jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 113 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional implementasi metode simple additive weighting (saw) pada sistem pendukung keputusan untuk menyeleksi saham prima ratna kusumawardani1, achmad solichin2 1,2) prodi teknik informatika, fakultas teknologi informasi universitas budi luhur, jakarta 1)ratna.kusumawardani@budiluhur.ac.id, 2)achmad.solichin@budiluhur.ac.id abstrak pada penelitian ini dibahas mengenai sistem pendukung keputusan untuk menyeleksi saham prima. masalah yang terjadi dalam penelitian adalah adanya kalangan umum maupun profesional yang masih melakukan analisis fundamental secara manual dalam pengambilan keputusan pembelian saham. penggunaan sistem pendukung keputusan diharapkan dapat membantu dalam proses pengolahan data saham yang memiliki kategori prima menjadi lebih efektif. metode simple additive weighting (saw) ini dipilih karena mampu menyeleksi alternatif terbaik dari sejumlah alternatif. dalam hal ini alternatif yang dimaksudkan yaitu saham prima berdasarkan kriteria-kriteria yang ditentukan. penelitian dilakukan dengan menentukan nilai bobot untuk setiap atribut, kemudian dilakukan proses perankingan yang akan menentukan alternatif yang optimal, yaitu saham terbaik. hasil penelitian berupa aplikasi sistem pendukung keputusan penyeleksi saham prima yang dibangun dengan bahasa pemrograman java dan basisdata mysql. aplikasi ini berguna untuk memilih alternatif yang terbaik untuk mendapatkan saham prima. para investor yang akan berinvestasi di saham, tidak akan salah membeli saham karena sudah memiliki daftar nama-nama saham prima. kata kunci: saw, spk, seleksi saham, perankingan, pendukung keputusan abstract in this study we proposed the decision support system for selecting prime stock. problems that occur in the research is the general public as well as professionals who are still doing fundamental analysis in decision making stock purchases manually. the use of a decision support system is expected to assist in the data processing stocks that have become more effective prime category. simple additive weighting (saw) method have been selected because it is able to select the best alternative from a number of alternatives. in this case the alternative meant that prime stocks based on specified criteria. research carried out by determining the weight value for each attribute, then do ranking process that will determine the optimal alternative, which is the best stock. results of the research is a decision support system application prime stock selectors that is built using the java programming language and mysql database. this application allows you to choose the best alternative to get prime stock. the investors who will invest in stocks, will not go wrong b uying stocks because it already had a list of names of the prime stocks. keywords: saw, dss, stock selection, ranking, decision support pendahuluan investasi merupakan suatu langkah seseorang dalam pemenuhan kebutuhan di masa yang akan datang. dewasa ini, dunia investasi tidak lagi didominasi oleh jenis investasi konvensional seperti tabungan atau deposito di bank. para investor saat ini mulai tertarik untuk menanamkan modalnya melalui pembagian kepemilikan perusahaan yang ditandai dengan surat berharga yang disebut saham. proses investasi ini dilakukan dengan cara jual beli sejumlah saham yang akan menentukan persentasi kepemilikan seorang investor terhadap perusahaan yang bersangkutan. proses jual beli tersebut dilakukan dengan cara lelang di suatu tempat perdagangan khusus yang disebut dengan bursa saham atau pasar modal (haryadi, 2013). kalangan umum maupun profesional masih banyak yang melakukan analisis fundamental secara manual dimana hal itu akan memakan waktu yang lama dan kurang efektif dalam mengolah data saham yang sangat banyak jumlahnya. analisis fundamental memerlukan pemahaman beberapa teknik dan teori, serta sulit dilakukan oleh orang awam (fahrurrozy, 2006; http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 114 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional falani, sugiono, & junaedi, 2012; soemapradja, logahan, & ongowarsito, 2014). permasalahan yang terjadi pada saat penyeleksian saham adalah keterbatasan kemampuan sumber daya manusia dalam hal ini bagian admin dalam mencari kriteria-kriteria yang diinginkan dalam waktu yang lebih singkat (fahrurrozy, 2006; falani et al., 2012). oleh karena itu diperlukan pengembangan suatu perangkat lunak yang dapat mempercepat pengambilan keputusan dalam memilih saham prima. pada penelitian ini dikembangkan sebuah sistem pendukung keputusan menggunakan metode simple additive weighting (saw). metode ini dipilih karena mampu menentukan nilai bobot untuk setiap atribut, kemudian dilanjutkan dengan proses perankingan yang akan menyeleksi alternatif terbaik dari sejumlah alternatif. dalam hal ini, alternatif yang dimaksud adalah saham prima berdasarkan kriteria-kriteria yang ditentukan. metode saw sering dikenal dengan istilah penjumlahan terbobot. konsep dasar metode simple additive weighting (saw) adalah mencari penjumlahan terbobot dari rating kinerja pada setiap alternatif pada semua atribut (kusumadewi, 2003). metode saw membutuhkan proses normalisasi matrik keputusan (x) ke suatu skala yang dapat diperbandingkan dengan semua rating alternatif yang ada. sistem pendukung keputusan yang dikembangkan, diterapkan untuk menyeleksi saham prima pada cv. bintang semesta. metode ini dipilih karena mampu menyeleksi alternatif terbaik dari sejumlah anternatif. dalam hal ini alternatif yang dimaksud adalah saham-saham yang terdaftar di bursa efek indonesia (bei). penelitian dilakukan dengan mencari penjumlahan terbobot dari nilai yang didapat pada setiap alternatif kemudian dilakukan proses perangkingan yang akan menentukan alternatif yang optimal yaitu saham prima. berdasarkan penelitian pada jurnal yang berjudul sistem pendukung keputusan penilaian proses belajar mengajar menggunakan metode simple additive weighting (saw) (usito, 2013), penulis menggunakan metode yang sama yaitu metode simple additive weighting (saw), namun diterapkan pada objek penelitian yang berbeda dan data yang berbeda pula. pada penelitian sebelumnya (usito, 2013), metode saw diterapkan di bidang pendidikan, sedangkan pada penelitian ini diterapkan pada data saham. tabel 1. rangkuman penelitian terkait no. paper tujuan penelitian metode kriteria 1 (yobioktabera, susanto, & wijayanti, 2012) untuk mengetahui pattern minat suatu sekolah atau institusi pendidikan terhadap jenis artikel tertentu simple additive weighting sistem pendukung keputusan yang dibangun ini berguna untuk mengetahui pattern minat suatu sekolah atau institusi pendidikan terhadap jenis artikel tertentu. 2 (usito, 2013) penilaian proses belajar mengajar yang dilakukan oleh dosen. simple additive weighting kriteria: tingkat kehadiran mengajar, ketepatan memulai dan mengakhiri kuliah, kesesuaian materi dengan silabus, kemudahan penyampaian materi untuk dipahami, memotivasi belajar dalam mendalami mata kuliah. 3 (oktaputra & noersasongko, 2014) spk kelayakan pemberian kredit motor pada perusahaan leasing. simple additive weighting kriteria: kepribadian, uang muka, kemampuan, jaminan, kondisi 4 (pohan & wibowo, 2017) spk pemilihan vendor pada pt. samudera indonesia ship management fuzzy-anp & topsis kriteria: delivery, price, quality, service 5 (prayogo, 2018) spk pemilihan karyawan teladan pt. bank rakyat indonesia simple additive weighting kriteria: absensi, produktivitas, tugas individual, tanggung jawab, penilaian supervisor 6 (bunajjar & solichin, 2018) rekomendasi lokasi cabang toko untuk gerai pulsa anp & topsis kriteria: price, quality, enviromental, facilities. 7 (mardiana & tanjung, 2019) sistem pendukung keputusan pemilihan perguruan tinggi swasta topsis kriteria: akreditasi, jumlah mahasiswa, jumlah dosen, biaya, fasilitas, jumlah jurusan. hasil penelitian ini dapat berguna untuk memilih alternatif yang terbaik untuk mendapatkan saham prima. dengan demikian, para investor yang akan berinvestasi di saham, tidak http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 115 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional akan salah membeli saham karena sudah memiliki daftar nama-nama saham prima. metode penelitian langkah penelitian untuk menyelesaikan permasalahan, dalam penelitian ini dilakukan beberapa langkah dan metode penelitian, sebagai berikut: a. studi pustaka metode ini dilakukan untuk mengumpulkan data dengan mencari dan membaca buku-buku referensi, jurnal, paper dan karya ilmiah lainnya yang dapat menunjang penelitian ini. b. analisis dokumen dokumen yang diperoleh kemudian dipelajari dan dianalisis untuk mengetahui bentuk sistem cara kerja yang akan dibangun. c. rancangan sistem metode ini dilakukan dengan membuat rancangan layar, flowchart, database dan lainlain sesuai dengan hasil analisis. d. implementasi rancangan sistem yang sudah dibuat akan diimplementasikan berdasarkan hasil analisis. kemudian hasil analisa akan dituangkan dalam kode-kode dengan menggunakan bahasa pemrograman tertentu. e. uji coba sistem setelah sistem selesai dibangun, maka dilakukan uji coba terhadap sistem yang dibangun. pengujian dilakukan dengan metode black box. teknik analisis data metode simple additive weighting (saw) sering dikenal dengan istilah metode penjumlahan terbobot. konsep dasar metode simple additive weighting (saw) adalah mencari penjumlahan terbobot dari rating kinerja dari masing-masing alternatif pada semua atribut. metode simple additive weighting (saw) membutuhkan proses normalisasi matriks keputusan (x) ke suatu skala yang dapat diperbandingkan dengan semua rating alternatif yang ada. kriteria-kriteria yang dibutuhkan salah satunya adalah laba bersih, laba usaha, pendapatan dan per, penentuan kriteria dapat digolongkan ke dalam 2 kriteria : a. benefit benefit adalah nilai maksimum dari suatu kriteria. adapun kriteria yang dapat digolongkan ke dalam kriteria benefit adalah pendapatan, laba kotor, laba usaha, laba bersih, aset. b. cost cost adalah nilai minimum dari suatu kriteria. adapun kriteria yang dapat digolongkan ke dalam kriteria cost adalah per. terdapat 4 tahapan yang harus dilakukan pada metode simple additive weighting (saw) dan diterapkan pada penelitian ini, yaitu: a. pengumpulan kriteria tabel 2 menyajikan kriteria yang digunakan untuk seleksi saham prima. selain itu, ditetapkan skala pembobotan seperti pada tabel 3. tabel 2 tabel kriteria kriteria keterangan c1 pendapatan c2 laba kotor c3 laba usaha c4 laba bersih c5 aset c6 per tabel 3 skala pembobotan skala pembobotan bobot cukup 5 rendah 2.5 sangat rendah 1 sangat tinggi 10 tinggi 7.5 b. pembobotan kriteria tabel 4 merupakan tabel pembobotan kriteria yang berguna untuk menampung data-data pembobotan kriteria yang sudah dipilih oleh admin. tabel 4 contoh pembobotan kriteria alternatif kriteria c1 c2 c3 c4 c5 c6 a1 5 2.5 2.5 2.5 5 2.5 a2 2.5 1 1 1 7.5 10 a3 10 1 10 10 10 1 a4 2.5 1 2.5 2.5 2.5 1 a5 10 7.5 5 7.5 7.5 5 a6 7.5 2.5 5 2.5 5 10 keterangan : a1 = adro a2 = born a3 = bumi a4 = brau a5 = atpk a6 = ptba c1 = aset (benefit) c2 = laba bersih (benefit) c3 = laba kotor (benefit) c4 = laba usaha (benefit) c5 = pendapatan (benefit) c6 = per (cost) c. matrik keputusan matrik keputusan dibuat untuk melakukan normalisasi data pembobotan setiap alternatif. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 116 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional normalisasi dipisahkan antara kriteria yang bersifat benefit dan cost. d. perankingan hasil normalisasi selanjutnya diranking dengan mengurutkan dari nilai terbesar ke nilai terkecil. hasil penelitian dan pembahasan desain sistem implementasi merupakan salah satu tahapan dalam pembuatan program. sistem pendukung keputusan ini dibangun dengan menggunakan bahasa pemrograman java dan basis data menggunakan mysql. berikut ini beberapa tampilan layar sistem pendukung keputusan yang telah dikembangkan dalam penelitian ini. gambar 1. tampilan form data saham gambar 2. tampilan form data kriteria gambar 1 adalah tampilan form data saham. pada menu ini dapat mengubah dan menghapus data saham dengan cara pilih salah satu data yang berada di dalam tabel. sementara itu, gambar 2 menampilkan form data kriteria untuk menambahkan, mengubah dan menghapus data kriteria. bobot kriteria dapat dikelola melalui halaman data bobot (gambar 3). di halaman ini, pengguna dapat menambahkan, mengubah dan menghapus data bobot. gambar 3. tampilan from data bobot gambar 4 menyajikan tampilan layar analisis saham. pada menu ini, pengguna dapat melakukan analisis saham dengan menentukan bobot pada masing-masing kriteria. kriteria yang digunakan adalah aset, laba bersih, laba kotor, laba usaha, pendapatan dan per. gambar 4. tampilan halaman input analisis saham hasil analisis saham ditampilkan pada gambar 5. pada menu ini, pengguna dapat melihat hasil dari pemilihan saham. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 117 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional gambar 5. tampilan hasil analisis saham gambar 6. tampilan hasil analisis saham dalam bentuk grafik selain dalam bentuk tabel, sistem juga mampu menampilkan hasil analisis dalam bentuk grafik seperti disajikan pada gambar 6. untuk pengolahan lebih lanjut, sistem juga dapat menampilkan hasil analisis dalam bentuk microsoft excel. pengujian sistem pada penelitian ini dilakukan pengujian sistem dengan metode pengujian black box. pengujian bertujuan untuk mengetahui fungsionalitas dari sistem atau aplikasi yang dihasilkan. tabel 5 menyajikan cuplikan hasil pengujian perangkat lunak dengan metode black box. berdasarkan hasil pengujian, sistem pendukung keputusan yang dibangun dapat berjalan dengan baik. tabel 5. hasil pengujian sistem dengan blackbox # modul fungsionalitas hasil 1 login menginputkan username dan password dengan benar berhasil masuk ke halaman pengguna 2 login menginputkan username dan/atau password yang salah menampilkan pesan gagal 3 data saham menambahkan, mengubah dan menghapus data saham berhasil tersimpan di db dan tampil di layar 4 data kriteria menambahkan, mengubah dan menghapus data kriteria berhasil tersimpan di db dan tampil di layar 5 analisis saham menjalankan modul analisis data saham berhasil ditampilkan hasil perhitungan dan perangkinan saham dalam bentuk tabel dan grafik 6 analisis saham mengekspor data hasil analisis ke file ms excel berhasil menyimpan ke file dan dapat dibuka dengan program ms excel simpulan simpulan berdasarkan penelitian yang telah dilakukan, dapat ditarik beberapa kesimpulan, antara lain: 1) permasalahan dapat diselesaikan dengan mengimplementasikan sistem pendukung keputusan dengan metode simple additive weighting (saw). 2) aplikasi ini dibangun sebagai alat bantu bagi cv. bintang semesta untuk memilih alternatif yang terbaik untuk mendapatkan saham prima. 3) hasil akhir dari perhitungan simple additive weighting (saw) ini berupa diagram batang dan dapat dieksport ke microsoft excel. 4) berdasarkan hasil pengujian fungsionalitas dengan metode pengujian black box disimpulkan bahwa aplikasi dapat berjalan dengan baik dan dapat menampilkan hasil analisis saham prima bagi penggunanya. saran selain menarik beberapa kesimpulan, peneliti juga memberikan saran dalam penerapan aplikasi sistem pendukung keputusan yang telah http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 118 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dihasilkan dalam penelitian ini. spesifikasi kebutuhan perangkat keras dan perangkat lunak harus dipenuhi agar aplikasi dapat bekerja dengan baik dan optimal. selain itu, penelitian ini dapat dikembangkan untuk menyelesaikan permasalahan di bidang lain. daftar referensi bunajjar, k., & solichin, a. (2018). determining the location of a wholesale pulse business branch with anp and topsis methods. international journal of advanced studies in computer science and engineering (ijascse), 7(11), 1–5. fahrurrozy. (2006). sistem penunjang keputusan investasi saham pada bursa efek jakarta. uin syarif hidayatullah. falani, a. z., sugiono, j. p., & junaedi, h. (2012). sistem pendukung keputusan investasi saham berbasis fuzzy logic. in proceeding seminar nasional fakultas teknik (snft) (hal. b-1-b-9). universitas muhammadiyah sidoarjo. haryadi, r. (2013). start up trader : jangan jadi trader sebelum baca buku ini ! visimedia pustaka. kusumadewi, s. (2003). artificial intelligence (teknik dan aplikasinya). yogyakarta: graha ilmu. mardiana, t., & tanjung, s. s. (2019). sistem pendukung keputusan pemilihan perguruan tinggi swasta menggunakan topsis. jurnal riset informatika, 1(2), 25–34. oktaputra, a. w., & noersasongko, e. (2014). sistem pendukung keputusan kelayakan pemberian kredit motor menggunakan metode simple additive weighting pada perusahaan leasing hd finance. program studi sistem informasi. universitas dian nuswantoro. pohan, f., & wibowo, a. (2017). integrasi model pendukung keputusan evaluasi pemilihan vendor dengan fuzzy analytical network process dan topsis studi kasus pt. samudera indonesia ship management. jurnal teknik, 6(2), 83–91. prayogo, j. (2018). sistem pendukung keputusan karyawan teladan pt. bank rakyat indonesia dengan metode simple additive weighting. jurnal riset informatika, 1(1), 35– 42. soemapradja, t. g., logahan, j. m., & ongowarsito, h. (2014). pengembangan aplikasi simulasi perdagangan saham dengan sector rotation dan linear programming. binus business review, 5(1), 418–428. usito, n. j. (2013). sistem pendukung keputusan penilaian proses belajar mengajar menggunakan metode simple additive weighting (saw). universitas diponegoro semarang. https://doi.org/10.1017/cbo978110741532 4.004 yobioktabera, a., susanto, h., & wijayanti, s. (2012). perancangan e-learning cerdas berbasis dss dengan menggunakan metode simple additive weighting pada smp n 9 semarang, 2012(semantik), 444–447. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 107 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. rancang bangun sistem informasi manajemen disposisi surat menyurat dengan menggunakan model rad (rapid application development) instianti elyana1, ishak kholil2, frans eduard schaduw3 1administrasi bisnis universitas bina sarana informatika www.bsi.ac.id instianti.iny@bsi.ac.id 2 sistem informasi, stmik nusa mandiri www.nusamandiri.ac.id ishak.ihk@nusamandiri.ac.id 3administrasi bisnis universitas bina sarana informatika www.bsi.ac.id frans.fes@bsi.ac.id abstract—many numbers of letters are made and accepted in the administrative administration section, so that data search will be inefficient in terms of time and energy using a manual system. so that at this time a more structured letter management administration system is needed in order to speed up the search for existing data and create reports. this filing application has the following capabilities: this filing application is run on a personal computer network in the administrative space, administrative officers can access this filing application by entering the correct user login, can add, edit, cancel, delete and save incoming mail and out, can search incoming letters and outgoing letters based on the sender and subject matter, search results can be sorted according to the letter id or date of the letter, can print reports based on search results that have been done based on the letter id or date of the letter. using the rad model will make it easier to design a correspondence disposition information system. keywords: correspondence letter, outgoing letter, entry letter, administration abstrak—banyak jumlah surat yang dibuat dan diterima dibagian administrasi tata usaha, sehingga pencarian data akan menjadi tidak efisien dalam hal waktu dan tenaga dengan menggunakan sistem manual. sehingga pada saat ini diperlukan suatu sistem administrasi manajemen surat yang lebih terstruktur agar dapat mempercepat pencarian data yang ada dan pembuatan laporan. aplikasi kearsipan ini mempunyai kemampuan sebagai berikut: aplikasi kearsipan ini dijalankan pada jaringan personal komputer pada bagian ruang tata usaha, petugas tata usaha dapat mengakses aplikasi kearsipan ini dengan memasukkan login user yang benar, dapat melakukan menambah, mengedit, membatalkan, menghapus dan menyimpan surat masuk dan keluar, dapat melakukan pencarian surat masuk dan surat keluar berdasarkan pengirim dan perihal, hasil pencarian dapat dilakukan pengurutan berdasar id surat atau tanggal surat, dapat mencetak laporan berdasarkan hasil pencarian yang telah dilakukan berdasarkan id surat atau tanggal surat. dengan menggunakan model rad akan lebih memudahkan dalam perancangan sistem informasi disposisi surat menyurat. kata kunci:.surat menyurat, surat keluar, surat masuk, administrasi pendahuluan surat menyurat merupakan hal yang tidak dapat dihindari dalam suatorganisasi/perusahaan karena hal ini memegang peranan penting dalam proses administrasi. surat merupakan rekaman kegiatan ataupun peristiwa dalam suatu instasi yang harus tersimpan dalam jangka waktu tertentu untuk kebutuhan tertentu. manajemen surat menyurat dibutuhkan untuk mengatur http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 108 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. jalannya suatu prosedur dalam suatu perusahaan, agar surat tertata baik dan tidak tercecer yang berdampak pada kerugian bagi perusahaan(ferdinandus, sandy, 2012). pengelolaan surat masuk dan surat keluar dikatakan penting karena banyak informasi yang dibutuhkan perusahaan. data surat masuk dan surat keluar harus tersimpan dengan baik agar dapat memudahkan dalam pengelolaannya. pengelolaan surat masuk dan surat keluar yang menggunakan cara manual (saputra & famukhit, 2014) yaitu dengan melakukan pencatatan data surat kedalam buku kendalanya adalah apabila buku tersebut hilang atau rusak maka data pun akan ikut hilang. dan jika pihak perusahaan menyimpan dokumen fisik surat yang ada akan ikut hilang dan rusak menyulitkan perusahaan dalam mencari dokumen surat yang dibutuhkan. peran sebuah tool dalam pengolahan data dan memiliki peran yang sangat penting bagi sebuah organisasi/perusahaan, tentu tool yang dimaksud adalah sebuah system informasi yang bertugas mengelola data dengan jumlah yang relative cukup banyak setiap harinya, apalagi disebuah organisasi/perusahaan besar pasti memerlukan suatu alat bantu yang memiliki tingkat kecepatan perhitungan dan penyampaian data yang tinggi. alat bantu tersebut berupa perangkat keras dan perangkat lunak. keunggulan aplikasi berbasis komputer untuk memproses data akan meningkatkan efektifitas dan produktifitas, serta efisiensi proses dan prosedur yang ada diorganisasi/perusahaan. pengunaan sistem aplikasi pengelolaan surat masuk dan surat keluar pada pt angkasa pura 1 semarang sudah menggunakan sistem informasi yang terkomputerisasi baik pada penginputan, pencarian data surat masuk/ surat keluar dan pembuatan laporan dapat dilakukan kapan saja dalam waktu yang cepat karena data tersimpan dengan aman dan terstruktur serta tidak terjadi lagi keterlambatan dalam pencarian data. (sugiharti & triliani, 2015). permasalahan surat menyurat dengan sistem konvensional pernah dibahas pada studi kasus kantor desa tanjungsari kotawinangun kebumen ini adalah dimana bentuk pelayanan surat menyuratnya masih menggunakan sistem yang konvensional (priyadi & lestari, 2018). bentuk permasalahan yang sering timbul adalah sulitnya pelaporan surat masuk (suherman, 2017) dan keluar dan pencariaan data surat masuk dan surat keluar, pada era informasi saat ini, permasalahan yang sering timbul akibat dari belum termanfaatkannya teknologi dengan baik, teknologi harus dapat menjadi solusi permasalahan tersebut. yaitu bagaimana mengolah data sedemikian rupa untuk menghasilkan informasi yang berguna, dan mudah digunakan oleh pengguna informasi (sasongko & diartono, 2009). untuk itu dibutuhkan pemanfaatan teknologi informasi untuk mendukung proses administrasi surat menyurat hal ini tentu untuk mempercepat pembuatan laporan dan pencarian data. karena itu perlu dibangun suatu sistem untuk pengelolaan dan pengarsipan berkas-berkas surat yang berupa aplikasi kearsipan untuk memudahkan dalam sistem surat menyurat dalam sebuah perusahaan. berdasarkan kebutuhan perusahan adalah membuat perangkat lunak untuk workflow pengelolaan surat menyurat lebih khususnya pada bagian pengelolan surat masuk dan keluar. pengembangan sebuah perangkat tentu harus didukung oleh metode yang tepat, unified modeling language (uml) dapat membantu tim pengembangan proyek berkomunikasi, mengeksplorasi potensi desain, dan memvalidasi desain arsitektur perangkat lunak atau pembuat program. komponen atau notasi uml diturunkan dari 3 (tiga) notasi yang telah ada sebelumnya yaitu grady booch, ood (object-oriented design), jim rumbaugh, omt (object modelling technique), dan ivar jacobson oose objectoriented software engineering)(haviluddin, 2013). bahan dan metode langkah-langkah penelitian dapat dilihat pada diagram berikut ini : gambar 1. bagan penelitian http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 109 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. gambar 2 : rad model business modeling aliran informasi antar fungsi bisnis dimodelkan dalamcara yang menjawab pertanyaan-pertanyaan berikut: informasi apa yang mendorong bisnis proses? informasi apa yang dihasilkan? siapa yang menghasilkannya? kemana perginya informasi? siapa yang memprosesnya? data modeling aliran informasi didefinisikan sebagai bagian dari fase pemodelan bisnis disempurnakan menjadi satu set objek data yang diperlukan untuk mendukung bisnis acteristics (disebut atribut) dari setiap objek diidentifikasi dan hubungan antara objek-objek ini didefinisikan proces modeling objek data yang didefinisikan dalam fase pemodelan data ditransformasikan untuk mencapai aliran informasi yang diperlukan untuk mengimplementasikan fungsi bisnis. memproses deskripsi dibuat untuk menambah, mengubah, menghapus, atau mengambil a objek data. application generation. rad mengasumsikan penggunaan teknik generasi keempat (bagian 2.10). daripada membuat perangkat lunak menggunakan generasi ketiga konvensional bahasa pemrograman proses rad bekerja untuk menggunakan kembali komponen program yang ada (bila memungkinkan) atau membuat komponen yang dapat digunakan kembali (bila perlu). dalam semua kasus, alat otomatis digunakan untuk memfasilitasi pembangunan perangkat lunak. testing and turnover karena proses rad menekankan penggunaan kembali, banyak komponen program telah diuji. ini mengurangi waktu pengujian keseluruhan. namun, komponen baru harus diuji dan semua antarmuka harus dilakukan sepenuhnya. hasil dan pembahasan model rad mengadopsi model waterfall dan pembangunan dalam waktu singkat yang dicapai dengan penerapan : 1. component based construktion ( pemprograman berbasis komponen bukan prosedural ) 2. penekanan pada penggunaan ulang ( reuse ) komponen perangkat lunak yang telah ada. 3. pembangkitan kode program otomatis atau semi otomatis 4. multiple ( banyak team) business modeling prosedur surat masuk surat masuk dari pengirim baik dari luar atau pun melalui email kemudian diterima oleh administrasi fo yang akan di catat data surat masuk dan surat keluar pensortiran surat, disposisi surat serta pengarsipan dan penerimaan surat keluar, pensortiran surat, pengiriman serta pengarsipan. prosedur surat keluar surat surat yang keluar akan dicatat oleh administrasi fo dimasukkan kedalam sistem kemudian diberikan no surat barulah surat akan keluar ketujuan masing-masing. laporan surat menyurat surat menyurat akan diinformasikan ke perusahaan melalui sistem informasi disposisi surat masuk dan surat keluar dibangun untuk memudahkan pengguna sistem manajemen disposisi dapat tersimpan dan terkelola dengan baik. data modelling sistem informasi disposisi surat ini dimulai surat yang diterima oleh administrasi untuk disortir berdasarkan tujuan surat yang harus dicatat pada form/buku surat masuk, surat akan didisposisikan ke tujuan masing-masing. begitupun dengan surat yang keluar akan dicatat oleh admin berdasarkan tujuan surat tersebut. tujuan utama memahami alur bisnis dan segala pengetahuan yang terkait operasional proses surat menyurat erd ( entity relationship diagram) model ini dimaksudkan untuk menjelaskan hubungan antar data dalam basis data berdasarkan objek-objek dasar data yang mempunyai hubungan antar relasi. dari hasil pengamatan surat menyurat di suatu perusahaan dibuat lah erd dengan penggambaran sebagai berikut : http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 110 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. kelola surat masuk surat keluar no_sukel tgl_sukeltujuan no_sm pengiriman status tgl_sm prihal administrasi level kode admin email user_name password 1 m kelola1 m file scand sm disposisi surat masuk tujuan disposisi user_name user_name gambar 5. rancangan erd semua surat yang masuk dan keluar diterima dan diteliti tujuannya adalah untuk memudahkan dibuka dan dicatatnya informasi kedalam lembar kendali surat masuk dan keluar yang telah disediakan oleh sistem informasi. proses disposisi surat oleh pengolah mengikuti aturan dan pola yang telah disepakati. @kode admin user_name password level email @no sm perihal tgl_sm pengiriman status file scand sm tujuan disposisi @@[@kode admin] tujuan @no_surat keluar tgl_sm tujuan disposisi @@[@kode admin] administrasi surat masuk surat keluar gambar 6. rancangan lrs proses modeling dalam proses modeling menggunakan unified modeling language (uml) pemodelan mengunakan uml dapat memberikan gambaran pada sistem yang penulis bahas yaitu surat yang diterima oleh administrasi akan disortir terlebih dahulu untuk mengetahui tujuan surat, seletah proses sortir surat akan disiposisikan kebagian/divisi tujuan surat, dan administrasi mencatat dokumentasi penerimaannya sebagai arsip. sedangkan untuk surat keluar, administrasi menerima surat yang dibuat oleh bagian/divisi, untuk dilakukan sortir tujuan surat agar tertulis alamat yang jelas, setelah dilakukan sortir surat akan dikirim melalui kurir, dan administrasi mengarsipkan bukti surat keluar.. metode ini juga bertujuan menyatukan teknik pemodelan berorientasi objek yang terstandarisasi. sistem informasi disposisi surat ini adalah dimulai surat yang diterima oleh admin untuk disortir berdasarkan tujuan surat yang harus dicatat pada form/buku surat masuk, surat akan didisposisikan ke tujuan masing-masing. begitupun dengan surat yang keluar akan dicatat oleh admin berdasarkan tujuan surat tersebut. requirement untuk mengambarkan kebutuhan dari sebuah system dengan mengunakan use case diagram menggambarkan manfaat system jika dilihat menurut pandangan orange yang berada diluar system ( actor ). diagram ini menjelaskan fungsionalitas suatu system atau kelas dan bagian system berinteraksi dengan dunia luar. komponen pembentuk diagram use case adalah: a. aktor menggambarkan pihak-pihak yang berperan dalam system. b. use case aktifitas atau sarana yang disiapkan oleh bisnis atau system. hubungan link actor mana saja yang terlibat dalam use case ini. gambar 3. rancangan use case activity diagram diagram aktiftas lebih memfokuskan diri pada eksekusi dan alur system dari pada bagaimana system dari pada bagaimana system ini dirakit. diagram ini tidak hanya memodelkan software melainkan memodelkan model bisnis uc use case model administrasi kelola surat masuk login surat keluar laporan disposisi «extend» http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 111 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. juga. diagram aktivitas menunjukkan aktivitas system dalam bentuk kumpulan aksi-aksi. sumber : hasil penelitian, 2019 gambar 4. rancangan activity diagram proses application generation datadata sistem pengolahan data memerlukan interface masukkan ( input ). dimana data-data membentuk keluaran output dalam bentuk informasi. sumber : ( hasil penelitian, 2019) gambar 8. desain input data disposisi desain input data disposisi digunakan untuk menginput, menambahkan, mencari data surat masuk dan menghapus. dengan memasukkan no agenda kemudian surat akan dapat mudah untuk dapat mencarinya. sumber : ( hasil penelitian, 2019) gambar 9. desain input data surat masuk desain input data surat masuk dengan memasukkan data surat input no agenda, tanggal agenda dan memasukkan no surat. desain ini memudahkan untuk mencari surat masuk dapat juga untuk menghapus, menyimpan, mencari dan menambahkan surat dengan tetap menggunakan no agenda. act activ ity diagram kelola surat masuk start tampilan surat masuk tambah t ambah ? menampilkan no agenda dan tanggal agenda secara otomatis input data surat masuk simpan simpan ? proses simpan data tersimpan gagal tersimpan merge simpanmerge t ambah cari data surat input kata kunci yang akan dicari ditemukan ? menampilka data yang dicari data tidak sesuai hapus hapus ? proses hapus data surat masuk hapusmerge cari finish http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 112 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. testing and turnover testing login no sekenario pengujian test case hasil yang diharapkan hasil pengujian kesimpulan 1 mengosongkan semua field yang berada di halaman baru semua field atau semua text box kosong sistem akan menolak akses jika no agenda tidak dimasukkan sesuai harapan valid 2 hanya mengisi 5 field saja lalu klik cari no.agenda : 1231, tanggal agenda: 6 agustus 2019 , no.surat : 1444, sisanya kosong sistem akan menolak akses daftar dan menampilkan pesan “kesalahan pada pengisian form (masih kosong)” sesuai harapan valid 3 mengisi semua field yang ada semua field di isi sistem menerima akses sesuai harapan valid testing masuk no sekenario pengujian test case hasil yang diharapkan hasil pengujian kesimpulan 1 mengosongkan semua field yang berada di halaman baru semua field atau semua text box kosong sistem akan menolak akses jika no agenda tidak dimasukkan sesuai harapan valid 2 hanya mengisi klik simpan semua field terisi sistem dapat mengakses masuk sesuai harapan valid 3 mengisi semua field yang ada semua field di isi sistem menerima akses sesuai harapan valid 4 mengisi hanya dengan 5 field no. agenda : 12341, tgl. agenda : 6 agustus 2019, no surat : 12351, pengirim : pt bumi reza, hal : penting sistem akan menolak akses masuk dan menampilkan pesan “kesalahan pada pengisian form (masih kosong)” sesuai harapan valid kesimpulan dari pembahasan diatas dapat dianalisa bahwa sebuah organisasi/ perusahaan sudah sangat tidak layak mengunakan sisten konvensional dalam pengelolaan surat menyuratnya diera informasi saat ini karena sifat pendataan masih dilakukan pencatatan biasa dan belum terolah secara maksimal terutama untuk, pencatatan, pencarian surat surat yang sudah pernah diterbitkan atau surat surat yang sudah pernah diterima. dengan solusi yang diusulkan adalah pengunaan system informasi agar dapat mengelola data surat, baik surat masuk maupun surat keluar sehingga surat-surat tersebut dapat dicari kapan saja dengan cepat apabila diperlukan. referensi ferdinandus, sandy, h. w. (2012). perancangan aplikasi surat masuk dan surat keluar pada pt. pln (persero) wilayah suluttenggo. american journal of germanic linguistics and literatures, 3(2), 161–174. https://doi.org/10.1017/s10408207000006 9x haviluddin. (2013). summary for policymakers. memahami penggunaan uml (unified modelling language) haviluddin program, 9(2), 1–6. https://doi.org/10.1017/cbo97811074153 24.004 priyadi, d. a., & lestari, e. w. (2018). perancangan sistem informasi pelayanan surat menyurat pada kantor desa tanjungsari kutowinangun kebumen berbasis desktop. jurnal teknik komputer, iv(2), 84–91. https://doi.org/10.31294/jtk.v4i2.3444 saputra, k. a., & famukhit, m. l. (2014). perancangan sistem informasi pengelolaan surat masuk dan surat keluar pada mts guppi jetiskidul. ijns indonesian journal on networking and security, 3(4). https://doi.org/10.1123/ijns.v3i4.979 sasongko, j., & diartono, d. a. (2009). rancang bangun sistem informasi manajemen surat, xiv(2), 137–145. sugiharti, e., & triliani, s. e. (2015). perancangan aplikasi surat masuk dan keluar pada pt. angkasa pura 1 semarang. scientific journal of informatics, 1(1), 39–52. https://doi.org/10.15294/sji.v1i1.3640 suherman, y. (2017). sistem informasi kearsipan tata kelola surat pada kantor inspeksi bri kota padang. jurnal resti (rekayasa sistem dan teknologi informasi), 1(1), 26–33. https://doi.org/10.29207/resti.v1i1.7 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 11 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. perancangan sistem informasi penjualan daur ulang botol bekas (pet) berbasis web aad aediyansyah program studi manajemen informatika amik bsi jakarta aadafz.crb@gmail.com abstract plastic bottle recycling is the use of recycling unused bottles into various kinds of goods or products for our daily needs, this product is also not only useful for decoration but also can be used as a daily necessity product. currently there are not many sites selling used plastic bottles online. in this case the author tries to analyze the information system for recycling sales of web-based plastic bottles. some sales data collection processes sometimes occur in recording errors, inaccurate sales reports are made and delays in searching for the required data. in addition, at the moment there are still many factories that are currently poorly managed and conventional, and the data collection method for the sale of their goods is not well managed. this sales information system development model uses the waterfall model, because it is easy in the process of applying information systems development. the purpose of this study is to provide convenience to consumers in online purchases, without having to come directly to the factory. make it easy for consumers to get information about products in detail through the website. keywords: sales information system, waterfall model, sales of used bottles abstrak daur ulang botol plastik merupakan pemanfaatan daur ulang botol-botol yang sudah tidak terpakai menjadi berbagai macam bahan barang ataupun produk kebutuhan kita sehari-hari, produk ini juga bukan hanya bermanfaat hiasan semata tetapi juga dapat dimanfaatkan sebagai suatu prodak kebutuhan sehari hari. saat ini banyak belum banyak situs penjualan botol plastik bekas secara online. dalam hal ini penulis mencoba menganalisa sistem informasi penjualan daur ulang botol plastik berbasis web. beberapa proses pendataan penjualan kadang terjadi kesalahan dalam pencatatan, kurang akuratnya laporan penjualan yang dibuat dan keterlambatan dalam pencarian data-data yang diperlukan. selain pada saat ini masih banyak pabrik-pabrik yang cara pendataan penjualanan barangnya saat ini masih kurang dikelola dengan baik dan masih konvensional, dan tidak terdata dengan baik. model pengembangan system informasi penjualan ini menggunakan model waterfall, dikarena mudah dalam proses penerapan pengembagan system informasi. tujuan penelitian ini untuk memberikan kemudahan pada konsumen dalam pembelian secara online, tanpa harus datang langsung ke pabrik. memudahkan konsumen untuk mendapatkan informasi tentang produk secara detail melalui website. kata kunci: sistem informasi penjualan, model waterfall, penjualan botol-botol bekas pendahuluan dalam kehidupan sehari-hari kita pasti menggunakan berbagai macam peralatan yang menggunakan bahan dasar botol plastik untuk keperluan ataupun untuk hiasan rumah sehari hari. daur ulang botol plastik merupakan pemanfaatan daur ulang botol-botol yang sudah tidak terpakai menjadi berbagai macam bahan barang (sari, sunarko, & hardati, 2016) ataupun produk kebutuhan kita sehari-hari, produk ini juga bukan hanya bermanfaat hiasan semata tetapi juga dapat dimanfaatkan sebagai suatu prodak kebutuhan sehari hari. saat ini banyak belum banyak situs penjualan botol plastik bekas secara online. dalam hal ini penulis mencoba menganalisa sistem informasi penjualan daur ulang botol plastik berbasis web. beberapa proses pendataan penjualan kadang terjadi kesalahan dalam pencatatan (christian & ariani, 2018), kurang akuratnya laporan penjualan yang dibuat (suci, zuraidah, & wajhillah, 2016) dan keterlambatan dalam pencarian data-data yang diperlukan (sintawati & sari, 2017). http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 12 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. selain pada saat ini masih banyak pabrik-pabrik yang cara pendataan penjualanan barangnya saat ini masih kurang dikelola dengan baik dan masih konvensional (frieyadie, 2014), (rachmawati, septiana, & yulianti, 2016) dan tidak terdata dengan baik. dengan adanya permasalahan tersebut maka harus ada suatu perkembangan dalam penjualan daur ulang botol-botol agar mudah di jangkau oleh pembeli, sebab itu penulis mencoba untuk membuat sebuah media pemasaran botol-botol daur ulang dengan membangun situs penjualan secara online atau lebih sering disebut dengan e-commerce dengan menggunakan fasilitas internet, agar pembeli tidak kesulitan untuk mendapatkan produk yang diinginkan, tanpa perlu datang langsung ke pabrik untuk membeli prodak yang akan di beli tersebut. model pengembangan system informasi penjualan ini menggunakan model waterfall, dikarena mudah dalam proses penerapan pengembagan system informasi. tujuan penelitian ini untuk memberikan kemudahan pada konsumen dalam pembelian secara online, tanpa harus datang langsung ke pabrik. memudahkan konsumen untuk mendapatkan informasi tentang produk secara detail melalui website. metodologi penelitian teknik pengumpulan data metode pengumpulan data yang digunakan dengan beberapa metode, meliputi : 1. metode observasi dalam pengumpulan data ini penulis mengadakan pengamatan pada situs website yang menjual daur ulang botol-botol bekas dengan melihat jalannya proses penjualan produk tersebut. 2. metode studi pustaka pengumpulan data dan informasi yang diperlukan dengan cara mempelajari buku-buku yang berkaitan dengan permasalahan yang dibahas, sehingga didapatkan dasar ilmiah yang kuat. model pengembangan perangkat lunak rekayasa perangkat lunak ini juga mengumpulkan kebutuhan pada tingkat bisnis strategis, yaitu: 1. analisa kebutuhan software tahap ini sangat menekan pada masalah pengumpulan kebutuhan penggunaan sistem dengan menentukan konsep system, fungsional sistem yang menghubungkan dengan lingkungan sekitar yang menghasilkan spesifikasi sistem. 2. desain merupakan tahapan proses multi langkah yang focus pada desain pembuatan program perangkat lunak struktur data, arsitektur data perangkat lunak dan representasi antar muka. 3. pengkodean pembuatan kode program pada tahap ini desain harus di implementasikan ke dalam program perangkat lunak. hasil dari tahap ini adalah program computer yang sesuai dengan desain yang telah di buat. 4. pengujian merupakan tahapan yang focus pada secara logika dan fungsional dan memastikan semua sudah di uji. metode yang digunakan untuk pengujian yaitu black box testing. 5. implementasi pada tahap ini perancangan perangkat lunak direalisasikan sebagai serangkaian program atau unit program. kemudian pengujian unit melibatkan fertivikasi bahwa setiap unit program telah memenuhi spesifikasinya. tiap aktivitas yang digunakan untuk dapat melakukan evaluasi suatu atribut atau kemampuan dari program atau sistem dan menentukan apakah memenuhi kebutuhan atau hasil yang diharapkan hasil dan pembahasan analisa kebutuhan software analisa kebutuhan untuk website penjualan daur ulang botol bekas yang diusulkan dengan beberapa prosedur diantaranya a. kebutuhan pengunjung a.1. pengunjung dapat melihat halaman utama a.2. pengunjung dapat melihat profil a.3. pengunjung dapat melihat barang yang dijual a.4. pengunjung dapat melihat panduan berbelanja a.5. pengunjung dapat melihat testimoni pembeli a.6. pengunjung dapat melakukan daftar akun baru b. kebutuhan pembeli b.1. pembeli dapat melakukan login b.2. pembeli dapat mengecek lupa password b.3. pembeli dapat memasukan ke keranjang belanja b.4. pembeli dapat melihat history transaksi b.5. pembeli dapat menginput testimoni http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 13 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. b.6. pembeli dapat melakukan konfirmasi pembayaran c. kebutuhan penjualan c.1. penjualan dapat mengganti password c.2. penjualan dapat mengelola data kota c.3. penjualan dapat mengelola data barang c.4. penjualan dapat mengelola data pembeli c.5. penjualan dapat mengelola pemesanan barang c.6. penjualan dapat mengelola konfirmasi pembayaran c.7. penjualan dapat mengelola laporan pemesanan c.8. penjualan dapat mengelola laporan pembayaran perancangan (desain) analisa kebutuhan untuk website penjualan daur ulang botol bekas yang diusulkan dengan beberapa prosedur diantaranya a. desain sistem informasi 1. desain fungsional sistem dengan usecase diagram a. usecase diagram fungsional kebutuhan pengunjung sumber: (aediyansyah, 2016) gambar 1. rancangan fungsional kebutuhan pengunjung b. usecase diagram fungsional kebutuhan pembeli. sumber: (aediyansyah, 2016) gambar 2. rancangan fungsional kebutuhan pembeli c. usecase diagram fungsional kebutuhan penjual sumber: (aediyansyah, 2016) gambar 1. rancangan fungsional kebutuhan penjual b. desain user interface 1. rancangan user interface halaman awal web. halaman pelanggan adalah halaman yang dapat diakses oleh user yang telah melakukan uc use case model pengunj ung melihat halaman utama melihat halaman profile melihat barang yang dij ual melihat panduan berbelanj a melihat testimoni pembeli melakukan daftar akun baru uc use case model pembeli melakukan login mengecek lupa passw ord melihat keranj ang belanj a melihat history transaksi menginput testimoni konfirmasi pembayaran uc use case model penj ual mengelola data kota mengelola data barang mengelola data pembeli mengelola data pemesanan barang mengelola konfirmasi pembayaran mengelola laporan pemesanan mengelola laporan pembayaran http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 14 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. registrasi. melalui halaman pembeli ini, pengunjung dapat melakukan pembelian produk. home profil barang panduan testimoni cari barang xxxxxxxxx cari kategori transfer pengiriman header footer sms:9xxx bbm:9xxx [id user : xxxx] transaksi keranjang belanja history transaksi testimoni [input] konfirmasi logout gambar gambar gambar koleksi barang xxxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxx stok:99 jenis barang;xxx rp.999999 beli xxxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxx stok: 99 jenis barang:xxx rp.999999 rp.999999 beli beli xxxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxxxxxxxxxx stok: 99 jenis barany:xxx xxxxxxxxxxx rek.9999999999 gambar gambar sumber: (aediyansyah, 2016) gambar 4. rancangan halaman awal 2. rancangan user interface keranjang belanja halaman keranjang belanja merupakan halaman dimana digunakan pembeli untuk menampung data-data barang yang akan dipesan. home profil barang panduan testimoni cari barang xxxxxxxx cari layanan pelanggan transfer pengiriman header footer sms:9xxx bbm:9xxx [id user : xxxx] transaksi keranjang belanja history transaksi testimoni[input] konfirmasi logout gambar nama barang stok harga total (rp) grade total : gambar ubah data lanjutkan keranjang belanja xxxxxxxxx 99 rp.999999 rp.999999 rp.999999 xxxxxxxxxx rek.9999999999 gambar gambar sumber: (aediyansyah, 2016) gambar 5. rancangan halaman keranjang belanja 3. rancangan user interface mengelola data barang. halaman mengelola data barang merupakan halaman dimana digunakan penjual untuk mengelola meta data item barang yang dijual. header · home · password admin · data kota · data barang · data pelanggan · pemesanan barang · konfirmasi transfer · laporan · logout no nama barang tools edit delete +add data data barang kode stok harga (rp.) footer 99 9999 xxxxxxxxxxxx 99 rp.999999 sumber: (aediyansyah, 2016) gambar 6. rancangan halaman mengelola data barang c. desain basis data 1. rancangan entity relationship diagram berikut rancangan basis data secara abstrak dengan menggunakan perangkat rancangan basis data entity relationship diagram (erd). konfirmasi pelanggan pemesanan barang melakukan melakukan punya isi no_pemesanan kd_prlanggan jumlah_transfer keterangan tanggal nm_pelanggan email no_telepon username password kd_pelanggan tgl_daftar kd_pelanggan tgl_pemesanan nama_penerima alamat_lengkap kd_provinsi kd_kota kode_pos status_bayar no_pemesanan no_telepon id no_pemesanan harga kd_kategori file_gambar keterangan harga_jual harga_modal nm_barang kd_barang 1 m 1 m 1 1 m 1 m id stok 1 1 nm_pelanggan kd_pelanggan kd_barang dikirim kota kd_kota nm_kota biaya_kirim kd_kota jumlah sumber: (aediyansyah, 2016) gambar 8. rancangan erd sistem daur ulang botol 2. rancangan logical record struktur berikut rancangan lrs yang merupakan hasil dari pemodelan entity relationship (er) berserta atributnya sehingga bisa terlihat hubunganhubungan antar entitas. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 15 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sumber: (aediyansyah, 2016) gambar 8. rancangan lrs sistem daur ulang botol pengkodean (code) pengkodean (code) merupakan tahap dimana dilakukan setelah melakukan kegiatan desain atau perancangan. adapun pengkodean tidak ditampilkan dikarenakan yang akan ditampilkan adalah source code program dimana sangat banyak sekali. untuk itu penulis hanya menyebutkan bahasa script yang digunakan untuk membangun system informasi ini. adapun bahasa script yang digunakan php, html, css, dan beberapa bahasa script pendukung. pengujian (testing) pengujian pada pembuatan system informasi penjualan daur ulang botol bekas, dengan menggunakan metode black box testing. berikut contoh pengujian pada login pembeli. tabel 1. pengujian login n o scenario pengujian test case hasil yang diharapka n hasil penguji an kesimpul an 1. mengosongk an isian username dan password lalu langsung mengklik tombol login usernam e: (kosong) password : (kosong) login anda salah sistem menampilk an “data username dan password yang anda masukan belum benar”. sesuai harapan valid 2. mengisi username dan mengosongk an password kemudian klik tombol login usernam e: bambang password : (kosong) login anda salah sistem menampilk an menampilk an “data password kosong, silahkan isi dengan benar”. sesuai harapan valid 3. mengisi usernam login sesuai valid username dan password dengan data yang benar kemudian klik tombol login e: bambang passwor d: bambang berhasil menampilk an pesan “selamat datang di website kami” harapan sumber: (aediyansyah, 2016) kesimpulan sistem informasi yang dibangun saat ini menggunakan model waterfall, dikarena untuk memudahkan membangun sistem informasi pengelolaan pendataan botol-botol bekas untuk daur ulang. memberikan kemudahan informasi pada konsumen dalam pembelian secara online, tanpa harus datang langsung ke pabrik. sistem dapat menyediakan informasi sesuai yang dibutuhkan pengelola daur ulang botol bekas, laporan transaksi pemesanan botol daur ulang, dan laporan penjualan. dengan adanya fasilitas tersebut maka pengelola akan lebih mudah, cepat dan akurat dalam membuat laporan karena pengolahan datanya dilakukan oleh sistem. referensi aediyansyah, a. (2016). laporan akhir penelitian “perancangan sistem informasi penjualan daur ulang botol bekas (pet) berbasis web.” jakarta. christian, a., & ariani, f. (2018). rancang bangun sistem informasi peminjaman perangkat demo video conference berbasis web dengan metode waterfall. jurnal pilar nusa mandiri, 14(1), 131–136. retrieved from http://ejournal.nusamandiri.ac.id/ejurnal/i ndex.php/pilar/article/view/802 frieyadie, f. (2014). penggunaan model rad untuk pembangunan sistem informasi penjualan tiket bus online. jurnal pilar nusa mandiri, 10(2), 204–208. retrieved from http://ejournal.nusamandiri.ac.id/ejurnal/i ndex.php/pilar/article/view/359 rachmawati, y., septiana, l., & yulianti, s. d. (2016). sistem informasi penjualan alat tulis kantor berbasis web pada cv. sumber rezeki jakarta. in seminar nasional ilmu pengetahuan dan teknologi komputer (p. inf283–inf.288). jakarta: pppm stmik nusa mandiri jakarta. http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 16 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. retrieved from https://konferensi.nusamandiri.ac.id/prosid ing/index.php/sniptek/article/view/231 sari, i. y., sunarko, s., & hardati, p. (2016). tingkat pengetahuan warga kampus di fakultas ilmu sosial universitas negeri semarang tentang pengelolaan sampah. edu geography, 4(3), 50–56. retrieved from https://journal.unnes.ac.id/sju/index.php/e dugeo/article/view/13815 sintawati, i. d., & sari, a. m. (2017). perancangan sistem informasi penjualan perlengkapan tidur berbasis web studi kasus toko batik galinah jakarta. paradigma jurnal komputer dan informatika, 19(2), 127–130. https://doi.org/10.31294/p.v19i2.2331 suci, f., zuraidah, e., & wajhillah, r. (2016). perancangan sistem informasi persediaan barang pada pt. cipta niaga semesta sukabumi berbasis intranet. seminar nasional ilmu pengetahuan dan teknologi komputer, 135– inf.142. retrieved from http://konferensi.nusamandiri.ac.id/prosidi ng/index.php/sniptek/article/view/253 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2 march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.467 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 171 evaluation of machine learning using the k-nn algorithm to determine the quality of meat before consumption feronika-1*), masrizal-2, ibnu rasyid munthe-3 1,3 department of information system, 2department of informatics management 1,2,3 universitas labuhanbatu sumatera utara, indonesia 1*) feronika@gmail.com, 2masrizal120405@gmail.com, 3ibnurasyidmunthe@gmail.com (*) corresponding author abstract meat is one of the sources of animal protein for humans, and one of the requirements that must be met so that the human body does not lack protein, especially animal; this protein can be obtained from beef, chicken, and other meats, but the most important thing here is the content contained in meat, whether it has been contaminated with chemicals, e.g., chicken that has been injected with chemicals that cause the chicken to look fat, or beef whose flexibility has decreased and the ph is getting more acidic. this research tries to predict meat quality by looking at two parameters: flexibility and acidity. the programming language used is r language, using the k-nn method or algorithm to determine the meat's condition suitable for consumption. in detail, it will be processed in machine learning using the k-nn algorithm; there are two criteria for consumption of meat, namely good or not good for consumption; in detail, the output will be explained using a specific graph using a plot function, and array data will be specifically classified to represent values. the value of 2 variables, namely feasible or not suitable for consumption. keywords: machine learning, k-nn algorithm, r language, meat, acidity prediction, flexibility prediction abstrak daging adalah salah satu sumber protein hewani bagi manusia, dan salah satu syarat yang harus dipenuhi agar tubuh manusia tidak kekurangan protein khususnya hewani, protein ini bisa didapatkan dari sapi, ayam, dan daging lainnya, namun yang terpenting disini adalah kandungan yang terdapat pada daging, apakah sudah terkontaminasi dengan zat kimia, misalnya ayam potong yang telah disuntik dengan zat kimia yang menyebabkan ayam kelihatan gemuk, atau daging sapi yang tingkat kelenturannya sudah berkurang dan ph yang semakin asam. riset ini mencoba untuk melakukan prediksi kualitas daging dengan melihat dua parameter yaitu kelenturan dan keasaman. bahasa pemrograman yang digunakan adalah r languag e, menggunakan metode atau algoritma k-nn yang dapat menentukan kondisi daging layak untuk dikonsumsi. secara detail akan diolah pada machine learning menggunakan algoritma k-nn ini, terdapat dua kriteria daging konsumsi yaitu bagus atau kurang bagus untuk dikonsumsi, secara detail output akan dijelaskan menggunakan specific graph menggunakan plot function, dan data array akan secara spesifik diklasifikasikan untuk merepresentasikan nilai-nilai dari 2 variable yaitu layak atau tidak layak konsumsi. kata kunci: machine learning, k-nn algorithm, r language, daging, prediksi keasaman, prediksi kelenturan introduction the economic factor is an essential and decisive factor to be able to escape the recession in indonesia in 2023, so what needs to be done by farmers is to utilize the existing land for several types of livestock, including broiler breeders. beef or chicken in the freezer for a long time causes the level of flexibility in the beef to be reduced or hard. likewise, with cattle farms, broiler breeders, and other breeders such as rabbits, goats, and others whose type of cuisine tends to be "sate." researchers need to write and share in detail new methods such as aiot & lorawan (adi & kitagawa, 2020), (liani et al., 2021), (mukti et al., 2021). machine learning and k-nn algorithm (lv et al., 2021), (du & li, 2019), ((jia, 2022) in solving quality problems of beef, chicken, and other types of meat consumption. one of the indonesian people's favorite foods is 'meatballs', like a resident of malang, east java, indonesia, is one of the fans of meatballs with many variants of meatballs. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.467 jurnal riset informatika vol. 5, no. 1 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 172 meatballs are also made from meat, e.g., beef. however, if when it is processed into meatballs, you see the quality of the beef being grilled, is it healthy, fresh, or the condition of the meat is no longer red or bluish. figure 1. k-nn algorithm overview figure 1 can be seen as an example of a binary classification with k=3. the green dot in the middle is the test sample x. the labels of the three neighbors are 2×(+1), and 1×(-1) yields the majority prediction (+1). k-nn algorithm ((pawlovsky & matsuhashi, 2017),(palacios & suzuki, 2019) uses a distance matrix, and it concludes that the closest parameter is the best conclusion. equation 1 shows the formula for this k-nn algorithm (chang & liu, 2011), (yao & cao, 2022), (salim et al., 2020), (song et al., 2020), (li et al., 2020). dist(x,x’)≥ max dist(x,x’’), (x’’,y’’) ⋲ 𝑆𝑥 ................... (1) dist(x, z)=(∑ |𝑋𝑟 − 𝑍𝑟 | 𝑑 𝑟=1 𝑝 ) 1/𝑝 ................................. (2) h(x)=mode ({y”( x’’,y’’) ⋲ 𝑆𝑥 }) .................................. (3) √∑ 𝑥𝑖 − 𝑦𝑖 2𝑘 𝑖=1 .................................................................. (4) figure 2. k-nn algorithm for the feasibility of meat consumption furthermore, figure 2 explains the method used in this manuscript where the k-nn method (yao & cao, 2022), (song et al., 2020), (sun et al., 2020), (sushmitha & jagadeesh, 2022) can solve the problem of eligible or not like meat consumed by the community, especially in indonesia. research methods this research will focus on using the k-nn algorithm (zhai, 2022), (li et al., 2020), (jia, 2022), (sushmitha & jagadeesh, 2022), (sun et al., 2020), (yunneng, 2020), (palacios & suzuki, 2019) and (setia & garg, 2021). this algorithm is used to classify new objects based on attributes and samples from training data, and this algorithm uses the predicted value of the new instance value. step-by-step to running of k-nn algorithm in general: 1. choose k nearest neighbors randomly 2. map dataset to vector space 3. separate the dataset into training data and test data 4. calculate the distance, d, between the test data and the training data 5. sorting d from smallest to largest 6. take and separate k-sorting data 7. observe the majority class 8. classify test data by majority jurnal riset informatika vol. 5, no. 2 march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.467 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 173 furthermore, it will be explained in full in the following flowchart, where the value of k must be determined first, then calculate the distance, sort it, and classified the test data. moreover, pseudo code is made to understand the flow of the program so that readers do not experience difficulties in understanding (setia & garg, 2021). figure 3. k-nn algorithm flowchart pseudocode_1 // x: training data // y: class labels of x //x: unknown sample classify (x,y,x) for i = 1 to m do compute distance d(x,..,x) end for compute set 1 containing indicates for the k smallest distances d (x,...x). end for compute set 1 containing indicates for the k smallest distances d (x,..x). return majority label for {yi where i e i} results and discussion the following table data determines the value to look for or predictions that will give the best quality value on meat quality. moreover, table 1 is a classification of meat quality. table 1. classification of meat quality brand acidity value flexibility value category a 7 1.2 good b 6 1.7 not good c 8 1.5 good d 5 1.3 not good e 9 1.0 good f 9.5 1.4 good g 8.3 0.8 not good h 7.5 1.1 ? furthermore, the next step is to run the program using r language using k-nn algorithm (salim et al., 2020), (riquelme et al., 2020); the display on the website page is data taken; this is stage 1. next is the relationship between flexibility and uniformity of product data as specifically shown in figure 4. code_1_running x<-c(7,6,8,5,9,9.5,8.3,7.5) > y<c(1.2,1.7,1.5,1.3,1.0,1.4,0.8,1.1) > bagus_x<-c(7,8,9,9.5) > bagus_y<-c(1.2,1.5,1.0,1.4) > kurang_x<-c(6,5,8.3) > kurang_y<-c(1.7,1.3,0.8) > test_x<-(7.5) > test_y<-(1.1) > merk_bagus<-c('a','c','e','f') > merk_kurang<-c('b','d','g') > merk_test<-c('h') >plot(x,y,col="blue",main="dataproduk" ,cex=1.3,pch=16,xlab="keasamaan",ylab= "kelenturan") step-by-step to running of k-nn algorithm on this project: 1. group product data into product array objects according to their respective categories 2. group the product brand data (label) into the brand array object according to their respective categories 3. plot an array of products using different symbols 4. plot array brands 5. plot legend p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.467 jurnal riset informatika vol. 5, no. 1 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 174 figure 4. the relationship between flexibility and uniformity of product data step 2 is to determine the value of k; for example, if k is 3, then k=3, we can choose k odd, 1, 3, or 5. then step 3 is to calculate the distance (d) between the test data (h) and neighbors; based on the results of observations of the type of data and the shape of the graph plot, then we can use the euclidean distance formula to determine the distance d as the figure 5. euclidean distance (d) = √(𝑥2 − 𝑥1) 2 + (𝑦2 − 𝑦1) 2 figure 5. the euclidean distance (d) then we will determine the coordinate value, the coordinate value is seen in table 2. table 2. coordinate value number coordinate 1 a(7, 1.2) 2 b (6, 1.7) 3 c(8,1.5) 4 d(5,1.3) 5 e(9,1.0) 6 f (9.5,1.4) 7 g(8.3,0,8) 8 h(7.5,1.1) 𝑑𝐻𝐴 = √7.5 − 7 2 + 1.1 − 1.22 = 0.509902 𝑑𝐻𝐵 = √7.5 − 6 2 + 1.1 − 1.72 = 1.615549 𝑑𝐻𝐶 = √7.5 − 8 2 + 1.1 − 1.52 = 0.640312 𝑑𝐻𝐷 = √7.5 − 5 2 + 1.1 − 1.32 = 2.507987 𝑑𝐻𝐸 = √7.5 − 9 2 + 1.1 − 1.02 = 1.503333 𝑑𝐻𝐹 = √7.5 − 9.5 2 + 1.1 − 1.42 = 2.02237 𝑑𝐻𝐺 = √7.5 − 8.3 2 + 1.1 − 0.82 = 0.85440 stage 4 is sorting the results of the d calculations from the smallest to the most significant d. then choose d as much as the value of k, namely k = 3 pieces, before sorting the results of the d calculation as following table 3. table 3. distance calculation d d value classification ha 0.509902 a (good) hb 1.615549 b (good) hc 0.640312 c (not good) hd 2.507987 d (not good) he 1.503333 e (not good) hf 2.022375 f (good) hg 0.854400 g (good) the next step is to determine a good product with a cross and determine the test data. the plot results can be seen in figure 3. in figure 3, those marked with a cross are of good quality, while those not marked with a cross are data that are not of good quality. the box is a sample of test data. moreover, the relationship between flexibility and uniformity of product data is presented explicitly in figure 6. code_2_running >points(bagus_x,bagus_y,col="red",pch= 4,lwd=2,cex=2) >points(test_x,test_y,col="black",pch= 22,lwd=2,cex=2) figure 6. the relationship between flexibility and uniformity of product data conclusions and suggestions conclusion using the k-nn algorithm using r language, the meat quality can be determined by several parameters, namely the value of flexibility and the value of acidity, which is inputted for processing or the data to be trained by the k-nn algorithm. good quality of the meat was found at acidity seven and flexibility 1.2, 8 and 1.5, 9 and 1.0, and 9.5 and 1.4 suggestion the new method can be compared to produce an error comparison value (%) of the quality of processed meat or meat consumed by the jurnal riset informatika vol. 5, no. 2 march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.467 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 175 community if it is seen not only as two parameters but can be more. references adi, p. d. p., & kitagawa, a. 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(2022). improving knn algorithm efficiency based on pca and kd-tree. proceedings 2022 international conference on machine learning and knowledge engineering, mlke 2022, 83–87. https://doi.org/10.1109/mlke55170.2022. 00021 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.527 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 263 extreme programming method for integrated service system website development in rejosari village eka supriyati-1, muhamad azrino gustalika-2*) teknik informatika-1,-2 institut teknologi telkom purwokerto purwokerto, indonesia 19102002@ittelkom-pwt.ac.id-1, *azrino@ittelkom-pwt.ac.id-2 *) corresponding author abstrak the rejosari village hall provides a manual letter submission service which is sometimes problematic, including when residents are about to submit an application letter, they have to come directly to the village hall office while the residents are still out of town. apart from that, there was no media information which resulted when they were going to submit the requirements for the letters they brought were not in accordance, then from the data collection, and the letters were still in the books. therefore we need a service system for the submission of letters. this integrated service system for residents of rejosari village is a web-based information system, the use of technology in the form of a website makes it easier to receive all forms of existing information. the extreme programming (xp) method is applied in developing this system, a software engineering process that refers to an object-oriented approach. the stages of this method start from the planning, design, coding and testing stages using black box testing with descriptive analysis techniques, which produce tests in the form of a proportion value of 96.42% and have a possible interpretation. in addition, this system can impact progress in the field of informatics in the form of information media as well as learning materials. keywords: extreme programming (xp); information system; integrated services abstract balai desa rejosari menyediakan pelayanan pengaujuan surat masih bersifat manual yang terkadang bermasalah, diantaranya pada saat penduduk akan mengajukan surat permohonan harus langsung datang ke kantor balai desa dan sementara penduduk tersebut masih diluar kota. selaian itu tidak adanya media informasi yang diberikan akibatnya ketika akan mengajukan surat persyaratan yang dibawa belum sesuai, kemudian dari pendataan suratnya masih dalam pembukuan. oleh karena itu diperlukan suatu sistem pelayanan untuk pengajuan surat. sistem pelayanan terpadu bagi warga desa rejosari ini merupakan sistem informasi berbasis web, pemanfaatan teknologi berupa website memudahkan dalam menerima segala bentuk informasi yang ada. dalam proses pengembangan sistem ini diterapkan metode extreme programming (xp) yang merupakan proses rekayasa perangkat lunak yang mengacu pada pendekatan berorientasi objek. tahapan metode ini dimulai dari tahap perencanaan, perancangan, pengkodean dan pengujian menggunakan pengujian blackbox dengan teknik analisis deskriptif yang menghasilkan pengujian berupa nilai persentase sebesar 96,42% dan memiliki interpretasi yang sangat layak. selain itu sistem ini dapat memberikan dampak kemajuan dalam bidang ilmu informatika berupa media informasi sekaligus sebagai bahan pembelajaran. kata kunci: extreme programming (xp); sistem informasi; pelayanan terintegrasi introduction in the era of revolution 4.0, digital technology is growing, for example, in information systems. according to the ministry of communication and information of the republic of indonesia, developing information systems can provide many advantages in various fields. one of them is in the field of public services. public service is an action that is needed by the community in all administration by public service providers (bazarah & pujiastuti, 2022). one type of public service is administrative service. the administration itself is a series of recording information in the form of information that is useful in action and facilitates a relationship (wiryananta, k., safitri, r., & prasetyo, 2020). one of the forms of implementing administrative services is integrated p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.522 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 264 system services in the village. based on law number 6 of 2014 concerning villages (bender, 2016) states that the village government must carry out its duties properly to serve all the administrative needs of the village community. rejosari village is in bojong district, pekalongan regency, central java province. based on the central bureau of statistics for pekalongan regency, the village has a population of around 1,972. the administrative service for application letters is still manual at the village hall, which must come to the village hall office with only one person in charge. therefore, sometimes people experience problems. based on the results of observations and interviews that have been conducted, some obstacles are often experienced, including when residents are still out of town and temporarily really need an application letter at an urgent time, so they cannot come directly to the village hall office. in addition, there are no information media provided. as a result, when residents are going to submit an application letter, the requirements needed are not suitable, so residents must complete the requirements first. it is less effective. then for the data collection process, the application letter is still in the form of books which must be calculated first if you report the total in one year. thus the need for a service system submission of application letters. the application letter submission service system will be designed as a website to solve existing problems. technology can be accessed via a website on a browser via an internet connection to obtain all available information. this research also applies the extreme programming (xp) method. the extreme programming (xp) method is an object-oriented approach in software engineering. in addition, this method is more efficient, adaptive and flexible in the system development process (widiastuti & cakranegara, 2022). that way, in making this system, there is involvement from the village as admin and residents as users. so that this system becomes adaptive to all changes that exist, and it is hoped that this research will make it easier for rejosari village hall officers and residents of rejosari village to carry out service processes and administrative submissions in the form of application letters with more significant test results than previous studies from research conducted by julisatya et al. regarding the development of a public service system with a test result of 88% and noer azni septiani et al. regarding the creation of a village information system that applies the extreme programming (xp) method. research methods extreme programming (xp) method (shrivastava et al., 2021) is one of the software engineering processes that refers to an objectoriented approach. in addition, this method is more efficient, adaptive and flexible in the system development process, and the core values of extreme programming include communication, courage, simplicity, feedback and hard work. stages in extreme programming (supriyatna & puspitasari, 2021): figure 1. extreme programming planning the initial stage of this method is planning. several planning activities were carried out during this phase, including: identifying problems, analyzing needs, and determining implementation schedules during system development (hartawan et al., 2021). the results of this stage are based on data collection by conducting interviews and observations. design design is a system architectural modelling process in the form of creating wireframes or mockups with figma tools while also designing databases using draw.io (lamada et al., 2022) coding coding is a stage in implementing the design process using a programming language (sudarsono, 2020). this stage uses visual studio code and xampp tools, which use a bootstrap framework while using php and javascript. testing system testing is the final stage in this method, where the results of the implementation will be tested to determine the feasibility of the system being built or whether it is to the client's needs (wanti et al., 2021), and at this final stage, it jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.527 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 265 uses the black box testing method which focuses on system functionality. data collection at the data collection stage (dewi, 2022) observation process is carried out (ahmadi et al., 2021) by monitoring directly the process of submitting a letter at the rejosari village hall by aiming to know the conditions of the existing problems to provide appropriate solutions. in addition, it also conducts an interview process with related parties in the system creation process, which aims to obtain information regarding the process of submitting an application letter at the rejosari village hall. blackbox testing the black box testing method (supriyono, 2020) requires the lower and upper limits to be tested with the number of entry data fields or based on its functionality (mahendra & asmarajaya, 2022). the method of testing black box testing is inputting data on a form so that the output results are by the inputs (purnama et al., 2022). descriptive analysis techniques descriptive analysis functions to produce percentage values from test respondents (rombey et al., 2019) and is usually used in testing the functionality of a system with the formalities of the calculation as follows(sopian, 2018) : 𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑆𝑐𝑜𝑟𝑒 𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑆𝑐𝑜𝑟𝑒 x 100 % ............................ (1) table 1. eligibility percentage presents eligibility level 81% 100% very worth it 61% 80% worthy 41% 60% enough 21% 40% not feasible ≤ 20% very unworthy based on the table above, there are four categories of system testing feasibility from the results of descriptive analysis calculations. results and discussion the results of this study can be seen from the data collection stage to the system website testing stage according to the flowchart in figure 2. at the data collection stage, based on the results of observations, interviews, and literature study then, proceed with the system development stage, which starts with planning in the form of identifying problems and analyzing needs, so that it can be continued with the design stage in the form of system modelling, system ui and database, from the design stage directly implemented into the coding stage as well as system testing. figure 2. research flowchart system development in developing the system using the extreme programming (xp) stage, which produces the following system implementation design: admin implementation results (village officials) dashboard page, figure 3, is the admin dashboard page menu which contains information on total incoming and outgoing data based on user submissions. in addition, there is also a letter format that can be downloaded. figure 3. dashboard page p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.522 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 266 entry page, data then figure 4 is the admin login data page menu which contains information from the user who submitted. besides that, the admin can also use the data search feature. and the admin can find out whether the user's submission has been processed. figure 4. entry page data data page out, in figure 5, is the data page menu. on this page, submit an application that the admin has processed. besides that, the admin can also use the data search feature. figure 5. data page out user implementation page (resident) the list page in figure 6 is a list page menu. on this page, the user inputs the appropriate personal data so they can register and input their id card and family card in jpg, jpge and png formats with a maximum size of 2mb. figure 6. list page dashboard page, figure 7, is the user dashboard page menu. this page displays information about the types of requirements and how to use the system. figure 7. dashboard page page, figure 8, is the form page menu. on this page, a user submitting a certificate must input data along with the required pdf document of less than two mb. figure 8. from page history page, then in figure 9 is the submission history page menu. this page displays information regarding submission information that has been carried out, whether the admin has processed it or not. figure 9. history page requirements page, next in fig 10, is the submission requirements page menu. this page displays information on various types of requirements that need to be prepared by the user for each type of submission made. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.527 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 267 figure 3. requirements page system testing techniques figure 11 is the final stage of making the system using the extreme programming method, which is the testing stage of the entire system's appearance. at this stage, black box testing is used to determine whether the system can be used in the calculations using descriptive analysis techniques. and 17 testers tested this system with details of 6 testers from institutions by lecturers five testers from the expert field of the system in the form of a website with 58 scenarios consisting of 42 scenarios for user pages then 14 scenarios for admin pages. for three village device testers, it consisted of 17 scenarios from the admin page and three testers from villages in the youth, adult and elderly categories consisting of 45 user page scenarios and 14 scenarios from the admin page. figure 4. system testing the following is the result of black box theatre testing using descriptive analysis technique calculations: 𝑌𝑒𝑠 = 783 812 𝑥 100 % = 96,42% 𝑁𝑜 = 29 812 𝑥 100 % = 3,57% the calculation results above can be interpreted when converted to the feasibility percentage table in the black box testing test with a value of 96.42%. conclusions based on the results of implementing the method and creating an integrated server system website. it can be concluded as follows: this study applies the extreme programming (xp) method to produce an integrated service system website that can provide information about the requirements for submitting letters and making letters so that they can carry out data collection and archiving. and testing this 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(2020). a new decade for social changes. technium social sciences journal, 7, 312–320. https://techniumscience.com/index.php/soc ialsciences/article/view/332/124 207 the effectiveness analysis of random forest algorithms with smote technique in predicting lung cancer risk ita yulianti1, ami rahmawati2*), tati mardiana3 sistem informasi akuntansi kampus kota sukabumi universitas bina sarana informatika ita.iyi@bsi.ac.id1 sistem informasi2*), sains data3 universitas nusa mandiri ami.amv@nusamandiri.ac.id2*), tati.ttm@nusamandiri.ac.id3 (*) corresponding author abstrak jika dibandingkan dengan jenis kanker lainnya, sebagian besar penduduk penderita kanker meninggal karena kanker paru-paru. seseorang perlu melakukan tes skrining melalui rontgen, ct scan, dan mri untuk mendeteksi penyakitnya. namun, sebelum melakukan proses tersebut, dokter biasanya akan melakukan anamnesis dan pemeriksaan fisik terlebih dahulu untuk mempelajari gejala dan kemungkinan faktor risiko kanker paru-paru. kumpulan data kanker paru memiliki ketidakseimbangan kelas sehingga mempengaruhi kinerja algoritma random forest dalam memprediksi risiko kanker paru. penelitian ini bertujuan untuk menerapkan teknik smote untuk meningkatkan kinerja algoritma random forest dalam memprediksi risiko kanker paru. pada penelitian ini pengolahan dan analisis data menggunakan bahasa pemrograman python. hasil pengujian menunjukkan nilai akurasi sebesar 88% dengan nilai auc sebesar 0,93. saat menggunakan algoritma random forest untuk memperkirakan risiko kanker paru-paru, teknik smote berguna dalam menangani ketidakseimbangan kelas dalam kumpulan data. kata kunci: kanker paru-paru, python, random forest, smote abstract when compared with other types of cancer, most of the population with cancer die from lung cancer.a person needs to do a screening test through x-rays, ct scans, and mri to detect the disease. however, before carrying out the process, the doctor will ordinarily investigate a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer. the lung cancer data set has a class imbalance that affects the performance of the random forest algorithm in predicting the risk of lung cancer. this study aims to employ the smote technique to the random forest algorithm to increase accuracy in predicting lung cancer risk. in this research, data processing and analysis use the python programming language. the test results show an accuracy value of 88% with an auc value of 0.93. when employing the random forest method to forecast lung cancer risk, the smote technique is useful in dealing with class imbalances in the data set. keywords: lung cancer, python, random forest, smote introduction cancer is one of the leading causes of significant morbidity and mortality worldwide (sofia & tahlil, 2018). in 2018, the number of deaths from cancer reached 9.6 million people. lung, prostate, colorectal, stomach, liver, and breast cancers are the biggest causes of cancer deaths every year (who, 2022). when compared with other types of cancer, most of the population with cancer died from lung cancer (rattan et al., 2018). according to who data from 2020, lung cancer is the leading cause of death, with 1.80 million cases (who, 2021). in indonesia itself, the majority of lung cancer sufferers are male due to smoking habits which can make the risk of getting cancer higher (bulan et al., 2017). lung cancer is difficult to detect because many cases of this cancer appear and show symptoms when its development has reached a certain stage (makaju et al., 2018). screening tests through x-rays, ct scans, and mris can detect the 208 disease in the lungs (kurnia et al., 2016). however, before carrying out this process, the doctor will usually do a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer (american cancer society, 2022). in line with the development of science and technology today, various fields ranging from education, economics, social, health, and many others are increasingly taking advantage of the role of technology, especially in the decision-making process. the many applications of this technology certainly encourage researchers to facilitate human work, for example in the health sector, namely to predict certain diseases using data mining techniques. this technique has many methods/algorithms that can be used, one of which is a random forest which is a modified algorithm from the decision tree. this approach has the advantage of being able to process a random selection of features in image datasets or in the form of attributes/parameters, resulting in a low error rate (sari et al., 2020). the random forest algorithm is generally widely used in previous studies including to measure the severity of disease in apple leaf images (ratnawati & sulistyaningrum, 2019). in addition, this algorithm is also better than the xgboost algorithm in overcoming the imbalance class in the classification of hepatitis c disease levels (syukron et al., 2020). therefore, the purpose of this study was to analyze and measure the effectiveness of the random forest algorithm in predicting the risk of lung cancer through a physical examination in the form of perceived symptoms and habits or lifestyles. patients with lung cancer usually have symptoms including shortness of breath, chest pain that does not improve, cough with blood, changes in the shape of the fingers, difficulty swallowing, and weight loss (syifa et al., 2016). not only that but in this study, the rf algorithm will also be integrated with the smote technique to overcome if there is a class imbalance in the dataset used so that it can improve the performance of the algorithm and the predicted outcome obtained is optimal. this research is expected to provide novelty in the form of modeling that can be developed into a system that can predict the disease. although not all lung cancer can be prevented, knowing the early detection of the risk of this disease, allows a person to immediately carry out a follow-up examination (screening) and be treated by a doctor so that it can reduce the risk of lung cancer. research methods the following is an overview of the research framework used to describe all the stages carried out in this research: figure 1. research methode framework the figure above describes the stages or steps taken to achieve the results of this research. this stage begins with selecting a dataset according to the topic raised, namely lung cancer. the next stage is preprocessing which includes removing duplicates, feature scaling and over-sampling smote. in the preprocessing stage, the dataset is processed first before entering the modeling stage. this is done so that the dataset used does not have problems that can complicate the processing. then move on to the modeling stage, the dataset is divided into training data and testing data with a ratio of 80:20 using split validation combined with cross validation using the tested algorithm, namely random forest. finally, the results of the algorithm test are validated with the help of a confusion matrix which gives output in the form of accuracy values and auc graphs. data collection the dataset in this research is a secondary dataset obtained from the kaggle repository with csv format which has a total of 16 attributes including the target class. 209 preprocessing in this research, there are three stages of preprocessing the data used, namely, removing duplicates, feature scaling and over-sampling smote. the purpose of removing duplicates is to prevent duplication of data by filtering and deleting the same data in the dataset (hendra & fitriyani, 2021). meanwhile, feature scaling is carried out to examine the diversity of values that occur in each variable and to balance the scale so that it has the same range of values (aripin, 2021). class balance in the application of classification algorithms is important to pay attention to so that the resulting performance has a good prediction. therefore, to overcome the class imbalance in the research dataset, a resampling technique is used, namely oversampling, this technique was chosen because it can balance the dataset that is lacking in the minority class without reducing the dataset (sulistiyono et al., 2021). the oversampling algorithm used is synthetic minority over-sampling technique (smote), where this algorithm is an algorithm that can be called similar to random oversampling (ros) but the difference is that in smote, existing samples are not duplicated randomly but are made with the nearest neighbor concept (arifiyanti & wahyuni, 2020). the workings of the smote algorithm are to interpolate the original data and then enter the artificial data generated in the minority class so that the data obtained varies (indrawati, 2021). in general, the function of this algorithm is written with smote (x, n, k), where x is the minority data, while n is the percentage of the total instances to be created, and k is the number of closest instances of the instance being searched for using the euclidean distance formula as following (sulistiyono et al., 2021): 𝑑𝑖𝑠𝑡 = √(𝑋1 − 𝑌1) 2 + (𝑋2 − 𝑌2) 2 + ⋯ + (𝑋𝑛 − 𝑌𝑛 ) 2…….(1) modeling random forest was first introduced by breiman in 2021, which is one of the decision treebased machine learning methods that is often used because it has the advantage of having high dimensions with a faster process that functions, especially on subset features (ardiningtyas & paulina heruningsih prima, 2021). this algorithm can be explained as a non-parametric model with the concept of combining learning models to improve performance in both regression and classification cases (aripin, 2021). there are three steps in the development of the random forest algorithm, namely (religia et al., 2021): 1. set sampling is part of training k, 2. making each decision tree model, and 3. collection of k trees into the rf model the random forest algorithm is a development method from the cart decision tree, so it is not surprising that in building the decision tree the cart method is used which begins by finding the entropy value first (aripin, 2021) using equation (2) below: 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑌) = − ∑ 𝑖𝑝(𝑌)𝑙𝑜𝑔2𝑝(𝑌) ......................... (2) then proceed with calculating the information gain using eq. (3) 𝐼𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝐺𝑎𝑖𝑛 (𝑌, 𝑎) = 𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑌) − ∑ 𝑣𝑎𝑙𝑢𝑒𝑠(𝑎) |𝑌𝑣| |𝑌𝑎| (𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑌𝑣)) ................................ (3) results and discussion dataset collecting data that is processed in this study is public data obtained from the kaggle website. the dataset was collected from the lung cancer prediction system's online site. the following is a description of the lung cancer dataset as shown in table 1. table 1. lung cancer dataset description lung cancer dataset number of attributes number of instances 16 309 from the data obtained, there are attributes, namely: gender, age, smoking, yellow fingers, anxiety, peer pressure, chronic disease, fatigue, allergy, wheezing, alcohol consuming, coughing, shortness of breath, swallowing difficulty, chest pain, and lung cancer. an explanation of each attribute will be described in table 2. table 2. description of data attributes attributes value gender m (male), f (female) age 21 – 87 smoking yes = 2, no = 1 yellow fingers yes = 2, no = 1 anxiety yes = 2, no = 1 peer pressure yes = 2, no = 1 chronic disease yes = 2, no = 1 fatigue yes = 2, no = 1 allergy yes = 2, no = 1 wheezing yes = 2, no = 1 alcohol yes = 2, no = 1 210 coughing yes = 2, no = 1 shortness of breath yes = 2, no = 1 swallowing difficulty yes = 2, no = 1 chest pain yes = 2, no = 1 class lung cancer yes, no data exploration and visualization data exploration and visualization are carried out to obtain information and understand the attributes in the dataset. exploration and visualization of several attributes are presented in graphical form as follows: figure 2. age attribute the results in the first graph (see figure 2.) show that the age attribute that dominates people at risk for lung cancer is the age of 60-70 years. figure 3. yellow fingers attribute then, the second result is in the form of a pie chart that shows the percentage of yellowing fingers symptoms experienced by people who are at risk of lung cancer by 42.39%. figure 4. shortness of breath attribute based on the results of exploration and visualization of further data, it shows that shortness of breath is a symptom that is often experienced by people who are at risk of lung cancer. figure 5. smoking attribute exploration of data for smoking attributes shows that active smokers are more susceptible to lung cancer. 211 figure 6. age dan alcohol attribute the next graph (see figure 6.) proves that the age range of 50-80 years and consuming alcohol is more at risk of lung cancer. figure 7. coughing attribute in addition, there is also another attribute that is the most common symptom experienced by people at risk for lung cancer, namely cough (see results of data exploration and visualization in figure 7.). figure 8. wheezing attribute finally, the graphic results from the wheezing attribute show that loud, high-frequency breathing sounds are often experienced by people who are at risk for lung cancer. modeling results a. remove duplicate removing duplicates is a step taken to remove duplication of data. in the lung cancer dataset, there are 33 duplicates, so removing duplicates is done. the number of tuples in the dataset, which was originally 309, has changed to 276. b. feature scaling in this research, feature scaling is carried out, namely to make numerical data have the same range of values. the scaling feature used is minmaxscaler to scale data values into a range. c. synthetic minority over-sampling technique (smote) figure 9. class lung cancer based on figure 9, it can be seen that there is a very large distribution of data between classes, where the yes class has a sample size of more than 212 250 data while the no class only has a sample number of fewer than 50 data so that the class is not balanced. to deal with imbalanced classes, in this study, smote was used. smote will select a point from the minority class and calculate the k nearest neighbors for this point. d. modeling results with random forest to determine and test the classification model, a data split was carried out, namely dividing the data into two parts. 80% for training data and 20% for test data. the training data is used to build the model while the test data is used for valid performance evaluation. in addition, crossvalidation was also carried out on the test data with a value of k = 10. the results of the tests carried out were to produce values of accuracy (confusion matrix), precision, recall, and auc. the results of the confusion matrix can be seen in table 3. table 3. confusion matrix of rf algorithm classification classified as positive negative positive 4 5 negative 2 45 based on table 3, there are details on the number of true positive = 4, false positive = 5, false negative = 2, and true negative = 45. from this data, accuracy, precision, and recall can be calculated. the test result data can be seen in figure 10 and table 4. table 4. results of random forest test performance random forest accuracy 0,88 precision 0,90 recall 0,95 figure 10. results of random forest test in addition, the test results for the random forest algorithm can also be seen based on the roc graph which can be seen through the resulting auc value of 0.93. this proves that the accuracy of the model is included in the excellent classification category. the resulting roc graph can be seen in figure 11. figure 11. auc random forest meanwhile, the precision and recall graphs are visualized through a graph that can be seen in the following figure. figure 12. prc random forest conclusions and suggestions conclusion from the research results obtained, the lung cancer dataset has a class imbalance. to overcome the class imbalance, smote (minority over sampling technique) was used and the random forest algorithm was used for classification testing. testing the lung cancer dataset yielded an accuracy of 88% with an auc value of 0.93. while the precision value is 0.90 and the recall value is 0.95. based on this explanation, it can be concluded that the application of the smote technique to the random forest algorithm can improve the performance of unbalanced data classification, so that the level of effectiveness of the resulting algorithm test becomes more optimal. suggestion 213 for further research, it is hoped that the modeling resulting from this research can be redeveloped by being integrated using optimization algorithms and implemented into a gui application that can be easily accessed by everyone, as well as adding image datasets so that prediction results are better. references american cancer society. 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(2022). cancer. world health organization. https://www.who.int/healthtopics/cancer#tab=tab_1 jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 167 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penerapan data mining terhadap penjualan pipa pada cv. gaskindo sentosa menggunakan metode algoritma apriori golda tm napitupul1, anggi oktaviani2, dahlia sarkawi3, ita yulianti4 1sistem informasi sekolah tinggi manajemen informatika dan komputer nusa mandiri www.nusamandiri.ac.id goldatmn0904@nusamandiri.ac.id 2teknik informatika sekolah tinggi manajemen informatika dan komputer nusa mandiri www.nusamandiri.ac.id anggi.aov@nusamandiri.ac.id 3administrasi perkantoran universitas bina sarana informatika www.bsi.ac.id dahlia.dls@bsi.ac.id 4sistem informasi universitas bina sarana informatika www.bsi.ac.id ita.iyi@bsi.ac.id abstrak cv. gaskindo sentosa merupakan salah satu perusahaan manufaktur yang bergerak di bidang penjualan pipa. guna meningkatkan kualitas pelayanan terhadap konsumen, perusahaan tersebut dituntut untuk dapat mengatasi permasalahan yang seringkali muncul diantaranya, kurangnya atau tidak ada (habis) stok persediaan dari jenis pipa yang paling diminati. hal tersebut dapat disebabkan karena pola perilaku belanja konsumen yang selalu berubah-ubah dan tidak dapat diprediksi. oleh karena itu, dalam upaya mengatasi permasalahan yang terjadi, penelitian ini dibuat dengan tujuan untuk memprediksi penjualan pipa pada cv. gaskindo sentosa dengan menerapkan algoritma apriori sehingga dapat diketahui pola perilaku konsumen dan diharapkan dapat meningkatkan penjualan pada perusahaan tersebut. adapun untuk data yang digunakan yaitu dengan memanfaatkan data history dari semua transaksi yang pernah terjadi di cv. gaskindo sentosa. dari hasil penelitian ini, diperoleh bahwa algoritma apriori dapat membantu mengembangkan strategi pemasaran untuk memasarkan produk lain dengan menganalisa kelebihan dari nilai jual produk yang paling laris terjual. kata kunci: algoritma apriori, data mining, penjualan produk, pipa abstract cv. gaskindo sentosa is one of the manufacturing companies engaged in pipe sales. in order to improve the quality of service to consumers, the company is demanded to be able to overcome problems that often arise including, lack or not (out of stock) inventory of the most desirable types of pipes. this can be caused by the ever-changing and unpredictable patterns of consumer shopping behavior. therefore, in an effort to overcome the problems that occur, this study was made with the aim to predict the sales of pipes in the cv. gaskindo sentosa by applying a priori algorithm so that it can be known patterns of consumer behavior and is expected to increase sales at the company. as for the data used, namely by utilizing data history of all transactions that have occurred in the cv. gaskindo sentosa. from the results of this study, it was found that a priori algorithm can help develop marketing strategies to market other products by analyzing the advantages of selling the most selling products. keywords: a priori algorithms, data mining, product sales, pipes http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 168 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pendahuluan dalam dunia bisnis, setiap perusahaan sudah tentu harus bisa bersaing dan berpikir bagaimana caranya agar perusahaan bisa terus berkembang dan dapat memperluas skala bisnisnya tersebut. agar dapat meningkatkan penjualan produk yang dijual, para pelaku usaha harus memiliki berbagai strategi yang dilakukan. salah satu caranya yaitu dengan memanfaatkan seluruh data transaksi penjualan yang telah terjadi pada perusahaan itu sendiri. cv. gaskindo sentosa merupakan salah satu perusahaan manufaktur yang bergerak di bidang penjualan pipa dengan jenis pipa diantaranya pipa spiral, pipa seamless, pipa carbon steel, pipa bakrie, pipa gasket, gasket tombo, gasket non asbestos, reducer tee, reducer concentric dan masih banyak lagi jenis yang lainnya. guna meningkatkan kualitas pelayanan terhadap konsumen pada cv. gaskindo sentosa, perusahaan dituntut untuk dapat mengatasi permasalahan yang seringkali muncul diantaranya, kurangnya atau tidak ada (habis) stok persediaan dari jenis pipa yang paling diminati oleh konsumen sehingga dapat berdampak terhadap penurunan penjualan. hal tersebut dapat disebabkan karena pola perilaku belanja konsumen saat ini yang selalu berubah-ubah dan tidak dapat diprediksi setiap harinya. maka dari itu, untuk mengatasi permasalahan yang terjadi pada cv. gaskindo sentosa, diperlukan suatu model khusus agar dapat memprediksi penjualan periode selanjutnya dengan memanfaatkan data melalui pengamatan history transaksi. data tersebut tidak hanya berfungsi sebagai arsip bagi perusahaan, tetapi juga dapat diolah menjadi informasi yang berguna dalam upaya peningkatan penjualan dan pemasaran produk. ada beberapa peneliti yang sebelumnya sudah melakukan penelitian mengenai prediksi penjualan diantaranya; (purnia & warnilah, 2017), pada penelitian tersebut untuk mendapatkan informasi produk yang paling laris dan diminati tiap konsumen dari suatu database transaksi digunakan algoritma apriori sehingga menghasilkan model dari penelitian yang dapat digunakan untuk pengembangan dalam meningkatkan jumlah jual dan pemasaran produk. selain itu, algoritma apriori juga dapat diimplementasikan sebagai metode yang dapat memprediksi penentuan tata letak barang seperti penelitian yang telah dilakukan oleh (syahdan & sindar, 2018) sehingga dapat mempermudah dan mennghemat waktu para konsumen dalam menemukan letak barang yang dicari. terakhir, penelitian yang dilakukan oleh (choiriah, 2019) yang memanfaatkan algoritma apriori dalam memprediksi penjualan e-tiket sehingga dapat diketahui pola frekuensi tiket yang paling banyak terjual. algoritma apriori termasuk jenis rule asosiasi dalam data mining yang memiliki cara kerja dengan mencari frekuensi set items yang dijalankan di sekumpulan data yang kompleks (pane, 2013). analisis apriori juga bisa dikatakan sebagai suatu proses dimana pencarian semua rule apriori dilakukan dengan melihat syarat minimum untuk support dan confidence-nya. dari model pengetahuan yang dihasilkan oleh algoritma tersebut dapat digunakan untuk memprediksi kecenderungan data yang akan datang (warumu, buulolo, & ndururu, 2017). berdasarkan penjelasan tersebut, penelitian ini bertujuan untuk memprediksi penjualan pipa pada cv. gaskindo sentosa dengan menerapkan algoritma apriori sehingga dapat diketahui pola perilaku konsumen dan diharapkan dapat meningkatkan penjualan pada perusahaan tersebut. metode penelitian jenis penelitian penelitian ini menggunakan pendekatan kuantitatif dan berupa penelitian terapan. target/subjek penelitian target/subjek dalam penelitian ini akan menghasilkan prediksi penjualan pada cv. gaskindo sentosa dengan menggunakan data history transaksi yang sudah terjadi dan menerapkan teknik data mining yaitu algoritma apriori dengan cara mencari rule prediksinya. model pengembangan sistem model pengembangan sistem digunakan dalam penelitian ini melibatkan teknik data mining, dimana data mining ini merupakan rangkaian proses untuk menemukan nilai tambah dari kumpulan data yang besar berupa pengetahuan yang selama ini tidak diketahui secara manual (kusrini & taufiq, 2007). dalam proses penggunaannya data mining selalu melibatkan teknik statistik, matematika, kecerdasaan buatan, dan mesin pembelajaran yang biasanya digunakan untuk mengektraksi dan merekongnisi informasi yang bermanfaat dan pengetahuan yang terbentuk dari berbagai database besar dan kompleks. data mining bertujuan untuk mencari pola atau hubungan yang biasanya tidak disadari kebenarannya berdasarkan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 169 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional hasil analisis otomatis dari data yang berjumlah besar atau kompleks (kusrini & taufiq, 2010). berdasarkan uraian yang telah disampaikan, ada beberapa hal penting yang terkait dengan data mining, diantaranya: 1. data mining merupakan suatu prosedur otomatis yang menghasilkan prediksi berdasarkan data yang sudah ada. 2. data yang akan dianalisi yaitu berupa kumpulan data yang kompleks . 3. data mining bertujuan untuk menemukan relasi yang memungkinkan dapat menghasilkan manifestasi yang bermanfaat. proses data mining terdiri dari beberapa fase yang saling terkait dan tidak harus dijalankan secara linear (lukman & imam sunoto, 2017). metodologi cross-industry standard process model for data mining (crisp-dm) merupakan upaya untuk standarisasi proses data mining yang memiliki enam fase saling terkait meliputi bussines understanding, data understanding, data preparation, modelling, evaluation, dan deployment yang digunakan untuk menggambarkan proses data mining (maimon & rokach, 2010). sumber: (maimon & rokach, 2010) gambar 1. proses crisp-dm algoritma apriori adalah salah satu algoritma yang menggunakan teknik asiosiasi dengan melakukan pencarian frequent itemset (gunadi & sensue, 2012). algoritma apriori juga merupakan salah satu algoritma yang dapat digunakan pada implementasi analisis pemasaran dengan menemukan setiap rule pada asosiasi yang telah memenuhi syarat yakni batas support dan confidence-nya. setiap rule asosiasi ditemukan dengan cara menggunakan parameter, sehingga pembentukan rules yang didapat menghasilkan nilai prediksi yang akurat. rule asosiasi dinyatakan dengan beberapa atribut yang biasanya sering disebut sebagai (affinity analysis) atau (market basket analysis). analisis asosiasi atau association rule pada data mining merupakan salah satu teknik data mining untuk mencari aturan suatu pada gabungan item. yang menarik dari analisis ini yaitu salah satu tahapannya dalam menghasilkan algoritma yang efisien dengan menganalisis pola frekuensi tinggi (frequent pattern mining). cara kerja algoritma ini adalah dengan memeriksa perkembangan calon itemset dari hasil frekuensi itemset dengan support-based pruning yang bertujuan untuk menghapus itemset yang tidak berpengaruh dengan memilih minimal support. sedangkan prinsip dari algoritma apriori itu sendiri adalah bila mana itemset dikategorikan sebagai frekuensi itemset yang mempunyai support lebih dari yang ditetapkan sebelumnya, maka setiap subset-nya juga termasuk golongan frekuensi itemset, begitupun sebaliknya. support merupakan suatu parameter yang membuktikan besarnya tingkat dominasi suatu item/itemset dari total transaksi yang terjadi. parameter ini memastikan apakah suatu item/itemset disebut layak untuk dicari nilai confidence-nya; contohnya seperti, dari total transaksi yang ada, seberapa besar tingkat dominasi yang membuktikan bahwa item a dan b memungkinkan dibeli secara bersamaan. berikut tahapan yang dilakukan dalam perhitungan dengan algoritma apriori: 1. mencari 3 nilai yang paling banyak terjual. untuk langkah pertama yaitu dengan mencapai nilai penjualan yang paling tinggi dalam suatu data transaksi selama sebulan dengan langakah-langkah: a. menentukan daftar merek pipa. b. menentukan data penjualan pipa. 2. melakukan pengelompokan 3 merek pipa yang paling laku terjual. 3. melakukan representasi data transaksi . setelah pengelompokan 3 merek yang dilakukan pada tahap 2 selanjutnya data juga dapat direpresentasikan. 4. pembuatan format tabular bila sudah diketahui nilai penjualan terbesar setiap bulannya maka di buatlah format tabular agar dapat dianalisis dengan algoritma apriori. 5. analisa pola frekuensi tinggi tahapan ini dilakukan dengan mencari full item yang telah termasuk kedalam syarat minimum dari nilai support dalam database. nilai support pada sebuah item dapat ditemukan dengan menggunakan rumus sebagai berikut: 𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴) = ∑𝑇𝑟𝑎𝑛𝑠𝑎𝑘𝑠𝑖 𝑚𝑒𝑛𝑔𝑎𝑛𝑑𝑢𝑛𝑔 𝐴 ∑𝑇𝑟𝑎𝑛𝑠𝑎𝑘𝑠𝑖 *100....................(1) sementara itu, untuk perhitungan 2 itemset dapat diperoleh dengan menggunakan rumus berbeda dari sebelumnya yaitu: http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 170 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴, 𝐵) = 𝑃 (𝐴 ∩ 𝐵) 𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴, 𝐵) = ∑𝑇𝑟𝑎𝑛𝑠𝑎𝑘𝑠𝑖 𝑚𝑒𝑛𝑔𝑎𝑛𝑑𝑢𝑛𝑔 𝐴 𝑑𝑎𝑛 𝐵 ∑𝑇𝑟𝑎𝑛𝑠𝑎𝑘𝑠𝑖 *100..........(2) dalam pencarian pola frekuensi tinggi akan dihentikan apabila kombinasi sudah tidak memenuhi syarat support yang sudah di tentukan. 6. pembentukan aturan asosiasi setelah langkah kelima dilakukan yakni menemukan semua pola frekuensi tinggi, langkah selanjutnya adalah mencari rule asosiasi yang telah termasuk kedalam syarat minimum confidence dengan cara menghitung confidence atau asosiatif menggunakan ketentuan berikut: 𝐴 → 𝐵 ............................................................................ (3) dengan minimum confidence 70% dari nilai confidence tersebut, kemudian dihasilkan rumus berikut: 𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒(𝐵|𝐴) = ∑transaksi mengandung a dan b ∑transaksi mengandung a .............................. (4) pencarian nilai confidence juga dilakukan sampai nilainya sudah tidak memenuhi syarat minimum confidence lagi. aturan asosiasi final dari analisis yang telah dilakukan dalam tahap ini akan terlihat asosiasi yang terbentuk dengan menggunakan perhitungan algoritma apriori. data, intrumen, dan teknik pengumpulan data untuk menunjang penulisan penelitian ini, penulis menggunakan beberapa metode pengumpulan data yaitu: 1. observasi (field research) pada tahapan ini pengumpulan data dilakukan dengan mengamati proses kegiatan jual beli pada cv gaskindo. 2. wawancara (conversation research) pada tahap ini wawancara dilakukan langsung kepada pemilik dan karyawan yang terlibat dalam transaksi penjualan produk di cv. gaskindo sentosa untuk mendapatkan data yang diperlukan dalam penelitian. 3. studi pustaka (library research) untuk melengkapi informasi-informasi yang berhubungan dengan penelitian ini, penulis mengumpulkan data dari sumbersumber yang berhubungan dengan objek maupun target penelitian ini. sumber-sumber bacaan ini dapat berupa artikel, jurnal atau sumber dari situs internet yang berhubungan dengan objek yang akan diteliti. teknik analisis data setiap data yang diperoleh akan dianalisa supaya dapat diketahui tentang apa saja yang dibutuhkan pada proses asosiasi data dalam penyelesaian masalah (yanto & khoiriah, 2015). dalam penelitian ini, teknik analisis data yang digunakan adalah analisis data kuantitatif yang merupakan suatu analisis data yang dipergunakan jika kesimpulan yang diperoleh dapat dibuktikan dengan angka dan perhitungan rumus yang ada keterkaitannya dengan analisis objek penelitian. data tersebut berupa jumlah penjualan dari tiap jenis produk yang nantinya akan diterapkan kedalam algoritma apriori sehingga dapat menghasilkan prediksi penjualan periode berikutnya di cv. gaskindo sentosa. hasil penelitian dan pembahasan analisa kebutuhan pada tahapan ini dilakukan analisa semua kebutuhan yang diperlukan untuk menerapkan algoritma apriori dalam memprediksi penjualan produk di cv. gaskindo sentosa. data yang dibutuhkan untuk menerapkan algoritma tersebut digunakan data primer penjualan produk pipa yang diperoleh dari cv. gaskindo sentosa dengan list sebagai berikut: tabel 1. list nama material pipa no nama material 1 stud bolt 2 stud anchor 3 union tee 4 tee 5 union connector 6 red union 7 male connector 8 needle valve 9 ball valve langkah selanjutnya yaitu melakukan pengelompokkan 3 merk pipa yang paling banyak terjual berdasarkan data transaksi penjualan pada cv. gaskindo sentosa dimulai dari januari 2018 sampai dengan desember 2018, sehingga menghasilkan pola transaksi yang disajikan pada tabel dibawah ini: tabel 2. pola transaksi penjualan pipa di cv. gaskindo sentosa pada tahun 2018 no nama material pipa 1 stud anchor, red union, stud bolt 2 stud anchor, red union, union tee 3 stud anchor, union tee, red union 4 stud anchor, tee, union tee 5 red union, union tee, stud bolt http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 171 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 6 stud anchor, red union, union tee 7 tee, union tee, red union 8 stud bolt, tee, red union 9 stud anchor, tee, stud bolt 10 stud anchor, union tee, red union 11 stud anchor, tee, stud bolt 12 stud anchor, red union, union tee penentuan itemset tahapan ini diawali dengan proses pembentukan c1 atau biasa disebut itemset dengan jumlah minimun support = 30% dan dihitung menggunakan rumus (1) yang menghasilkan nilai support sebagai berikut: tabel 3. hasil support dari tiap itemset itemset support stud anchor 75% red union 75% tee 41,67% stud bolt 41,67% union tee 66,67% setelah hasil perhitungan c1 didapat, maka dapat dilanjutkan proses pembentukan c2 (kombinasi 2 itemset) dengan menggunakan rumus (2). tabel 4. support dan kombinasi 2 itemset itemset support stud anchor, red union 50% stud anchor, tee 25% stud anchor, stud bolt 25% stud anchor, union tee 50% red union, tee 16,67% red union, stud bolt 25% red union, union tee 58,33% tee, stud bolt 25% tee, union tee 16,67% stud bolt, union tee 8,33% minimal support yang ditentukan yaitu 30%, maka kombinasi 2 itemset yang tidak memenuhi minimal suppport akan dihilangkan, sehingga menghasilkan nilai berikut: tabel 5. minimal support 2 itemset 30% itemset support stud anchor, red union 50% stud anchor, union tee 50% red union, union tee 58,33% terakhir, dilanjutkan proses pembentukan c3 (kombinasi 3 itemset) dengan jumlah minimal support = 30% yang menghasilkan itemset sebagai berikut: tabel 6. minimal support 3 itemset 30% itemset support stud anchor, red union, union tee 41,67% aturan/rule asosisasi final setelah semua pola frekuensi tinggi ditemukan (c1 c2, dan c3) barulah dicari aturan asosiasi dengan hasil pola frekuensi yang disajikan dalam tabel 7. tabel 7. hasil pola frekuensi tinggi yang memenuhi syarat itemset support stud anchor, red union 50% stud anchor, union tee 50% red union, union tee 58,33% stud anchor, red union, union tee 41,67% langkah berikutnya yaitu mencari aturan asosiasi yang memenuhi syarat minimum untuk confidence pada tabel 7. dengan menghitung confidence atau asosiatif a→b, dengan minimum confidence 70% menggunakan rumus (3) dan (4). tabel 8. hasil confidence atau asosiasi aturan confidence jika membeli stud anchor,maka akan membeli red union 6/9 66,66% jika membeli red union,maka akan membeli stud anchor 6/9 66,66% jika membeli stud anchor,maka akan membeli union tee 6/9 66,66% jika membeli union tee,maka akan membeli stud anchor 6/8 75% jika membeli red union,maka akan membeli union tee 7/9 77.78% jika membeli union tee,maka akan membeli red union 7/8 87,5% jika membeli stud anchor dan red union, maka akan membeli union tee 5/6 83,33% jika membeli stud anchor dan union tee, maka akan membeli red union 5/6 83,33% langkah terakhir, pembentukan asosiasi final terurut diperoleh berdasarkan minimal support dan minimal confidence yang telah ditentukan sehingga dapat dideskripsikan pada tabel dan grafik dibawah ini: tabel 9. rule asosiasi final rule support confidence stud anchor, red union 50% 85,71% stud anchor, union tee 50% 85,71% red union, union tee 58,33% 77,78% stud anchor,red union, union tee 41,67% 83,33% http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 172 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional gambar 2. hasil pembentukan aturan asosiasi final penjualan terbanyak. berdasarkan tabel 9. dan gambar 2., dapat diketahui produk mana yang paling laris terjual produknya pada cv. gaskindo sentosa. simpulan dan saran simpulan berdasarkan pembahasan diatas, maka dapat diperoleh kesimpulan bahwa penelitian algoritma apriori dapat membantu mengembangkan strategi pemasaran untuk memasarkan produk lain dengan menganalisa kelebihan dari nilai jual produk yang paling laris terjual. hal tersebut dapat dilihat dari hasil penelitian yang menunjukkan bahwa penjualan produk pipa paling banyak terjual pada cv. gaskindo sentosa, dengan melihat produk yang memenuhi minimal support dan minimal confidence, produk yang banyak terjual tersebut adalah stud anchor, red union, stud bolt, union tee. dari aturan asosiasi final yang diketahui jika membeli union tee maka akan membeli red union dengan support 50% dan confidence 87,5%. jika membeli red union maka akan membeli union tee dengan support 50% dan confidence 77,78%. jika membeli stud anchor, red union maka akan membeli union tee dengan support 33,33% dan confidence 83,33%. jika membeli stud anchor, union tee maka akan membeli red union dengan support 33,33% dan confidence 83,33%. saran berdasarkan kesimpulan diatas maka dapat diambil saran bahwa penerapan algoritma apriori dapat di selaraskan dengan aplikasi tanagra dan dengan mendefinisikan lebih banyak variable yang kompleks serta melibatkan lebih banyak jenis produk untuk asosiasi aturan final. daftar referensi choiriah, w. (2019). analisis penjualan e-tiket menggunakan algoritma apriori pada cv. guti mulia wisata. zonasi: jurnal sistem informasi, 1(1). gunadi, g., & sensue, d. . (2012). penerapan data mining market basket analysis terhadap data penjualan produk buku dengan menggunakan algoritma apriori dan frequent pattern growth (fp-growth). jurnal telematika mkom, 4(1). kusrini, & taufiq, l. e. (2007). algoritma data mining. yogyakarta: cv andi offset. kusrini, & taufiq, l. e. (2010). algoritma data mining. yogyakarta: cv andi offset. lukman, & imam sunoto. (2017). komparasi algoritma multilayer perceptron dan support vector machine dalam pemilihan beasiswa. jurnal sap, 2(1), 114–128. maimon, & rokach. (2010). data mining and knowledge discovery handbook. new york: springer. pane, d. k. (2013). implementasi data mining pada penjualan produk elektronik dengan algoritma apriori (studi kasus: kreditplus). pelita informatika budi darma, 4(3), 25–29. purnia, d. s., & warnilah, a. i. (2017). implementasi data mining pada penjualan kacamata menggunakan algoritma apriori. implementasi data mining pada penjualan kacamata menggunakan algoritma apriori, 2(2), 31–39. syahdan, s. al, & sindar, a. (2018). data mining penjualan produk dengan metode apriori pada indomaret galang kota. jurnal nasional komputasi dan teknologi informasi (jnkti), 1(2). https://doi.org/10.32672/jnkti.v1i2.771 warumu, f. t., buulolo, e., & ndururu, e. (2017). implementasi algoritma apriori pada analisa pola data penyakit manusia yang disebabkan oleh rokok. komik (konferensi nasional teknologi informasi dan komputer), 1(1), 176–182. yanto, r., & khoiriah, r. (2015). implementasi data mining dengan metode algoritma apriori dalam menentukan pola pembelian obat. citec journal, 2(2), 102–113. stud anchor,red union,union tee stud anchor,red union stud anchor,union tee red union, union tee stud anchor,red union,union tee 5 6 6 7 5 41.67% 50% 50% 58.33 41.67 56% 67% 7% 78% 56% grafik apriori support count support confidence http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 119 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasiona sistem pendukung keputusan penerimaan asisten laboratorium menggunakan metode multi factor evaluation process (mfep) painem1, hari soetanto2 prodi sistem informasi, fakultas teknologi informasi universitas budi luhur, jakarta 1)painem@budiluhur.ac.id 2) prodi teknik informatika, fakultas teknologi informasi universitas budi luhur, jakarta 2)hari.soetanto@budiluhur.ac.id abstrak asisten lab ict merupakan mahasiswa fakultas teknologi informasi dan fakultas ekonomi dan bisnis yang aktif. asisten laboratorium mempunyai tugas mendampingi dosen pada saat dosen mengajar praktikum di lab ict, dan harus memahami materi praktikum dosen yang sedang mengajar, dan harus mampu memperbaiki komputer yang bermasalah. asisten bukan jabatan permanen di lab ict. asisten lab bukan merupakan jabatan yang permanen, sehingga setiap tahun ajaran baru dibuka pendaftaran calon asisten. banyak mahasiswa yang mendaftar menjadi calon asisten tetapi jumlah calon asisten yang diterima terbatas. untuk menjadi asisten lab ada beberapa kriteria yang dijadikan sebagai penilaian, yaitu nilai hardware, nilai psikotes, nilai kompetensi, nilai wawancara, nilai project1, nilai project2 dan nilai absensi. untuk menentukan asisten yang diterima berdasarkan beberapa kriteria diatas belum ada metode tertentu dalam mengambil keputusan. untuk membantu dalam mengambil keputusan maka dibuat sistem pendukung keputusan untuk penentuan penerimaan asisten lab. metode yang digunakan digunakan dalam sistem pendukung keputusan adalah multi factor evaluation process (mfep). prototype yang dibuat menggunakan bahasa pemrograman php. kata kunci: mfep, spk, penerimaan asisten, pendukung keputusan abstract the lab ict assistant is an active information technology and faculty of economics and business student. the laboratory assistant has the task of assisting the lecturer when the lecturer teaches practicum at lab ict, and must understand the lecturer practicum material that is teaching, and must be able to repair the problematic computer. assistants are not permanent positions in lab ict. the lab assistant is not a permanent position, so that every new school year the registration of prospective assistants is opened. many students register to be prospective assistants but the number of prospective assistant candidates is limited. to become a lab assistant there are several criteria that are used as assessments, namely hardware value, psychological value, competency value, interview value, project1 value, project value and attendance value. to determine the assistants who are accepted based on the above criteria there is no particular method in making decisions. to assist in making decisions, a decision support system is made for determining the acceptance of the lab assistant. the method used in decision support systems is the multi factor evaluation process (mfep). the prototype is made using the php programming language. keywords: mfep, dss, lab assistant, decision support pendahuluan lab ict merupakan laboratorium komputer pusat yang digunakan untuk kuliah praktikun fakultas teknologi informasi, fakultas ekonomi dan bisnis, fakultas ilmu komunikasi, fakultas teknik di universitas budi luhur. lab ict juga digunakan untuk mendukung kegiatan pengabdian kepada masyarakat bagi dosen universitas budi luhur. lab ict saat ini dibawah direktorat digitalisasi penunjang akademik. lab ict saat ini mempunyai 13 ruang praktikum. masing-masing ruangan praktikum berkapasitas 35 komputer. mahasiswa yang http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 120 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional menggunakan lab ict untuk praktikum kurang lebih 12.000 mhs setiap harinya. jumlah asisten lab ict saat ini hanya 22 asisten. jumlah tersebut masih kurang untuk memenuhi kebutuhan lab ict. asisten bukan merupakan jabatan yang permanen. sehingga setiap tahun ajaran baru ada pembukaan pendaftaran asisten untuk mahasiswa fakultas teknologi informasi dan fakultas ekonomi dan bisnis. peminat untuk menjadi asisten lab banyak, kurang lebih 20-45 pendaftar setiap tahun ajaran baru. tetapi tidak semua peminat akan menjadi asisten. penentuan menjadi asisten ada beberapa kriteria dalam penilaiaan antara lain : nilai hardware, nilai psikotes, nilai kompetensi, nilai wawancara, nilai project1,nilai project2 dan nilai absensi. banyaknya kriteria dalam penentuan penerimaa asisten maka membutuhkan waktu lama dalam perhitungannya dan belum ada metode yang digunakan untuk penentuan penerimaan asisten. berdasarkan latar belakang diatas maka dibutuhkan sistem pendukung keputusan untuk penentuan penerimaan asisten. metode yang digunakan dalam sistem pendukung keputusan penentuan penerimaan asisten adalah metode multi factor evaluation process. sistem pendukung keputusan yang dibuat diharapkan dapat membantu menyelesaikan permasalahan dalam penentuan penerimaan asisten lab. penelitian sistem pendukung keputusan dengan metode mfep sudah banyak dilakukan. sebagai contoh hutabri (2015) mengembangkan spk dalam pemilihan kelas unggul menggunakan metode mfep. selanjutnya andoko dkk. menerapkan metode mfep pada sistem pendukung keputusan penentuan kelayakan pemberi pinjaman (andoko, alfiarini, & yanto, 2018) dan djunaedi dkk juga mengembangkan sistem pendukung keputusan penilaian kinerja pegawai (djunaedi, subiyakto, & fetrina, 2017). tabel 1 menyajikan beberapa penelitian terkait penerapan metode mfep dalam spk. konsep sistem pendukung keputusan (spk) atau decision support system pertama kali diungkapkan pada awal tahun 1971 oleh michael s. scott morton (turban, 2001) dengan istilah management decision system. menurut kusrini, pada (turaina, gustia e, & cici, 2017) spk merupakan sistem informasi interaktif yang menyediakan informasi, pemodelan dan pemanipulasian data. spk berfungsi membantu pengambil keputusan dengan memberikan alternatif-alternatif keputusan yang tepat. tabel 1. penelitian terkait penerapan metode mfep dalam spk no peneliti metode judul kriteria kesimpulan 1. (t. henny febriana harumy, 2016) mfep sistem penunjang keputusan penentuan jabatan manager menggunakan metode mfep pada cv. sapo durin kedisiplinan (0,2) keaktifan (0,15) total anggota(0,3) jumlah anggota(0,25) kegigihan (0,15) aplikasi ini digunakan untuk memudahkan perusahaan dalam menentukan siapa yang berhak dipromosikan sebagai manajer 2. (purnomo, nurdin, & nangi, 2017) mfep penerapan multi factor evaluation process (mfep) untuk penilaian guru (studi kasus: man 1 kota kendari) tanggungjawab(1-4) kejujuran(1-4) kerjasama (1-4) kedisplinan (1-4) kehadiran (1-4) perhitungan yang dihasilkan oleh sistem adalah sesuai dengan hasil pengujian yang dihasilkan melalui perhitungan yang dilakukan secara manual 3 (diwanda et al., 2016) mfep sistem penunjang keputusan penilaian kinerja karyawan menggunakan metode mfep pada pt.konsuil wilayah sulawesi tenggara kecakapan (4) watak dan tingkah laku (5) loyalitas (5) dengan adanya aplikasi ini pengambil keputusan dapat memberikan penilaian kinerja karyawan secara obyektif dengan menimbang berbagai faktor yang mempunyai pengaruh penting terhadap alternatif pilihan pengambil keputusan 4. (hutabri, 2015) mfep spk dalam pemilihan siswa kelas unggul 1. administrasi: nilai un (0,40), piagam sistem penunjang keputusan dalam http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 121 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasiona no peneliti metode judul kriteria kesimpulan menggunakan metode mfep di smpn 2 solok (0,60) 2. akademik: pengetahuan umum (0,40), ipa (0,60) 3. wawancara: tata krama (0,40), kepribadian (0,60) pemilihan calon siswa kelas unggul yang dibuat telah membantu kepala sekolah dalam menerima informasi dan mengambil keputusan dalam penerimaan calon siswa kelas unggul dengan cepat dan tepat metode mfep (multi factor evaluation process) merupakan metode yang menjadi fundamental dari pengembangan metode pada sistem pendukung keputusan (latif, jamil, & abbas, 2018). teknik penyelesaian metode ini yaitu dengan penilaian subyektif dan intuitif terhadap indikator atau faktor penyebab dari sebuah masalah yang dianggap penting. pertimbangan – pertimbangan tersebut yaitu dengan memberikan bobot (weighting system) berdasarkan skala prioritas berdasarkan tingkat kepentingannya. adapun algoritma penyelesaian metode ini yaitu : 1. langkah 1 : mendefinisikan terlebih dahulu kriteria – kriteria atau faktor-faktor yang menyebabkan masalah beserta bobotnya 2. langkah 2 :menghitung nilai bobot evaluasi (nbe) 3. langkah 3 :menghitung total bobot evaluasi (tbe) 4. langkah 4 : lakukan perangkingan untuk mendapatkan keputusan adapun rumus yang digunakan untuk menghitung nilai (nbe) pada metode mfep menggunakan persamaan (1). 𝑁𝐵𝐸 = 𝑁𝐵𝐹 ∗ 𝑁𝐸𝐹 ……………………………………….. (1) keterangan : nbe : nilai bobot evaluasi nbf : nilai bobot factor nef : nilai evaluasi factor sementara itu, persamaan (2) merupakan rumus yang digunakan untuk menghitung nilai tbe pada metode mfep. 𝑇𝐵𝐸 = ∑ 𝑁𝐵𝐸(𝑛)𝑘𝑛=1 ………………………………….. (2) keterangan : tbe : total bobot evaluasi nbe : nilai bobot evaluasi k : jumlah kriteria metode penelitian langkah penelitian untuk menyelesaikan permasalahan, dalam penelitian ini dilakukan beberapa langkah dan metode penelitian, seperti disajikan pada gambar 1 dan dijelaskan sebagai berikut: a. studi pustaka metode ini dilakukan untuk mengumpulkan data dengan mencari dan membaca buku-buku referensi, jurnal, paper dan karya ilmiah lainnya yang dapat menunjang penelitian ini. b. analisis dokumen dokumen yang diperoleh kemudian dipelajari dan dianalisis untuk mengetahui bentuk sistem cara kerja yang akan dibangun. c. rancangan aplikasi / prototype metode ini dilakukan dengan membuat rancangan layar, flowchart, database dan lainlain sesuai dengan hasil analisis. d. pembuatan prototype rancangan sistem yang sudah dibuat akan diimplementasikan berdasarkan hasil analisis. kemudian hasil analisa akan dituangkan dalam kode-kode dengan menggunakan bahasa pemrograman tertentu. e. pengujian dan analisis setelah sistem selesai dibangun, maka dilakukan uji coba terhadap sistem yang dibangun. pengujian dilakukan dengan metode black box dan pengujian persepsional dengan iso 9126. f. pembuatan laporan dan publikasi berdasarkan hasil pengujian yang dilakukan, dibuat laporan penelitian dan publikasi hasil penelitian. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 122 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional gambar 1. langkah penelitian kerangka pikir gambar 2 menampilkan kerangka pikir yang digunakan dalam penelitian ini. kerangka pikir mencakup analisis terhadap kondisi saat ini di laboratorium ict universitas budi luhur. selanjutnya dirumuskan permasalahan penelitian dan rancangan solusi dengan menerapkan metode mfep dalam sistem pendukung keputusan. gambar 2. kerangka pikir hasil dan pembahasan perhitungan mfep secara manual pada tabel 2 disajikan kriteria – kriteria yang dibutuhkan dalam penentuan penerimaan asisten lab ict tabel 2. kriteria penerimaan asisten lab ict kode kriteria nama kriteria k001 nilai hardware k002 nilai psikotes k003 nilai kompetensi k004 nilai wawancara k005 nilai project 1 k006 nilai project 2 k007 nilai absensi sementara itu, pada tabel 3 ditampilkan bobot yang akan digunakan untuk setiap alternatif adalah dibutuhkan dalam penentuan penerimaan asisten lab ict tabel 3. pembobotan kriteria langkah selanjutnya adalah melakukan penilaian untuk setiap alternatif, dalam hal ini calon asisten untuk setiap kriteria. tabel 4 merupakan hasil penilaian calon asisten berdasarkan berdasarkan test yang sudah diikuti. tabel 4. nilai alternatif n o nama n h np s n k n w np 1 np 2 n a 1. irennada 60 65 75 72 60 60 90 2. nurul azzahra 65 67 70 62 60 60 94 3. erika nur komala sari 80 66 65 60 60 60 93 4. amirudin 68 66 60 70 69 62 89 5. m. verdiansyah 90 67 70 74 76 90 86 6. nur fahmi aziz 76 50 70 68 60 65 84 7. muhamad satrio aditomo 75 55 80 65 73 0 83 8 selfiana halfiana 77 65 60 65 69 60 81 9 kamil salim 75 65 60 65 70 60 79 kode kriteria alternatif bobot k001 nilai hardware 20% k002 nilai psikotes 10% k003 nilai kompetensi 20% k004 nilai wawancara 10% k005 nilai project 1 10 % k006 nilai project 2 10% k007 nilai absensi 20% http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 123 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasiona n o nama n h np s n k n w np 1 np 2 n a 10 harry setiawan 92 72 70 70 71 0 73 11 juan kalyzta 92 70 70 72 65 65 70 12 sanding riyanto 93 65 65 72 65 65 69 13 kris setiawati 75 66 70 70 71 60 67 14 grace a l simamora 68 56 60 68 50 61 64 15 muhamad rifki adnan 92 66 65 70 68 70 63 16 muhamma d iqbal aryabima 67 65 70 70 72 68 62 17 yogi angga putra 76 67 65 68 72 60 49 18 muhamma d arya java 72 66 65 60 50 0 44 keterangan : nh=nilai hardware, nps=nilai psikotes, nk=nilai kompetensi, nw=nilai wawancara, np1= nilai project1, np2=nilai project2, na=nilai absensi. berdasarkan hasil perhitungan diatas maka didapatkan perankingan dari total bobot evaluasi (tbe) seperti pada tabel 5. tabel 5. hasil perangkingan berdasarkan tbe no nama alternatif tbe 1. muhammad verdiansyah 79.9 2. juan kalyzta 73.6 3. erika nur komalasari 72.2 4. sanding riyanto 72.1 5. irennada 71.9 6. muhammad rifki adnan 71.4 7. nurul azzahra 70.7 8. nur fahmi aziz 70.3 9. amirudin 70.1 10. selfiana halfiana 69.5 11. kris setiawati 69.1 12. kamil salim 68.8 13. harry setiawan 68.3 14. muhammad iqbal a 67.3 15. muhammad satrio a 66.9 16. yogi angga putra 64.7 17. grace a l simamora 61.9 18. muhammad arya java 53.8 rancangan prototipe tabel 6, tabel 7, dan tabel 8 merupakan struktur tabel utama yang digunakan dalam aplikasi sistem pendukung keputusan penentuan penerimaan asisten lab ict : tabel 6. tb_kriteria no nama field type keterangan 1. kode_kriteria varchar (16) kode kriteria 2. nama_kriteria varchar (225) nama kriteria tabel 7. tb_alternatif no nama field type keterangan 1. kode_alternatif varchar (16) kode kriteria 2. nama_alternatif varchar (225) nama kriteria tabel 8. tb_detailalternatif no nama field type keterangan 1. kode_kriteria varchar(16) kode kriteria 2. kode_alternatif varchar(16) kode alternatif 3. nilai integer(3) nilai alternatif prototipe aplikasi pendukung keputusan dikembangkan dengan bahasa pemrograman php dan perangkat basis data mysql. gambar 3 dan gambar 4 merupakan contoh tampilan aplikasi yang telah dikembangkan pada penelitian ini. pada gambar 3 tampilan layar untuk menginput nilai alternatif dari masing-masing kriteria. sedangkan pada gambar 4 akan ditampilkan daftar hasil perankingan total bobot evaluasi (tbe) dari semua alternatif di urutkan secara ascending (urut dari nilai terbesar ke nilai terkecil). gambar 3. tampilan halaman tambah nilai alternatif http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 124 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pengguna dapat menambahkan nilai alternatif dengan memilih nama alternatif (calon asisten) dan menginput nilai masing-masing komponen. nilai yang diinputkan dalam rentang 0100. setelah semua nilai diinput, pengguna dapat menekan tombol simpan. gambar 4. laporan perankingan tbe gambar 4 menyajikan laporan perhitungan total bobot evaluasi (tbe) untuk setiap alternatif. pelaporan disajikan secara terurut dari nilai tbe yang terbesar hingga terkecil. nilai tbe berada pada rentang 0-100. pada contoh laporan di gambar 4, nilai tbe terbesar diperoleh oleh alternatif (calon asisten) dengan nama muhammad verdiansyah. pengujian sistem pengujian yang pertama menggunakan metode pengujian perangkat lunak black box. menurut roohullah jan dan j. watkins dalam (jaya, 2018), black box testing merupakan teknik pengujian perangkat lunak yang berfokus pada spesifikasi fungsional dari perangkat lunak. untuk memastikan sistem bekerja dengan maksimal maka dilakukan pengujian sistem. pada penelitian menggunakan pengujian sistem dengan metode black box. hasil pengujian sistem dengan menggunakan metode black box terlihat pada tabel 9. tabel 9: hasil pengujian sistem no halaman hal diuji hasil 1. login notifikasi login berhasil dan masuk ke halaman menu utama sukses 2. kriteria menu sukses no halaman hal diuji hasil menampilkan kode kriteria, nama kriteria, bobot, aksi untuk ubah dan hapus,tambah, dan proses pencarian kriteria 3. alternatif menu menampilkan kode alternatif, nama alternatif, aksi untuk ubah dan hapus,tambah, dan proses pencarian alternatif sukses 4. transaksi nilai alternatif menu menampilkan kode alternatif, nama alternatif , semua nilainilai kriteria dan menginput ilainilai kriteria untuk masing-masing alternatif serta searching. sukses 5. transaksi perhitungan menu untuk menampilkan ranking hasil sukses 6. laporan menu untuk laporan per alternatif dan menampilkan laporan ranking semua alternatif / total bobot evaluasi sukses 7. logout menu untuk keluar dari aplikasi sukses berdasarkan hasil pengujian yang dilakukan terhadap sistem yang dibangun, sistem berjalan optimal sesuai dengan kebutuhan yang diperlukan. dimana sistem yang dibangun sesuai dengan memberikan hasil sesuai dengan yang diinginkan oleh user. seluruh fungsionalitas sistem sudah diujicoba dan terbukti dapat berjalan dengan baik. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 125 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasiona selain menggunakan metode blackbox, pada penelitian ini juga dilakukan pengujian persepsional dengan metode pengujian iso 9126. international organization for standarization (iso) dalam iso strandard 9126 telah mengusulkan beberapa karakteristik untuk melakukan pengujian terhadap kualitas sebuah perangkat lunak. (andoko et al., 2018), iso-9126 mengidentifikasikan enam karakteristik sebuah perangkat lunak dikatakan berkualitas, yaitu functionality, reliability, usability, efficiency, maintability dan portability. dalam sistem pendukung keputusan penentuan penerimaan asisten lab mengusulkan beberapa karakteristik functionality, realiability, usability, efficiency untuk melakukan pengujian terhadap aplikasi tersebut berdasarkan jawaban dari 7 (tujuh) responden yang mengisi kuesioner indikator kualitas perangkat lunak menurut iso 9126, dapat diukur dengan persamaan (3). %𝑆𝑘𝑜𝑟 = 𝑆𝑘𝑜𝑟 𝐴𝑘𝑡𝑢𝑎𝑙 𝑆𝑘𝑜𝑟 𝐼𝑑𝑒𝑎𝑙 𝑥 100% ............................... (3) gambar 5. hasil pengujian dengan iso 9126 berdasarkan analisis data yang diperoleh dari kuesioner, dapat disimpulkan bahwa total keseluruhan skor hasil pengujian kualitas aplikasi berdasarkan empat aspek kualitas perangkat lunak aplikasi (functionality, reliability, usability, efficency) dengan standar iso 9126 adalah 90 % dan termasuk kategori sangat baik. untuk functionaly dan usability perlu ditingkatkan karena nilai yang didapatkan kurang dari 90 %. simpulan berdasarkan perhitungan serta perancangan sistem pendukung keputusan, sehingga disimpulkan bahwa pengambilan keputusan penentuan penerimaan asisten laboratorium information and communication technologi (lab ict) dapat dilakukan dengan menggunakan metode multi factor evaluation process (mfep). pemberian bobot faktor kriteria akan mempengaruhi penilaian dan hasil perhitungan metode mfep. hasil uji coba untuk perhitungan mfep yang dilakukan oleh sistem sesuai dengan hasil pengujian yang dilakukan secara manual. berdasarkan pengujian sistem dengan metode black box maka sistem berjalan secara maksimal sesuai dengan fungsinya. berdasarkan pengujian aplikasi dengan iso 9126 dengan menggunakan karakteristik functionality, realiability, usability, efficiency didapatkan nilai 90 % atau sangat baik. daftar referensi andoko, alfiarini, & yanto, r. (2018). penerapan metode multi factor evaluation process pada sistem pendukung keputusan penentuan kelayakan pemberi pinjaman ( studi kasus nsc finance kota lubuklinggau ), 4(2), 113– 122. diwanda, s. a., ode, l., sagala, h. s., informatika, j. t., teknik, f., & oleo, u. h. (2016). sistem pendukung keputusan penilaian kinerja karwayan menggunakan metode multi factor evaluation process pada pt. konsuil wilayah sulawesi tenggara. semantik, 2(1), 341–348. djunaedi, a., subiyakto, a., & fetrina, e. (2017). sistem pendukung keputusan penilaian kinerja pegawai (studi kasus : pt . pln (persero distribusi jakarta raya area pondok gede)). jurnal sistem informasi, 10(1), 37–44. hutabri, e. (2015). spk dalam pemilihan siswa kelas unggul menggunakan metode mfep di smp n 2 solok. edik informatika, 1(2), 55–63. jaya, t. s. (2018). pengujian aplikasi dengan metode blackbox testing boundary value analysis. jurnal informatika: jurnal pengembangan it poltek tegal, 03(02), 45– 48. https://doi.org/10.30591/jpit.v3i1.647 latif, l. a., jamil, m., & abbas, s. h. (2018). sistem pendukung keputusan teori dan implementasi. deepublish. purnomo, r., nurdin, a., & nangi, j. (2017). penerapan multifactor evaluation process (mfep) untuk penilian guru (studi kasus: http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 126 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional man 1 kota kendari). seminar nasional riset kuantitatif terapan, (april), 76–79. t. henny febriana harumy, i. s. (2016). sistem penunjang keputusan penentuan jabatan manager menggunakan metode mfep pada cv. sapo durin. seminar nasional teknologi informasi dan multimedia 2016, 6–7. turaina, r., gustia e, & cici. (2017). sistem penunjang keputusan penerimaan calon tenaga honorer di sma n 1 junjung sirih kab. solok menggunakan metode multifaktor evaluasi proses (mfep). jurnal momentum, 18(2), 60–66. https://doi.org/10.21063/jm.2016.v18.2.6066 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 173 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional perancangan dan pembuatan aplikasi e-learning menggunakan model waterfall pada sekolah menengah atas ari puspita1, muhammad fahmi2, yuyun yuningsih3 1sistem informasi universitas bina sarana informatika ari.arp@bsi.ac.id 2teknik informatika stmik nusa mandiri fahmi.mmf@nusamandiri.ac.id 3sistem informasi stmik nusa mandiri yuyun.yyg@nusamandiri.ac.id abstrak berbagai konsep dan teknik baru dalam pengajaran telah banyak dikembangkan untuk menggantikan metode tradisional yang hanya mengandalkan pada metode pengajaran satu arah di kelas. salah satu metode pengajaran yang sedang berkembang di masa sekarang adalah e-learning. e-learning dapat membantu para pengajar dalam mendistribusikan bahan ajar mereka tanpa harus berada di kelas dengan menggunakan internet, hal ini dapat memaksimalkan waktu pembelajaran di kelas yang terbatas. pengembangan sistem informasi dalam pembuatan perangkat lunak menggunakan model waterfall dan dalam pembuatan perangkat lunak ini menggunakan php sebagai bahasa script yang digunakan untuk membuat halaman website dan mysql sebagai database tempat penyimpanan data. memudahkan para guru untuk dapat mendistribusikan materi pelajaran untuk siswa dapat dengan mudah mendapat materi pelajaran. website e-learning ini dapat dijadikan media mengerjakan soal-soal ujian berupa pilihan ganda dan mengumpulkan tugas-tugas yang di berikan oleh guru. kata kunci: website, e-learning, waterfall abstract if the teacher is not present and the learning time is limited by the school, then automatically the learning process will be hampered. new concepts and techniques in teaching have been developed to replace traditional methods that rely solely on one-way teaching methods in the classroom. one of the teaching methods that are developing in the present is e-learning. e-learning can help teachers in distributing their teaching materials without having to be in the classroom by using the internet, this can maximize the time of learning in a limited classroom. development of information system in making software using waterfall method and in making this software using php as script language which is used to create website page and mysql as database of data storage. with this e-learning can help the learning process to be more optimal. make it easier for teachers to distribute subject matter for students and also students can easily get the subject matter. this e-learning website can be used as media to do exam questions in the form of multiple choice and collect tasks given by the teacher. keywords: website, e-learning, waterfall pendahuluan pendidikan merupakan suatu sarana yang sangat penting bagi kelangsungan hidup manusia, hal ini disebabkan karena pendidikan adalah sektor yang dapat menciptakan kecerdasan manusia dalam melangsungkan kehidupannya. (aryadhi, parmiti, putu, & mahadewi, 2015) dalam penerapan pembelajaran di sekolah menengah atas , guru harus mampu memberikan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 174 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pengetahuan kepada peserta didik mengenai konsep-konsep yang terkandung dalam mata pelajaran yang diajarkan. namun pada kenyataannya, penyampaian materi masih sering dilakukan secara konvensional (salim, 2015). hal ini akan menyebabkan pembelajaran cendrung membosankan (inayati, subroto, & supardi, 2012) karena hanya mendengarkan ceramah dari guru, yang akan mengakibatkan siswa kurang termotivasi (wulandari, 2012) dan malas untuk belajar. sistem pembelajaran kebanyakan yang digunakan bersifat tatap muka di dalam kelas dengan adanya kehadiran guru dan siswa yang melakukan komunikasi di tempat dan waktu yang sama dan telah ditentukan. jika guru dan siswa tidak melakukan interaksi di dalam kelas pada proses pembelajaran maka sistem pembelajaran seperti ini menjadi kurang efisien dan tidak berhasil. proses pembelajaran merupakan upaya menyeimbangkan keaktifan yang dimiliki peserta didik dan pendidik (wahyuningsih & makmur, 2017). apabila peserta didik aktif maka pendidik berada pada posisi pasif, sebaliknya jika peserta didik pasif maka pendidik harus aktif. keaktifan pendidik dalam hal ini bukan untuk mengatur setiap tindakan belajar peserta didik, melainkan berfungsi untuk mendorong peserta didik agar tergugah kesadarannya untuk belajar. dengan latar belakang masalah yang telah dikemukakan sebelumnya, salah satu metode pengajaran yang sedang berkembang di masa sekarang adalah e-learning. e-learning dapat membantu para pengajar dalam mendistribusikan bahan ajar mereka tanpa harus berada di kelas dengan menggunakan internet, hal ini dapat memaksimalkan waktu pembelajaran di kelas yang terbatas. pada penelitian sebelumnya (romindo, 2017) dengan adanya e-learning ini dapat membantu proses belajar mengajar agar lebih optimal. sistem pembelajaran e-learning juga dapat mempermudah para guru untuk mengembangkan materi yang di ajarkan dan memberikan materi tambahan . dan siswa juga dapat mengakses dari mana saja. web e-learning juga dapat menjadi wadah untuk berdiskusi membahas materi yang memang belum dapat di paham . siswa juga dapat mengerjakan soal latihan yang sudah disiapkan oleh guru sehingga siswa dapat mengetahui seberapa pemahaman siswa tersebut mengenai materi pembelajaaran yang dibahas. dari hasil penelitian (wicaksono, winarno, & sunyoto, 2015) perancangan dan implementasi e-learning dapat mendukung project based learning yang sudah berhasil dibangun menggunakan lms moodle yang digunakan sebagai alternatif dalam proses belajar mengajar. untuk itu penulis bermaksud untuk membuatkan aplikasi elearning untuk sekolah menengah atas dengan model pengembangan waterfall. menurut (yohendra, saputra, & thjin, 2013) perancangan sistem informasi dengan metodologi object oriented dan waterfall mengimplementasikannya dengan menggunakan bahasa pemrograman php dan database menggunalan mysql, sehingga terbentuk sistem elearning yang akan digunakan pada proses belajar mengajar. setelah aplikasi e-learning sudah selesai di buat tahapan selanjutnya adalah tahap implementasi didalam tahapan implementasi tersebut adanya evaluasi sesuai dengan desain intruksional. agar dapat memperbaiki sistem dan melakukan penyesuaian terhadap tahapan tahapan yang sebelumnya desain instruksional merupakan proses dinamis yang dapat berubah-ubah sesuai dengan informasi dan evaluasi yang diterima bertujuan untuk meningkatkan hasil pembelajaran peserta didik sehingga tujuan pembelajaran dapat tercapai. (hanum, 2013) tujuan dari penelitian ini adalah untuk memfasilitasi dan mempermudah komunikasi antara guru dengaan siswa/i melalui forum diskusidan memperoleh materi pelajaran secara lengkap yang dapat mengoptomalkan proses belajar di kelas. metode penelitian jenis penelitian penelitian yang penulis buat merupakan pendekatan kualitatif dan penelitian terapan. waktu dan tempat penelitian tempat penelitian dilakukan pada sekolah menengah atas di daerah bekasi. waktu penelitian pada bulan april 2019 sampai dengan agustus 2019 target/subjek penelitian target penelitian pada penelitian ini adalah guru – guru yang mengajar pada sekolah menengah atas di daerah bekasi model pengembangan sistem metode penelitian yang digunakan dalam penelitian ini menggunakan kualitatif didasarkan pada siklus hidup pengembangan sistem (sdlc) dengan model waterfall (sukamto & shalahuddin, 2018), model ini dapat dapat menghasilkan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 175 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional perangkat lunak yang mampu meningkatkan kerja sistem e-learning dan dapat mengurangi kesalahkesalahan yang disebabkan oleh faktor manusia (human error).(muthia, amalia, puspita, & lestari, 2019) model waterfall terbagi menjadi 5 tahap yaitu 1. analisis kebutuhan perangkat lunak penulis melakukan pengumpulan kebutuhan secara intensif untuk mespesifikasikan kebutuhan perangkat lunak agar dapat dipahami perangkat lunak seperti apa yang dibutuhkan oleh user. 2. desain pada tahap desain penulis melakukan proses yang fokus pada desain pembuatan program perangkat lunak mulai dari rancangan antar muka, pembuatan erd (entity relationship diagram) sampai dengan pembuatan struktur navigasi. pada tahap ini mentranslasi kebutuhan perangkat lunak dari tahap analisis kebutuhan ke representasi desain agar dapat diimplementasikan menjadi program pada tahap selanjutnya. dalam pembuatan database aplikasi e-learning ini menggunakan mysql, dimana pengertian mysql merupakan software database management system (rdbms) yang dapat mengelola database yang sangat cepat, dapat menampung data dalam jumlah sangat besar, dapat diakses oleh banyak user (rofiah, 2018) 3. pembuatan kode program tahap ini penulis diharuskan mentranslasikan desain ke dalam program perangkat lunak. hasil dari tahap ini adalah program komputer sesuai dengan desain yang telah dibuat pada tahap desain. 4. pengujian pengujian pada program ini fokus pada logik dan fungsional dan memastikan semua bagian sudah diuji, pada hal ini penulis menggunakan sistem pengujian black box testing hal ini dilakukan untuk meminimalisir kesalahan (error) dan memastikan keluaran yang dihasilkan sesuai dengan yang diiinginkan penulis. 5. pendukung (support) dan pemeliharaan (maintenance) melakukan pengoperasian perdana kepada beberapa orang dan melakukan pemeliharaan, seperti penyesuaian atau perubahan dengan situasi sebenarnya. data, intrumen, dan teknik pengumpulan data metode pengumpulan data adalah factor yang penting dalam proses dan keberhasilan suatu penelitian. hal ini berkaitan dengan bagaimana cara mengumpulkan data dan siapa sumber yang dapat memberikan informasi data yang dapat digunakan dalam penelitian tersebut:(yuningsih, 2019) 1. observasi penulis melakukan pengamatan langsung terhadap kegiatan yang bekaitan dengan masalah yang diambil pada sekolah menengah atas 2. wawancara dalam penulisan tugas akhir ini untuk mendapatkan informasi secara lengkap, maka penulis melakukan suatu metode tanya jawab dengan kepala sekolah menengah atas 3. studi pustaka selain melakukan kegiatan diatas penulis juga melakukan studi kepustakaan melalui literatur-literatur atau referensi-referensi yang ada di beberapa perpustakaan maupun internet sebagai bahan perbandingan maupun referensi yang berhubungan dengan masalah berkaitan dengan penulisan tugas akhir ini. hasil penelitian dan pembahasan analisa kebutuhan perangkat lunak sekolah merupakan suatu lembaga yang dirancang sebagai tempat pengajaran siswa atau murid di bawah pengawasan guru. sedangkan perpustakaan adalah tempat bagi para murid mendapatkan ilmu selain dari guru, dengan demikian untuk mempermudah siswa untuk meminjam dan membaca maka di butuhkan suatu sistem e-learning. tahap analisa kebutuhan merupakan tahap perencanaan mengenai sistem informasi yang di inginkan . perencanaan mengenai sistem informasi yang akan di bangun. dan menganalisa apa saja yang dibutuhkan dalam membangun aplikasi elearning ini. dalam penelitian ini dilakukan perencanaan dengan membuat analisa kebutuhan sistem a. kebutuhan penguna pada pembuatan website elearning ini telah diperoleh kebutuhan-kebutuhan yang dapat menghubungkan dan saling beriteraksi dalam lingkungan sistem yang telah diinginkan yaitukebutuhan pengguna yang meliputi skenario kebutuhan pengujung, siswa, guru, dan admin. setiap pengguna memiliki kebutuhan informasi yang berbeda-beda, yaitu: 1. pengunjung a. dapat melihat informasi sekolah http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 176 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional b. dapat memberikan umpan balik kepada admin 2. siswa a. dapat mendownload modul b. dapat melihat kelas dan teman satu kelas c. dapat melihat daftar mata pelajaran d. dapat mengerjakan quiz online e. dapat melakukan upload tugas f. dapat melihat nilai g. dapat merubah profil 3. guru a. dapat meng-upload modul b. dapat melihat matapelajaran yang diajar dan melihat siswa c. dapat membuat quiz dan topik quiz d. dapat membuat penugumpulan tugas dan lihat tugas yang telah diterima e. dapat memberikan nilai tugas siswa f. dapat melihat daftar nilai siswa 4. admin a. dapat mengelola data siswa b. dapat mengelola data guru c. dapat mengelola data kelas d. dapat mengelola data mata pelajaran b. kebutuhan sistem pada website elearning tentunya terdapat sistem yang digunakan untuk memproses semua kebutuhan yang diperlukan untuk mengendalikan website. setiap sistem mempunyai kegunaan masing-masing yaitu: 1. sistem membatasi administrator agar melakukan login terlebih dahulu untuk masuk ke halaman admin dan logout untuk keluar. admin dapat menambah, meng-edit, dan menghapus data siswa, guru, kelas dan matapelajaran. 2. sistem membatasi siswa dan guru untuk melakukan login terlebih dahulu untuk masuk ke menu utama. 3. sistem dapat menambah, meng-edit, dan menghapus data siswa, guru, kelas dan matapelajaran yang tedapat pada halaman admin 4. sistem dapat mengkalkulasi nilai hasil quiz 5. sistem dapat mengirim file tugas dari siswa ke folder yg sudah ditentukan 6. dapat mengirim pesan dari pengunjung ke admin desain tahapan desain ini dapat dilakukan dengan pengambaran aplikasi e-learning yang akan dibangun adalah sebagai berikut: 1. perancangan database: perancangan basis data menghasilkan pemetaan tabel-tabel yang digambarkan dengan entity relationship diagram (erd). gambar 1. entity relationship diagram (erd)aplikasi e-learning http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 177 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional rancangan logical record structure aplikasi elearning sumber : (frieyadie, 2019) gambar 2. logical record structure aplikasi elearning 2. perancangan navigasi aplikasi e-learning ini mempunyai perancangan struktur navigasi a. struktur navigasi pengunjung login siswa login guru contac us about gambar 3.struktur navigasi pengunjung b. struktur navigasi siswa download modul pilih mata pelajaran latihan quiz upload tugas halaman soal ujian halaman cetak bukti ujian halaman cetak bukti upload tugas nilai profil edit profil gambar 4.struktur navigasi siswa c. struktur navigasi guru lihat mata pelajaran upload modul buat quiz pengumpulan tugas upload soal quiz daftar nilai profil edit profil gambar 5. struktur navigasi guru d. struktur navigasi admin dashboard manajemen siswa manajemen guru tambah guru edit guru tambah siswa manajemen kelas manajemen matapelajaran profil edit profil edit siswa tambah kelas edit kelas tambah matapelajaran edit matapelajaran gambar 6. struktur navigasi admin 3. perancangan user inteface a. halaman utama pada halaman utama menampilkan informasi mengenai profil sekolah dan terdapat dua tombol yaitu “saya siswa” dan “saya guru” ini untuk menampilkan popup modal untuk login baik itu siswa maupun guru. gambar 6.halaman utama pengunjung http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 178 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional b. halaman login siswa pada halaman ini terdapat form login siswa dimana siswa wajib menginputkan email berserta password jika meng-klik tombol login dan hanya siswa yang berhasil login yang dapat masuk ke halaman dashboard gambar 7.halaman login siswa c. login guru pada halaman ini terdapat form login guru dimana guru wajib menginputkan email berserta password dan meng-klik tombol login dan hanya guru yang berhasil login yang dapat masuk ke halaman dashboard guru. gambar 8.halaman login guru d. contact us pada halaman ini terrdapat form untuk menghubungi admin website berguna untuk melaporkan mengenai apa yang berkaitan dengan website dan menjadi wadah saran atau masukkan dari user untuk pengembangan website. gambar 9.halaman contact us e. dashboard guru pada halaman ini menyediakan menumenu yang berkaitan dengan proses pembelajaran elearning guru seperti membuat tugas, upload modul, memberi nilai tugas dan lain-lain. gambar 10.halaman dashboard guru f. login admin pada halaman ini terdapat form login admin dimana admin wajib menginputkan email berserta password dan meng-klik tombol login dan hanya admin yang berhasil login yang dapat masuk ke halaman dashboard admin. gambar 11.halaman login admin g. dashboard admin pada halaman ini menyediakan menumenu yang berkaitan dengan penunjangan website elearning seperti pengelolaan master dat. gambar 12.halaman dashboard admin code program code program yang digunakan dalam pembuatan aplikasi e-learning ini adalah menggunakan bahasa pemograman php . http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 179 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pengujian program pengujian terhadap program yang dibuat menggunakan blackbox testing yang fokus terhadap proses masukan dan keluaran program. a. pengujian terhadap form login siswa tabel.1 hasil pengujian blackbox testing form login siswa no skenario pengujian test case hasil yang diharapkan hasil pengujian kesimpulan 1. user email dan password tidak diisi kemudian klik tombol login user email: (kosong) password: (kosong) sistem akan menolak akses user dan menampilkan “harap isi bidang email dan password” sesuai harapan valid 2 mengetikkan user email dan password tidak diisi atau kosong kemudian klik tombol login user email: (setiawanjaka59 gmail.com) password: (kosong) sistem akan menolak akses user dan menampilkan “harap isi bidang email ” sesuai harapan valid 3 mengetikkan password dan user email tidak diisi atau kosong kemudian klik tombol login user email: (kosong) password: (123) sistem akan menolak akses user dan menampilkan “harap isi bidang password ” sesuai harapan valid 4 mengetikkan user email dengan data yang benar dan password dengan data yang salah kemudian klik tombol login user email: (setiawanjaka59@gmail .com) password: (3219) sistem akan menolak akses user dan menampilkan“password/ email salah!” sesuai harapan valid 5 mengetikkan user email dengan data yang salah dan password dengan data yang benar kemudian klik tombol login user email: (setiawanjaka5129 @gmail.com) password: (123) sistem akan menolak akses user dan menampilkan “password/ email salah!” sesuai harapan valid 6 mengetikkan user email dengan data yang benar dan password dengan data yang benar kemudian klik tombol login user email: (setiawanjaka59@gmail . com) password: (123) sistem akan menerima akses user dan menampilkan halaman dashboard siswa sesuai harapan valid simpulan dan saran simpulan program elearning dapat membantu meningkatakan proses belajar dan mengajar antara siswa dan guru. dengan adanya website elearning proses belajar lebih efisien karena pembelajaran bisa dimana saja dengan penerapan sistem pembelajaran elearning guru dapat memantau perkembangan belajar tiap siswanya. saran berdasarkan kesimpulan di atas penulis membuat beberapa saran untuk mengembangkan aplikasi e-learning ini agar lebih baik lagi dengan adanya maintenance berkala jika sudah diterapkan seperti mengelola database program yang harus diperhatikan agar adanya backup database secara berkala agar data selalu aman apabila terjadi kerusakan software atau hardware yang tidak diinginkan pada server penyimpan database dan menghambat kinerja website itu sendiri sekolah dan guru dapat meman-faatkan media pembelajaran e-learning seba-gai salah satu solusi pemanfaatan internet se-bagai sumber dan media belajar. penambahan beberapa fitur yang dirasa masih kurang seperti adanya metode pembelajaran berbasis video dan fitur chatting atau kirim pesan antar siswa maupun guru. referensi aryadhi, w., parmiti, d. p., putu, l., & mahadewi, p. (2015). pengembangan e-learningdengan model waterfall pada mata pelajaran ipa kelas viii. journal edutech universitas pendidikan ganesha jurusan teknologi pendidikan, 3(1). http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 180 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional frieyadie. (2019). logical record structure (lrs). retrieved october 9, 2019, from http://frieyadie.web.id/logical-recordstructure-lrs/ hanum, n. s. (2013). keefektifan e-learning sebagai media pembelajaran (studi evaluasi model pembelajaran e-learning smk telkom sandhy putra purwokerto) the effectiveness of e-learning as instructional media (evaluation study of e-learning instructional model insmk telkom san. jurnal pendidikan vokasi, 3(1), 90–102. inayati, i., subroto, t., & supardi, k. i. 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(2012). efektifitas computer aided learning (cal) dalam pembelajaran kosakata bahasa inggris siswa sekolah dasar. jurnal pilar nusa mandiri, 8(2), 108–115. retrieved from http://ejournal.nusamandiri.ac.id/index.php /pilar/article/view/481 yohendra, saputra, d., & thjin, s. (2013). perancangan dan kajian penerapan e learning : studi kasus : cyber solution. seminar nasional sistem informasi indonesia, (desember). yuningsih, y. (2019). metode delone dan mclean dalam kepuasan konsumen terhadap aplikasi shopee, 6(1), 55–64. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.463 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 165 a real-time web information system based on a global positioning system for monitoring environmental pollution eko gustriyadi-1, volvo sihombing-2, masrizal-3, puput dani prasetyo adi-4 1,3 sistem informasi, 2 manajemen informatika universitas labuhanbatu sumatera utara, indonesia 1gustriyadiieko@gmail.com, 2volvolumbantoruan@gmail.com, 3masrizal120405@gmail.com, 4badan riset dan inovasi nasional (brin) jakarta, indonesia 4pupu008@brin.go.id (*) corresponding author abstract this research will discuss monitoring pollution in waterways in real time based on gps. a website-based information system is an essential factor for information media, not only database-based but can be communicated with gps. gps is a satellite system that can determine the point of an area with longitude and latitude parameters. the global positioning system is one of the parameters used in this study. longitude and latitude are the primary keys to getting the point in a particular area or point. in research, this location is used in sensor or environmental pollution monitoring. in this paper, we try to review the projects carried out and perform analysis, management, and governance on the server and local host. the program is made by developing the frontend and backend sides. development can be done on desktop based programming and then extended to mobile by manipulating and modifying programs using javascript, json, and other building scripts for better performance and suitable for deployment on various platforms such as mobile-based. this system is very efficient in determining various parameters, for example, the environmental pollution factor. from testing, the gps data is not perfect, all data can be sent, but the accuracy of gps data can reach 96%. this is due to data errors during uplinking and downlinking data. keywords: information systems; global positioning system; environmental monitoring, real-time, longitude-latitude abstract pada riset ini akan membahas pemantauan pencemaran pada saluran air secara realtime berbasis gps. sistem informasi berbasis website merupakan faktor penting untuk media informasi, tidak hanya berbasis database tetapi dapat dikomunikasikan dengan gps. gps merupakan sistem satelit yang dapat mengetahui titik suatu daerah dengan parameter bujur dan lintang, global positioning system merupakan salah satu parameter yang digunakan dalam penelitian ini, dimana parameter bujur dan lintang merupakan kunci utama untuk mendapatkan suatu titik dalam suatu daerah atau titik tertentu, dalam penelitian ini merupakan lokasi yang digunakan dalam pemantauan sensor atau pemantauan pencemaran lingkungan. dalam naskah ini, kami mencoba meninjau proyek-proyek yang telah dilakukan dan melakukan analisis, manajemen, dan tata kelola di server dan localhost. program dibuat dengan mengembangkan sisi frontend dan backend. pengembangan dapat dilakukan pada pemrograman berbasis desktop dan selanjutnya diperluas ke mobile dengan memanipulasi dan memodifikasi program menggunakan javascript, json, dan skrip bangunan lainnya untuk kinerja yang lebih baik dan cocok untuk ditempatkan di berbagai platform seperti berbasis mobile. sistem ini sangat efisien digunakan untuk mengetahui berbagai parameter dalam sistem, misalnya faktor pencemaran pada suatu lingkungan. dari pengujian data gps belum sempurna, semua data dapat terkirim namun akurasi data gps dapat mencapai 96%. hal ini dikarenakan adanya kesalahan data pada saat proses uplink dan downlink data. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.463 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 166 kata kunci: sistem informasi; sistem penentuan posisi global; pemantauan lingkungan, realtime, bujurlintang introduction conventional systems carry out periodic checks using manual methods, for example, checking water conditions or water quality in areas full of pollution, for example, water pollution in crowded settlements. for example, every week, officers from the cleaning service and health services check the ph of water in areas such as sewers or water reservoirs adjacent to residential areas; this is done to determine the level of pollution that may occur in well water or air pollution. preventive efforts are needed to overcome pollution, which uses conventional methods. in this research, automatic detection or real-time monitoring (moushi et al., 2018) of pollution that occurs in certain areas is carried out using a combination of website-based (hnatushenko et al., 2018) and realtime based technology (benitez cortes et al., 2019), (adi et al., 2022), realtime here is novelty research where data can be appropriately read using gps in real-time (aji et al., 2022). sensor data can be recognized quickly (hnatushenko et al., 2018), (benitez cortes et al., 2019), (liani et al., 2021), (mukti et al., 2021), (vinnikov et al., 2021), (xie et al., 2022), (shi et al., 2018). this method provides quick action for health and hygiene workers to cope with leaking waterways from farm points and other polluting factors such as factory areas. figure 1. gps iot prototype system to monitor river water pollution from the previous research, ganesh ram, aravind dhandapani (june 2022), with his research entitled, development of ph value detection sensor for water to detect and automate water neutralization process, at the end of the research is the detection of quality water for people usage. it indicates the same with its auto-monitoring system. figure 1 obtains the descriptions of this gps-based monitoring (vinnikov et al., 2021), (radhika et al., 2022), (shi et al., 2018). the first is how the power consumption of sensor nodes, the overall prototype, the application server, and the internet server is obtained. the first is how to analyze river water and how much ph is produced by the river water according to the point where the gps module is installed (want et al., 2018), (sunehra et al., 2020). then the next thing is regulating the power consumption taken from sunlight. then how to transmit water ph sensor data to the internet server, attenuation, and quality of service from lorawan devices (moushi et al., 2018), (anand et al., 2019). figure 2. water ph level in an area from gps data furthermore, the prototype data analyzed the value of uplink and downlink data from the lorawan server. from here, we can discover the quality and service of the device or prototype built in the test or monitoring area. at the same time, the website is a system that will display data from mysql or mariadb using python programming code. then display the data simultaneously from the stored ph sensor to mariadb. moreover, mariadb sends the data to the server in real-time. in this case, php and javascript are needed to convert and call data from mariadb or mysql db so that data can be displayed in real time on the website we build. an area monitoring system with water ph level data is shown in figure 2. talking about the website is the same as talking about management information systems. in this case, the water pollution information system is based on the website and the internet of things (iot) (arta et al., 2022), (zhang et al., 2022), (radhika et al., 2022), (adi, sihombing, et al., 2021), jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.463 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 167 (adi, mustamu, et al., 2021). this management information system is a planning control that provides detailed, fast, and accurate information. this fast value is taken from the internet of things, a system that was built (munsadwala et al., 2019), (arta et al., 2022), while the accuracy comes from data sensors installed in the environment, referred to as sensor calibration, for example, water ph. until ph water data can be obtained by all people easily using an ip address or homepage address of application servers such as thingspeak, the things network, or domains in general. figure 3. iot to web of things finally, from the discussion on the internet of things, the water ph monitoring system combined with the website service will turn into a web of things, as shown in figure 3. the role of web technology influences this, the script code used and the combination of gps data (adi, sihombing, et al., 2021) and iot sensors (xie et al., 2022), (want et al., 2018), (xie et al., 2022) based on mysql database which is changed to real-time graph and data table form. then a new problem arises, namely how to determine the incoming data because the incoming data on this web application is not limited in number. to overcome this, a limitation of the data sensor to the server or website server is needed. research methods the flowchart in figure 4 shows the method used in this research. from the method, it can be seen that the initial data is obtained from the sensors contained in the end node, and then the conversion process is carried out to get the sensor data displayed on the web server and internet server and continued on the website pages. the first time is to initialize the gps lib library. in the lora module, run the gps module (anand et al., 2019), and ensure that the ph water is connected correctly and calibrated, then the ph water data is sent to the internet via the application server, and sensor data can be seen in real time on the website. in figure 5, the gps module works with a satellite system (sunehra et al., 2020), (shi et al., 2018). gps detects the longitude and latitude layout, uses uart connected to the microcontroller and power supply, sends data to the gui using python and mariadb, then converts to php and javascript and displays it to the website. figure 4. the system work figure 5. communication data from a satellite to a website the system as a whole can be seen in figure 5 to facilitate an understanding of the system to be built. from figure 5, it can be concluded that the gps module contained in the prototype reads position data from the ph water sensor, and then from that data, it is processed into mysql data form and displayed on the website pages. the processes p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.463 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 168 occur the conversion process of the sensor data entered in the mysql database belonging to the raspberry pi 4.0, namely mariadb, which is then converted to php and javascript. (𝑥 − 𝐴1) 2 + (𝑦 − 𝐵1) 2 + (𝑧 − 𝐶1) 2 − (𝑐(𝑡1 − 𝑑)) 2 = 0 ............ (1) (𝑥 − 𝐴2) 2 + (𝑦 − 𝐵2) 2 + (𝑧 − 𝐶2) 2 − (𝑐(𝑡2 − 𝑑)) 2 = 0 ............ (2) (𝑥 − 𝐴3) 2 + (𝑦 − 𝐵3) 2 + (𝑧 − 𝐶3) 2 − (𝑐(𝑡3 − 𝑑)) 2 = 0 ............ (3) (𝑥 − 𝐴4) 2 + (𝑦 − 𝐵4) 2 + (𝑧 − 𝐶4) 2 − (𝑐(𝑡4 − 𝑑)) 2 = 0 ............ (4) in equations 1-4, x, y, and z are the rectangular coordinates of the gps on the receiver, while a, b, and c are the coordinates of the gps satellites; moreover, satelite positions define (𝐴𝑖, 𝐵𝑖 , 𝐶𝑖 , ), from spherical coordinates (𝜌𝑖 , 𝜑𝑖 , 𝜃𝑖), then the relationship between the satellite and spherical positions is as in equations 5-7. 𝐴𝑖 = 𝜌 cos(𝜑𝑖 ) ................................................................................ (5) 𝐵𝑖 = 𝜌 cos(𝜑𝑖 ) sin(𝜃𝑖 ) ................................................................ (6) 𝐶𝑖 = 𝜌 sin(𝜑𝑖 ) ................................................................................. (6) results and discussion the display on the website page is data taken from the end-node sensor. the system other than the real-time mechanism can also be seen by adding the crud system (create, read, update, delete). this system needs to be made on the website based. as shown in the following figure 6. figure 6. data on the web page’s example furthermore, figure 7 and figure 8 are examples of a web page for adding data, in which there is a crud that can change data easily. figure 7. additional data menu example gps performs specific location tracking at a minor area known as ward and street. this is where gps performs additional important points when tracking, as shown in figure 9 and figure 10. figure 8. add data districts example figure 9. add data ward example figure 10. add data street example furthermore, this gps-based web feature has three view features openstreetmap view, street view, and satellite point view. openstreetmap view is shown in figure 11, and the display is just a line of street data added to the web. while street point view can be seen in figure 12, these are the points where measurements are made. to make it more transparent, change the satellite point view menu so that the satellite displays specific points, namely longitude and latitude, and the results of capture images when we perform measurements. in detail can be seen in figure 14. figure 11. openstreetmap view figure 12. street point view jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.463 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 169 the point in figure 12 can be more specific and detailed if the user adds data. for example, the data is water ph and water temperature. the data taken is the measurement of the ph sensor located on the sensor and attached to the gps module as a single unit. the ph sensor, gps, and lora module are installed as a single unit, and we can take this ph water and gps data as primary data. moreover, satellite point view and details data view is shown in figure 13 and figure 14, and this is gps data that has been combined or synchronized with longitude and latitude data on google maps. figure 13. satellite point view figure 14. details data view conclusions and suggestions conclusion the overall system of this environmental monitoring website can work well and provide specific information for users. this gps-based detection system has been tested and shows high accuracy, but for iot devices, it must continue to be updated and maintained at any time, for example, once a week. from testing gps data not to perfect, all data can be sent but can reach 96% accuracy of gps data. this is because there is a data error during the uplink and downlink process data. the data obtained is data on the position of ph water with the points that have been fixed so that we can automatically get real-time data on ph water with the gps module's value. suggestion there must be data settings; therefore, gps data with ph water data is not sent continuously. data can be sent weekly, then delay or automatic sleep every 5 hours. this is done to reduce packet loss or packet data error (bit) and data effectiveness and mysql data capability in storing data in the database, so it is also necessary to increase the database capacity to be more significant. finally, an increase in programming ability towards a more specific mobile is needed for accessibility, flexibility, and user convenience. references adi, p. d. p., indarti, n., wahyu, y., sudarmanto, b. a., mukti, f. s., & parenreng, j. m. 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(2022). planning bus networks by exploiting gps trajectories collected by iot‐enabled vehicles. international journal of communication systems. https://doi.org/10.1002/dac.5229 127 comparative analysis of the k-nearest neighbor algorithm on various intrusion detection datasets andri agung riyadi 1), fachri amsury 2), irwansyah saputra 3), tiska pattiasina 4), jupriyanto 5) sains data1, sistem informasi2,3,5 universitas nusa mandiri andriagu1603@nusamandiri.ac.id1, fachri.fcy@nusamandiri.ac.id2, irwansyah.iys@nusamandiri.ac.id3, jupriyanto.kahar@gmail.com5 teknologi informasi universitas bina sarana informatika elleoratiska07@gmail.com4 abstrak keamanan di dalam jaringan komputer dapat menjadi rentan, hal ini disebabkan kita memiliki kelemahan dalam membuat kebijakan keamanan, konfigurasi sistem komputer yang lemah atau bug pada perangkat lunak. intrusion detection adalah mekanisme mengamankan jaringan komputer dengan cara mendeteksi, mencegah, dan menghalangi usaha ilegal untuk mengakses informasi yang bersifat rahasia. mekanisme ids dirancang untuk dapat melindungi sistem dan mengurangi dampak kerusakan yang ada dari setiap serangan di dalam jaringan komputer karena melanggar kebijakan keamanan komputer meliputi ketersediaan, kerahasiaan, dan integritas. teknik data mining telah digunakan untuk memperoleh pengetahuan yang berguna dari penggunaan dataset-dataset ids. beberapa dataset ids yang umum digunakan adalah nsl-kdd, 10% kdd, full kdd, corrected kdd99, unsw-nb15, adfa windows, caida, dan unm telah digunakan untuk mendapatkan tingkat akurasi menggunakan algoritma k-nearest neighbors (k -nn). dataset ids terbaru yang disediakan oleh canadian institute of cybersecurity yang berisi sebagian besar skenario serangan terbaru bernama dataset cicids2017. eksperimen pendahuluan menunjukkan bahwa pendekatan menggunakan metode k-nn pada dataset cicids2017 berhasil menghasilkan nilai rata-rata akurasi deteksi intrusi tertinggi dibandingkan dataset ids lainnya. kata kunci: intrusion detection system, k-nearest neighbors, machine learning, network security abstract because we have flaws in developing security rules, inadequate computer system settings, or software defects, security in computer networks can be vulnerable. intrusion detection is a computer network security method that detects, prevents, and blocks unauthorized access to confidential information. the ids method is intended to defend the system and minimize the harm caused by any attack on a computer network that violates computer security policies such as availability, confidentiality, and integrity. data mining techniques were utilized to extract relevant information from ids databases. the following are some of the most widely utilized ids datasets nsl-kdd, 10% kdd, full kdd, corrected kdd99, unsw-nb15, adfa windows, caida, dan unm have been used to get the accuracy rate using the k-nearest neighbors algorithm (k-nn). the latest ids dataset provided by the canadian institute of cybersecurity contains most of the latest attack scenarios named the cicids2017 dataset. preliminary experiment shows that the approach using the k-nn method on the cicids2017 dataset successfully produces the highest average value of intrusion detection accuracy than other ids datasets. keywords: intrusion detection system, k-nearest neighbors, machine learning, network security introduction the number of internet users around the world has exploded in the previous two decades. hundreds of thousands of institutions and millions of people communicate with each other every day over the internet. as a result of these advancements, the number of attacks on internet networks continues to rise on a daily basis. data integrity and privacy become a significant concern. the three principles of network security are confidentiality, integrity, and availability, and network security 128 attempts to defend the network from assaults on these three principles. an attempt to violate these three key characteristics is referred to as a network attack (bace & mell, 2001). there is a lot of software that protects data and networks from incoming threats, such as firewalls, antivirus, data encryption, and user authentication, but it can't protect against all attacks. a lot of studies have been done on this subject to tackle this problem. intrusion detection systems (ids) was created to track and filter network activity by detecting threats and alerting network administrators (chung & wahid, 2012). the misuse detection method and the anomaly detection method are the two basic approaches for ids. ineffective against all forms of threats, yet each has its own set of strengths and weaknesses (lin, ke, & tsai, 2015). misuse detection is a methodical strategy to detect an assault on a computer network by comparing actions or looking for patterns that have previously been designated as attack symptoms. the abuse detection method is effective for detecting known assaults, but it is unable to detect fresh attacks (zhang, li, gao, wang, & luo, 2015). anomaly detection is useful at identifying novel assaults, with the exception that it is not very effective at known detection rates, resulting in a high fpr (kim, lee, & kim, 2014). data mining techniques have been used to obtain useful knowledge from the use of ids datasets. some ids datasets that are commonly used are nsl-kdd, 10% kdd, full kdd, corrected kdd99, unsw-nb15, adfa windows, caida, dan unm have been used to get the accuracy using the k-nn algorithm approach (hamid, et al., 2018). cicids2017, one of the latest ids datasets from the canadian cybersecurity institute (cic) at new brunswick university (unb), was analyzed for research purposes (sharafaldin, habibi lashkari, & ghorbani, 2018). the cicids2017 dataset is created using a modern framework that takes into account your organization's policies and conditions and uses coefficients that can be individually determined for each criterion (gharib, sharafaldin, lashkari, & ghorbani, 2016). the solution to overcome the challenges of fraud detection and anomaly detection technologies and maximize the capabilities of the two technologies is to use a hybrid approach(depren, topallar, anarim, & ciliz, 2005). for use with ids, three hybrid methods are recommended: fraud detection and subsequent anomaly detection, anomaly detection and subsequent fraud detection, or fraud detection and anomaly detection at the same time. the ids hybrid method uses a combination of many results from independent training of fraud detection and anomaly detection. for example, in the hybrid method, if at least one of the two methods classifies network traffic as an attack, then network traffic is considered an attack. in this case, the detection rate is high, but the ids's fpr is still high. conversely, if the hybrid method considers network traffic as an attack only if both methods are classified as attacks, the fpr will be low, but many attacks in the network traffic will be ignored (kim et al., 2014). false positive rate (fpr) is when the ids system detects benign or normal activity on the computer network and classifies it as a dangerous attack. this research uses the k-nearest neighbor algorithm approach to measure the attack detection accuracy of the cicids2017 dataset. the algorithm method is not used in the cicids2017 dataset. research methods in conducting research, will use knowledge discovery in databases (kdd) method consisting of five stages, namely data selection, preprocessing, transformation, data mining, interpretation, or evaluation (fayyad, 1997). the cicids2017 dataset will be used as the latest standard dataset for research and evaluation studies in the field of ids, performing analysis to further identify the data, creating the initial findings, and then evaluating the quality of the data. the cicids2017 dataset consists of 3.1 million records with 85 attributes, including one attribute used as a label. dataset attributes have seven attack categories and one normal category. the preprocessing process includes removing duplicate data, checking for inconsistent data, removing low-value or completely useless features, converting labels of all attack types to attack labels, and fixing data errors. feature selection is used to determine which features are important and to discard low-quality and uncorrelated features. given the number of records in the cicids2017 dataset, you should perform data sampling for efficiency reasons. in this study, we obtained a 1% sample from the cicids2017 dataset. the fitted model is used to compare the result of the precision value using the k-nn algorithm approach with the value of k = 5, 6, 7, 8, 9. results obtained in the form of accuracy, precision, and recall values are produced by comparison with other ids datasets. literature study a. intrusion detection systems and cicids2017 dataset intrusion detection systems (ids) are a very important part of protecting information systems in computer networks. the research report written by anderson (1980) whose purpose was to 129 enhance the audit capabilities of computer security and customer surveillance capabilities of the system, served as the initial concept of ids (anderson, 1980). there are three commonly used approaches to ids systems: misuse detection, anomaly detection, and hybrid detection (mchugh, christie, & allen, 2000). hybrid detection uses the ids approach. this method combines the use of misuse detection and anomaly detection to improve the ability of both attack detection methods. there are three ways to implement hybrid methods on ids, namely the use of the misuse detection method followed by the anomaly detection method, the anomaly detection method followed by the method of misuses detection or integrating the method of misuse detection, and the anomaly detection method at the same time (kim et al., 2014). cicids2017 is a dataset made by the university of new brunswick's (unb) canadian institute for cybersecurity (cic). cicids2017 was created using a modern framework that takes into account organizational policies and conditions with coefficients that can be individually determined for each criterion. this dataset consists of approximately 3.1 million records with more than 80 attributes, where 1 attribute is used as a label. the attributes in the dataset have 7 attack categories and 1 benign or normal category. the 7 categories of attacks in this dataset are heartbleed attack, botnet, dos attack, brute force attack, ddos attack, infiltration attack, and web attack. b. methodology data mining is the application of special algorithms to extract patterns from data (fayyad, 1997). data mining is about solving problems by analyzing existing data in the database (witten et al., 2005). there are numerous techniques and methods for carrying out various types of data mining tasks. this method is classified into three major data mining paradigms, which are as follows: predictive modeling, discovery, and deviation detection. data mining and knowledge discovery in databases (kdd) are frequently used interchangeably to describe the process of uncovering hidden information in a large database (agushinta & irfan, 2008). although they have different concepts, data mining and kdd are related. data mining is one of the stages in the kdd process. one of the ten best data mining techniques is the k-nearest neighbors (k-nn) classification algorithm method. the k-nearest neighbors (k-nn) method uses famous ciceroprinciplepares cum paribus facility congregant (birds of a feather flock together or equals with equals easily associate) (mucherino, papajorgji, & pardalos, 2009). the accuracy, precision, and recall of eight different ids datasets were compared using the k-nn algorithm. in the nsl-kdd dataset, the k-nn algorithm outperforms other algorithms in terms of accuracy, precision, and recall (hamid et al., 2018). the k-nn method does not generate a classifier from the data in a training set, but rather uses the training set every time classification is required; thus, the k-nn method is often referred to as a lazy classifier. classification employs the analogy-based k-nn algorithm method, which compares test records with training records that have similarities. the k-nearest neighbors (k-nn) algorithm is a method for classifying objects that are based on learning data that is close to the object. this technique is very simple and straightforward to use. similar to clustering techniques, namely grouping new data based on its distance from some existing data or its nearest neighbor. the first step is to calculate the distance to a neighbor before searching for data. then, to define the distance between two points, namely the point on the training data and the point on the testing data, the euclidean formula is used with equation (1), as follows: 𝑑(𝑎, 𝑏) = (𝑥 + 𝑎)𝑛 = ∑ (𝑋𝑖 − 𝑌𝑖)² 𝑛 𝑖=0 ………… (1) explanation: x: data 1 y: data 2 i: feature n d (a,b): euclidean distance n: number of features in the concept of data mining, a confusion matrix is a method that is commonly used to calculate accuracy. if the dataset only has two classes, one is considered positive and the other is considered negative. table 1. confusion matrix data class positive negative positive true positives (tp) false negatives (fn) negative false positive (fp) true negative (tn) accuracy is defined as the degree of similarity between predicted and actual values. precision is the degree of agreement between the information requested by the user and the response provided by the system. precision values are calculated by dividing the number of positive examples correctly classified by the number of positive examples labeled as positive by the system. 130 the recall rate is the system's success rate in rediscovering information. the recall value is calculated by dividing the number of correctly classified positive samples by the number of positive examples in the data. 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝐹𝑁+𝐹𝑃+𝑇𝑁 ............................................ (2) 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃+𝐹𝑃 ......................................................... (3) 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃+𝐹𝑁 ................................................................ (4) research framework development the ideas for this research were proposed using references from previous evaluations regarding data collection in several ids datasets, beginning with the use of the cicids2017 dataset as a standard dataset for researchers in the field of intrusion detection systems (ids) (sharafaldin et al., 2018). data will be preprocessed by removing features that have been written twice, discarding wrong notes, no value, or incomplete data, and so on (alshammari & nur zincir-heywood, 2007; radford, richardson, & davis, 2018). the dataset's data is labeled with two labels: benign and attack. the benign traffic represents normal network traffic, while the rest is an attack. it is feared that data cleansing or feature selection, which eliminates features that are less valuable or completely useless, will render research results irrelevant. to prepare for the data mining process, data reduction and data splitting are performed. the k-nearest neighbors (k-nn) approach will be used as a data mining algorithm to improve intrusion detection accuracy with values of k = 5, 6, 7, 8, 9. results and discussion the data mining algorithm method approach is used at the classification stage to determine the accuracy of attack detection in the cicids2017 dataset. the algorithm used is the k-nn algorithm with values of k = 5, 6, 7, 8, 9. a. k-nearest neighbour figure 1 depicts the accuracy, precision, and recall values in the cicids2017 dataset using the k-nn algorithm with values of k = 5, 6, 7, 8, 9. figure 1. classification results using the k-nn algorithm b. comparison with another dataset table 2 compares the accuracy, precision, and recall values in several other ids datasets using the k-nn algorithm with the value of k = 5, 6, 7, 8, 9. table 2. comparison of the results of the k-nn algorithm on various ids datasets dataset neighborhood 5 6 7 8 9 accuracy precision recall accuracy precision recall accuracy precision recall accuracy precision recall accuracy precision recall full kdd99 0.7342 0.722 0.734 0.70979 0.721 0.71 0.72028 0.736 0.72 0.65734 0.624 0.657 0.6958 0.633 0.696 corrected kdd 0.6682 0.67 0.668 0.71495 0.707 0.715 0.48598 0.496 0.486 0.71962 0.722 0.72 0.71028 0.701 0.71 nslkdd 0.7853 0.677 0.785 0.92 0.92 0.92 0.97592 0.959 0.976 0.9875 0.977 0.988 0.77193 0.77 0.772 10% kdd 0.8421 0.874 0.842 0.57142 0.571 0.571 0.64285 0.629 0.643 0.71428 0.706 0.714 0.5 0.521 0.5 unsw 0.4285 0.351 0.429 0.57142 0.571 0.571 0.66083 0.655 0.661 0.8421 0.874 0.842 0.82456 0.83 0.825 caida 0.6428 0.413 0.643 0.42857 0.762 0.351 0.5 0.521 0.5 0.71428 0.706 0.714 0.64285 0.413 0.643 adfa windows 0.7142 0.714 0.714 0.64285 0.413 0.643 0.82456 0.83 0.825 0.91228 0.92 0.912 0.85308 0.858 0.853 unm dataset 0.6382 0.626 0.638 0.79906 0.794 0.799 0.66822 0.67 0.668 0.72429 0.712 0.724 0.57943 0.53 0.579 cicis2017 0.9688 0.9172 0.926 0.9697 0.919 0.929 0.9676 0.9157 0.922 0.9683 0.916 0.925 0.9664 0.9113 0.921 131 the k-nn algorithm with the value of k= 5, 6, 7, 8, 9 is used in figure 2 to calculate the average value of accuracy, precision, and recall. figure 2. comparison of the average values of accuracy, precision, and recall of the k-nn algorithm on various ids datasets we represented cicids2017 for comparison with several other existing ids datasets; as shown in figure 1, the highest accuracy value of the cicids2017 dataset was obtained using the k-nn algorithm with the value of k = 6, which equals 96.97%. table 2 shows the accuracy, precision, and recall values in some ids datasets, with the nslkdd dataset having the highest level of accuracy using the k-nn algorithm with k = 8. conclusions and suggestions conclusion the goal of this study is to detect network anomalies using machine learning methods. because of its up-to-dateness, wide attack diversity, and various network protocols, the cicids2017 dataset was used in this context. the average value of accuracy, precision, and recall uses the k-nn algorithm with the value of k= 5, 6, 7, 8, 9 on the cicids2017 dataset higher than other ids datasets which are 96.8160%, 91.5840%, 92.4640% as seen on figure 2. suggestion based on the conclusions obtained, several suggestions can later be done for future research that researchers can use a more varied algorithm and up-to-date ids datasets. references agushinta, d. 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(2015). detecting anomalies from big network traffic data using an adaptive detection approach. information sciences, 318, 91–110. doi:10.1016/j.ins.2014.07.044 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.556 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 425 clickbait detection in indonesia headline news using indobert and roberta muhammad edo syahputra-1*, ade putera kemala-2, dimas ramdhan-3 computer science department1*), 2, data science / school of computer science3 bina nusantara university jakarta, indonesia muhammad.syahputra002@binus.ac.id1 , ade.kemala@binus.ac.id2, dimas.ramdhan@binus.ac.id3 (*) corresponding author abstract this paper explores clickbait detection using transformer models, specifically indobert and roberta. the objective is to leverage the models specifically for clickbait detection accuracy by employing balancing and augmentation techniques on the dataset. the research demonstrates the benefit of balancing techniques in improving model performance. additionally, data augmentation techniques also improved the performance of roberta. however, it resulted differently for indobert with slightly decreased performance. these findings underline the importance of considering model selection and dataset characteristics when applying augmentation. based on the result, indobert, with a balanced distribution, outperformed the previous study and the other models used in this research. this study used three dataset distribution settings: unbalanced, balanced, and augmented with 8513, 6632, and 15503 total data counts, respectively. furthermore, by incorporating balancing and augmentation techniques, the research surpasses previous studies, contributing to the advancement of clickbait detection accuracy, contributing to the advancement of clickbait detection accuracy with 95% accuracy in f1-score with unbalanced distribution. however, the augmentation method in this study only improved the roberta model. moreover, performance might be boosted by gathering more varied datasets. this work highlights the value of leveraging pre-trained transformer models and specific dataset-handling techniques. the implications include the necessity of dataset balancing for accurate detection and the varying impact of augmentation on different models. these insights aid researchers and practitioners in making informed decisions for clickbait detection tasks, benefiting content moderation, online user experience, and information reliability. the study emphasizes the significance of utilizing state-of-the-art models and tailored approaches to improve clickbait detection performance. keywords: clickbait detection; transformer; deep learning; data augmentation abstrak makalah ini mengeksplorasi pendeteksian clickbait menggunakan model transformer, khususnya indobert dan roberta. tujuannya adalah untuk memanfaatkan model khusus untuk akurasi deteksi clickbait dengan menggunakan teknik penyeimbangan dan augmentasi pada dataset. penelitian menunjukkan manfaat teknik penyeimbangan dalam meningkatkan kinerja model. selain itu, teknik augmentasi data juga meningkatkan kinerja roberta. namun hasil yang didapatkan berbeda bagi indobert dengan sedikit penurunan kinerja. temuan ini menggarisbawahi pentingnya mempertimbangkan pemilihan model dan karakteristik dataset saat menerapkan augmentasi. berdasarkan hasil tersebut, indobert dengan distribusi berimbang mengungguli penelitian sebelumnya serta model lain yang digunakan dalam penelitian ini. penelitian ini menggunakan tiga skema dataset untuk melakukan eksperimen yaitu distribusi unbalanced sebanyak 8513 total data, balanced 6631 data, dan augmented data dengan total 15503 data. selain itu, dengan menggabungkan teknik penyeimbangan dan augmentasi, penelitian ini melampaui penelitian sebelumnya, dengan mendapatkan akurasi sebesar 95% pada model indobert yang divalidasi menggunakan metode f1 score. namun, berdasarkan eksperimen metode augmentasi tidak memberikan kenaikan pada model indobert. sebaliknya metode augmentasi cukup efektif pada model roberta. selain itu, dengan menambahkan jumlah data yang lebih bercvariasi dapat meningkat performa model secara signifikan. pekerjaan ini menyoroti nilai memanfaatkan model transformer yang telah dilatih sebelumnya dan teknik mailto:muhammad.syahputra002@binus.ac.id mailto:ade.kemala@binus.ac.id2 mailto:dimas.ramdhan@binus.ac.id p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.556 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 426 penanganan kumpulan data tertentu. implikasinya termasuk perlunya penyeimbangan dataset untuk deteksi yang akurat dan berbagai dampak augmentasi pada model yang berbeda. wawasan ini membantu peneliti dan praktisi dalam membuat keputusan yang tepat untuk tugas deteksi clickbait, yang menguntungkan moderasi konten, pengalaman pengguna online, dan keandalan informasi. studi ini menekankan pentingnya memanfaatkan model canggih dan pendekatan yang disesuaikan untuk meningkatkan kinerja deteksi clickbait. kata kunci: clickbait detection; deep learning; deep learning; data augmentasi introduction clickbait has become a mainstream approach in online media, where the headline is not aligned with the content. usually, the headline uses catchy or exaggerated words to attract the reader’s attention. this approach considerably negatively impacts society (bondielli & marcelloni, 2019; zhou et al., 2022). in a survey (bondielli & marcelloni 2019), the clickbait approach potentially leads to a polarized society. the existence of social media with its algorithm can amplify the distribution of information, including news, which could shape society’s discourse in a public space (shukai et al., 2017). such phenomenon also appears in the united states, uk, and arguably worldwide social media. many studies have been conducted exploring methods to tackle this clickbait problem. an early approach using a machine learning classifier is explored (abbas et al., 2019; chakraborty et al., n.d.; manjesh et al., 2018; potthast et al., 2016). however, the studies mentioned still lack performance in capturing meaning and validating using fleiss’ within the context of headline news (zheng et al., 2021). however, recent studies using the deep learning approach have shown promising results (agrawal, n.d.; kim, 2014; zhou et al., 2022). a study (aju et al., 2022) conducted an empirical study comparing the performance of the machine learning and deep learning approaches. the result shows that bert provides maximum efficiency by outperforming the machine learning method with 10% accuracy. also, in the study (oliva et al., 2022), the divergence measures technique in deep learning to tackle dataset availability in clickbait detection outperforms the machine learning approach in accuracy. however, there is still a gap to be filled in indonesian clickbait detection. since there are still only a few studies focusing on bahasa indonesia using the deep learning approach. moreover, the availability of the dataset in bahasa indonesia is also one of the gaps in this research and in deep learning research in general. also, one of the disadvantages of the deep learning approach is that it takes more time to train since a large dataset is needed to extract and learn features (sirusstara et al., 2022a). therefore, this research aims to explore and leverage a pre-trained model to build a classifier model for indonesian headline news compared to the previous research (sirusstara et al., 2022a) using the same dataset from (hadiyat, 2019). in addition, this research utilized the dataset for balancing and augmentation to improve model accuracy, which was not utilized in the previous study. research methods the methodology proposed in this research consists of three phases: obtaining the dataset, preprocessing, and training using deep learning models. dataset this research obtained the dataset from (william & sari, 2020). the dataset contains more than 15000 indonesian headline news from 12 news publishers. every headline is annotated as clickbait and non-clickbait by the 3 annotators method (fleiss & cohen, 1973). based on annotation results, the dataset has several versions. the version used in this research is mainly from the inter-annotator agreement score of 0.42, the highest fleiss’ k score with distribution, as shown in table 1. table 1. dataset label distribution class total count clickbait 3316 non-clickbait 5297 total 8513 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.556 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 427 pre-processing considering the amount of headline news dataset, which is relatively small, this research used augmentation techniques such as eda (wei & zou, n.d.) and bootstrapping (stine, 2016) to balance the distribution. since the deep learning technique requires a large amount of data to achieve great results, constructing the dataset will most likely affect the model's performance dramatically. in addition, data augmentation has been proven to be effective and improve performance in many tasks, including computer vision (perez & wang, 2017) and speech recognition (park et al., 2019). data augmentation is a technique that utilizes the available data to synthesize new similar data. therefore, it allows researchers to overcome the scarcity of the dataset available. furthermore, adding more variety to the data also leads to avoiding overfitting. bootstrapping in this research, a simple balancing method is conducted using resample method. in this method, a random resampling is proceeded by generating extra data points to the dataset based on bootstrapping procedure (stine, 2016). table 2 is shown the difference in dataset distribution after resampling. table 2. before and after resampling class total count clickbait 3316 non-clickbait 3316 total 6632 eda another approach to data augmentation used in this research is eda (easy data augmentation). in general, eda consists of four approaches synonym replacement (sr), random insertion (ri), random swap (rw), and random deletion (rd). in the first step, sr’s function is initialized to randomly selects n number of words and replaces these words randomly based on selected synonyms from ir’s function. ir generates synonyms in n times randomly, excluding stop words to be replaced in sr with random positions. also operated in n times, rs randomly selects two words in a sentence to swap their positions. finally, rd measures these selected words in a sentence in a probability of p. table 3 shows the dataset distribution after eda. table 3. before and after augmentation class total count clickbait 9539 non-clickbait 5964 total 15503 deep learning technique this paper focuses on deep learning algorithms, specifically using large models from indonesian bert (koto et al., 2020; wilie et al., 2020) or called indobert. indobert uses the same architecture as the original bert with different pre-training datasets, which are unlabeled textcorpus. bert uses a multi-headed attention mechanism as its method (vaswani et al., n.d.) and has been proven to outperform other methods in many nlp benchmarks. in other words, bert is currently known as the state-of-the-art in nlp research. a paper from (koto et al., 2020) proposed indobert, which heavily pre-trained the model using mostly news datasets such as kompas, tempo, liputan6, wikipedia, etc. in total, indobert trained over 220m corpus words. indobert trained purely as a masked language model using the huggingface framework. they also followed the default configuration for bert-base, which has 12 hidden layers, 12 attention heads, and feed-forward hidden layers of 3,072d. the model used an adam optimizer and linear scheduler in pre-training. moreover, the paper also shows the model's high performance in many tasks, such as summarization, sentiment analysis, named entity recognition, etc. unlike (koto et al., 2020), (wilie et al., 2020) also proposed indobert with a larger and more general pre-training dataset. the model trained over 4 billion corpus indonesian words from many sources such as wikipedia, webpage articles, and twitter. therefore, this research implemented a model from (wilie et al., 2020). however, the model from (koto et al., 2020) is also considered to see the impact of specifically pre-training datasets from news headlines that are dominantly used in the research, which is suitable for this research. for training both indobert models, this research used adam optimizer with a learning rate 2e-5 and batch size of 64 and 10 epochs using one gpu nvidia rtx 3080ti. furthermore, this research added more than 3000 headline news to the dataset and trained on both models with the same configuration. then, examine the result for further analysis. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.556 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 428 results and discussion model comparison table 4 shows the experimental results of indobert (wilie et al., 2020) and roberta model from hugging face by cahya, in which the model has been trained with the indonesian dataset. overall, indobert architecture trained with a balanced dataset and without augmentation outperformed other models with (95%) accuracy of f1-score. this accuracy also outperformed previous research using xlm-roberta with (91%) accuracy of f1-score. table 4. experiment results model train test split 8:2 average precision average recall f1-score train test indobenchmark/indobert-base-p1 imbalance 0.99 0.93 0.96 0.86 0.94 balance 0.9882 0.9396 0.9259 0.9176 0.9508 imbalance + augment 0.9964 0.98 0.907 0.87 0.93 balance + augment 0.9952 0.936 0.904 0.934 0.94 cahya/roberta-base-indonesian-522m imbalance 0.98 0.92 0.87 0.92 0.93 balance 0.9839 90.36 0.84 0.9182 0.91 imbalance + augment 0.9925 0.7766 0.6935 0.7589 0.8119 balance + augment 0.993 0.9164 0.8764 0.9131 0.9308 this study also experimented with the same model as the previous study, adding the balancing and augmentation method to the dataset. the augmentation method combined with a balanced dataset improved the accuracy by 4%. thus, this study's model performed better than the previous one. also, based on the experiment results shows that balanced data distribution could improve the accuracy of both models. whereas the augmentation method only boosts performance on the roberta model. data distribution results table 4 also shows the experiment results based on the type of dataset presented in the table. according to the results, they are revealing distinct trends for the indobert and roberta. the experiment using a dataset with a balanced distribution exhibited improved accuracy for indobert and roberta. in contrast with the augmentation method, the model only improves roberta. however, the augmentation method only enhances the performance of the roberta model while slightly decreasing the performance of indobert. the eda augmentation method dramatically increases dataset distribution, as table 3 illustrates. this method leverages the indonesian wordnet, incorporating operations such as synonym replacement, insertion, swap, and deletion, which introduce noise into the training data. although the eda method employs simple operations, the unilingual indonesian wordnet is better suited for our dataset, which consists of indonesian headline news. the amount of augmented dataset could be tuned and equated for each augmentation method. however, this paper used the default parameters, thus producing a different amount of augmented dataset. comparison with previous study table 5 compares the results of this study with the best results from the previous study. table 5 showcases the performance of indobert and roberta models using the eda augmentation method and the previous research's best method, including roberta and xlm-roberta models without data augmentation. the results indicate that when combined with balanced data distribution and the augmentation method, the models in this study outperformed the previous research in all testing scenarios. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.556 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 429 table 5. model comparison with previous study model augmentation type precision recall f1-score indobert (this study) normal 0.96 0.86 0.94 indobert (this study) eda 0.92 0.91 0.95 robert (sirusstara et al., 2022b) normal 0.87 0.874 8738 roberta eda 0.87 0.91 0.93 xlmroberta (sirusstara et al., 2022b) normal 0.91 0.91 0.91 conclusions and suggestions this research employed two architectural models and three dataset settings: imbalance, balance, and augmented. despite utilizing the eda augmentation method, indobert demonstrated superior performance with 95% accuracy in the f1score when the distribution was balanced. however, as observed in previous studies, augmentation improved accuracy in the roberta model without balancing an augmentation method towards the dataset. to further advance clickbait detection, exploring additional deep learning architectures for future research would be beneficial. furthermore, collecting more diverse datasets could enhance performance, and investigating the parameters in the augmentation models could be another avenue for future investigation. references abbas, m., ali memon, k., & aleem jamali, a. 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(2016). an introduction to bootstrap methods. http://dx.doi.org/10.1177/00491241890180 02003, 18(2–3), 243–291. https://doi.org/10.1177/004912418901800 2003 vaswani, a., brain, g., shazeer, n., parmar, n., uszkoreit, j., jones, l., gomez, a. n., kaiser, ł., & polosukhin, i. (n.d.). attention is all you need. wei, j., & zou, k. (n.d.). eda: easy data augmentation techniques for boosting performance on text classification tasks. 6382–6388. retrieved september 23, 2022, from http://github. wilie, b., vincentio, k., indra winata, g., cahyawijaya, s., li, x., lim, z. y., soleman, s., mahendra, r., fung, p., bahar, s., purwarianti, a., & bandung, i. t. (2020). indonlu: benchmark and resources for evaluating indonesian natural language understanding (pp. 843–857). https://aclanthology.org/2020.aacl-main.85 william, a., & sari, y. (2020). click-id: a novel dataset for indonesian clickbait headlines. data in brief, 32, 106231. https://doi.org/10.1016/j.dib.2020.106231 zheng, j., yu, k., & wu, x. (2021). a deep model based on lure and similarity for adaptive clickbait detection. knowledge-based systems, 214, 106714. https://doi.org/10.1016/j.knosys.2020.106 714 zhou, m., xu, w., zhang, w., & jiang, q. (2022). leverage knowledge graph and gcn for finegrained-level clickbait detection. world wide web, 25(3), 1243–1258. https://doi.org/10.1007/s11280-02201032-3 jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.555 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 419 analysis of indonesian language dataset for tax court cases: multiclass classification of court verdicts ade putera kemala-1*, hafizh ash shiddiqi-2 data science1*, computer science2 school of computer science, bina nusantara university jakarta, indonesia ade.kemala@binus.ac.id1*), hafizh.shiddiqi@binus.ac.id2) (*)coresponding author abstrak pajak adalah kewajiban yang timbul akibat adanya undang-undang, menciptakan kewajiban bagi warga negara untuk memberikan sebagian pendapatan mereka kepada negara. pengadilan pajak berperan sebagai otoritas peradilan bagi wajib pajak yang mencari keadilan dalam sengketa pajak. penelitian ini menyajikan analisis dataset pengadilan pajak dalam bahasa indonesia dengan tujuan melakukan klasifikasi multiclass untuk memprediksi putusan pengadilan. sebelum digunakan dataset melalui tahap pra-pemrosesan untuk membersihkan data, proses augmentasi data menggunakan metode oversampling dan label weighting untuk mengatasi ketidakseimbangan kelas. dua model, yaitu bi-lstm dan indobert, digunakan untuk melaksanakan proses klasifikasi. penelitian ini menghasilkan model akhir dengan akurasi 75,83% menggunakan model indobert. hasil penelitian menunjukkan efektivitas kedua model dalam memprediksi putusan pengadilan. penelitian ini memiliki implikasi dalam memprediksi kesimpulan pengadilan dengan informasi kasus yang terbatas, dan memberikan wawasan berharga untuk proses pengambilan keputusan hukum. temuan ini berkontribusi pada bidang analisis data hukum, menampilkan potensi teknik nlp dalam memahami dan memprediksi hasil pengadilan, sehingga meningkatkan efisiensi proses hukum. kata kunci: nlp; tax; bert; deep learning; klasifikasi abstract tax is an obligation that arises due to the existence of laws, creating a duty for citizens to contribute a certain portion of their income to the state. the tax court serves as a judicial authority for taxpayers seeking justice in tax disputes, handling various types of taxes on a daily basis. this paper presents an analysis of an indonesian language dataset of tax court cases, aiming to perform multiclass classification to predict court verdicts. the dataset undergoes preprocessing steps, while data augmentation using oversampling and label weighting techniques address class imbalance. two models, bi-lstm and indobert, are utilized for classification. the research produced a final result of model with 75.83% using indobert model. the results demonstrate the efficacy of both models in predicting court verdicts. this research has implications for predicting court conclusions with limited case details, providing valuable insights for legal decisionmaking processes. the findings contribute to the field of legal data analysis, showcasing the potential of nlp techniques in understanding and predicting court outcomes, thus enhancing the efficiency of legal proceedings. keywords: nlp; tax; bert; deep learning; classification introduction taxation, from an economic standpoint, refers to the transfer of resources from the private sector to the public sector. from a legal perspective, taxation is an obligation that arises due to the existence of laws, creating a duty for citizens to contribute a certain portion of their income to the state (sutedi, 2022). tax is a compulsory contribution imposed by the government on taxpayers, whether individuals or corporations, and it is enforced based on legal provisions. the government does not provide direct compensation to taxpayers; however, tax revenue should be utilized for the maximum prosperity of the people and the needs of the state (halim et al., 2014). taxation in indonesia is primarily regulated in the constitution through article 23a of the undang undang dasar (uud) tahun 1945 (pracasya, 2021) “taxes and other compulsory p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.555 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 420 levies for the purposes of the state are regulated by law”. based on the collecting institutions, taxes in indonesia are levied by both central and regional institutions. the types of taxes managed by the central government include income tax (pph), value-added tax (ppn), luxury goods sales tax (ppnbm), stamp duty (bea materai), land and building tax for plantations, forestry, and mining. these taxes are mostly managed by the directorate general of taxes and the ministry of finance (farouq, 2018). taxes play a crucial role in the life of a nation, particularly in the implementation of development, as taxes serve as a source of state revenue to finance expenditures, including development expenses. the general functions of taxes are budgetary, regulatory, stability, and income redistribution functions (rohendi, 2014). the tax court is a judicial body that exercises judicial authority over taxpayers or tax payers seeking justice in tax disputes they encounter. the tax court has the same status, rank, and independence as other equivalent courts. this tax court operates within the framework of state administration and has an organizational structure that ultimately reports to the mahkamah agung (supreme court) (sandra, 2021). nlp (natural language processing) research has been flourishing in recent years due to the advancements in context-based models in nlp research, revolutionalized by the publication of the transformer model (vaswani et al., 2017). one of the developments stemming from the transformer model is the bert (bidirectional encoder representations from transformers) model (devlin et al., 2018), which is an architecture created by stacking the encoder component of the transformer model. previously, the popular method involved using word vectors like word2vec (church, 2017) or glove(pennington et al., 2014) combined with deep neural networks such as lstm (yu et al., 2019). using bert architecture, it is possible to create a pre-trained model that is trained with a huge amount of unlabeled data to provide it with a general understanding of language. subsequently, the pre-trained model can be further fine-tuned using a small amount of labeled data to adapt it to a specific task. the bert model has achieved remarkable results in various nlp tasks, such as classification (sun et al., 2019), question answering (wang et al., 2019), and named entity recognition (church et al., 2020). one of the implementation of the bert architecture, pre-trained on the indonesian language, is referred to as indobert (wilie et al., 2020). this model is trained with dataset called indo4b which is a 23gb collection of corpus dataset, including wikipedia, twitter, newsletter data. the resulting model has achieved state-of-the-art results in several nlp tasks specific to indonesian language. the aim of this paper is to analyze the dataset of legal cases from the indonesian tax court and attempt to predict the court verdict using available data by leveraging the capabilities of the bert model for multiclass classification tasks. due to the limited availability of textual dataset in the indonesian language (ferdiana et al., 2019), there is still a gap that needs to be addressed in indonesian natural language processing research. therefore, this research aims to explore and utilize pre-trained models to build a classifier model for analyzing and classifying court-based data. research methods this quantitative research focuses on analyzing and performing a multiclass classification task to predict court verdicts based on provided indonesian language text data. most of the research was conducted in the virtual space, utilizing cloud services provided by google for the required computational tasks. the execution timeframe for this research was from june 1, 2023, to july 7, 2023. figure 1. flowchart research figure 1 shows the steps involved in this research, starting with the dataset processing and utilizing the data to develop two nlp models. each jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.555 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 421 model has different mechanisms, resulting in different treatments for the training dataset. once the training process is completed, the accuracy of the models is evaluated. dataset the dataset used is the indonesian tax court verdict summary, which is a secondary dataset and is open source, obtained through kaggle (christian, 2021). this dataset consists of 12,283 text data entries describing the content of tax disputes in the tax court along with the court decisions. each data row represents a case in the tax court. the dataset consists of 7 columns. the detailed explanations for each column can be found in table 1. table 1. dataset features no column desc 1 text textual data from court documents 2 nomor_putusan court decision number 3 tahun_pajak tax year 4 jenis_pajak tax type 5 tahun_putusan year of court decision 6 pokok_sengketa main dispute 7 jenis_putusan type of verdicts the dataset contains tax disputes handled by the tax court from 2005 to 2020. among these 7 columns, the jenis_pajak (tax type) and pokok_sengketa (main dispute) columns will be used as features for analysis, while the jenis_putusan (verdict type) column will be used as the label column. in this stage, we will attempt a multiclass classification task using the dataset. we will analyze the dataset, clean it, and determine which parameters to use. based on preliminary inspection, we are particularly interested in using the pokok_sengketa (main dispute) as the primary feature for determining the verdict. this column contains the textual main object of discussion regarding the dispute related to this legal case. figure 2. tax type distribution figure 3. court verdict distribution figures 2 and 3 display the statistical data distribution based on the jenis_putusan (decision type) and jenis_pajak (tax type) columns. it is evident that based on the jenis_putusan column, there are three types of verdicts with higher frequencies which is : mengabulkan seluruhnya (approved completely), mengabulkan sebagian (partially approved), and menolak (rejected). for convenience, menolak will be referred to as label '0', mengabulkan sebagian as label '1', and mengabulkan seluruhnya as label '2'. in figure 2, it is shown that there are four major types of tax cases in the dataset: beacukai (customs), ppn (value-added tax), pph (income tax), and gugatan (tax lawsuit). based on this preliminary examination, we will only utilize these four types of cases that have verdicts falling into the three most common categories in the dataset. table 2 displays the final data to be used for training and testing. there is a significant imbalance in the dataset labels, and various approaches will be employed to address this issue later. table 2. data distribution data total count class distribution (0 : 1 : 2) all data 11380 4011 : 1464 : 5905 beacukai 4371 1340 : 265 : 2766 ppn 3992 1201 : 773 : 2018 pph 1730 573 : 404 : 753 gugatan 1287 897 : 22 : 368 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.555 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 422 models there are two models used in this research. the first model is the bi-lstm model. for this algorithm, the dataset is further pre-processed by removing stopwords and punctuation. afterward, the dataset is split into an 80:20 distribution as training data and testing data. the resulting training data is fed into a bi-lstm network to train the model. to ensure the best result, we employ a gridsearch method (akiba et al., 2019) to find the optimal hyperparameters for the model. the search space consists of the embedding dimension ranging from 50 to 300, lstm units ranging from 64 to 256, and dropout rates ranging from 0 to 0.5. the best hyperparameters generated by this method and the default initial values to train the model can be observed in table 3. the resulting models then evaluated with testing data to find the accuracy of the model. table 3. hyperparameter searching hyperparameter range best value default value embedding dimension 50 – 300 271 100 lstm unit 64 – 256 75 128 dropout 0 – 0.5 0.11 0.5 the second model used in this research is called indobert, which is an implementation of the bert architecture specifically for the indonesian language. for this model, pre-processing is not required since the model focuses on the context of the sentences as a whole. deleting or modifying the sentences may remove or alter the context. the data split used remains the same, with an 80:20 ratio for the train data and test data. the model is trained with the following hyperparameter settings: maximum input length of 512, batch size of 16, epoch of 10, and a learning rate of 5e-06. several scenarios were tested with this model, and further details of these scenarios will be explained in the following section. imbalance dataset to address data imbalance, two methods will be employed namely : label weighting and oversampling. label weighting is a technique used to address the issue of imbalanced datasets in machine learning. it involves assigning different weights to the labels or classes in the dataset based on their frequency or importance (madabushi et al., 2020). with label weighting, each label will be assigned a different weight based on its frequency distribution. label weighting helps in giving more importance to the minority classes on the other hand, for the oversampling method, the eda (easy data augmentation) technique (wei & zou, 2019) combined with wordnet will be used to generate new synthetic data using similar words from the wordnet corpus. it is worth noted that only the train data will be oversampled to prevent data testing leakage. there are several steps implemented by the eda method to perform data augmentation, including replacing words with their synonyms, deleting a percentage of words, rearranging the positions of words in a sentence, and randomly inserting words into sentences. these steps are randomly applied to each data row. this augmentation will result in a more balanced dataset, where all classes with fewer instances will be augmented to match the size of the majority class. the results of the oversampling technique are presented in table 4. table 4. data distribution after oversampling data class distribution (0 : 1 : 2) beacukai 2166 : 2180 : 2195 ppn 1950 : 1833 : 1607 pph 924 : 630 : 607 gugatan 731 : 630 : 846 results and discussion the overall results of the research are presented in table 5. several insights can be derived from these results. table 5. research result model data acc indobert all data normal 75.83% weighted label 75.04% beacukai data normal 79.66% weighted label 78.29% oversampling 65.49% ppn data normal 80.48% weighted label 79.50% oversampling 51.81% pph data normal 66.48% weighted label 65.61% oversampling 43.06% gugatan data normal 86.05% weighted label 81.78% oversampling 64.34% bi-lstm all data normal parameter 67.36% best parameter 67.57% the first insight observed from the results is that context-based models like bert outperformed word vector-based models with deep jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.555 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 423 neural networks such as bi-lstm. even with the best parameters applied to the bi-lstm model, there was no significant increase in performance compared by using the initial hyperparameter in bilstm model. for the indobert model, several scenarios were tested. the first scenario involved fine-tuning the model using all available data. there was no significant difference between using normal distribution data or weighted distribution data; both scenarios achieved an accuracy of 75%. after observing the performance using all the data, it was decided to attempt dividing the data by tax types. the rationale behind this is that different types of taxes may involve distinct wording and contextual elements in each legal case. the results indicated that the fine-tuning process using beacukai and ppn data yielded better results compared to the previous approach. it is worth noting that among all tax types, pph (income tax) proved to be the most challenging for the model to classify accurately. regarding the gugatan (tax lawsuit) data, its comparatively higher performance may be attributed to significant label imbalances within the testing data. there were only sufficient data for two labels, while the last label had very little representation. as a result, the model primarily focused on classifying the two main labels, which was relatively easier than classifying all three labels, leading to a more significant improvement in performance. it is also observed that using the weighted label and oversampling methods does not contribute to an improvement in the models' performance. in the case of oversampling, where several words in a sentence may be altered, removed, or added, it can potentially change the contextual meaning of the text, making it more challenging for the bert model to accurately analyze the true context of the data. conclusions and suggestions after analyzing the data, performing the necessary preprocessing data for each model it is concluded that context-based model like bert performed best on a multiclass classification task. interestingly, neither label weighting and oversampling method yielded a better result in this particular case. this research has implications for predicting court conclusions with limited case details, providing valuable insights for legal decision-making processes. the findings contribute to the field of legal data analysis, showcasing the potential of nlp techniques in understanding and predicting court outcomes, thus enhancing the efficiency of legal proceedings. suggestion for future research is to use ‘text’ column in dataset as a new features and extract relevance information from it in order to perform multiclass classification with a better result. references akiba, t., sano, s., yanase, t., ohta, t., & koyama, m. 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(2019). a review of recurrent neural networks: lstm cells and network architectures. neural computation, 31(7), 1235–1270. 191 the effect of amount of data on results of accuracy value of c4.5 algorithm on student achievement index data anton sunardi 1*), sienny rusli 2, christina juliane 3 information system master's program stmik likmi https://likmi.ac.id antonsunardi@gmail.com1*), siennyrusli@gmail.com2, christina.juliane@likmi.ac.id3 (*) corresponding author abstract of the many academic data, data in the form of an achievement index needs to be used in-depth so that it does not become a display of numbers and information only. this achievement index evaluation data reflects the educational process students and teaching staff carries out in an educational process. this study aims to measure the accuracy of data mining processing based on differences in test data by analyzing the c4.5 algorithm using rapidminer as a data processing tool and determining the decisions students can make and academic institutions in developing study strategies and educational curricula to be maximized. the data processing is carried out by classifying the student achievement index data at a private university using data analysis test equipment. the data source comes from kaggle.com, which consists of 1687 data that have been processed and processed. the conclusion from the results of this study is that the amount of data turns out to have a significant influence on the accuracy value of the c4.5 algorithm, where an accuracy rate of 91.69% is obtained from the test results of 1687 data with four main attributes, namely ipk1, ipk2, ipk3, ipk4 and correctly or not as a label. keywords: the amount of data, c4.5, achievement index, data mining abstrak dari sekian banyak data akademik, data berupa indeks prestasi perlu dimanfaatkan secara mendalam agar tidak menjadi tampilan deret angka dan informasi saja. data evaluasi indeks prestasi ini merupakan cerminan dari proses pendidikan yang dilakukan pelajar, mahasiswa, dan tenaga pengajar dalam suatu proses pendidikan. penelitian ini bertujuan untuk mengukur tingkat akurasi pengolahan data mining berdasarkan perbedaan jumlah data uji dengan menganalisa algoritma c4.5 menggunakan rapidminer sebagai alat bantu olah data, dan untuk mengetahui keputusan yang dapat diambil oleh pelajar, mahasiswa dan institusi akademis dalam menyusun strategi studi dan kurikulum pendidikan agar lebih maksimal. proses olah data dilakukan dengan mengklasifikasikan data indeks prestasi mahasiswa pada sebuah perguruan tinggi swasta menggunakan alat uji analisis data. sumber data berasal dari kaggle.com yang terdiri dari 1687 data yang telah diproses dan diolah. kesimpulan dari hasil penelitian ini adalah jumlah data ternyata memiliki pengaruh signifikan terhadap nilai akurasi algoritma c4.5, dimana tingkat akurasi sebesar 91.69 % didapatkan dari hasil uji terhadap 1687 data dengan 4 atribut utama yaitu ipk1, ipk2, ipk3, ipk4 dengan tepat atau tidak kelulusan sebagai label. kata kunci: jumlah data, c4.5, indeks prestasi, data mining introduction from time to time, along with the rapid development of the world of data and education, a person is required to improve knowledge and skills to have a good and quality thinking pattern to plan strategies in the face of future competition. performance evaluations are stored and collected by various universities in the form of student achievement index data that universities can use as one of the supports that the management of education organizers can use to determine their educational strategies. the way to use historical data is to process it using data mining methods, one of which is by applying data classification methods so that patterns and rules can produce helpful information to support students and educational institutions in developing learning strategies and educational strategies. mailto:antonsunardi@gmail.com1 mailto:siennyrusli@gmail.com2 mailto:christina.juliane@likmi.ac.id3 192 in previous studies, the level of accuracy in processing student graduation data using several methods can also be tested using machine learning or deep learning techniques, as has been done by (maryanto, 2017). from the explanation in the research, data processing requires a tool that can measure the level of accuracy. according to 2020 college statistics (handini et al., 2020) the amount of student data recorded nationally compared to the number of students enrolled in the ministry of education and culture has a significant difference. therefore, research is needed to apply extensive data collection to support the industrial revolution as a breakthrough in rapid technological progress. research that has been done previously by (budiman & ramadina, 2015) regarding predictions using the data mining classification algorithm conducted by (windarti & suradi, 2019) applies large amounts of data to data analysis concepts that can help readers, especially students and teaching staff and related agencies. their field determines educational strategies by considering various aspects, especially the influence of gpa and the accuracy of the student achievement index data results. this research is presupposed to provide benefits for students as an early reminder about the potential for untimely graduation so that students can develop a more effective study plan strategy. for academic institutions to foreknown provide information in the form of patterns and images that can be used to determine policies in minimizing the student's potential untimely graduation, which is not timely in the scientific field, it is hoped that this research can provide changes in data mining testing techniques. the classification method uses the c4.5 algorithm for varying amounts of data so that this research can be used as a reference for parties in need. research methods the method used in data collection and reference consists of analytical techniques used to classify data and is done by selecting large amounts of data sourced from kaggle.com, then sorting and cleaning steps to be arranged according to research needs, namely 1687 data divided into five research attributes. data processing is carried out using the rapidminer data processing tool to find c4.5 accuracy through careful calculations to find the best level of precision to produce data references to be implemented. the data is applied through the process of extracting patterns from data using training data of 80% and testing data of 20%, then conducted a comparison test of the accuracy rate of the c4.5 algorithm, with the number of data 100, 400, 900, 1250, 1450, and 1687 from a private university in indonesia, referring to the student achievement index. the research flow can be presented in figure 1 below: figure 1. research flow research type according to (chapman et al., 2000), crispdm is a method that connects the background to be achieved to data usage and provides an overview of the data cycle. research using similar techniques was conducted by (sabna & muhardi, 2016) regarding the data cycle of the data wealth of college students. this study uses six stages of the data processing process, namely: 1. business understanding the focus of this stage is more on understanding the objectives and requirements than turning this knowledge into the purpose of extracting data. 2. data understanding data understanding begins with data collection, then getting to know the data, identifying data quality, looking for insights, and detecting groups of data parts that can generate hypotheses on confidential information. 3. data preparation (amir & abijono, 2018) it is posited that the stages in the preparation of data are processed in several locations and can be non-sequential as modelling tools are performed labelling, attribute selection, transformation, and data cleaning to build the final dataset. rapidminer is a data processing tool used to find patterns, designs, knowledge, and evaluation of large amounts of data. it is an opensource learning machine that contains data tools for pre-processing, classification, rule, and association 193 so that it is easy to visualize stated. (muis & affandes, 2015) 4. modelling this research refers to previous research by (romadhona, suprapedi, & himawan, 2017). in his discussion of data modelling using the c4.5 algorithm decision tree, he explains that there is a higher level of accuracy when compared to id3 and chaid algorithms. therefore, modelling is determined and adjusted to achieve optimal values at the modelling stage. at this stage, the authors tested the amount of data against the level of accuracy by comparing data < 1000, namely 100, 400, and 900, and data >1000 data, namely 1250, 1400, and 1687. (hermawanti, asriyanik, & sunarto, 2019) found an accuracy rate of 68.42% using 145 test data, comparing < 1000 data i.e. 100, 400, 900 and data > 1000 data namely 1250, 1400 and 1687. this study used training data of 80% test data of 20% to produce optimal accuracy values referring to discussions done previously by (musu, ibrahim, & heriadi, 2021) 5. evaluation at the evaluation stage, an assessment of the data that has been generated from accuracy values based on confusion matrix, under curve area values (auc) and execution time (et). (olson & shi, 2007) his book entitled introduction to the science of business data excavation explains that a confusion matrix produces four types of classifications, namely true positive (tp), true negative (tn), false positive (fp), and false-negative (fn). the formulation of the confusion matrix in table form is described in table 1 below: table 1. confusion matrix table (olson and shi 2007) true value true false predicted value true tp (true positive) correct result fp (false positive) unexpected result false fn (false negative) missing result tn (true negative) correct absence of result information: true positive (tp): the amount of positive and predicted data to be accurate as positive. false positive (fp): the amount of harmful data but predicted as positive. false negative (fn): the amount of data that is positive but predicted as negative. true negative (tn): the amount of negative and predicted data to be accurate as negative. referring to research conducted by (azhari, situmorang, & rosnelly, 2021), it is mentioned that precision is the accuracy of getting information from accurate positive and negative class data. in addition to the accuracy value, there is also a value to recall specific information obtained from the recall value. the comparison value in testing against research data is obtained from the system's predictive value and the tester's prediction value called accuracy. once the results of the confusion matrix calculation are obtained, estimations for precision, recall, and accuracy values can be calculated as in the measures in formula 1 below: precision = 𝑇𝑃 𝑇𝑃+𝐹𝑃 ............................................................... (1) recall = 𝑇𝑃 𝑇𝑃+𝐹𝑁 .................................................................... (2) accuracy = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 .............................................. (3) formula1. table precision, recall, and accuracy (olson & shi, 2007) formula with the following description: tp: true positive fp: false positive tn: true negative fn: false negative 6. deployment according to (saefulloh & moedjiono, 2013), after the formation of the model, further analysis and measurements are carried out at the previous stage. at this stage, the most accurate model or rule is applied to predict timely graduation and can then be used to evaluate new data. the concept of data deployment refers to the application of a model to predict the accuracy results of 91.69% of 1687 data that students, educators, and readers can use to monitor data processing strategy plans with the implementation of data mining if the purpose of the model is to increase knowledge about data, knowledge. what is obtained needs to be arranged and presented so that students, educators, and related institutions can use it? the implementation stage produces research reports that can be used for repetitive data mining. students or students, educators, and educational institutions that carry out deployment steps need to be understood to redevelop necessary knowledge so that the results have use value. 194 procedure this research was conducted by referring to various literature studies in the form of research results in journals and books seized according to related research needs as a reference for writing. the steps of the data excavation and processing process procedure using rapidminer that we do can be presented in figure 2 below: figure 2. process of processing data, methods, knowledge, evaluation using rapidminer (author source) results and discussions (megna, 2021) in his research in 2021, he believed that large amounts of data are enormous data sets in volume but grow exponentially with the time that traditional data does not have, so it requires management tools that can store or process it to be efficient. based on this, the diversity of data formats that are increasingly complex and grow over time requires data governance using techniques and technology. here are the results obtained based on the crisp-dm methodology theory 1. business understanding the purpose of the study was to determine the amount of data in the dataset that affects the degree of accuracy in the decision tree classification method with the c4.5 algorithm. 2. data understanding the student achievement index dataset is in the form of public data taken from kaggle.com, with 1687 data and four attributes containing student achievement index values and 1 point of appropriate or inappropriate graduation used as a label. the initial data is presented in table 2 and table 3 below: table 2. initial dataset table no sai1 sai2 sai3 sai4 yes / no 1 2.30 1.97 1.80 1.56 no 2 1.81 1.68 1.57 1.86 no 3 3.07 3.00 2.75 3.21 no 4 2.71 2.33 2.61 1.98 no 5 3.17 3.02 3.28 2.96 no 6 3.16 3.45 3.02 3.06 no 7 2.72 2.50 2.92 3.00 no . . . . . . 1687 3.18 3.05 3.05 3.27 yes table 3. dataset description table attribute name information sai1 student achievement index 1 sai2 student achievement index 2 sai3 student achievement index 3 sai4 student achievement index 4 yes / no right or not graduation 3. data preparation in the preparatory stage, the dataset was adjusted to the data mining processing process using rapidminer, and the decision tree classification method was carried out with the c4.5 algorithm and then obtained 1687 data with four attributes. student achievement index and one attribute are appropriate or not timely as labels as presented in table 4 below: table 4. pre processing data table no sai1 sai2 sai3 sai4 yes / no 1 2.30 1.97 1.80 1.56 no 2 1.81 1.68 1.57 1.86 no 3 3.07 3.00 2.75 3.21 no 4 2.71 2.33 2.61 1.98 no 5 3.17 3.02 3.28 2.96 no 6 3.16 3.45 3.02 3.06 no 7 2.72 2.50 2.92 3.00 no . . . . . . 1687 3.18 3.05 3.05 3.27 yes 195 according to (dengen, kusrini, & luthfi, 2020) the calculation steps in the c4.5 algorithm decision tree using manual calculation methods are as follows: the calculation steps in the c4.5 algorithm decision tree using manual calculation methods are as follows: 1. prepare dataset samples 2. calculate the entropy value using the formula:: entropy (s) = ∑ − 𝑝𝑖 ∗ log 2 𝑝𝑖𝑛𝑖=1 ............................ (4) information: s: set n: number of partitions s pi: proportion of si to ss 3. calculate the gain value of each attribute, followed by selecting the highest gain value gain (s,a) = entropy(s) ∑ ∗ entropy(si)ni=1 ...... (5) information: s: set a: attributes n: number of attribute partitions a | si |: the amount of data on the ith partition | s |: number of cases in s 4. modelling this stage progressed by testing <1000 data with 100, 400, and 900 samples and testing data >1000 with examples taken from 1250, 1400, and 1687. this study processed the composition of training data by 80% and testing data by 20%. using rapid miner, data testing was executed to get performance vector results through the presentation in table 5 and table 6. table 5. in performance, vector data < 1000 obtained calculation results for 100 data with an accuracy result of 70% with confusion matrix on prediction "yes" and prediction result "yes" of 1, and on prediction "yes" and prediction result "no" by 5. the results of the performance vector 400 data calculation were obtained with an accuracy result of 85% with confusion matrix on prediction "yes" and prediction result "yes" of 0, and on projection "yes" and prediction result "no" of 5. performance vector calculation results from 900 data obtained an accuracy result of 86.11% with confusion matrix on prediction "yes" and prediction result "yes" of 1, and on prediction "yes" and prediction result "no" of 12. table 5. rapidminer vector performance table (data < 1000) data < 1000 amount of data performance vector 100 accuracy 70.00 % confusion matrix true no yes no 1 5 yes 1 13 400 accuracy 85.00 % confusion matrix true no yes no 0 5 yes 7 68 900 accuracy 86.11 % confusion matrix true no yes no 1 12 yes 13 154 table 6. rapidminer vector performance table (data > 1000) data > 1000 amount of data performance vector 1250 accuracy 86.60 % confusion matrix true no yes no 0 6 yes 20 224 1400 accuracy 90.36 % confusion matrix true no yes no 0 3 yes 24 253 1687 accuracy 91.69 % confusion matrix true no yes no 2 3 yes 25 307 table 6. in performance, vector data >1000 obtained calculation results for 1250 data with an accuracy result of 86.6% with confusion matrix on prediction "yes" and prediction result "yes" of 0, and on prediction "yes" and prediction result "no" of 6. the results of the performance vector 1400 data calculation were obtained an accuracy result of 90.36% with confusion matrix on prediction "yes" and prediction result "yes" of 0, and on prediction "yes" and prediction result "no" of 3. performance vector 1687 data obtained an accuracy result of 91.69% with confusion matrix on the forecast "yes" and the prediction result "yes" of 196 2, and on the prediction "yes" and the development of the prophecy. "no" of 3. 5. evaluation the evaluation is seen from the results of calculating accuracy values based on confusion matrix using rapidminer obtained results as table 7 below: table 7. c4.5 algorithm data evaluation table amount of data accuracy algorithm of c4.5 100 70.00 % true no true yes class precision prediction no 1 5 16.67 % prediction yes 1 13 92.86 % class recall 50.00 % 72.22 % 400 85.00 % true no true yes class precision prediction no 0 5 0.00 % prediction yes 7 68 90.67 % class recall 0.00 % 93.15 % 900 86.11 % true no true yes class precision prediction no 1 12 7.69 % prediction yes 13 154 92.22 % class recall 7.14 % 92.77 % 1250 89.60 % true no true yes class precision prediction no 0 6 0.00 % prediction yes 20 224 91.80 % class recall 0.00 % 97.39 % 1400 90.36 % true no true yes class precision prediction no 0 3 0.00 % prediction yes 24 253 91.34 % class recall 0.00 % 98.83 % 1687 91.69 % true no true yes class precision prediction no 2 3 40.00 % prediction yes 25 307 92.47 % class recall 7.41 % 99.03 % table 7 shows the percentage value of c4.5 algorithm calculation accuracy against the number of data > 1000, i.e., 1250, 1400, and 1687 and the amount of information < 1000, i.e., 100, 400, and 900. the c4.5 algorithm showed higher accuracy in > 1000 data, namely 1250, 1400, and 1687. it indicates that data processing using rapidminer from several data samples has a high degree of accuracy on data amounting to > 1000. the highest accuracy obtained is 91.69%, with 1687 data. 6. deployment the results of this research are enclosed conceive writing comprise information on differences in the results of calculating the accuracy value of the c4.5 algorithm, which is affected by the amount of data. this paper can be functioned as an information and reference for research by using the following data classification methods. conclusions and suggestions conclusion the study concluded that the achievement index data processing test using rapidminer against <1000 data showed a lower graduation accuracy rate than the graduation accuracy rate with the number of data > 1000. the difference in the amount of data tested causes a difference in the results of the accuracy value. the highest accuracy percentage is shown in data 1687 at 91.69%, while the test data amounting to 100 produced the lowest accuracy value of 70%. 197 suggestion to obtain the outcome of the achievement index grouping, appropriate data support is needed. to aim for a more accurate potential value in the c4.5 algorithm technique, the suitability of the type of data and the amount of data is essential because it has a significant effect. the more data tested, the accuracy level will be determined, therefore it is concluded that the amount of data influences the result of processing the accuracy value. reference amir, s., & abijono, h. 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of the pulses issued. the problem that often arises is the difference in parameters possessed by the kkks including the character of data filling. the problem that often arises is the difference in parameters possessed by the kkks including the character of data filling. especially for kkks with oil products there is flushing which should not be included as a lifting volume. the value of flushing is different for each kkks so it is necessary to equalize the perception with the kkks regarding the procedure for recording data including the time of filling in the database. there is still a time difference at the ctp point, the time at the poller migas control center and the time in the database server so that there is a partial data flowrate that has been filled but has not yet been displayed on the web. the purpose of this study is to increase the effectiveness and efficiency of business management and trade in oil and gas, especially in terms of monitoring the volume of oil and gas lifting. installation of a monitoring system that is an integration of a number of software applications, hardware and a number of procedures that are capable of verifying and tracking the truth of the lifting volume reports submitted by the kkks. keywords: scada, supervisory control and data acqucition, oil and gas lifting volume monitoring system abstrak selama ini masyarakat dapat mengontrol sesuatu dari jarak jauh dengan menggunakan remote control, akan tetapi pengontrolan tersebut terhambat oleh jarak. apabila jarak antara alat yang dikontrol dengan pengontrol itu melewati batas toleransinya, maka peralatan tersebut tidak dapat berfungsi sesuai dengan yang diinginkan. selain itu juga adanya kendala biaya terhadap jarak. jarak semakin jauh maka biaya pulsa yang dikeluarkan semakin besar. permasalahan yang sering muncul yaitu perbedaan parameter yang dimiliki kkks termasuk karakter pengisisan data. permasalahan yang sering muncul yaitu perbedaan parameter yang dimiliki kkks termasuk karakter pengisisan data. khusus untuk kkks dengan produk minyak terdapat flushing yang seharusnya tidak dimasukkan sebagai volume lifting. nilai flushing ini berbeda-beda untuk tiap kkks sehingga perlu penyamaan persepsi dengan kkks tentang tata cara recording data termasuk waktu pengisian database. masih terdapat perbedaan waktu di titik ctp, waktu di poller migas control center dan waktu di database server sehingga ada sebagian data flowrate yang terisi tetapi belum sempat ditampilkan di web. tujuan dari penelitian ini untuk peningkatan efektifitas dan efisiensi pengelolaan usaha dan tata niaga minyak dan gas bumi, khususnya dalam hal monitoring volume lifting minyak dan gas bumi. pemasangan sistem monitoring yang merupakan integrasi dari sejumlah aplikasi perangkat lunak, perangkat keras serta sejumlah prosedur yang mampu melakukan verifikasi serta penelusuran terhadap kebenaran laporan volume lifting yang disampaikan oleh pihak kkks kata kunci: scada, supervisory control and data acqucition, sistem monitoring volume lifting minyak dan gas bumi http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 28 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. pendahuluan inovasi di dalam teknologi telekomunikasi berkembang dengan cepat dan selaras dengan perkembangan karakteristik masyarakat modern yang memiliki mobilitas tinggi, mencari layanan yang fleksibel, serba mudah dan memuaskan serta mengejar efisiensi di segala aspek. kebutuhan akan sistem untuk pengendalian jarak jauh semakin meningkat sejalan dengan era globalisasi dimana perpindahan dan pergerakan manusia semakin luas dan cepat. selama ini masyarakat dapat mengontrol sesuatu dari jarak jauh dengan menggunakan remote control, akan tetapi pengontrolan tersebut terhambat oleh jarak. apabila jarak antara alat yang dikontrol dengan pengontrol itu melewati batas toleransinya, maka peralatan tersebut tidak dapat berfungsi sesuai dengan yang diinginkan. pengontrolan melalui jalur telepon merupakan hal yang lumrah, tetapi sistem ini kerap digunakan untuk sistem fix-point to point. selain itu juga adanya kendala biaya terhadap jarak. jarak semakin jauh maka biaya pulsa yang dikeluarkan semakin besar. teknologi jaringan komputer merupakan solusi yang dapat dimanfaatkan untuk mengatasi fix-point to point dan biaya, serta menjadi model fleksibel multi point to multi point. pesatnya perkembangan dunia jaringan komputer akhir-akhir ini, memicu berkembangnya teknologi baru yang memanfaatkan teknologi jaringan komputer sebagai media untuk mewujudkan impian manusia akan sebuah aplikasi pengoperasian peralatan dari tempat lain yang sangat jauh tanpa harus berada di tempat tersebut. permasalahan yang sering muncul yaitu perbedaan parameter yang dimiliki (masruroh & prasetyorini, 2015), perbedaan monitoring (pradikta, pradikta, affandi, & setijadi, 2013) kkks termasuk karakter pengisisan data. khusus untuk kkks dengan produk minyak terdapat flushing yang seharusnya tidak dimasukkan sebagai volume lifting. nilai flushing ini berbedabeda untuk tiap kkks sehingga perlu penyamaan persepsi dengan kkks tentang tata cara recording data termasuk waktu pengisian database. masih terdapat perbedaan waktu di titik ctp, waktu di poller migas control center dan waktu di database server sehingga ada sebagian data flowrate yang terisi tetapi belum sempat ditampilkan di web. dalam penelitian ini melakukan riset tentang sistem monitoring volume lifting minyak dan gas bumi berbasis scada (supervisory control and data acquisition) atau biasa dikenal dengan online realtime system merupakan suatu sistem yang dibangun dan diimplementasikan di titik custody transfer point (ctp) dan melakukan akuisisi data secara online realtime dari setiap titik pantau, sehingga data-data tersebut dapat dipantau dan dimonitor secara realtime. tujuan dari penelitian ini untuk peningkatan efektifitas dan efisiensi pengelolaan usaha dan tata niaga minyak dan gas bumi, khususnya dalam hal monitoring volume lifting minyak dan gas bumi. pemasangan sistem monitoring yang merupakan integrasi dari sejumlah aplikasi perangkat lunak, perangkat keras serta sejumlah prosedur yang mampu melakukan verifikasi serta penelusuran terhadap kebenaran laporan volume lifting yang disampaikan oleh pihak kkks. stabilitas akses dan akuisisi data melalui sistem integrasi data produksi dan lifting minyak dan gas bumi secara online realtime. adanya penerapan sistem monitoring volume lifting minyak dan gas bumi secara online dan realtime di 33 ctp (costudy transfer point). memperoleh data atau informasi volume produksi dan volume lifting minyak dan gas serta pembagiannya sesuai dengan masingmasing daerah penghasil secara transparan, akurat dan cepat. bahan dan metode a. topologi jaringan berikut topologi jaringan sistem monitoring lifting minyak dan gas bumi. 2 u dell blade server 1955 internet astinet (isp) internet router cisco 1841 make data 4400 brocade 200e silk worm fiber optic from web database f ib e r o p ti c f ro m g is d a ta b a s e fire wall cisco pix 501 (linux) microtic vpn server patch panel ket : kabel lan utp kabel fo cliet for viewer switch 3com 24 port modem tellabs 8110 sumber : (pt.astron-optima, 2012) gambar 1. topologi jaringan sistem monitoring volume lifting minyak dan gas bumi b. arsitektur jaringan & skema jaringan arsitektur jaringan yang digunakan pada sistem monitoring volume lifting minyak dan gas http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 29 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. bumi adalah operasi jaringan model clinet-server. sistem operasi jaringan client-server memungkinkan jaringan untuk mensentralisasi fungsi dan aplikasi kepada satu atau lebih dedicated file server. internet modem tellabs 1180 router cisco 1800 firewall cisco pix 501 switch 3com printer hp pcl 6 server dell client clientclient clientclient sumber : (pt.astron-optima, 2012) gambar 2. skema jaringan sistem monitoring volume lifting minyak dan gas bumi c. keamanan jaringan keamanan jaringan pada sistem monitoring volume lifting minyak dan gas bumi pada saat ini sudah cukup bagus, selain menggunakan perangkat hardware firewall cisco pix 501 series yang digunakan sebagai internet gateway dan melakukan fungsi nat (network address translation) yang memberikan akses ip private agar dapat masuk ke jaringan internet. pada sistim ini juga tiap pc sudah terpasang antivirus mcafee yang selalu terupdate. d. sistem scada (supervisory control and data acquisition) di dunia industri, sistem otomatis sangat diminati karena dapat menjamin kualitas produk yang dihasilkan, memperpendek waktu produksi dan mengurangi biaya untuk tenaga kerja manusia. salah satu pengendali yang paling popular, khususnya untuk sistem yang bekerja secara sekunsial, ialah programmable logic controller (plc). berikut ini review plc dan otomasi sistem dari kepanjangan plc, kita dapat mengetahui definisi sederhana dari plc itu sendiri. 1. programmable dapat diprogram (software based). 2. logic bekerja berdasar logika yang dibuat, logika disini biasanya menunjuk pada logika boolean yang hanya terdiri dari 2 keadaan, on atau off. 3. controller pengendali (otak) dari suatu sistem. secara umum, cara kerja sistem yang dikendalikan plc cukup sederhana. a. plc mendapatkan sinyal input dari input device. b. akibatnya plc mengerjakan logika program yang ada di dalamnya. c. plc memberikan sinyal output pada output device. sumber: (wicaksono, 2012) gambar 3. diagram hubungan plc dan input/output device dari penjelasan diatas, didapatkan definisi sebagai berikut : 1. plc input device : benda fisik yang memicu eksekusi logika/program pada plc. contoh : saklar dan sensor. 2. plc output device : benda fisik yang diaktifkan oleh plc sebagai hasil eksekusi program. contoh : motor dc, motor ac, solenoid dan lain-lain. pada bagian ini akan dijelaskan tentang definisi plc. menurut nema (national electrical manufacturers association-usa) definisi plc ialah “alat elektronik digital yang menggunakan programmable memory untuk menyimpan intruksi dan untuk menjalankan fungsi-fungsi khusus seperti : logika, sequence (urutan), timing (pewaktuan), penghitungan dan operasi aritmatika untuk mengendalikan mesin dan proses.” definisi lain menyebutkan bahwa plc ialah “komputer industri khusus untuk mengawasi dan mengendalikan proses industri menggunakan bahasa pemrograman khusus untuk kontrol industri (ladder diagram), didesain untuk tahan terhadap lingkungan industri yang banyak gangguan (noise, vibration, shock, temperature, humidity).” plc terbagi dari beberapa komponen utama. untuk memahaminya, perhatikan gambar yang http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 30 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. menampilkan hubungan plc dengan peralatan lain berikut ini : sumber: (wicaksono, 2012) gambar 4. hubungan plc dengan peralatan lain dari gambar 4 di atas nampak bahwa plc memiliki komponen yang terhubung dengan input device dan output device. plc juga terhubung dengan pc untuk kebutuhan pemrograman (umumnya menggunakan rs 232 serial port). secara umum plc terbagi dalam beberapa komponen berikut : 1. power supply 2. processor 3. memory 4. input and output device 5. programming device jika suatu plant atau sistem otomatis masih berukuran kecil, tingkat komplesitas rendah dan tidak memerlukan akurasi yang tinggi maka skema otomasi sistem dengan plc saja sudah cukup. namun, jika kompleksitas plant relative besar dan akurasi yang dibutuhkan dalam sistem relatif tinggi maka sangat diperlukan suatu sistem scada. skema sistem scada sederhana yang diimplementasikan melalui program komputer seperti dibawah ini : sumber: (wicaksono, 2012) gambar 5. skema system scada sederhana dalam pengendalian sistem hasil dan pembahasan a. sistem monitoring volume lifting minyak dan gas bumi berbasis scada (supervisory control and data acqucition). sistem monitoring volume lifting minyak dan gas bumi berbasis scada atau biasa dikenal dengan online realtime system merupakan suatu sistem yang dibangun dan diimplementasikan berbasiskan scada di titik custody transfer point (ctp) dengan melakukan akuisisi data volume secara online realtime dari setiap titik pantau, sehingga data-data volume lifting tersebut dapat dipantau secara realtime melalui migas control center. proses ini dilakukan menggunakan scada sistem, yaitu sistem yang dirancang untuk mengumpulkan data dan menampilkan pergerakan volume lifting secara online realtime di migas control center. data yang dikumpulkan akan disimpan dalam database untuk divisualisasikan kemudian ditransfer ke dalam sistem web. sistem ini dibangun untuk mendukung pengguna sistem (end user) dalam melakukan pengawasan terhadap realisasi volume lifting minyak dan gas bumi di setiap titik custody transfer point (ctp) secara realtime. hasil dari monitoring lifting secara realtime ini akan digunakan sebagai salah satu referensi dalam melakukan monitoring lifting di kkks. scada adalah sebuah akronim untuk supervisory control and data acquisition. a. data acquisition merupakan proses mendapatkan informasi dari proses-proses yang terdistribusi secara luas. b. supervisory control adalah menghitung dan memberikan instruksi-instruksi pengendalian untuk fasilitas-fasilitas proses yang letaknya jauh (remote facilities). sumber : (pt.astron-optima, 2012) gambar 6. rancangan konsep dasar sistem monitoring volume lifting minyak dan gas bumi berbasis scada konsep sistem monitoring lifting pada dasarnya merupakan kegiatan pengawasan terhadap realisasi volume lifting serta perhitungan valve status (open/close) switch position (on/off) pump (start/stop) low tank level alarm high tank level alarm fire alarm pressures flow rates temperatures tank levels p r o c e s s variables alarms device status f ie l d in t e r f a c e http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 31 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. alokasi lifting untuk setiap daerah penghasil. sistem monitoring ini merupakan integrasi dari penggunaan teknologi akuisisi data secara onlinerealtime serta teknologi berbasis web yang memungkinkan untuk mengirim data volume secara online. di samping pemanfaatan berbagai teknologi informasi dan komunikasi, sistem ini juga ditunjang dengan sejumlah pedoman operasional yang meliputi beberapa prosedur pelaksanaan yang terkait langsung dengan kegiatan monitoring. penggunaan teknologi tersebut di atas ditujukan untuk membantu proses akuisisi data, pengolahan data dan proses pelaporan volume produksi dan lifting minyak dan gas bumi kepada semua stakeholder. sedangkan melalui penerapan sejumlah prosedur, maka implementasi sistem ini juga ditunjang dengan beberapa aktifitas berupa kegiatan validasi/verifikasi data serta kegiatan audit lapangan untuk membuktikan kebenaran hasil perhitungan setiap kkks. sumber : (pt.astron-optima, 2012) gambar 7. rancangan alur proses implementasi dari supervisory control implementasi dari supervisory control ditujukan untuk : a. manajemen titik pengaturan (set point) untuk beberapa sistem kendali b. optimalisasi untuk memperoleh titik operasi yang terbaik menggunakan algoritma-algoritma kontrol lanjut. (cascade controller, ratio controller, override control, dan lain-lain). b. data sistem pada gambar di bawah ini terlihat secara lebih rinci data-data volume yang dibutuhkan, meliputi volume produksi dan volume lifting serta metode akuisisi data tersebut. sumber : (pt.astron-optima, 2012) gambar 8. rancangan sistem akusisi data dan integrasi akuisisi dan integrasi data secara efisien memungkinkan untuk peningkatan usaha dan tata niaga migas yang melibatkan banyak stakeholder termasuk pihak kkks dan pihak pemerintah daerah yang memiliki kepentingan dalam pendapatan daerah sebagaimana diatur dalam undang-undang. dengan adanya data akuisisi volume lifting yang dilakukan terus-menerus secara berkesinambungan dan terintegrasi secara nasional, maka pemerintah daerah bersama dengan pemerintah pusat dapat memantau serta mengelola pendapatan daerah serta pusat secara lebih akurat dan dapat membuat perencanaan serta melaksanakan pembangunan dengan lebih baik dan akurat. dari penjelasan konsep sebagaimana dijelaskan di atas, maka dapat disusun usulan platform aplikasi (software) secara umum set point 1 process controller sensoractuator process controller sensoractuator process controller sensoractuator set point 2 set point 3 loop #1 loop #2 loop #3 supervisory control http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 32 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sebagaimana terlihat pada diagram blok di bawah ini. sumber : (pt.astron-optima, 2012) gambar 9. rancangan alur proses akusisi data dan reporting terlihat jelas pada diagram di atas bahwa melalui kombinasi akuisisi data secara online realtime dengan menggunakan teknologi sistem scada dengan teknologi web akan dapat mengumpulkan data-data yang diperlukan dalam perhitungan volume lifting sebagai dasar dari pengawasan (controlling) terhadap laporan lifting yang disampaikan setiap bulannya oleh pihak kkks. selanjutnya dengan menggunakan angka volume hasil monitoring akan dilakukan validasi terhadap laporan lifting dengan menetapkan nilai ambang batas yang telah disepakati oleh ditjen migas. jika masih dalam ambang batas maka secara otomatis laporan lifting kontraktor dinyatakan valid, jika diluar ambang batas, maka akan dilakukan klarifikasi atau audit untuk memastikan penyebab penyimpangan tersebut. c. proses tapping data kegiatan ini meliputi komunikasi antara migas control center dengan lapangan atau ctp yang di hubungkan dengan sistem komunikasi telkom vsat. adapun mekanisme perolehan data tersebut dapat dilihat seperti gambar dibawah ini : sumber : (pt.astron-optima, 2012) gambar 10. rancangan alur mekanisme perolehan data dari ctp ke migas control center keterangan gambar 10 diatas: a. komputer hmi milik dari kkks di integrasikan dengan komputer hmi migas. b. setelah kedua komputer tersebut terhubung maka proses tapping / capture data setelah kedua komputer tersebut terhubung maka proses tapping/capture data dilakukan dengan cara penyeragaman value tage name dengan name text yang berbeda dengan kkks. hal ini di lakukan untuk mengantisipasi adanya indentifikasi yang double/ganda pada hmi. c. setelah data masuk ke komputer hmi migas, komputer yang telah terhubung denga vsat telkom yang terhubung langsung dengan pusat data yang di migas control center (mcc). d. data dikirim ke mcc yang melewati sentral telekomunikasi jakarta dan di sambungkan ke receiver yang ada di gedung migas dan bermuara pada modem vsat yang berada di ruang server lt. 6. e. data di teruskan ke poller a dan poller b dengan media penyimpanan sql server 2000. tahap display data dan penyesuaian data dengan ctp/lapangan : a. proses ini dimulai dari pengumpulan data hardcopy atau data lifting perhari dari masing-masing ctp. data pembanding yang field device rtu/efm front end interface lifting volume raw data communication flowcal client workstation online field data validation online and manual data importing reporting flowcal server oracle database archiving and storage field validation data web server web access for data online web acsess for reporting online data online well info well test production duration entitas volume transfer volume lifting self assesment reporting online active stakeholder access publication passive stakeholder access report realtime monitoring report web base monitoring report online realtime online realtime web online web online web online http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 33 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. berasal dari lapangan biasanya diperoleh dari bagian finance. data tersebut akan dikirimkan secara terus menerus ke pihak migas control center secara email atau fax. pengiriman dilakukan paling lambat tanggal 15 pada bulan berikutnya. salah satu contoh data adalah sebagai berikut : b. proses penarikan data dari sql server 2000 yang telah diperoleh di migas control center. data yang diperoleh harus terlebih dahulu di pilah-pilah berdasarkan masing-masing ctp dan tanggalsumber : pt. astron optima c. secara umum taping data dilakukan melalui ipc migas atau rtu scadapack. untuk kkks dengan produk oil data yang diambil antara lain flowrate, gross volume dan net volume. sedangkan untuk kkks dengan produk gas, data yang ditaping yaitu flowrate, volume today, volume yesterday dan volume monthly. protokol yang digunakan akan mengikuti ketersediaan port komunikasi yang disyaratkan kkks, misalnya menggunakan tcp/ip atau modbus rs232/485. data tersebut kemudian diteruskan ke migas control center melalui vsat. d. pemeliharaan data data yang telah diambil dari berbagai ctp akan dikumpulkan dan diolah sedemikian rupa mulai dari penyeragaman skala, satuan/unit, nilai maksimum dan nilai minimumnya. selain itu proses rekam data membutuhkan script tersendiri untuk masing-masing ctp yang akan memberikan perintah pencatatan maupun perhitungan. proses ini berguna terutama untuk kkks yang tidak memiliki parameter yang dibutuhkan migas, sehingga dibutuhkan perhitungan terlebih dahulu dari parameter yang ada. seperti contoh untuk kkks dengan produk gas yang tidak memiliki angka volume yesterday, maka data volume totalizer yang diambil harus diolah terlebih dahulu sehingga menghasilkan angka volume yesterday. maka dibuat perintah untuk menghitung volume totalizer sehingga diperoleh nilai volume yesterday sebesar (y-x). e. database untuk penyimpanan database dari wonderware intouch digunakan software microsoft sqlserver 2000 dan insql 9.0 dengan struktur pengisian data seperti terlihat pada gambar 11. wonderware intouch 9.5 insql mssqlserver 2000 tabel : scadametervalue scadameterinfo scadaflowratevalue scadamapping active factory report insert data sumber : (pt.astron-optima, 2012) gambar 11. rancangan alur proses pengisian data dari wonderware intouch ke mssql server data yang diambil dari masing-masing titik ctp dimasukkan ke dalam table scadametervalue, sementara table scadameterinfo berfungsi untuk menyimpan data informasi titik ctp meliputi lokasi dan nama kkks yang bersangkutan. tabel scadamaping dan scadaflowratevalue berfungsi sebagai interface ke sistem web. waktu penyimpanan data diatur dalam perintah wonderware intouch. permasalahan yang muncul biasanya terdapat perbedaan waktu di titik ctp, waktu di poller migas control center dan waktu di database server sehingga ada sebagian data flowrate yang terisi tetapi belum sempat ditampilkan di web. oleh karena itu perlu disesuaikan waktu antara poller dan database server. f. pelaporan (reporting) untuk sistem pelaporan di hmi wonderware terdapat dua jenis pelaporan yaitu laporan harian dan laporan detail masing-masing titik ctp. untuk laporan harian digunakan untuk memantau nilai lifting per hari tiap-tiap ctp. laporan harian mencakup volume lifting semua ctp yang sedianya dicetak ke dalam bentuk hardcopy setiap hari. sedangkan laporan detail digunakan untuk melihat volume lifting secara terperinci dalam batas waktu yang dapat ditentukan oleh pengguna. tampilan laporan detail menggunakan activex histclientactivedatagrid yang menampilkan data dari ms sqlserver melalui insql. permasalahan yang sering muncul yaitu delay antara database server dengan poller yang tidak mungkin dihilangkan melainkan diperkecil. http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 34 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. kesimpulan setelah melakukan pemasangan sistem monitoring volume lifting minyak dan gas bumi berbasis scada (supervisory control and data acqusition) maka dapat diambil kesimpulan sebagai berikut: 1) diperoleh data yang akurat sebagai pembanding antara volume lifting yang dimonitor secara online realtime dengan volume lifting yang dilaporkan kkks. 2) diperoleh informasi volume lifting minyak dan gas bumi yang akurat pada titik-titik pantau secara online realtime. 3) melalui pemanfaatan fungsi system monitoring volume lifting minyak dan gas bumi berbasis scada dapat diperoleh data sebagai dasar bagi perhitungan jumlah dana bagian daerah penghasil migas yang transparan dan sesuai dengan ketentuan. 4) dengan teknologi vsat data yang dikirimkan ke migas control center dari tiap site / ctp akan aman dan cepat. untuk pengiriman data menggunakan teknologi vsat, dimana teknologi ini menggunakan topologi star, sedangkan untuk koneksinya menggunakan jenis koneksi point to multipoint. saran yang bisa diberikan untuk pengembangan sistem monitoring volume lifting minyak dan gas bumi berbasis scada (supervisory control and data acqusition) adalah dengan mengganti teknologi pengiriman data yang menggunakan teknologi vsat dengan teknologi vpn (virtual private network) karena data yang dibutuhkan untuk sistem ini tidak begitu banyak/besar. referensi 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(2015). model penjadwalan pengiriman pasokan pada strategi multi-supplier dengan variasi harga dan lead time untuk permintaan stokastik. jurnal teknik industri, 17(1), 35– 46. https://doi.org/10.9744/jti.17.1.35-46 pradikta, r., pradikta, r., affandi, a., & setijadi, e. (2013). rancang bangun aplikasi monitoring jaringan dengan menggunakan simple network management protocol. jurnal teknik its, 2(1), a154–a159. https://doi.org/10.12962/j23373539.v2i1.2 265 pt.astron-optima. (2012). lembar kerja pt. astron optima. jakarta. wicaksono, h. (2012). scada software dengan wonderware touch dasar-dasar pemrograman (1st ed.). yogyakarta: graha ilmu. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 1 an interactive medium to introduce sasando traditional music using multimedia development life cycle method salam irianto nadeak-1*), yusmar ali-2, djaka suryadi-3 1,2 design grafis politeknik negeri media kreatif https://polimedia.ac.id/ salamirianto66@gmail.com, yusalim@gmail.com 3islamic economics stai asy-syukriyyah https://asy-syukriyyah.ac.id/ djakasuryadi@asy-syukriyyah.ac.id (*) corresponding abstract actionscript-based programming is one of the software which includes applications that teachers widely use to create interactive learning media in the world of education. actionscript-based programming technology is a type of graphic animation software that can create a graphic object that can be animated without using other supporting software. at present, there are many educational circles to expedite the process of learning activities, especially for students. interactive learning media is one means of delivering subject matter that is very important to apply today. in implementing student learning at school, it is necessary to present a practical and theoretical learning system which is the main point in helping to develop student competence. one form of culture in indonesia is the traditional musical instrument sasando. sasando belongs to the chordophone instrument because it is played by picking it. the form of sasando itself is in the form of a guitar, violin or harp. the central part of the sasando is in the form of a long bamboo tube. in the middle, rounded from top to bottom, there is a wedge to stretch the strings. developing interactive learning media requires a software development method; one of the development methods used is the mdlc (multimedia development life cycle) method. making the mdlc has five stages: concept, design, material collecting, assembly, and testing. keywords: adobe flash; interaktive media; sasando; graphic abstrak pemrograman yang berbasis actionscript merupakan salah satu perangkat lunak yang didalamnya termasuk aplikasi yang banyak digunakan para guru untuk membuat media pembelajaran interaktif dalam dunia pendidikan. teknologi pemrograman yang berbasis actionscript ini adalah salah satu jenis software animasi grafis yang dapat membuat objek grafis yang dapat dianimasikan tanpa menggunakan software pendukung lainnya. saat ini banyak kalangan pendidikan untuk memperlancar proses kegiatan pembelajaran, khususnya bagi siswa. media pembelajaran interaktif merupakan salah satu sarana penyampaian materi pelajaran yang sangat penting untuk diterapkan saat ini. dalam pelaksanaan pembelajaran siswa di sekolah, perlu dihadirkan sistem pembelajaran praktis dan teoritis yang menjadi poin utama dalam membantu mengembangkan kompetensi siswa. salah satu bentuk budaya yang ada di indonesia adalah alat musik tradisional sasando. sasando termasuk ke dalam alat musik chordophone karena dimainkan dengan cara dipetik. bentuk sasando sendiri berupa gitar, biola atau kecapi. bagian tengah sasando berbentuk tabung bambu panjang. di bagian tengah, membulat dari atas ke bawah, terdapat baji untuk meregangkan senar. pengembangan media pembelajaran interaktif membutuhkan metode pengembangan perangkat lunak; salah satu metode pengembangan yang digunakan adalah metode mdlc (multimedia development life cycle). dalam pembuatan mdlc terdapat lima tahapan yaitu concept, design, material collecting, assembly, dan testing. kata kunci: adobe flash; media interaktif; sasando; grafis p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 jurnal riset informatika vol. 5, no. 2 june 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 2 introduction the indonesian nation has various cultures stretching from sabang to merauke. one of the cultures they have is traditional musical instruments. however, in fact, from time to time, people's interest, one of which is the youth of the current generation, is decreasing towards indonesian culture, especially in traditional arts furthermore, today's younger generation is more interested in learning modern musical instruments than traditional ones (julia et al., 2019). it is not strange if traditional musical instruments are only known by name, but how to play them and what the instrument sounds like is still being determined. the research will convey through interactive learning media about a traditional musical instrument originating from the island of rote, east nusa tenggara, the sasando, using the mdlc method (astuti et al., 2019). sasando has a varied sound, so it can be played in various genres, such as traditional music, pop, and other music that is not electric. in the rote community, sasando is often played to accompany dances, songs, poems, and other entertainment events (francis, 2017). lack of knowledge about regional musical instruments is a problem that is currently being experienced. in reality, not all people have the opportunity to learn and play this instrument due to limited tools and costs. sasando belongs to the chordophone instrument because plucking the strings is how to play it. the form of sasando itself is in the form of a guitar, violin or harp (tukan et al., 2020). the central part of the sasando is in the form of a long bamboo tube. they are rounded from top to bottom in the middle and have wedges for stretching the strings. what these wedges do is give each string a different sound effect. then the sasando tube is placed in a container of woven palm leaves like a fan (magalhaes, 2022). learning media is a tool used to convey teaching material, and learning media has an essential role in supporting the quality of the teaching and learning process (wahid et al., 2020). media is anything that can be used to transmit messages and stimulate the mind, arouse enthusiasm, attention, and willingness of students so that it can encourage the learning process in students. media can also make learning more exciting and fun. actionscript-based programming is specifically designed by programmers and a professional standard authoring tool application program used to create attractive animations and bitmaps to develop interactive and dynamic websites. it is designed with the ability to create reliable and lightweight two-dimensional animations, so actionscript-based is widely used to build and provide animation effects on websites and learning media. in addition, this application can also be used to create animations, logos, movies, games, navigation on websites, animated buttons, banners, interactive menus, form fields, e-cards, screen servers, and other web applications (fadila et al., 2019). in addition to the community, it is necessary to take care of the traditional sasando music tools as part of cultural values through playing them; of course, the community wants the sasando music tool to be widely known throughout the world. through the production of smes, sasando is also sold as a source of income to support the economy of families in the community of east nusa tenggara. promotion efforts carried out massively online have led sasando sales to penetrate the international market, such as germany, australia, and mexico. they were selling sasando musical instruments at varying prices for electric versions with prices above 3,500,000 idr and acoustic versions of around 600,000 1,500,000 idr per unit (muntasir, 2022). the sales value in one booking can penetrate up to tens of millions of rupiah. on various occasions, efforts to promote sasando musical instruments continue to be carried out by displaying them at various events, such as meetings, festivals, and music concerts. based on the description above, the research objective is to increase student interest in learning, especially in schools introducing the sasando musical instrument. research methods the research method created by the author uses a type of qualitative method that tends to be descriptive. the qualitative method is an investigative process that aims to understand a social situation and event (sugiyono, 2022). the following are several stages in collecting data through qualitative methods. the interview was conducted by asking several questions to the relevant informant to obtain information, aiming to obtain data that could explain a research problem. after that, observations were carried out with systematic observation and recording of an object of remote observation using the zoom application through the art teacher at the school for the incident being investigated (creswell et al., 2011). the observation is carried out to know the conditions during the learning process. furthermore, to jurnal riset informatika vol. 5, no. 2. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 3 complete the data needed, namely conducting a literature study, namely steps taken by searching for data through reading literature and reference sources from journals that support and are related to the application of actionscript-based technology as an interactive medium for the introduction of the traditional sasando musical instrument using the mdlc model in schools that will be made, as a reference in collecting the necessary data in order to obtain a valid theoretical basis (ahmad et al., 2021; riswandari et al., 2021). the software development implemented in this study uses the multimedia development life cycle (mdlc) methodology. the development of multimedia in interactive learning must carry out well-designed design stages and systematic arrangements to be used effectively in learning (astiti et al., 2021; purhita et al., 2021) figure 1. design method mdlc (roedavan et al., 2022) the first stage, namely the concept, is the stage for determining the direction and objectives of the research. in addition, the concept also determines the type of application, such as presentations and other interactive. this aims to create an exciting and modern multimedia learning process by utilizing technology in the current era. it can increase interest in learning, especially cultural arts in indonesia, one of which is to hone students' abilities to learn better to know the traditional musical instrument, the sasando. the second stage, namely designs, is an ongoing phase in developing specifications for program architecture, design, and supporting materials or materials used by the application to be made. the third stage of material collecting is the stage where collecting materials must match what is needed in making interactive media. these materials include images, photos, animation, video, audio, text and other complementary materials. the fourth stage is assembly, which is the stage where all materials or interactive media objects that have been obtained are then assembled and arranged according to the design. this interactive learning media is processed and created using actionscript-based software. the last stage is testing, which is carried out to find out which interactive learning media that is designed has been completed according to the previous stages. then a black box table verification test is carried out. the research was conducted at one of the vocational schools in jakarta by introducing the traditional sasando musical instrument with an application as an interactive learning medium. data and instrument data were obtained from the interviews, with various inputs obtained as material for consideration in the study. in general, using interactive media is constructive for students in introducing traditional sasando music because, with interactive learning media, students understand more than just reading the material in the module. so far, the learning process takes place when conveying the culture of traditional musical instruments using modules and delivery through the powerpoint application as material the teacher presents. it triggers why many students are not interested in learning traditional musical instruments because of the coals of teaching or the way of learning that has been lived. hopefully, this interactive application will further encourage students to understand and know more about traditional musical instruments, especially the sasando musical instrument. results and discussion from the results of interviews and interviews conducted at the school, information is obtained that so far, students can only get information from the teacher about this traditional musical tool from the submitted. this interactive media innovation allows students to understand existing material easily. initialization the flowchart below in figure 1 is the initial concept of interactive media applications that schools will use in introducing traditional musical p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 jurnal riset informatika vol. 5, no. 2 june 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 4 instruments. the main menu consists of 4 menus: historical, material, video, and quiz. this menu can be used to see the various overall kinds of traditional music, a traditional sasando instrument, in this study. the application can also be known history and learning about the musical instrument. applications can also play existing videos of how the sound is generated from the music device. the last menu is a quiz to find out how much understanding students are in knowing the sasando music instrument that has been learned. figure 2. flowchart process design method mdlc is not only an evolution in software development but also revolutionizes the old software development methods. the design concept results provide information about the prototype to be developed, which will later become a product used in the school environment. figure 2 above is the flowchart process design of the interactive learning media to introduce the traditional sasando musical instrument. blue print design blueprint design in mdlc refers to the process of designing schematic designs and details related to the multimedia production to be made. at this stage, the multimedia designer will collect information about the needs, goals, and target users, then design the multimedia structure and interface and create a sketch or storyboard about the multimedia content to be produced. in the blueprint design, the multimedia designer will also make visual designs, layouts and layouts that match the themes and multimedia concepts produced. assessment preparation in mdlc, asset preparation is an essential stage in multimedia development because the quality and completeness of multimedia assets will affect the quality of the final product. if multimedia assets are not properly prepared, the final product will not look professional and may not meet user expectations. asset preparation involves collecting, editing and storing all the multimedia assets used in the final product. the multimedia development will also determine each asset's format, size, resolution and quality to ensure that everything conforms to predetermined needs and specifications. product development product development refers to the multimedia product development stage, which is the last stage of the development cycle. at this stage, multimedia products planned, designed, and prepared for assets will be developed into a final product that can be implemented and distributed to users. at the product development stage, the team will integrate all previously prepared multimedia assets into a complete multimedia product. this involves the process of developing an application or website, incorporating audio, video and graphics, as well as the integration of interactive elements such as buttons, menus and animations. a. main menu this design image is the main menu display page from several menu buttons, among others, in the form of history buttons, material, video, and quizzes, as shown in figure 3. figure 3. main menu jurnal riset informatika vol. 5, no. 2. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 5 b. historical menu figure 4 provides information about the historical page and displays two buttons, section 1 and section 2. the button functions to display the history of the traditional sasando musical instrument figure 4. historical menu c. material menu the material pages are designed neatly and interactively to make it easier for users to get the necessary information. the material display of this menu displays six buttons for discussion of the traditional sasando musical instrument, as shown in figure 5. figure 5. material menu d. menu videos figure 6 is a video page display with two buttons to play educational videos about the traditional sasando musical instrument. button 1 is video 1, a video in .flv, .mkv, .mp4, .m4p, .mpeg format, while the second button is video 2 in .avi, .gif, .wmf, .3gp format. figure 6. video e. quiz menu in the final display design, the quiz will display a main quiz page with the start button to work on the quiz, as shown in figure 7 figure 7. quiz menu the information in the materials related to the subject matter was obtained from interviews with teaching teachers and several related literature and sources regarding the traditional sasando musical instrument. this interactive learning media was made at the assembly stage using action script 3.0 programming on adobe flash professional cs6 software. while making this interactive learning media, the developer makes scene after scene designed attractively to attract students' desires to learn traditional musical instruments. after the assembly stage, the last stage is the testing stage. at the testing stage, the method used for testing is using a black box; this aims to find out whether, functionally, the processes in the application program are running according to purpose so that this interactive learning media can be used properly (joosten, 2021). the overall results of assembly and testing produce application p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 jurnal riset informatika vol. 5, no. 2 june 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 6 tools that can be appropriately used in schools in lessons related to the traditional sasando music instrument testing and validation the testing and validation involve testing aspects such as functionality, security, performance and compatibility. the testing will ensure all features and functionality work correctly per the multimedia product's needs and objectives. in addition, the testing will also perform performance tests to ensure that the multimedia product works well on all desired platforms and devices. compatibility testing will also be carried out to ensure that multimedia products can run adequately on all desired operating systems, browsers and devices. table 1. table product testing no test scenario result decision 1 login user fill in the username & password system on dashboard success 2 signout signout button system on dashboard success 3 main menu displays the main menu the system can display the main menu success 4 historical menu displays the historical page the system can display the history page success 5 material menu displays the material page the system can display material page success 6 menu videos displays the videos page the system can display a videos page success 7 menu quiz displays the menu quiz page the system can display a videos page success conclusions and suggestions conclusion based on the results of the research and discussion described above regarding interactive learning media for the introduction of the traditional sasando musical instrument using the mdlc method, it can be concluded that the existence of this interactive learning media can increase understanding and knowledge of culture in indonesia, one of which is the traditional sasando musical instrument. the learning media has history, materials, videos, and quizzes. the test results show that interactive learning media can provide convenience to students in the learning process at school to understand better and know more about the traditional sasando musical instrument. the experience of making this application is also the first step in developing subsequent applications, which can be developed into applications for other types of traditional musical instruments. this hope aligns with the music teacher's desire to continue teaching traditional musical instruments to students so that traditional music remains popular with students. suggestion application-based research will continue to develop in line with technological developments that rotate. this developed application uses adobe flash technology, which will likely be developed using other technologies such as microsoft silverlight, ruffle or photon flash player. developing applications with this technology will increase the user's imagination, providing comfort to students studying it. reference ahmad, i., rahmanto, y., pratama, d., & borman, r. i. 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(2020). sasando gaya edon: kajian organologi dan penyeteman. resital: jurnal seni pertunjukan, 21(1), 28–40. https://doi.org/10.24821/resital.v21i1.3693 wahid, a. h., najiburrahman, rahman, k., faiz, qodriyah, k., hambali, el iq bali, m. m., baharun, h., & muali, c. (2020). effectiveness of android-based mathematics learning media application on student learning achievement. journal of physics: conference series, 1594(1), 012047. https://doi.org/10.1088/17426596/1594/1/012047 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.518 jurnal riset informatika vol. 5, no. 2 june 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 8 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 1 implementation of hybrid method in tourism place recommendation system based on image features steven christ pinantyo arwidarasto-1*), desti fitriati-2 department of informatics engineering universitas pancasila jakarta, indonesia1,2 stevenchristpa2001@gmail.com1, desti.fitriati@univpancasila.ac.id2 (*) corresponding author abstrak pada industri 4.0, terjadinya ledakan data tak terstruktur maupun berstruktur yang menghasilkan informasi pengetahuan yang sangat luas dan variatif. tentunya, manusia tidak dapat mengolah banyak informasi dalam waktu cepat, yang membuat eksistensi sistem rekomendasi menjadi berarti. sistem ini mempelajari informasi yang ada dan memberikan saran yang sesuai dengan apa yang diinginkan oleh pengguna. dewasanya, banyak sistem rekomendasi lebih menitikberatkan pada penggunaan metode content-based filtering dimana hasil rekomendasi didasari berdasarkan kemiripan fitur dari konen yang disukai oleh pengguna. hal ini membatasi variasi informasi yang relevan kepada pengguna. selain itu, dalam konteks tempat wisata, banyak penelitian yang belum menggunakan data gambar yang dapat memuat banyak objek fitur dalam satu frame sebagai faktor penentu dalam memberikan rekomendasi. hal ini membuat, dalam penelitian ini, penulis memproposalkan penambahan fitur gambar sebagai salah satu parameter penentu rekomendasi untuk mengetahui dampak penggunaan gambar pada performa model. adapun performa terbaik yang didapat yaitu 0.364 menggunakan matrik rmse menggunakan metode hybrid image. kata kunci: industri, rekomendasi, kemiripan, gambar, variasi abstract in the industrial 4.0 era, there is an explosion of unstructured and structured data that produces broad and varied knowledge information that humans cannot process quickly. this issue makes the existence of recommendation systems meaningful. this system studies the existing information and provides suggestions according to the user's will. in the past, many recommendation systems have focused more on content-based filtering methods where recommendation results are similar based on the features of the content that match the user's personality. this method limits the variety of information that is relevant to users. in addition, in the context of tourist attractions, many studies have not used image data that can contain many objects in one frame as a determining factor in providing recommendations. therefore, in this study, the authors propose to add image features as one of the parameters of the recommendation system to determine the impact of using image features on the model performance. the best performance obtained is 0.364 rmse metric using the hybrid image method. keywords: industrial, recommendation, similar, image, varied introduction the rapid development of the times has made much information appear to be used as a basis for decision-making (walek & fojtik, 2020). this resulted in a huge amount of information that made the processing stage difficult for decision-makers to process existing information. in this case, the recommendation system exists to provide relevant decision suggestions for its users based on preferences and historical data (geetha et al., 2018; mohammadpour et al., 2019). the use of recommendation systems in everyday life, among others, in the creative economy and tourism industries. in this sector, the recommendation system provides advice and outreach to people who want or like natural, cultural, educational, or religious exploration. for creative economy and tourism industries, a recommendation system requires information such as tourist categories, place names, locations, and descriptions of tourist attractions to be used as features of the content representation, p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 2 which the majority of the features mentioned are text-based features. there are several approaches widely used to select relevant items, namely collaborative filtering (c.f.) and content-based filtering (cbf), where both approaches have their own merits. for instance, the c.f. approach suggests that content is seen by other users with highly similar preferences based on historical records (geetha et al., 2018; ng, 2020). in contrast, the cbf approach offers content with similar features to the content users prefer (ng, 2020). one of the c.f. algorithms widely used for recommending content is matrix factorization (m.f.) which has proven to be one of the popular models in the netflix competition (koren et al., 2009). the challenges faced include finding the reliability of the ratings given by users to items. this reliability refers to the relevance of the ratings on a user-item transaction based on the difference in ratings of items. several studies conducted experiments to overcome the problem of rating relevance, one of which was to improve m.f. with a bernoulli probability (ortega et al., 2021). this research uses the movielens dataset, which contains user rating data for several films. this method obtains an evaluation score using a pointwise matrix, namely, mean absolute error (m.a.e.) of 0.093 (native) and 0.036 (enforced). another study was conducted to optimize m.f. using benchmark data, namely the movie lens, which uses the rating centrality method to determine the reliability of the ratings given by the user (wu et al., 2018). this method obtains an evaluation score with a pointwise matrix, namely rmse (root mean squared) of 0.898 at an alpha value of 80%. in contrast to the c.f. methods, several studies conducted cbf to recommend items. one of which uses the content-based image retrieval (cbir) method to retrieve relevant image data using v.g.g. 16 deep convolutional network architecture to select the dominant features of an image into vectors which ranked based on the highest score of cosine similarity matrix (rian et al., 2019). the metrics used in this study, namely f1 score and precision, respectively, 73% and 89.6%. looking at the capabilities of each approach, several studies then combined the c.f. and cbf approaches, known as the hybrid method, to help the c.f. method find out the personalization context of the user (context-aware recommendation model). in doing so, the combined effort could result in a more relevant selection of travel recommendations that draw on other users' histories and similar content. an example of the hybrid method combines the deep neural network method and the c.f. method (using the alternating least square (a.l.s.) algorithm) (biswas & liu, 2022). in this study, feature extraction was done using user-place representation to determine the relationship between both parameters and give relevant recommendations. this method obtains an rmse score of 0.0106. another example of research to optimize m.f. using the deep hybrid model architecture is combining m.f. and neural network (n.n.) using categorical and continuous features (çakır et al., 2019). this study used a listwise evaluation matrix, namely normalized discounted cumulative gain (ndcg) and hit ratio (h.r.), was used. the ndcg and h.r. scores obtained were 0.855 and 0.628 for recommendations based on items and 0.795 and 0.581 for user recommendations. based on these studies, the hybrid model uses personalized m.f. methods that utilize features from content aline with users' preferences. in the context of the creative economy and tourism industries, the content feature that is more dominant to attract the attention of its users is information about the sights of these tourist attractions. this opens up opportunities for using data features that dominantly have information related to object sightseeing as a personalization method for its users. regarding information about tourist sights, if the model processes text-based information, the user is limited by the semantic expression of the text. however, the presence of images can provide broader information about the atmosphere and conditions of the tourist destination to be visited. therefore, utilizing images as an information source can be a solution for finding relevant tourist attractions. for example, a study optimized m.f. architecture by combining m.f. with resnet50 to extract information from images (elsayed et al., 2022). this research uses the amazon fashion dataset, which contains products for clothing needs. the model obtained an a.u.c. matrix evaluation with 100 negative samples for each user of 0.8250. based on the literature studies that have been conducted, many recommendation system studies in the tourism sector still use text-based recommendations. examples of research within the scope of indonesia's creative economy and tourism industries use the smart (simple multi-attribute rating technique) method, which utilizes user preferences based on the most relevant attributes to users. this smart method gives weight to each attribute and calculates the relevance score for each jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 3 sample. the model obtained an average accuracy of 83.3% (sihotang et al., 2021). other studies used the hybrid method by combining c.f. and cbf approaches. the algorithm used for c.f. approaches is k-nearest neighbor (k.n.n.) to predict the rating value given by the user. in the case of the cbf approach, the features of the descriptive text features extracted by the tf-idf method. the model obtained an m.a.e. result of 0.3678 with k neighbors of 25% (lubis et al., 2020). based on several studies, this research aims to find a suitable algorithm using the hybrid method by combining the c.f. approach and image feature extraction from cbir. the c.f. approach with the m.f. algorithm was used to suggest tourist attractions to users from other users with similar preferences. meanwhile, the image feature extraction method performs the feature extraction process of tourist attractions images as additional data. research methods the following figure 1 shows the propose method in this study. figure 1. proposed method in fig. 1, the proposed method has several stages. these stages will be carried out to gain a suitable method for recommending tourism to people. these stages consist of: a. input data, tourism transaction, and tourism image at this stage, user-item transaction data and images of each tourist spot will be input via the input layer. as for image data, the data will be converted into a vector. b. fetch features at this stage, user-item transaction data is processed by selecting only the features needed in the form of user id features, tourist attraction i.d.s, and explicit ratings given by users. c. normalization next, the rating data is normalized to a value range of 0-1. the normalization used is the min-max normalization which uses the most significant value of the rating, which is 5, with the smallest value, 1. d. resize image image data is processed by setting the image's dimension format (length and width) to 100 using the opencv python package. e. color format conversion the image is converted from the blue green red (b.g.r.) color format to red green blue (rgb). this is because the library used to read images uses opencv, which orders b.g.r. f. feature extraction convolutional neural networks (cnn) convolution neural network (cnn) is one of the implementations of artificial neural networks in image processing. cnn consists of several convolution blocks that take image vectors and perform adaptive learning of spatial features from images (yamashita et al., 2018). cnn performs kernel striding operations to extract features from images at the convolution and pooling layers. after feature extraction, these features will be mapped to the fully connected layer as output results. the results of this output can be given to the regressor or classifier for later identifying suitable labels for the data. cnn has several well-known architectural implementations, including resnet-50, vgg-16, vgg-19, inceptionv3, and xception. in this study, the cnn architecture for feature extraction uses the resnet-50 architecture. g. embeddings embeddings are a compression method to map the input feature results into vectors (hrinchuk et al., 2019). in the recommendation system, embeddings are needed to get the latent vector user p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 4 id and tourism id. the second vector combines the dot product multiplication operation to produce a factorized matrix. h. matrix factorization this method performs user-item object mapping by performing dot product multiplication operations based on the vector values generated by the user and item embeddings (zhang, 2022). the use of matrix factorization also has the ability as dimensionality reduction because the dot product multiplication operation will only use vectors that already have values which can be seen in (1). r ≈ 𝑃𝑇 . q = ȓ ............................................................... (1) (zhang, 2022) equation (1) shows that p is the user-feature matrix, and q is the items-feature matrix. the dot product of two latent factors can predict the r rating of an item, which can be seen in (2). 𝑟𝑖𝑗 = 𝑝𝑖 𝑇 𝑞𝑗 = ∑ 𝑝𝑖𝑘 𝑇 𝑞𝑘𝑗 𝑘 𝑘=1 .......................................... (2) (zhang, 2022) equation (2) shows that r_ij is the predicted result of the dot product operation. the rating prediction results from the dot product operation will be compared with the actual results to obtain the loss value using the m.s.e. matrix, which can be seen in (3) and (4). 𝑒𝑖𝑗 2 = (𝑟𝑖𝑗 − ȓ𝑖𝑗 ) 2 = (𝑟𝑖𝑗 − ∑ 𝑝𝑖𝑘 𝑞𝑗𝑘 𝑘 𝑘=1 ) 2 ............... (3) 𝐿𝑜𝑠𝑠(𝑝, 𝑞) = ∑ 𝑒𝑖𝑗 2 + 𝜆 (||𝑝𝑖𝑘 || 2 + ||𝑞𝑗𝑘 || 2 ) ....... (4) (zhang, 2022) in equation (4), the loss function has λ as regularization, which will be used as a penalty. the loss value will be used by gradient descent to minimize the difference between the predicted and actual values, which can be seen in (5), and (6). 𝜕𝐶 𝜕𝑝𝑖𝑘 = ∑ 2(𝑟𝑖𝑗 − ∑ 𝑝𝑖 𝑞𝑗 𝑘 𝑘=1 )𝑗 (−𝑞𝑗𝑘 ) + 2𝜆𝑝𝑖𝑘 ..... (5) 𝜕𝐶 𝜕𝑞𝑗𝑘 = ∑ 2(𝑟𝑖𝑗 − ∑ 𝑝𝑖 𝑞𝑗 𝑘 𝑘=1 )𝑗 (−𝑞𝑖𝑘 ) + 2𝜆𝑝𝑗𝑘 ..... (6) (zhang, 2022) equations (5) and (6) above will be used by the optimizer for learning to minimize the loss between the predicted results and the actual value. data, instruments, and data collection techniques this study uses secondary data from 2021, namely indonesian tourism dataset data, which has several unique users of 300 accounts, several tourist attractions (tourism sites) 437 spread across five major cities in indonesia, and the total number of reservations along with ratings on a scale of 1-5 is 10,000 samples. this data is taken from a file in .csv format based on literature research. figure 2. distribution of reservations user-based based on figure 2, it can be concluded that the data distribution is not normally distributed. figure 3. distribution of ages based on figure 3, it can be concluded that the majority range of ages is between 27-30 figure 4. distribution of ratings based on figure 4, it also can be concluded that most ratings are above 1. in addition to text data, image data are obtained through google images randomly. the sources of image data were also varied; the majority were obtained from wikipedia, pegipegi, tokopedia, traveloka, and tiket. these images are used as a representation of tourist attractions, with a total of 437 images. results and discussion in this study, several experiments were conducted using several architectures, including; matrix factorization, neural collaborative filtering, hybrid method using text features, hybrid m.f. and n.c.f. methods with image features, and hybrid mfncf method with image features. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 5 table 1. algorithm comparison table algoritma metrics ndcg @5 acc@5 mse rmse m.a.e. matrix factorizati on (m.f.) 38.4% 52.7% 0.653 0.808 0.649 neural collaborati ve filtering (n.c.f.) 15.9% 51.8% 0.179 0.423 0.348 hybrid text feature 42.8% 49.9% 0.160 0.400 0.333 neural collaborati ve filtering image 62.8% 52.1% 0.219 0.468 0.376 matrix factorizati on image 28.2% 54.6% 0.158 0.398 0.332 hybrid m.f. image recommen dation (personaliz ed) 71.5% 53.8% 0.133 0.364 0.309 hybrid n.c.f. image recommen dation (personaliz ed) 85.8% 52.7% 0.133 0.364 0.311 hybrid m.f. +ncf image recomme ndation (personali zed) 60.2% 56.2% 0.142 0.376 0.317 based on table i, the results of the evaluation of the highest indonesian tourism dataset data based on the regression matrix were obtained by the hybrid m.f. image recommendation (personalized) and n.c.f. personalized methods, which had the lowest m.s.e., rmse, and m.a.e. values compared to other methods. the regression matrix measures 'how close' the predicted results are to the actual results and becomes the main matrix. table i shows that images as additional parameters for the model have a better average performance than those without image features. furthermore, the additional personalization data from the categories of tourist attractions preferred by users increased model performance, as indicated by a lower average m.a.e. evaluation than the model without personalization. based on the ndcg matrix, which is used to measure the quality of giving recommendations. based on these scores, the highest model is n.c.f. image personalized, with a score of 85.8%. based on the top-k accuracy matrix to get a slice of tourist attractions in the top recommendations for each user using the average value, the combined m.f. and n.c.f. image recommendation personalized model obtained the highest average accuracy value of 56.2% compared to other methods. this result is due to the model's nature which combined the linear and non-linear models, resulting in broader regression for the model. these combinations acted as ensembled learning with the output of mean value between the two algorithms. figure 5. architecture of hybrid models (mf and n.c.f. combined) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 6 based on fig. 5, the hybrid models combined m.f. and n.c.f. models and averaged their outputs to gain the most relevant score. for the image feature extractor, the author used resnet50. figure 6. set up personalization when a new user registers for the website, the website will redirect to the personalization setup page, where the user must choose four tourist attractions they like, which can be seen in fig. 6. figure 7. home page after setting up the personalization for a new user, the user will redirect to the home page, where the system provides top-5 recommendations based on the previous personalization setup or users' histories (old users), which can be seen in fig. 7. figure 8. booking page when the user plans to visit a tourist spot, the user can select the visit link to open the tourist place order form, which can be seen in figure 8. conclusions and suggestions conclusion based on the evaluation of the research that has been carried out on the proposed algorithms, the use of the hybrid method with image and personalization features can provide tourist recommendations. although, the evaluation of the m.s.e., rmse, and m.a.e. regression matrices where the prediction results are not very satisfactory due to a significant distance from the actual value. in this case, the use of the hybrid method is carried out by combining two examples of the implementation of the m.f. method, namely gmf and n.c.f., with the resnet50 cnn architecture. in addition, this model is supported by user personalization in the form of tourist categories that each user likes. based on the experimental results above, it was found that the hybrid n.c.f. + m.f. image (personalized) model has an ndcg accuracy of jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 7 60.2% for the top-5 with an accuracy top-5 of 56.2%. suggestion this recommendation system is not perfect and has room for improvement that can be improved further into a context-awareness based recommendation system where recommendations are made based on users' online patterns, length of interaction with each site that provides information on certain tourist attractions, and also providing a matrix of calculating relevance scores provided by the user. this can positively impact the accuracy of scoring by users for each tourist spot. in addition, based on existing data, descriptions of tourist attractions can be processed by the text sequence learning model to determine a suitable probability relationship between each description of tourist attractions. references 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(2022). an introduction to matrix p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.526 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 8 factorization and factorization machines in recommendation system, and beyond (pp. 1– 15). arxiv. https://doi.org/10.48550/arxiv.2203.11026 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 529 support vector classification with hyperparameters for analysis of public sentiment on data security in indonesia siti ernawati -1*), risa wati -2, nuzuliarini nuris -3 sistem informasi universitas nusa mandiri jakarta, indonesia www.nusamandiri.ac.id 1*)siti.ste@nusamandiri.ac.id sistem informasi universitas bina sarana informatika jakarta, indonesia www.bsi.ac.id 2risawati.rwx@bsi.ac.id, 3nuzuliarini.nzn@bsi.ac.id (*) corresponding author abstract the development of information technology makes increasing use of the internet. this raises the vulnerability of data security. cyber attacks in indonesia caused many tweets on social media twitter. some are positive, and some are negative. the problem of this study is to determine the public sentiment towards data security in indonesia, while the purpose of this study is how the response or evaluation of the government of indonesia to the many perceptions of people who lack confidence in data security in indonesia. data obtained from twitter with as much as 706 data was processed using python with a percentage of 10% test data and 90% training data. weighting is done using tf-idf, and then the data is processed using the support vector machine algorithm using the svc (support vector classification) library. support vector classification with rbf kernel classifies text well to obtain auc value with good classification category. utilizing one of the hyperparameter techniques, which is a grid search technique that can compare the accuracy of test results. the test results using svc with rbf kernel obtained an accuracy value of 0.87, precision of 0.82, recall of 0.94, and f1_score of 0.87. this study is expected to be used by decision-makers related to public confidence in data security in indonesia. keywords: data security; grid search ; hyperparameter; svc abstrak berkembangnya teknologi informasi membuat meningkatnya penggunaan internet. hal ini menimbulkan rentannya keamaan data. serangan siber di indonesia menimbulkan banyaknya cuitan pada media social twitter, ada yang beropini positif dan ada yang beropini negative. permasalahan dalam penelitian adalah untuk mengetahui sentimen masyarakat terhadap keamanan data di indonesia, sedangkan tujuan dari penelitian ini adalah bagaimana tanggapan atau evaluasi pemerintah indonesia terhadap banyaknya persepsi masyarakat yang kurang percaya terhadap keamanan data di indonesia. data diperoleh dari twitter dengan jumlah data sebanyak 706 data diolah menggunakan python dengan prosentase 10% data test dan 90% data training. dilakukan pembobotan menggunakan tf-idf selanjutnya data diolah menggunakan algoritma support vector machine dengan memanfaatkan library svc (support vector classification). support vector classification dengan kernel rbf mengklasifikasikan teks dengan baik memperoleh nilai auc dengan kategori good classification. memanfaatkan salah satu teknik hyperparameter yaitu teknik grid search yang dapat membandingkan keakuratan hasil uji. hasil uji menggunakan svc dengan kernel rbf didapatkan nilai akurasi sebesar 0.87, precision 0.82, recall 0.94 dan f1_score 0.87. penelitian ini diharapkan dapat dijadikan pengambil keputusan terkait kepercayaan masyarakat terhadap keamanan data di indonesia. kata kunci: grid search; hyperparameter; keamanan data; svc p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 530 introduction information technology is growing rapidly, causing an impact on many community activities. utilizing information technology resulted in the vulnerability of personal data security. every year internet users in indonesia increase, based on the results of a survey by the asosiasi penyelenggara jasa internet indonesia (apjii) in the 2021-2022 period reaching 210.03 million users (bayu, 2022). increased use of the internet makes the risk of hacking even higher. until april 2022, badan siber dan sandi negara (bssn) recorded cyber attacks in indonesia, reaching 100 million cases (andreya, 2022). the issue of personal data protection is very important. if there is theft of personal data such as names, addresses, emails, telephone numbers, bank account numbers to medical history in the thousands to millions, it can threaten national security (wisnubroto, 2021). regulation of personal data protection is important due to the emergence of various problems as the use of personal data in information technology-based transactions increases (rumlus & hartadi, 2020). kemenkominfo revealed that indonesia was the country that experienced the most cyber attacks (widyanuratikah, 2018). in the last few years, social media, one of which is twitter, has gotten much attention, and users can express their opinions about anything. users generate information that shows the user's views on a particular topic, which is very useful for analyzing public opinion (naz et al., 2018). the number of cyber attacks that occurred in indonesia made people have given opinions on social media such as twitter. various tweets appeared with both positive opinions and negative opinions. therefore, researchers will analyze public perceptions of data security in indonesia. the purpose of this study is to determine the value of performance metrics generated by svc using the hyperparameter grid search technique and is expected to be useful for the government of indonesia in evaluating the perception of people who lack confidence in data security in indonesia. the proposed model will analyze the sentiment of public perception of data security in indonesia using the svm algorithm with svc library and grid-search techniques. with two or more classes, svm is commonly used in classification problems (hsu, chang, & lin, 2010). the svm algorithm will find the best parameters (ahmad, aftab, bashir, hameed, et al., 2018) and compare the accuracy of the results obtained and then choose the best parameters. the most recommended kernel in svm is rbf, and this kernel nonlinearly maps samples into higher dimensional space (hsu et al., 2008). the parameters to be searched are gamma and c. to evaluate the performance of the proposed model using a confusion matrix by calculating the performance metrics of accuracy, precision, recall, and f1-score, also using the roc curve (receiver operator characteristic) and auc value (area under curve). the first related research compares twitter's sentiment classification algorithm to tokopedia's data leak incident. conducted observations of three different classifiers, it was concluded that of the total 494 tweets analyzed, support vector machine is the classifier with the best performance, resulting in the highest f1-score of 0.503583 (wibowo et al., 2021). the next research is the application of svm in various research fields such as text categorization, protein fold, remote homology detection, image classification, bioinformatics, hand-written character recognition, face detection, generalized predictive control, and many more. many researchers have shown that in classification techniques, svm is better than other algorithms (cervantes et al., 2019). research on gopay user sentiment analysis using lexicon based and support vector machine method shows svm classification method by comparing the kernel is quite good, for linear kernel get an accuracy value of 89.17% while the polynomial kernel of 84.38% (mahendrajaya et al., 2019). application of the support vector machine method for sentiment analysis of indihome services based on tweets (tineges et al., 2020) resulted in an accuracy of 87% with accuracy between the predicted results with the actual data (precision) of 86%, the success rate of the system in predicting a data (recall) of 95%, while for the average comparison value of precision and recall (f1-score) of 90%. research conducted on sentiment analysis of gojek on social media using the svm method (fitriyah et al., 2020) has the best overall accuracy rate of 79.19%. the accuracy is obtained from modeling using rbf kernel with cost=1000 and γ=0,00026. research methods the stages of research conducted are: 1. identification of problems and objects of research the problem of this study is to determine people's sentiment about data security in indonesia, while the purpose of this study is how the response or evaluation of the government of indonesia to the many perceptions of people who lack confidence in jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 531 data security in indonesia. the object of this study is the opinions written by people on social media. this opinion continues to increase over time, becomes a problem, and can be used by researchers to determine public sentiment by analyzing public opinion. figure 1. stages of research 2. data set using public data is data collected from twitter, namely data on public opinion about data security in indonesia. the data used as much as 706, labelled positive and negative and then processed using the python language. the percentage of test data and training data is 10% test data and 90% training data. 3. prepossessing preprocessing converts raw data into data that uses for subsequent data processing. several stages of preprocessing conducted in this study are: a. case folding converts each word in the dataset into lowercase (fikri et al., 2020). b. cleaning text removes unused characters such as double spaces, hashtags, links, retweets, mentions, and punctuation (rahmawati & sukmasetya, 2022). c. tokenization is the process used to divide text into single words (unigrams) or consecutive combinations of words (n-grams) (chiny et al., 2021). d. stopword removes words that have no meaning, such as the, in, and on (fikri et al., 2020). e. stemming is changing a word into a base word by removing affixes (rahmawati & sukmasetya, 2022). 4. weighting tf-idf (term frequency-inverse document frequency) is an algorithm for assigning weight to text (fikri et al., 2020). tf (term frequency) is the frequency of occurrence of a term in the document in question, while the idf is the relationship between the availability of a term in all documents (mahendrajaya et al., 2019). tf-idf is used to find out how often words appear in a document, the following formula in tfidf weighting (liu & yang, 2012). 𝑎𝑖,𝑗 = 𝑡𝑓𝑖,𝑗 ∗ 𝑙𝑜𝑔 ( 𝑁 𝑛𝑗 ) ....................................................... (1) 𝑡𝑓𝑖,𝑗 is term frequency of term j in document i. n represents the total number of documents in the dataset. nj is the number of the emergence of documents in term i. 5. support vector classification implementation with grid search support vector machine is an important and fundamental technique in machine learning (yin & li, 2019). svm is one of the widely used machinelearning techniques to detect the polarity of text (ahmad, aftab, bashir, & hameed, 2018). svm can predict both classification and regression. in implementing the performance model, researchers will use the sklearn svc library, a support vector classification, to implement the libsvm library (chang & lin, 2011). how svm works first, svm looks for a support vector in each class. the support vector is a sample from each class with the closest distance to other class samples. after the support vector obtains, svm then calculates the margin. we can think of margin as the path that separates two classes. margin is created based on the support vector where the support vector works as the edge of the road, or often we know as the shoulder of the road. svm seeks the largest margin or widest path to separate the two classes. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 532 figure 2. svm working technique (ahmad, aftab, bashir, & hameed, 2018) based on figure 2, svm will select the margin on the right because the 'path 'or margin on the right image is wider than the' path ' on the left. it is why images placed off to the left are said to have a "big margin," while images placed off to the right have a "small margin." 6. evaluation an important step in this evaluation is to measure the performance of the proposed model. this evaluation is used as a consideration to choose the best model. one technique to measure the proposed model's performance is the fusion matrix. the performance metrics calculated in the confusion matrix are accuracy, precision, recall, and f1-score. in addition to the confusion matrix, the researcher used the roc curve (receiver operator characteristic) and auc value (area under curve) in this evaluation stage. this evaluation phase will explain whether proven support vector classification is a good classification model for sentiment analysis of data security in indonesia. 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑃+𝑇𝑁) (𝑇𝑃+𝐹𝑃+𝐹𝑁+𝑇𝑁) ........................................... (2) 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝+𝑓𝑝 ............................................................ (3) 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝+𝑓𝑛 .................................................................... (4) 𝐹1 − 𝑆𝑐𝑜𝑟𝑒 = 2 ∗ (𝑟𝑒𝑐𝑎𝑙𝑙 ∗ 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛)/(𝑟𝑒𝑐𝑎𝑙𝑙 + 𝑝𝑟𝑒𝑠𝑐𝑖𝑠𝑖𝑜𝑛) .......................................................................... (5) results and discussion 1. classification of models this study collected data from social media twitter in the form of public opinion about data security in indonesia. the data used as much as 706 data, and then the data was separated manually into positive and negative opinions. with several positive opinions as much as 379 and negative opinions as much as 328 data. data sharing between test data and training data. comparison of data made 10% test data and 90% training data. based on figure 3, the negative words in the opinion include data, private, cyber, bocor, and bjorka. figure 3. word cloud negatif in figure 4, the positive words that appear are data, pribadi, aman, lindung, and undang-undang. positive and negative wordcloud shows the words used in the sentiment, the more words that appear, the larger the size of the word in the image. figure 4. word cloud positif after the data becomes positive and negative opinions, preprocessing the data removes data noise so that the data is used for further data processing. here is the form of data before and after preprocessing. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 533 table 1. preprocessing result tweet preprocessing result #kuliahasyik #digitalactivism #bjorka bjorka sosok hero indonesia? bjorka menunjukan bahwa betapa lemahnya sistem perlindungan dan keamanan data indonesia,seharusnya pemerintah kita sadar dengan kebocoran sistem pemerintah dari dulu sampek sekarang. bjorka sosok hero indonesia bjorka tunjuk betapa lemah sistem lindung aman data indonesia perintah sadar bocor sistem perintah sampek di tengah isu privasi dan kebocoran data warga yang gagal diamankan negara, @indihome memperbarui kebijakan. pengguna harus merelakan datanya untuk ???afiliasi telekom dan tujuan-tujuan lain.??? di poin 8, indihome tak bertanggung jawab jika data bocor karena ???gangguan keamanan.??? https://t.co/zmsx5o5l09 privasi bocor data warga gagal aman negara baru bijak guna rela data afiliasi telekom tuju tuju poin indihome tanggung data bocor ganggu aman 2. experiment using the svc model with grid search statistical approaches such as machine learning and deep learning work best when using numerical data. therefore, opinion data is a collection of words converted into numbers or numeric. this process is often called the weighting process. some techniques often used in the weighting process include bag of words, n-grams, word2vec, and tf-idf. this study uses tf-idf (term frequency-inverse document frequency) for weighting in sentiment analysis (chiny et al., 2021). tf-idf is used to find out how often words appear in a document, the following formula in tf-idf weighting (liu & yang, 2012). after the weighting process, the svc library implements on the support vector machine by building a model and utilizing several kernels. there are four kernels, linear, rbf, poly, and sigmoid. one hyperparameter technique will be used based on the four kernels: grid search. one technique to optimize the accuracy value is to use grid search (yan et al., 2022), a grid search technique to determine the result of a combination of parameters that are best for the performance of the proposed model (k et al., 2019). the parameters tested can be seen in table 2. table 2. parameters to be tested parameter description kernel linear, rbf, poly, sigmoid c 1, 100, 1000, 10000 gamma 0.01, 0.1, 1, 10, 100 table 3. svc model accuracy results with grid search kernel c gamma accuracy results linear 1 0.01 precision : [0.94 0.79] accuracy: 0.86 f1_score: 0.86 recall: 0.94 rbf 1 1 precision : [0.94 0.82] accuracy: 0.87 f1_score: 0.87 recall: 0.94 poly 1 1 precision : [0.87 0.85] accuracy: 0.86 f1_score: 0.85 recall: 0.76 sigmoid 100 0.01 precision : [0.94 0.79] accuracy: 0.86 f1_score: 0.86 recall: 0.94 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.481 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 534 table 3 shows the results of the svc model calculation with grid search. from the test results can be seen that the best parameter is c= 1 and gamma = 1, which produces a value of negative precision = 0.94, positive precision = 0.82, accuracy = 0.87, f1_score = 0.87 and recall = 0.94 3. evaluation to evaluate the performance of the proposed model using performance metrics of accuracy, precision, recall, and f1-score. performance metrics are calculated based on the value of the true positive (tp), false positive (fp), true negative (tn), and false negative (fn) class set. at the same time, the precision of the correct prediction is positive compared to the overall predicted outcome. figure 5. confusion matrix obtained a true negative value of 43.66%, false positive of 9.86%, false negative of 2.82%, and true positive of 43.66%. figure 5. confusion matrix 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑃 + 𝑇𝑁) (𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁 + 𝑇𝑁) = = (43.66+43.66) (43.66+9.86+2.82+43.66) = 0.87 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝+𝑓𝑝 = 43.66 43.66+9.86 = 0.82 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝+𝑓𝑛 = 43.66 43.66+2.82 = 0.94 (8) 𝐹1 − 𝑆𝑐𝑜𝑟𝑒 = 2 ∗ (𝑟𝑒𝑐𝑎𝑙𝑙 ∗ 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛)/(𝑟𝑒𝑐𝑎𝑙𝑙 + 𝑝𝑟𝑒𝑠𝑐𝑖𝑠𝑖𝑜𝑛) = 2 ∗ (0.94 ∗ 0.82)/(0.94 + 0.82) = 0.87 figure 6 illustrates the roc curve. the roc curve shows accuracy and visually compares classifications. the roc curve expresses the confusion matrix. roc is a two-dimensional graph with false positives as horizontal lines and true positives as vertical lines. this study obtained an auc value of 0.89, and the value entered into the category of good classification. figure 6. roc curve conclusions and suggestions conclusion based on the test results for sentiment analysis of indonesian public confidence in data security, svc with rbf kernel can classify text well, as evidenced by the auc value of 0.89. the grid search technique can compare the accuracy of the test results and then choose the best parameters. the test results using svc with rbf kernel obtained an accuracy value of 0.87, precision of 0.82, recall of 0.94, and f1_score of 0.87. this research model can be applied by agencies in decision-making related to public confidence in data security so that the government can evaluate the number of perceptions of people who lack confidence in data security in indonesia. suggestion further research can focus on comparing techniques or other classification methods that can create a new model by combining many classification and selection methods to produce more accurate values. references ahmad, m., aftab, s., bashir, m. s., & hameed, n. 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(2019). a semismooth newton method for support vector classification and regression. computational optimization and applications, 73(2), 477–508. https://doi.org/10.1007/s10589-01900075-z jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 121 web-based customer services management implementation for the sales division endang sri palupi sistem informasi universitas bina sarana informatika endang.epl@bsi.ac.id abstract with the growing business at pt mastersystem infotama, more and more customers and orders have been obtained. as time goes by, many sales go in and out, and staff change, so the customer database cannot be maintained and updated correctly. all leads and opportunities cannot be monitored and appropriately managed, and the daily activity of the sales division is also not monitored. implementation of customer relationship management (crm) helps maintain customer data and can be continuously updated to maintain good relations with all customers. prospects and opportunities can be managed and monitored, the daily activities of the sales division are neatly scheduled, and customer observations can be gathered, all of which can be done in one application, crm. with this implementation, sales at pt mastersystem infotama experienced a 20% increase in sales in 2018. this research uses the waterfall model, which has the advantage of being a gradual and more detailed method to minimize errors. this crm implementation produces a web-based crm application that can be accessed by all employees wherever they are connected to a lan. employees can access crm according to each division's capacity to make work easier. keywords: customer relationship management, crm implementation, business strategy abstrak dengan semakin berkembangnya bisnis di pt mastersystem infotama, semakin banyak pula pelanggan dan order yang telah didapatkan. seiring berjalannya waktu banyak sales keluar masuk adanya pergantian staff sehingga database pelanggan tidak dapat terpelihara dan terupdate dengan baik. semua leads dan opportunity tidak bisa termonitor dan termanage dengan baik, daily activity divisi penjualan juga tidak termonitor. untuk itu penulis ingin mengimplementasikan customer relationship management (crm) supaya semua database pelanggan dapat terpelihara dengan baik dan bisa terus diupdate sehingga dapat terus menjalin hubungan yang baik dengan semua pelanggan, leads dan opportunity dapat dimanage dan dimonitor,daily activity divisi penjualan terjadwal rapi, dan mendapatkan informasi untuk observasi pelanggan, semua dapat dilakukan dalam satu aplikasi yaitu crm. dengan implementasi ini penjualan di pt mastersystem infotama mengalami kenaikan penjualan 20% pada tahun 2018. penelitian ini menggunakan pemodelan waterfall yang memiliki kelebihan yaitu metodenya yang bertahap dan lebih detail sehingga meminimalisir kesalahan. implementasi crm ini menghasilkan aplikasi crm berbasis web yang dapat diakses seluruh karyawan dimanapun berada yang terhubung lan ataupun tidak. karyawan bisa mengakses crm sesuai kapasitasnya masing – masing divisi untuk mempermudah pekerjaan. kata kunci: customer relationship management, penerapan crm, strategi bisnis introduction with the development of an increasingly significant business at pt mastersystem infotama, the amount of customer data is increasing, and the orders coming in are increasing, thus requiring good data maintenance. coupled with the entry and exit of employees so that there is a change of old and new employees requires a database storage template that is easily accessible and updated by all employees in the system. the crm application does not require additional space to store data, such as saving manually. the data can be accessed in realtime on one platform so that if there is a change of staff, you do not have to look for data information in hundreds of files on the computer. (barantum, 2022) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 122 the research entitled implementation of crm in marketing information systems using a framework based on the react.js website uses an agile method that restores readiness to carry out changes in the development stage. while the authors conducted research using the waterfall method, waterfall is the most traditional sdlc development method, while agile is a method that is considered evolved and absorbs speed (agility). (muhammad, fitri, & nuraini, 2022) in the research on electronic customer relationship management (e-crm) design as an information system in improving digital library services for the unsri faculty of computer science, crm is implemented electronically using a web browser, email, call center, sms gateway, and chat. one of the aims of this e-crm is to make it easier for library service users to find information and access e-crm as customers. while this research is a website-based crm that can only be accessed via a browser, and companies only use crm, there is no customer access, so there is no feedback regarding service. (mira afrina, 2013) the research entitled implementation of crm for guest services in the berbah sub-district of sleman yogyakarta is used to provide information and monitor all guests who enter the berbah subdistrict of yogyakarta through the local rt head using an sms gateway. the use of crm in this study uses the sms gateway system. sms gateway is a device or system capable of handling basic sms operations (sending, receiving, reading, and deleting sms) that are outside the standard gsm network. the sms gateway can also add complementary features such as autoreply and sms broadcast. exit and entry of guests are informed and monitored via the sms gateway. in contrast to this study, crm is implemented using a website and helps store customer data and create customer opportunities and orders. (galih pamungkas, 2015) then the following research entitled crm implementation in web-based sales applications pt. buana telekomindo, this descriptive research aims to describe the business processes running at pt. buana telekomindo is then analyzed using the crm concept to see which processes can be removed, added, or modified. action research to improve business processes and design a crmbased information system consists of process design using dfd, database design using erd, and interface design. the similarity with this research is that it can monitor business processes so that they are more effective. the difference is that this research is in the crm application. there is also an updated and real-time customer database, and there are records of all customer purchases that have been made, so the information needed for reports is faster. (simarmata & hasibuan, 2019) it differs from heru saputro and colleagues' research entitled implementation of crm for optimizing web-based rengginang traditional food customer service and tawk because this research builds a rengginang online sales web using the waterfall method based on customer relationship management. so crm here is on the online sales web that customers can access, while the author implements crm that cannot be accessed by customers and only for business processes within the company. (h. saputro, 2018) research methods in this study, the authors used a qualitative descriptive method, a qualitative descriptive analysis method to analyze, describe, and summarize various conditions and situations from various data collected in the form of interviews or observations regarding the problems studied that occurred in the field. (i. made winarta, 2006). the reason for choosing a qualitative descriptive research design is that the researcher wants to describe the conditions observed in the field more precisely, transparently, and in-depth. the researcher formulates the problem, collects data using observation, interview, and literature study techniques, and then finds research conclusions and implements them through web-based crm. crm is a business strategy that integrates processes, people, and technology. help attract sales prospects, convert them into customers, and retain existing, satisfied, loyal customers. figure 1. crm concept the concept of crm is divided into three levels, namely: strategic crm for business jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 123 development, operational crm, namely business processes, and analytical crm for the utilization of customer data to increase company value. (supandi, yusuf, & fauziah, 2018) customers the customer database is constantly updated and fully maintained. all purchases and activities for customers are also recorded on crm. for example, in 2021, pt abc asked for an hpe product presentation and an antivirus price quote so that the sales division could continue to the next stage to increase sales to these customers. a. key performance indicators to see the achievements of the sales division. with this achievement, a performance evaluation will be carried out to achieve the company's target. b. monitoring to measure the performance of the customer service team and evaluate the quality of services provided to customers. c. technical services there is a contact center that stands by 24/7 hours, and any problems reported by customers will be resolved according to the sla (services level agreement) for each product and the problem. d. promotions schedule of promotional activities to be held by the company. for example, autocad product license flash sales from january 14, 2022, to january 20, 2022, at a 30% discount. e. payment the system will schedule payment due dates for customers who make transactions. if there has been no payment and it is past due, the sales division will help follow up with the customer. you can see records of orderly and disorderly customer payments so that the sales division can make policies for the future. types of research this study used the descriptive qualitative method. the qualitative research method is a research method based on the philosophy of postpositivism, used to examine the condition of natural objects (as opposed to experiments) where the researcher is the key instrument, data collection techniques are carried out by triangulation (combined), data analysis is inductive/qualitative, and the results of qualitative research emphasize the meaning of generalizations. (sugiyono, 2018) researchers use the qualitative descriptive research method to find knowledge or theory of research at a particular time. (mukhtar, 2013). figure 2. research framework time and place of research this research was conducted from june 2022 to august 2022 at pt mastersystem infotama, central jakarta. research target / subject this crm implementation is used internally for pt mastersystem infotama, especially the marketing division, to input customer data, leads, opportunities, quotations, and orders. the purchasing, marketing admin, finance, and warehouse divisions also follow up on orders that the marketing division has made according to their respective job desks, and all can be monitored through crm. all customer databases and marketing division sales data can be seen in one crm application. procedure this model is often called the "classic life cycle" or "waterfall model." this model belonged to the generic software engineering model and was introduced by winston royce around 1970. the linear sequential model is a software development p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 124 method with a sequential approach with a scope of activities: 1. requirements analysis at this stage, analyze the need to identify the problem to determine the right solution. explore and collect data and information in the company pt mastersystem infotama relating to the scope of work, sales, employees, products, and business processes. after the data is collected, the system requirements can be analyzed. system requirements in developing this application are functional requirements and non-functional requirements. requirements contain what processes can be carried out and what information the system produces. how the system should react to specific inputs and how the system should behave in certain situations. (yanto & asiah, 2021) figure 3. stages of the waterfall model table 1. functional requirements user system capabilities admin the system can log in the system can manage customer information data the system can input data the system can delete data the system can manage sales data the system can manage system user data sales the system can log in the system can input customer data the system can input leads the system can input the opportunity the system can input meeting schedules sales manager the system can log in the system can approve the opportunity the system can approve the opportunity the system can assign leads to other users the system can reject opportunities and orders the system can see the sales results of other users 2. design create a system design based on an analysis of system requirements using the unified modeling language (uml), such as use case diagrams and class diagrams. uml is a standard language widely used in the industrial world to define requirements, analyze and design, and describe architecture in object-directed programming. (rosa, as. salahuddin, 2015) 3. development the programming languages used are php and mysql as the database and dreamweaver 8 for the web display design. 5. testing (testing). after the program code is complete, testing can be carried out. testing focuses on the internal logic of the software's external functions, looks for any possible errors, and checks whether it matches the desired results. testing is done using a black box. black box testing focuses on the functional specifications of the software and a set of input conditions and performs functional testing of the program (mustaqbal, firdaus, & rahmadi, 2015). the results of black box testing can be concluded by calculating the percentage, which is calculated based on the number of questions received divided by the total number of all questions asked, then multiplied by 100%, and the result is a decision, namely whether the application system being tested is feasible to be implemented. (elza fadli hadimulyo, welly purnomo, 2019) table 3. test calculation range criteria score criteria 0,00 36.00 not good 36.01 52.00 not worth it 52.01 68.00 pretty good table 4. black box test results category response frequency tester total question approve 398 10 45 reject 52 10 45 thus the results of web-based crm implementation at pt mastersystem infotama obtained results of 88.44% and are very good and feasible to implement. 6. maintenance (maintenance). the last part of the development cycle is carried out after the software is used. it includes the following activities: a. corrective maintenance: she is correcting errors in software, which are only detected when the software is used. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 125 b. adaptive maintenance: adjustment to a new environment, such as an operating system, or as a demand for developing a computer system, such as adding a printer driver. c. perfective maintenance: suppose the user successfully uses the software. maintenance is intended to increase its capabilities by providing additional functions, improving performance, etc (bolung & tampangela, 2017) data, instruments, and data collection techniques the meaning of data collection techniques is the most strategic step in research because the primary purpose of research is to obtain data. (sugiyono, 2018) data collection techniques are data collection methods, namely techniques or methods that researchers can use to collect data. (riduwan, 2010) data collection techniques used in general are using: 1. interview technique according to (sarosa, 2017), interviews are one of the most widely used tools to collect qualitative research data. the researcher interviewed employees of the sales department, the finance department, the purchasing and warehouse division, and the it development and desktop sections to collect data. interviews were conducted face-to-face so researchers could get more detailed answers to the questions. 2. observation technique according to (fuad. anis, 2014), defining observation in qualitative research is a basic technique that can be done. at the beginning of the qualitative research, observations were made during the grand tour observation. the observation method is used in direct observation or sense of an object, condition, situation, process, or behavior. in this study, researchers chose to collect data using participatory observation techniques so that researchers could make observations of events that occurred and involve themselves directly in collecting data and information sought to answer questions that became problems in the study. (zhahara yusra, rufran zulkarnain, 2021) 3. literature study techniques researchers look for literature and references from various books, journals, and internal reports at pt mastersystem infotama that relate to the problems to be solved. a literature study is a data collection technique by reviewing books, literature, notes, and reports related to the problem being solved. (m. nazir, 1988) field notes are important because they are essential for various qualitative data collection techniques. the form of recording in the field, namely: a) fact notes: qualitative data from interviews in the form of descriptions or direct quotes and observations; b) theoretical notes: analysis while in the field to conclude the structure of the community under study, and the formulation of relationships topics. (variable) is essential in inductive research according to field facts; c) notes methodological: the researcher tries to use qualitative methods in the field, taking notes field events there are two notes: main note, second note memo/reflective: contents about constructive descriptive criticism (raya & raya, 2021) results and discussion figure 4. feature crm figure 4 shows the features of crm. on sales, the menu features sales activities such as following up with customers, meetings with customers and visiting customers so that they know more about customer needs. on the services menu, there is a feature account for customer names and customer contacts, where customers who contact the service desk have their data recorded in crm. then there is a marketing menu that contains leads, opportunities, and competitors, aiming to turn potential customers into customers so they can send price offers and get orders from customers. crm elements, namely customer services, are available 24/7 and can be contacted by customers. sales force automation can also be done because each division can see complete and updated customer data so that the product division can update the latest product info to customers. the marketing division can provide information related to promos and marketing activities to customers. the finance and tax divisions can provide information regarding changes in tax regulations and others—campaign management by holding several events in collaboration with principals and distributors. customers are invited based on profiling according to the product of the campaign. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 126 figure 5. crm login page in figure 5. the display of the login page on the crm user is asked to enter a user name. namely, the email registered with the company and password for the user name and password is the same as those registered in the active directory. figure 6. crm main page display in figure 6, the crm main page displays the menus designed in the class diagram: accounts, contacts, prospects, opportunities, activities, and so on. figure 7. account page display in figure 7, the account page display contains the customer company name data inputted by marketing—customers who have made purchases and potential customers who can be used as prospects. figure 8. account menu sales division users input customer data on the account menu, this data is real-time, and there is information on whom the user inputted it. figure 9. leads page view on the leads page in crm, there are customer data that have needs according to the products offered by sales, and in the future, they can become potential customers to achieve sales targets. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 127 figure 10. leads menu on the leads menu, the sales division inputs lead data, namely information regarding more detailed specifications regarding their needs in terms of product, timeline, budget, and pic, to be followed up further so they can generate opportunities and orders. figure 11. activities halaman page display in the activities menu, you can see the user's activities, whether to create a new opportunity, what products, and how much the price is. all reports are also sent to the email of each user who made it and his boss for approval. figure 12. opportunity page view the opportunity page will contain customer needs shortly; in the future, detailed information will be obtained from customers. after the opportunity appears, the salesperson usually makes a price offer. figure 13. sales chart after implementing web-based crm, there was an increase in sales in 2018, as shown in figure 13. conclusions and suggestions conclusion the test results using the black box are 88.44%. thus the system design is perfect and can be implemented. crm implementation at pt mastersystem infotama can make work easier, especially for the sales division, as all work can be done in one crm application. with crm, you can attract and get new customers, maintain existing customers, re-recruit old customers, and save on marketing costs for client services. work becomes more effective and efficient, and the sales division's daily schedule can also be seen as more organized to plan to increase sales and achieve company targets. suggestion p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.485 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 128 for further research, continue the implementation of crm in the order process, delivery, and billing, and all divisions can be integrated to monitor the course of business processes. references barantum. 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(2021). pengelolaan lkp pada masa pandemik covid19. journal of lifelong learning, 4(1), 15–22. https://doi.org/https://doi.org/10.33369/jol l.4.1.15-22 jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 97 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem deteksi wajah untuk identifikasi kehadiran mahasiswa dengan menggunakan metode eigenface pca husni sulaiman1, zahir zainuddin2, supriadi sahibu3 1 sistem informasi stmik bina adinata husninevergiveup@gmail.com 2 teknik informatika universitas hasanuddin makassar zainuddinzahir@gmail.com 3 pascasarjana sistem komputer stmik handayani makassar supriadi.dit@gmail.com abstrak pengenalan wajah merupakan salah satu cara pengenalan untuk keperluan identifikasi seseorang selain pengenalan sidik jari, suara, tanda tangan, retina mata dan sebagainya. teknik identifikasi kehadiran mahasiswa di stmik bina adinata masih bersifat konvensional, yaitu setiap mahasiswa hanya mengisi atau menandatangani absensi pada saat mengikuti perkuliahan, hal ini tentunya kurang efektif karena biasanya ada mahasiswa yang tidak mengikuti perkuliahan tapi tetap tercatat hadir di absen dikarenakan adanya seorang mahasiswa yang menandatangani absensi mahasiwa yang tidak sempat hadir pada saat perkuliahan, atau yang lebih sering disebut titip absen. tujuan dari penelitian ini adalah untuk membangun sebuah sistem deteksi wajah untuk identifikasi kehadiran mahasiswa dengan menggunakan metode eigenface pca dan open cv library. penelitian ini dilaksanakan di kampus stmik bina adinata. sistem ini dapat bekerja secara realtime dengan menggunakan metode eigenface pca (priciple component analysis). hasil penelitian yang diperoleh menunjukkan bahwa sistem dapat bekerja secara realtime atau secara langsung, jadi sistem dapat mendeteksi wajah mahasiswa yang sedang mengikuti perkuliahan sistem ini dapat mengenali citra wajah baik dalam posisi lurus maupun menyamping. sistem dapat mendeteksi bukan cuma 1 wajah saja, tetapi sistem dapat mendeteksi semua wajah yang tertangkap kamera. tingkat keberhasilan akurasi sangat dipengaruhi oleh pencahayaan, semakin terang pencahayaan maka tingkat keberhasilan akurasi juga semakin tingggi. total akurasi keseluruhan dari segi pencahayaan adalah 90 % dan total akurasi keseluruhan dari segi posisi wajah adalah 86,6 % kata kunci : deteksi wajah, metode eigenface pca, open cv. abstract identifying face recognition is one of the introductory approaches for the purpose of someone besides other biometric approaches such as fingerprint recognition, voice, signature, identifying eyes and so on. face recognition system which is included with the field of image processing can be combined with the attendance system so that it becomes one of the interesting things to do, the attendance system it can be applied with the way of identification face. purpose of this research is to build a face detection system to identify student attendance with eigenface pca method and open cv library. this research was carried out at the stmik bina adinata its a one of university, this system work in realtime using the eigenface pca (primary component analysis) method. the research results obtained indicate that the system work in realtime or directly, so the system detect the faces of students who are attending lectures. this system can recognize facial images both in a straight and sideways position. the system can detect not only one face, but the system can detect all faces caught on camera. the success rate accuracy very influenced by the lighting, the lighting that more brighter will make success of accuracy increasingly high. keywords: face detection, method of eigenface pca, open cv. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 98 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pendahuluan secara garis besar metode yang digunakan dalam proses pengenalan wajah ada 3 macam, yaitu metode holistik, metode berdasarkan ciri dan metode hybrid. pendeteksian wajah merupakan tahap awal yang penting dalam sistem pengenalanan wajah otomatis. dalam suatu citra tunggal, tujuan dari pendeteksian wajah adalah mengidentifikasi semua area yang ada dalam citra untuk menemukan area wajah dan area bukan wajah. (yang, kriegman, & ahuja, 2002) dalam pendeteksian citra, warna memiliki kepekaan yang tinggi terhadap perubahan cahaya, maka untuk mengatasinya dilakukan transformasi citra rgb ke dalam sebuah ruang warna yang komponen luminasi dan kromatiknya dipisahkan sehingga cukup digunakan kromatik saja untuk proses deteksi warna kulit (chang & robles, 2000). salah satu contoh pendeteksian citra yang bisa diterapkan adalah identifikasi kehadiran atau absesnsi berdasarkan wajah yang bisa digunakan untuk mengidentifikasi kehadiran mahasiswa (yusuf, ginardi, & ahmadiyah, 2016). teknik identifikasi kehadiran mahasiswa di stmik bina adinata masih bersifat konvensional (purnia & sumitro, 2015), yaitu setiap mahasiswa hanya mengisi atau menandatangani absensi pada saat mengikuti perkuliahan, hal ini tentunya kurang efektif (suhery & ruslianto, 2017) karena biasanya ada mahasiswa yang tidak mengikuti perkuliahan tapi tetap tercatat hadir di absen dikarenakan adanya seorang mahasiswa yang menandatangani absensi mahasiwa yang tidak sempat hadir pada saat perkuliahan, atau yang lebih sering disebut titip absen (hertyana, 2016). berdasarkan permasalahan tesebut maka diperlukan sebuah sistem deteksi wajah untuk identifikasi kehadiran mahasiswa, dengan adanya aplikasi detekasi wajah ini diharapkan dapat memberikan informasi yang lebih akurat tentang kehadiran mahasiswa pada saat mengikuti perkuliahan. wajah merupakan bagian dari tubuh manusia yang menjadi fokus perhatian di dalam interaksi sosial, wajah memainkan peranan vital dengan menunjukan identitas dan emosi. kemampuan manusia untuk mengetahui seseorang dari wajahnya sangat luar biasa. kita dapat mengenali ribuan wajah karena frekuensi interaksi yang sangat sering ataupun hanya sekilas bahkan dalam rentang waktu yang sangat lama. bahkan kita mampu mengenali seseorang walaupun terjadi perubahan pada orang tersebut karena bertambahnya usia atau pemakaian kacamata atau perubahan gaya rambut. oleh karena itu wajah digunakan sebagai organ dari tubuh manusia yang dijadikan indikasi pengenalan seseorang atau face recognition. face recognition atau pengenalan wajah merupakan salah satu teknologi biometrik yang banyak diaplikasikan khususnya dalam sistem security. sistem absensi dengan wajah, mengenali pelaku tindak kriminal dengan cctv adalah beberapa aplikasi dari pengenalan wajah, efisiensi dan akurasi menjadi faktor utama mengapa pengenalan wajah banyak diaplikasikan khususnya dalam sistem security tahapan pengenalan wajah : 1) pengenalan wajah (face recognition) yaitu membandingkan citra wajah masukan dengan suatu database wajah dan menemukan wajah yang paling cocok dengan citra masukan tersebut. 2) autentikasi wajah (face authentication) yaitu menguji keaslian/kesamaan suatu wajah dengan data wajah yang telah diinputkan sebelumnya. 3) lokalisasi wajah (face localization) yaitu pendeteksian wajah namun asumsi hanya ada satu wajah di dalam citra 4) penjejakan wajah (face tracking) yaitu memperkirakan lokasi suatu wajah di dalam video secara real time. 5) pengenalan ekspresi wajah (facial expression recognition) untuk mengenali kondisi emosi manusia. metode penelitian rancangan sistem berikut ini adalah gambaran tentang perancangan sistem deteksi wajah untuk identifikasi kehadiran mahasiswa dengan menggunakan metode eigenface pca di stmik bina adinata secara keseluruhan. gambar 1 : perancangan sistem http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 99 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional cara kerja sistem : tahap pertama yang harus dilakukan adalah, menginput biodata mahasiswa dan mengcapture wajah mahasiswa sebagai data latih dimana nantinya data ini akan di olah dan di cocokkan dengan data uji, data latih ini kemudian akan disimpan kedalam database. selanjutnya sistem akan melakukan proses perhitungan eigenface pca, setelah proses perhitungan selesai, selanjutnya sistem akan menyimpan hasil perhitungan tersebut kedalam bentuk file *xml. berdasarkan dari hasil perhitungan tersebut selanjutnya sistem akan mengidentivikasi atau memverifikasi wajah dari data latih dengan data uji, setelah di verifikasi sistem akan menampilkan hasil pengenalan wajah tersebut, kemudian menyimpannya lagi kedalam database. penyimpanan data dalam sistem ini penyimpanan data dapat disimpan dalam database dan juga dataset. database pada program ini menggunakan sqlitestudio. adapun rancangan database nya dapat dilihat pada tabel dibawah ini. tabel 1 : rancangan database name date type mahasiswa id integer nama lengkap text nim text jenis kelamin text kelas text absen text tanggal varchart jam time sedangkan untuk menyimpan citra wajah tiap mahasiswa penulis menggunakan data set. untuk menghubungkan citra wajah dengan database sistem harus di generate terlebih dahulu agar data yang ada di database dapat disesuaikan atau di cocokkan dengan data di dataset. jenis penelitian untuk menyempurnakan data – data yang dibutuhkan dalam penyusunan penelitian ini, maka penulis akan melakukan pengumpulan data dengan menggunakan 2 cara yaitu : 1) penelitian kepustakaan (library research), yaitu pengumpulan data dengan cara membaca buku melalui literature, tutorial – tutorial maupun artikel dari internet yang bersifat ilmiah yang ada hipotesisnya dengan materi pembahasan. 2) penelitian lapangan (field research),yaitu dilakukan dengan cara mengumpulkan data secara langsung kepada objek penelitian yaitu pada stmik bina adinata. waktu dan tempat penelitian penelitian ini akan dilaksanakan selama 5 bulan, mulai februari sampai juni 2018 di stmik bina adinata. target/subjek penelitian target penelitian ini adalah mahasiswa stmik bina adinata prosedur renacana kegiatan 1. pengumpulan data pada tahap ini merupakan tahap dimana penulis mengumpulkan data megenai obyek yang akan diteliti dengan cara melakukan observaasi langsung ke lapangan. 2. analisis dan desain sistem pada tahap ini peneliti akan melakukan perancangan sistem yang akan dibuat pada penelitian ini. 3. pembuatan aplikasi pada tahap ini peneliti akan mulai melakukan pembuatan aplikasi sistem deteksi wajah. 4. pengujian pada tahap ini peneliti akan mulai melakukan pengujian terhadap aplikasi yang telah dibuat. 5. implementasi tahap ini merupakan tahap penerapan aplikasi yang telah dibuat oleh peneliti. data, intrumen, dan teknik pengumpulan data adapun teknik pengumpulan data yang dilakukan oleh penulis yaitu dengan cara observasi, yaitu mengamati secara langsung pada saat proses perkuliahan berlangsung, dan meperhatikan pola perilaku dari mahasiswa pada saat perkuliahan. hasil penelitian dan pembahasan implementasi sistem aplikasi ini dibuat dengan bahasa pemrograman python dan dikombinasikan dengan menggunakan opencv library. sistem yang dibuat ini merupakan sistem yang berbasis desktop, dimana proses – proses dalam menjalankan sistem dibuat secara realtime dan mudah dipahami dalam penggunaannya. tahapan – tahapan algoritma eigenface perhitungan eigenface untuk gambar training 1) penyusunan flatvector matriks citra http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 100 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dari data training image ( г )yang tersediah langkah selanjutnya adalah menyusun seluruh data training menjadi satu matriks tunggal. representasikan semua matriks training menjadi matriks linier nx1 atau flatvector seperti gambar 3, 4 dan 5. gambar 2 : training 1 г1 = [ 3 5 1 3 4 2 0 3 4 ] → [ 3 5 1 3 4 2 0 3 4] gambar 3 : training 2 г2 = [ 3 2 3 0 2 2 2 0 2 ] → [ 3 2 3 0 2 2 2 0 2] gambar 4 : training 3 г3 = [ 3 2 2 3 0 2 4 3 0 ] → [ 3 2 2 3 0 2 4 3 0] menghitung nilai tengah atau mean (ψ) ψ = 1 3 ∑ гn3𝑛=1 ψ = 1 3 [ 3 5 1 3 4 2 0 3 4] + [ 3 2 3 0 2 2 0 0 2] + [ 3 2 2 3 0 2 4 3 0] = [ 3 3 2 2 2 2 2 2 2] menghitung nilai selisih antara training image ( г ) dengan nilai mean (ψ) ø1 = [ 3 5 1 3 4 2 0 3 4] [ 3 3 2 2 2 2 2 2 2] = [ 0 2 −1 1 2 0 −2 1 2 ] ø2 = [ 3 2 3 0 2 2 2 0 2] [ 3 3 2 2 2 2 2 2 2] = [ 0 −1 1 −2 0 0 0 −2 0 ] ø3 = [ 3 2 2 3 0 2 4 3 0] [ 3 3 2 2 2 2 2 2 2] = [ 0 −1 0 1 −2 2 2 1 −2] menghitung nilai matriks kovarian l=[ 0 2 − 1 1 2 0 − 2 1 2 0 − 1 1 − 2 0 0 0 − 2 0 0 − 1 0 1 − 2 2 2 1 − 2 ] [ 0 0 0 2 − 1 − 1 −1 1 0 1 − 2 1 2 0 − 2 0 0 2 −2 0 2 1 − 2 1 2 0 − 2] l [ 19 − 7 − 12 −7 10 − 3 −12 − 3 19 ] hitung nilai eigen value (λ) dan eigen vector (υ) dari matriks kovarian 0 = 𝜆 [ 1 0 0 0 1 0 0 0 1 ] [ 19 − 7 − 12 −7 10 − 3 −12 − 3 19 ] 0 = [ 𝜆 − 19 7 12 7 𝜆 − 10 3 12 3 𝜆 − 19 ] nilai eigen vector diperoleh dengan cara mensubtitusi eigen value kedalam persamaan (𝜆𝐼 − 𝐿) υ = 0 . eigen vector masing – masing eigen http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 101 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional value didapat berdasarkan masing – masing kolom eigen value dan kemudian dihimpun kembali menjadi satu matriks. untuk 𝜆1 = 1.1641 [ 𝜆 − 19 7 12 7 𝜆 − 10 3 12 3 𝜆 − 19 ] [ υ1 υ2 υ3 ] = [ 0 0 0 ] υ1 = [ −0.5870 −0.6354 −0.5018 ] untuk 𝜆2 = 15.4227 [ 𝜆 − 19 7 12 7 𝜆 − 10 3 12 3 𝜆 − 19 ] [ υ1 υ2 υ3 ] = [ 0 0 0 ] υ2= [ 0.3529 −0.7586 0.5477 ] untuk 𝜆3 = 31.4131 [ 𝜆 − 19 7 12 7 𝜆 − 10 3 12 3 𝜆 − 19 ] [ υ1 υ2 υ3 ] = [ 0 0 0 ] υ3 = [ −0.7286 0.1444 0.6695 ] maka c = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] menghitung nilai eigenface (µ) µ1 = υ 𝑥 ø1 µ1 = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] 𝑥 [ 0 2 − 1 1 2 0 −2 1 2 ] µ1 = [ 1.8102 − 1.1967 − 0.8703 −1.0474 − 2.6435 0.9242 −0.7913 0.7614 1.8404 ] µ2 = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] 𝑥 [ 0 − 1 1 −2 0 0 0 − 2 0 ] µ2 = [ −0.7058 2.0442 − 0.5870 1.5172 0.3466 − 0.6354 −1.0954 − 0.8372 − 0.5018 ] µ3 = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] 𝑥 [ 0 − 1 0 1 − 2 2 2 1 − 2 ] µ3 = [ −1.1044 − 0.8475 2.1631 −0.4698 2.2969 − 1.8060 1.8867 0.0758 − 0.2436 ] 2) proses pengenalan wajah proses pengenalan wajah dilakukan dengan cara mengenali gambar tes, kemudian mencocokkan dengan training image yang telah telah tersimpan. tahap pertama dimulai dari mengubah matriks persegi menjadi flatvector hingga memperoleh nilai eigenface (µ) seperti gambar dibawah ini gambar 5 : gambar test г new = [ 3 2 2 3 0 2 4 3 0 ] → [ 3 2 2 3 0 2 4 3 0] setelah merepresentasikan gambar test ke flatvector, kemudian mencari selisi (ø) antara gambar test dengan nilai mean (ψ). ø new = [ 3 2 2 3 0 2 4 3 0] [ 3 3 2 2 2 2 2 2 2] = [ 0 2 −2 1 2 0 −2 1 3 ] dari nilai selisih, maka nilai eigenface dapat dihitung. µ new = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] 𝑥 [ 0 2 − 2 1 2 0 −2 1 3 ] µ new = [ 1.8102 − 1.1967 − 1.0120 −1.0474 − 2.6435 1.7039 −0.7913 0.7614 3.0120 ] setelah nilai eigenface untuk gambar test diperoleh maka kita bisa melakukan identifikasi dengan menentukan jarak terpendek (ecludian distance) dengan eigenface dari eigenvector training image. ε1 = [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] [ −0.5870 0.3529 − 0.7286 −0.6354 − 0.7586 0.1444 −0.5018 0.5477 0.6695 ] ε1 = √ (0)2 + (0)2 + (−0.1417)2 + (0)2+(0)2 + (0.7797)2+(0)2+(0)2 + (1.1712)2 ε1 = √1.9997 = 1.4141 ε2 = [ 1.8102 − 1.1967 − 1.0120 −1.047 − 2.6435 1.7039 −0.791 0.7614 3.0120 ] [ −0.7058 2.0442 − 0.5870 1.5172 0.3466 − 0.6354 −1.09 − 0.8372 − 0.5018 ] ε2 = √ (1.2197)2 + (10.5034)2 + (0.1806)2 + (6.5772)2+(4.3685)2 + (5.4723)2 + (0.0924)2+(2.5555)2+(12.4171)2 ε2 = √43.386 = 6.5868 ε3 = [ 1.8102 − 1.1967 − 1.0120 −1.047 − 2.6435 1.7039 −0.791 0.7614 3.0120 ] [ −1.1044 − 0.8475 2.1631 −0.4698 2.2969 − 1.8060 1.8867 0.0758 − 0.2436 ] ε3 = √ (2.9146)2 + (0.3492)2 + (−3.1751)2 + (−0.5776)2+(−4.9404)2 + (3.5099)2 + (−2.678)2+(0.6856)2+(3.2556)2 ε3 = √73.999 = 8.6022 dari hasil perhitungan nilai ecludian distance gambar training 1, training 2 dan training 3 terhadap gambar test, maka nilai jarak eigenface http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 102 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional yang terkecil di identifikasikan lebih mirip antara training 1 dengan gambar tes, dibandingakan dengan training 2 dan training 3. pengujian black box setelah aplikasi ini dibuat maka tahap selanjutnya adalah tahap uji coba dari tampilan aplikasi. tahap uji coba ini dimulai dari memasukkan citra wajah sebagai data set, dimana data citra ini nantinya akan di cocokkan dengan data citra wajah yang dilakukan secara real time. untuk lebih jelaskan perhatikan gambar dibawah ini : pengujian menu inputan data mahasiswa tabel 2 : pengujian menu inputan data mahasiswa test factor hasil keterangan menu inputan data mahasiswa √ dosen dapat melakukan penginputan data mahasiswa yang mengikuti mata kuliah pengujian pengambilan citra wajah tabel 3 : pengujian pengambilan citra wajah test factor hasil keterangan proses pengambilan citra wajah √ semua mahasiswa yang mengikuti perkuliahan harus di data terlebih dahulu, bukan cuma data identitas tapi juga data mengenai citra wajah tiap mahasiswa. tabel 4 : pengujian pendeteksian 1 citra wajah test factor hasil keterangan proses pendeteksian 1 citra wajah √ pada tampilan ini sitem dapat mengenali 1 citra wajah secara real time dalam satu kali pendeteksian. pengujian pendeteksian 2 citra wajah tabel 5 : pengujian pendeteksian 2 citra wajah test factor hasil keterangan proses pendeteksian 2 citra wajah √ pada tampilan ini sitem dapat mengenali 2 citra wajah sekaligus secara real time dalam satu kali pendeteksian. pendeteksian 3 citra wajah http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 103 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel 6 : pengujian pendeteksian 3 citra wajah test factor hasil keterangan proses pendeteksian 3 citra wajah √ pada tampilan ini sitem dapat mengenali 3 citra wajah sekaligus secara real time dalam satu kali pendeteksian. pendeteksian ketika proses pembelajaran tabel 7 : pengujian pendeteksian ketika proses pembelajaran test factor hasil keterangan proses pendeteksia n ketika proses pembelajara n √ pada tampilan ini sitem dapat mengenali banyak citra wajah sekaligus secara real time dalam satu kali pendeteksian. pengujian laporan hasil absensi mahasiswa tabel 8 : pengujian laporan hasil absensi mahasiswa test factor hasil keterangan laporan hasil absesnsi √ pada tampilan ini sitem dapat menampilan laporan hasil absensi mahasiswa mahasiswa. tingkat persentase absensi pengenalan wajah untuk tingkat persentase hasil absensi pada pengenalan wajah ini dilakukan dengan uji coba terhadap 10 wajah, dimana tiap wajah tersebut memiliki 100 data set. untuk lebih jelasnya perhatikan tabel pengujian di bawah ini. pengujian citra wajah berdasarkan dari segi pencahayaan tabel 9 : pengujian citra wajah berdasarkan dari segi pencahayaan citra wajah yang diuji pencahayaan citra wajah pencarian terang terdeteksi normal terdeteksi redup terdeteksi terang terdeteksi normal terdetekasi redup terdeteksi terang terdeteksi normal terdeteksi redup terdeteksi terang terdeteksi normal terdeteksi redup tidak terdeteksi terang terdeteksi normal terdeteksi redup terdeteksi http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 104 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional citra wajah yang diuji pencahayaan citra wajah pencarian terang terdeteksi normal terdeteksi redup tidak terdeteksi terang terdeteksi normal terdeteksi redup tidak terdeteksi terang terdeteksi normal terdeteksi redup terdeteksi terang terdeteksi normal terdeteksi redup terdeteksi terang terdeteksi berdasarkan hasil pengujian yang telah dilakukan, maka diperoleh tingkat keberhasilan akurasi citra wajah berdasarkan dari segi pencahayaan terang, normal, dan redup adalah sebagai berikut : terang : 10 10 x 100 % = 100 % normal : 10 10 x 100 % = 100 % redup : 7 10 x 100 % = 70 % total akurasi keseluruhan dari segi pencahayaan adalah 27 30 x 100 % = 90 % pengujian citra wajah berdasarkan dari segi posisi wajah tabel 10 : pengujian citra ajah berdasarkan dari segi posisi wajah citra wajah yang diuji posisi citra wajah pencarian depan terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri tidak terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan tidak terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri tidak terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan tidak terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan terdeteksi depan terdeteksi samping kiri terdeteksi samping kanan terdeteksi berdasarkan hasil pengujian yang telah dilakukan, maka diperoleh tingkat keberhasilan akurasi citra wajah berdasarkan dari segi posisi wajah depan, samping kiri, dan samping kanan adalah sebagai berikut : depan : 10 10 x 100 % = 100 % samping kiri : 8 10 x 100 % = 80 % samping kanan : 8 10 x 100 % = 80 % http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 105 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional total akurasi keseluruhan dari segi posisi wajah adalah 26 30 x 100 % = 86,6 % pengujian citra wajah berdasarkan banyaknya wajah yang terdeteksi tabel 11 : pengujian citra wajah berdasarkan banyaknya wajah yang terdeteksi citra wajah yang diuji citra wajah pencarian 1 wajah terdeteksi 2 wajah terdeteksi 3 wajah terdeteksi banyak wajah terdeteksi aplikasi ini dapat mengenali bukan cuma 1 wajah, tetapi dia bisa mengenali banyak wajah selama wajah tersebut masih bisa terjangkau oleh kamera yang digunakan. simpulan dan saran simpulan berdasarkan dari uji coba sistem yang telah dibuat maka dapat di simpulkan sebagai berikut: sistem absensi yang telah dibuat mampu melakukan proses absensi secara realtime dengan mendeteksi wajah melalui metode pengenalan wajah yaitu eigenface pca (priciple component analysis) dengan tingkat total akurasi keseluruhan dari segi pencahayaan adalah 90 % dan total akurasi keseluruhan dari segi posisi wajah adalah 86,6 %. sistem ini dapat mengenali citra wajah baik dalam posisi lurus maupun menyamping. sistem dapat mendeteksi bukan cuma 1 wajah saja, tetapi sistem dapat mendeteksi semua wajah yang tertangkap kamera. tingkat keberhasilan akurasi sangat dipengaruhi oleh pencahayaan, semakin terang pencahayaan maka tingkat keberhasilan akurasi juga semakin tingggi. dan proses pendeteksian citra wajah juga berpengaruh terhadap jarak citra dengan kamera yang digunakan. saran pencahayaan dan jarak saat pengambilan citra wajah dan pengenalan wajah merupakan faktor yang sangat penting yang akan memepengaruhi keberhasilan akurasi pendeteksian citra. oleh karena itu ketika ingin menggunakan sistem ini dibutuhkan pencahayaan yang baik agar dapat mencapai akurasi yang lebih tinggi, dan juga untuk hasil maksimal sebaiknya menggunakan kamera dengan spesifikasi yang tinggi atau menggunakan kamera cctv. daftar referensi chang, h., & robles, u. (2000). ee368 final project report spring 2000: face detection. stanford. hertyana, h. (2016). pengaruh sistem absensi fingerprint terhadap kinerja karyawan pada pt. deltacomsel indonesia. jurnal teknik komputer, 2(2), 42–48. https://doi.org/10.31294/jtk.v2i2.1614 purnia, d. s., & sumitro, a. (2015). perancangan program absensi siswa realtime menggunakan sms gateway pada sma negeri 69 jakarta. seminar nasional ilmu http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 106 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pengetahuan dan teknologi komputer, 71inf.76. suhery, c., & ruslianto, i. (2017). identifikasi wajah manusia untuk sistem monitoring kehadiran perkuliahan menggunakan ekstraksi fitur principal component analysis (pca). jurnal edukasi dan penelitian informatika (jepin), 3(1), 9. https://doi.org/10.26418/jp.v3i1.19792 yang, m.-h., kriegman, d. j., & ahuja, n. (2002). detecting faces in images: a survey. ieee transactions on pattern analysis and machine intelligence, 24(1), 34–58. https://doi.org/10.1109/34.982883 yusuf, m., ginardi, r. v. h., & ahmadiyah, a. s. (2016). rancang bangun aplikasi absensi perkuliaha n mahasiswa dengan pengenalan wajah. jurnal teknik its, 5(2), a766–a770. https://doi.org/10.12962/j23373539.v5i2.1 7518 http://creativecommons.org/licenses/by-nc/4.0/ microsoft word 1015949202316419916_553-jri-53_379-386_tyas.docx jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.553 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 379 covid-19 social aid admission selection using simple additive weighting method as decision support tyas setiyorini, frieyadie*), aditiya yoga pratama informatika, sistem informasi universitas nusa mandiri jakarta, indonesia tyas.tys@nusamandiri.ac.id, frieyadie@nusamandiri.ac.id, aditiyayogapratama@gmail.com (*) corresponding author abstract the process of receiving covid-19 social assistance to residents who are recorded as social aid recipients in the rt.07 rw.10 kp. sukapura jaya area is still uneven. the second problem is that there is no particular mathematical calculation to determine the value of the weight of the criteria, especially for residents who are recorded as receiving covid-19 social aid in the rt.007 rw.10 kp. sukapura jaya area. the gradual decline in social aid programs so that the number that falls does not match the data of social aid recipients. this caused a polemic for rt administrators in distributing social aid programs. the decline in social aid programs does not match the number of citizens recorded. it overcomes citizens who cause social jealousy—analyzing the problems experienced by the rt management in the distribution of covid-19 social assistance, especially the rt.07 rw.10 kp. sukapura jaya area to residents who are recorded as recipients. selecting covid-19 social assistance recipients, especially in the rt.07 rw.10 kp. sukapura jaya area. so the application of methods as decision support is needed, and it is needed to help determine the weight of particular criteria for citizens who are recorded as more in need. this study proposes a decision support method using the simple additive weighting (saw) method, which is expected to help decision-making in solving problems for selecting covid-19 social aid recipients in the rt.07 rw.10 kp. sukapura jaya community. the purpose of the study is to select residents who are recorded to receive social aid who are more in need first will get covid-19 social aid. keywords: simple additive weighting method; covid-19; social assistance abstrak proses penerimaan bantuan sosial covid-19 kepada khususnya warga yang terdata penerima bansos di wilayah rt.07 rw.10 kp. sukapura jaya masih tidak merata. masalah kedua belum adanya perhitungan matematika khusus untuk menentukan nilai bobot kriteria khususnya warga yang terdata penerimaan bansos covid-19 yang ada di wilayah rt.007 rw.10 kp. sukapura jaya. turunnya bansos secara bertahap sehingga jumlah yang turun tidak sesuai dengan data penerima bansos. sehingga menimbulkan polemik buat pengurus rt dalam mendistribusikan bansos. turunnya bansos tidak sesuai dengan jumlah warga yang terdata dan mengatasi terjadi nya warga yang menimbulkan kecemburuan sosial. menganalisis permasalahan yang dialami pihak pengurus rt dalam pembagian bansos covid-19 khususnya wilayah rt.07 rw.10 kp. sukapura jaya terhadap warga yang terdata penerima. dengan adanya masalah terhadap penyeleksian penerima bantuan sosial covid-19 khususnya di wilayah rt.07 rw.10 kp. sukapura jaya. maka dibutuhkannya penerapan metode sebagai pendukung keputusan dan diperlukannya untuk bisa membantu dalam menentukan bobot kriteria khusus nya para warga yang terdata lebih menbutuhkan. pada penelitian ini mengusulkan metode pendukung keputusan menggunakan metode simple additive weighting (saw), yang diharapkan membantu pengambilan keputusan dalam memecahkan masalah untuk seleksi penerima bansos covid-19 pada masyarakat rt.07 rw.10 kp. sukapura jaya. tujuan penelitian untuk menyeleksian terhadap warga yang terdata menerima bansos yang lebih membutuhkan dahulu akan mendapatkan bansos covid-19. kata kunci: metode simple additive weighting; covid-19; bantuan sosial p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.553 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 380 introduction in indonesia, precisely in the capital city of dki jakarta, many companies laid off workers or worked at home while also being laid off due to the company experiencing a decline, not just workers in the company, but small traders, farmers, online motorcycle taxis and so on also experienced an economic crisis due to lack of income generation. many complain that necessities for daily life are getting more expensive (maleha et al., 2021) than the necessities of life caused by the covid-19 pandemic. for this reason, the dki jakarta provincial government helps its people who are struggling in their economy, so the dki jakarta provincial government distributes covid-19 social assistance consisting of various necessities. this is stated in law no. 1 of 2020, explaining the policy system and economic handling during the covid-19 pandemic (einstein et al., 2020). target bantuan sosial ini, kepada masyarakat yang berekonomi rendah hingga menengah yang mayoritas tinggal diperkampungan contohnya di wilayah rt.07 rw.10 kp. sukapura jaya jakarta utara. in the rt.07 rw.10 kp. area, sukapura jaya is an area consisting of the majority of people with low and medium economies. currently, the process of receiving covid-19 social assistance to especially residents who are recorded as social aid recipients in the rt.07 rw.10 kp. sukapura jaya area is still uneven. so that the receipt of covid-19 social assistance in the region caused a polemic of social jealousy because it was not correctly on target (santoso & suparmadi, 2019). the second problem is that there is no mathematical calculation to determine the weight value, especially for residents who are recorded as receiving covid-19 social aid in the rt.007 rw.10 kp. sukapura jaya area to assess who is more entitled to the assistance first. it is necessary to apply decision support methods to analyze criteria, especially citizens listed as recipients, to assess who is more entitled to the assistance. the goal is to help facilitate the rt management in their duties, select residents who are recorded as needing to get covid-19 social assistance, overcome the occurrence of residents who cause social jealousy, and help research accuracy in selection (jurnal et al., 2018). moreover, the third problem is the gradual decline in social aid programs so that the number that falls does not match the data of social aid recipients (aprilia et al., 2022). so that caused a polemic among rt managers. the simple additive weighting (saw) method determines the weight value of particular criteria and continues the ranking process that will select particular social aid recipients (astika et al., 2018). it is hoped that applying decision support methods with the saw method can help problems or solve problems in selecting social aid recipients so that there is no wrong target, overcome social jealousy (jayawardani & maryam, 2022; rizaldy, 2022), and selecting social aid recipients who need it first in the rt.07 rw.10 kp. sukapura jaya area. the previous research conducted by falentino sembiring et al. (fauzan et al., 2018), discussing the covid-19 social aid system, is still used manually in sundawenang village. this is in the provision of covid-19 social aid is still mistargeted at covid-19 social aid recipients. for this reason, the right solution is to develop a decision support system with the saw method that refers to relevant criteria. similarly, according to joni riadi et al. (rohmatin et al., 2020). discusses the system. the recipients of "raskin" in the alalak sub-district still use subjective assessments or estimates and assumptions. it is feared that this will cause inaccuracy in judging so that raskin does not reach people who need it. therefore, applying decision support using the saw method to solve personal assessment problems with the condition that the criteria for recipients of social assistance (bansos) are determined is the right solution. the saw method can also determine the weight value of specific criteria and continue the ranking process, which will select specific social assistance recipients. this will help overcome the problem of the uneven distribution of covid-19 social assistance in the rt.07 rw.10 kp area. sukapura jaya. this decision support system is expected to help determine raskin beneficiaries so that distribution is not on target. the aims of this research are as follows 1) to help facilitate the task of the rt manager in selecting residents who need covid-19 social assistance and to get the social assistance first. 2) to overcome the emergence of citizen social dissatisfaction caused by the uneven distribution of covid-19 social assistance in the area of rt.07 rw.10 kp. sukapura jaya. 3) to help improve accuracy in selecting recipients of covid-19 social assistance, especially in determining the priority of receiving social assistance for needy residents. materials and methods stages of research the preparation of this research required process steps to achieve the goals that have been set. these steps are depicted in figure 1. jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.553 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 381 figure 1. research steps diagram the following is an explanation of the research steps in figure 1. a. problem identification identifying a persistent problem is the initial stage of the research process. this stage builds on several underlying issues on the background of the problem. b. interview the second step is an interview. the interview stage is carried out face-to-face directly with the parties involved and has a role in providing the information needed. c. questionnaire dissemination the third step is the distribution of questionnaires. the distribution of questionnaires is carried out so that the process of the problem under study provides reciprocity in the form of data filled in by social aid recipients to be examined by the author. d. data collection the fourth step is data collection. in this process, data collection is carried out by filling out questionnaires from parties who receive and filling out questionnaires for data preparation at the next stage. e. data analysis the fifth step is data analysis, and this stage is the process of analyzing data for the needs of the following process in the data processing. f. data processing with the saw method the sixth step is processing, and with the simple additive weighting method, the data processing process is to apply calculations to the saw method to produce calculation data output to determine the ranking under study with the expected results. g. calculation results of the saw method the seventh step is a result of calculating data obtained from the research process that has been carried out. population and research sample the population of residents receiving covid-19 social aid in rt.07 rw.10 kp. sukapura jaya has filled out a selection criteria questionnaire for 150 heads of families who are recorded as recipients and select who gets social aid first. in determining the sample size of the population, the author used the slovin formula with a critical value of 5% and obtained a sample of 110 heads of families (kk) residents receiving social assistance. simple additive weighting (saw) saw is a weighted addition method (much ibnu subroto & kurniadi, 2022; putera et al., 2020). the basis of the concept of this method is to find the sum of the weights of the performance branch on each alternative in all attributes (habibur rahman arjuni & arif senja fitrani, 2022; hutahaean et al., 2022). this method also requires normalizing the decision matrix (x) (marpaung, 2018; pratama & yunita, 2022) to a scale that can be compared with all alternative ratings on each criterion. this method requires the decision maker to determine the weight of each tribute or criterion. the alternative management stages used (in this case, determine the selection of social aid recipients who first covid-19 in rt.07 rw.10 kp. sukapura jaya residents), namely: a. setting an alternative is aᵢ. b. determine the criteria used as a reference in decision-making, namely cⱼ. c. determine each criterion's preference weight or level of importance (w). d. determine the value of the matching branch for each alternative on each criterion. e. make a decision matrix (x) from the table of match branches of each alternative on each criterion. the x value of each alternative (aᵢ) on each criterion (cⱼ) has been determined. f. carry out the process of normalizing the decision matrix (x) to a scale that can be compared with all existing alternative branches on each criterion. g. the result of matrix normalization (rᵢⱼ) forms a normalized matrix (r). h. the final result of the preference value (vᵢ) is obtained from the sum of the multiplication of the normalized matrix row elements (r) with p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.553 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 382 the preference weight (w) corresponding to the matrix column elements (r). i. the ranking process is obtained based on the alternative with the largest to lowest total value to determine the selection of beneficiaries who first received the covid-19 social assistance at rt.07 rw.10 kp.sukapura jaya. because the saw method is one of the methods of the fmadm model, the determination of weights and variable values on each criterion must use fuzzy numbers. the criteria that have been determined are as table 1. table 1. criteria criterion (c) description c₁ age c₂ home ownership status c₃ number of family members c₄ status/type of work c₅ for c₆ deposit savings c₇ expenditure these criteria then determine the importance of the value criteria based on the weight values applied to the fuzzy numbers. table 2 below is the suitability rating for each alternative for each criterion. table 2 fuzzy numbers fuzzy numbers value low 1 currently 2 tall 3 table 2 displays the criteria based on the suitability of each alternative for each predetermined criterion, then the weight of each criterion has been converted to a fuzzy number. age table 3 shows the age criteria are the requirements needed for decision making, based on age. the description of the age value has been converted to fuzzy numbers. table 3 age value age fuzzy numbers value 25 – 30 years low 1 30 – 40 years currently 2 40> tall 3 home ownership status table 4 shows the criteria for home ownership status, which are the requirements for decision-making based on home ownership status. the description of the value of house ownership status has been converted to fuzzy numbers. table 4 assess home ownership status home ownership status fuzzy numbers value private house low 1 contracted currently 2 number of family members table 6 displays the criteria for the number of family members, which are the requirements for decision-making based on the type of work. the description of the value of the number of family members has been converted to fuzzy numbers. table 6. assess the number of family members number of family members fuzzy numbers grade 2-3 person low 1 3-5 people currently 2 five> tall 3 job status/type table 7 displays the criteria for status/type of work is a requirement needed for decision making, based on the type of work. the description of the job type value has been converted to fuzzy numbers. table 7 job type values type of work fuzzy numbers value unemployed/laid off tall 3 farmer/odd worker/trader/driver/security currently 2 private sector employee low 1 income table 8 shows the income criteria are the requirements for decision-making based on income. the description of earnings has been converted to fuzzy numbers. table 8 income value income fuzzy numbers value do not settle low 1 rp. 1.000.000 rp. 2.000.000 currently 2 rp. 2.000.000 rp. 3.000.000 tall 3 deposit savings table 9 displays the criteria for savings deposits which are the requirements for decisionmaking based on savings. the description of savings accounts has been converted to fuzzy numbers. table 9. value of deposit savings income fuzzy numbers value < rp. 1.000.000 low 1 rp. 1.000.000 rp. 2.000.000 currently 2 rp. 3.000.000 rp. 4.500.000 tall 3 jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.553 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 383 expenditure table 10 displays the criteria for expenditure requirements needed for decisionmaking by expenditure. the expenditure description has been converted to fuzzy numbers. table 10 expenditure value income fuzzy numbers value rp. 500.000 – rp. 1.000.000 low 1 rp. 1.000.000 rp. 1.500.000 currently 2 > rp. 2.000.000 tall 3 table 11 shows some of the criteria above, so the decision maker gives a weight value based on the level of importance of the required criteria. the weight value of each criterion is as follows: table 11. criteria importance level criteria ( c ) information c₁ 3 c₂ 2 c₃ 3 c₄ 3 c₅ 3 c₆ 3 c₇ 2 results and discussion result compatibility rating value of each alternative on each criterion table 12 determines the suitability rating of each alternative on each predetermined criterion. table 12. alternative compatibility ratings n am e a ge p o ss es si o n n u m b er o f f am il y t y p es o f jo b s in co m e sa v in gs e xp en se s a1 3 1 2 2 2 2 2 a2 3 1 2 2 2 2 2 a3 3 1 2 2 2 2 2 a4 3 1 1 2 2 2 2 a5 3 2 2 2 2 2 2 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... a106 3 1 1 1 3 3 1 a107 2 1 2 2 1 1 2 a108 2 1 2 2 2 2 3 a109 3 2 2 2 3 2 2 a110 3 1 2 2 3 2 3 decision matrix normalization (x) table 13 presents the results of the decision matrix normalization process (x) to a scale that can be compared with all alternative ratings in each criterion. ��� = ��� ��� ��� = if j is the profit attribute zᵢⱼ = ai and cj match twig values \]z zᵢ = largest of all rating values largest of all matching twig scores on each criterion. table 13. decision matrix normalization value name age possession number of family members types of jobs income savings expenditure needs a1 1 0,5 0,666666667 0,666666667 0,666666667 0,666666667 0,666666667 a2 1 0,5 0,666666667 0,666666667 0,666666667 0,666666667 0,666666667 a3 1 0,5 0,666666667 0,666666667 0,666666667 0,666666667 0,666666667 a4 1 0,5 0,333333333 0,666666667 0,666666667 0,666666667 0,666666667 a5 1 1 0,666666667 0,666666667 0,666666667 0,666666667 0,666666667 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... a106 1 0,5 0,333333333 1 1 1 0,333333333 a107 0,666666667 0,5 0,666666667 0,333333333 0,333333333 0,333333333 0,666666667 a108 0,666666667 0,5 0,666666667 0,666666667 0,666666667 0,666666667 1 a109 1 1 0,666666667 1 1 0,666666667 0,666666667 a110 1 0,5 0,666666667 1 1 0,666666667 1 preference value (vᵢ) table 14 shows the final results of the preference values obtained from the sum of the multiplication of the normalized matrix row elements (r) with the preference weights (w) corresponding to the matrix column elements (r). preference weight: 3, 2, 3, 3, 3, 3, 2. (c1 x 3) + (c2 x 2) + (c3 x 3) + (c4 x 3) + (c4 x 3) + (c5 x 3) + (c5 x 2) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.553 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 384 table 14. test result name age ownership number of family members type of work income deposit savings expenditure needs total a1 3 1 2 2 2 2 1,333333 13,33333 a2 3 1 2 2 2 2 1,333333 13,33333 a3 3 1 2 2 2 2 1,333333 13,33333 a4 3 1 1 2 2 2 1,333333 12,33333 a5 3 2 2 2 2 2 1,333333 14,33333 … … … … … … … … … … … … … … … … … … 108 a108 2 1 2 2 2 2 2 109 a109 3 2 2 3 3 2 1,333333 110 a110 3 1 2 3 3 2 2 based on the calculation results in table 14, we get the ranking results. here is table 15, showing 20 covid-19 social assistance receipts. table 15. data ranking results of recipients of covid-19 social assistance rank no. total 1 46 18 2 47 18 3 79 18 4 84 18 5 8 17,333 6 28 17,33333 7 36 17 8 56 17 9 101 16,33333 10 109 16,33333 11 21 16 12 38 16 13 45 16 14 48 16 15 110 16 16 102 15,33333 17 104 15,33333 18 15 15 19 20 15 20 30 15 … … … … … … 106 44 9 107 73 9 108 97 9 109 86 8 110 92 8 table 15 displays ranking calculation results where each sequence has the same score. even though it has the same score using the saw method, the committee gets priority results for covid-19 social assistance recipients. discussion by highlighting the strengths and advantages of the saw method and carefully evaluating its accuracy, reliability, and validity, the conclusions can show the effectiveness of the saw method as a reliable tool for selecting recipients of covid-19 social assistance in rt.07 rw.10 kp. sukapura jaya. in this respect, the discussion of the effectiveness of the saw method provides a more detailed understanding of the performance and reliability of the method and provides strong support for the research conclusions. conclusion berdasarkan uraian pembahasan penelitian yang telah dilakukan, maka kesimpulan yang dapat ditarik antara lain dengan metode simple additive weighting (saw) dapat melengkapi keputusan dalam penentuan khususnya bagi warga rt.07 rw.10 kp. sukapura jaya, yang tercatat menerima kebutuhan lebih dulu. proses pemilihan penerima bansos covid-19 dilakukan dengan metode ini, dimulai dengan penilaian kriteria kuesioner, pembobotan, pencocokan rating, normalisasi, dan ranking untuk menghasilkan skor dari setiap kriteria bagi warga rt.07 rw.10 kp—sukapura jaya, yang tercatat lebih dulu. hasil perhitungan penerapan metode tersebut adalah peringkat tertinggi hingga terendah yang merupakan hasil akhir pertimbangan pihak tertentu untuk memilih penerima bansos covid-19 yang membutuhkan terlebih dahulu. beberapa saran terkait penerapan metode saw ini dapat dikembangkan seiring dengan perkembangan teknologi yang merinci kebutuhan yang sangat dibutuhkan. pengembangan jurnal riset informatika vol. 5, no. 3 june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.553 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 385 lanjutan mungkin melibatkan penjelajahan teknik dan algoritme yang lebih canggih. misalnya, pendekatan hybrid yang menggabungkan saw dengan metode lain seperti analytical hierarchy process (ahp) atau technique for order of preference by similarity to the ideal solution (topsis) dapat meningkatkan akurasi dan reliabilitas hasil pengambilan keputusan. dengan menerapkan kombinasi yang tepat dari teknikteknik tersebut, diharapkan hasil seleksi penerima bansos covid-19 akan lebih optimal. reference aprilia, y., latifah, l., & ritonga, i. 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(2019). sosial untuk keluarga miskin dengan metoda simple additve weighting (saw). 2019, 4307(february), 21–28. https://doi.org/10.54314/jssr.v2i1.387 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 507 the implementation of moora methods to support refinement decision priority system in information technology fanni rahmah tsani-1*), umi chotijah-2 teknik informatika universitas muhammadiyah gresik kabupaten gresik, indonesia https://umg.ac.id/ fanirahmatsani039@gmail.com-1*), umi.chotijah@umg.ac.id-2 (*) corresponding author abstract maintenance of information and communications technology scoped on the lamongan regency government is the responsibility of the lamongan regency communications and information department. the application of information technology is closely related to the problems that appear, such as communication network interruption/damage. in this case, the report is provided by the user via whatsapp message, and no single point of contact is used for delivery, retard the refinement process and making it difficult for technicians to prioritize refinement. in this study, the authors built a decisionsupporting in order to assist technicians in making priority refinement. the multi-objective optimization based ratio analysis (moora) method is the appropriate method to apply for this study as it allows us to perform the ranking process based on different weighting attributes. the calculation process of the moora method is based on specified criteria and weightings. criteria are the type of damage, risk of a complaint, duration of the claim, and type of service. in one day, the three regional apparatuses with the highest scores are selected and recommendations for prioritized refinement are provided. in this study, we found that samples with high criterion weights and high criterion scores tended to be prioritized over other samples. the results moora calculated show the library service to be the best alternative with a value of 0.396 on ten regional apparatus tested. keywords: decision support system; repair priority; multi-objective optimization based on ratio analysis (moora); information technology abstrak pemeliharaan teknologi informasi dan komunikasi dilingkup pemerintahan kabupaten lamongan merupakan tanggung jawab dinas komunikasi dan informatika kabupaten lamongan. penerapan teknologi informasi tidak terlepas dari permasalahan yang timbul seperti adanya gangguan/kerusakan jaringan komunikasi. dalam hal ini teknisi kesulitan dalam menentukan prioritas perbaikan dikarenakan pelaporan yang diberikan pengguna melalui pesan whattsapp dan tidak digunakan kontak tunggal dalam penyampaiannya, sehingga memperlambat proses penyelesaian perbaikan. pada penelitian ini, penulis membangun suatu sistem pendukung keputusan yang bertujuan untuk membantu teknisi dalam menghasilkan suatu keputusan prioritas perbaikan. metode multi-objective optimization based ratio analysis (moora) adalah metode yang tepat diterapkan pada penelitian ini karena mampu melakukan proses perangkingan berdasarkan atribut bobot yang berbeda. proses perhitungan metode moora berdasakan kriteria dan bobot yang telah ditentukan. kriteria penilaian yang digunakan adalah jenis kerusakan, resiko komplain, lama permintaan, dan jenis pelayanan. dalam satu hari akan dipilih tiga perangkat daerah dengan nilai tertinggi untuk dilakukan rekomendasi prioritas perbaikan. dalam penelitian ini ditemukan bahwa sample dengan nilai kriteria yang tinggi dengan bobot kriteria yang tinggi cenderung mendapatkan prioritas yang lebih dibandingkan sample yang lain. hasil perhitungan moora menunjukkan dinas perpustakaan sebagai alternative tertinggi dengan nilai 0,396 pada sepuluh perangkat daerah yang diuji coba. kata kunci: sistem pendukung keputusan; prioritas perbaikan; multi-objective optimization based on ratio analysis (moora); teknologi informasi p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 508 introduction communication and informatics department of lamongan regency, is the regional apparatus which responsible for the maintenance of information and communication technology device for all regional apparatus that use communication networks and data exchange in carrying out their duties. the application of information technology cannot be separated from the problems that arise from disruption to information technology service resulting in service interruptions that could be affecting the performance of lamongan regency government. when performing their duties, technicians have faced problems in determining the priority of each issue in regional apparatus. furthermore, repair requests are not handled by a single contact person. therefore, it makes the data collection unorganized and slowing down response time (santoso, wijaya, & nugraha, 2019). to fulfill that needs, a decision support system is recommended to assist decision maker for prioritizing requests from the regional apparatus. a decision support system is defined as a computerized system that used to facilitate decision making (risykiyana, rosyid, chotijah, & mar’i, 2022). using a decision support system helps user make decisions (yunus & senung, 2021). currently, the lamongan department communications and information department needs an effective and efficient decision support system to expedite repairs. this study uses one of the multi-criteria decision making (mcdm) methods, namely the multi-objective optimization based on ratio analysis (moora) with the consideration of being able to carry out the process simultaneously optimizing two or more conflicting attributes (maharrani & somantri, 2020) where the attributes can be profitable (benefit) or unprofitable (cost) (fadli & imtihan, 2019) and can provide a better alternative assessment than other methods and carry out an easy and fast ranking process (pane & erwansyah, 2020). several studies applying the moora method were conducted in pt. indonesia comnets plus sbu regional sumbagsel that determining the level of urgency to improve the damaged towers is still being done manually. to determine the severe damage using moora (abdurrasyid, nugroho, dakhlan, arman, & mahayana, 2022), the same thing is applied to the priority of selecting tower construction areas because the high cost of building a tower is the reason for providers to be selective and right on target in determining the location of tower construction using the ahp method and moora (pane & erwansyah, 2020). from several studies that have been carried out, the data used is data that no longer has been updated, so the data cannot experience re-versioning of the running time series. this research provides objective, fast, and transparent input or recommendations in determining priorities for improving information technology so that the decisions to be taken will be effective and appropriate (pane & erwansyah, 2020). research methods types of research this research belongs to qualitative research. time and place of research this research was carried out from march 2022 to april 2022. the research was carried out at the lamongan regency communication and informatics department in the informatics application field, which is located on jalan kh. ahmad dahlan, lamongan regency. research target / subject this research targeted the efficiency of decision priority making. procedure 1. identification of problem often technicians have difficulty in determining refinement priorities due to the reporting that users provide via whatsapp messages and not using a single contact in their delivery, thus slowing down the refinement completion process. in this study, the authors built a decision support system that aims to assist technicians in making a priority refinement decision. the multiobjective optimization based on ratio analysis (moora) method is the right method to be applied to this study because it is able to carry out a ranking process based on different weight attributes so that the improvement priority results obtained are optimally and appropriate. 2. data, instruments, and data collection techniques this study used repair submission data from regional apparatus at the lamongan district communication and information department. in one day, 3 local officials will be selected for repairs. priority improvement activities need to be carried jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 509 out within regional apparatus so that decisions to be taken are more effective and optimal. the criteria used in carrying out priority repairs are the type of damage, the risk of complaints, the length of request, and the type of service. the followings are the techniques used for data collection: a) field research the research was carried out by direct observation of the problem to be studied and taking the data needed for research at the lamongan regency communication and information service. b) literature research previous research related to journal topics and used as a reference source. research conducted by (abdurrasyid et al., 2022) at pt. indonesia comnets plus sbu regional sumbagsel determines the level of urgency of repairs in towers using the moora method with 100% accuracy results, the same is also applied (pane & erwansyah, 2020) by applying the ahp and moora methods as determining the weight of the criteria and the best alternative to be selected in the selection of tower construction sites that have a level of accuracy at the seven locations tested. other research was also conducted by (akmaludin, sihombing, dewi, rinawati, & arisawati, 2021) testing conducted with the moora method in collaboration with the price-quality ratio approach, the results obtained were the selection of objectbased software applications. which can be done optimally and provide efficiency in the benefits and costs incurred. from the many studies used as reference sources, no decision support system has been found using versioning-type data. 3. data processing from filling out the repair form, the following sample data is obtained: table 1. the repair data regional apparatus type of damage the risk of complain demand hour type of services kec. maduran local network level 2 1 day public services kec. sekaran local network level2 10 minute public services gedung pkk internet network level 5 >24 our management kec. pucuk software level 2 2 our public services kec. brondong hardware level 2 1,5 our public services bakesbangpol internet network level 4 24 our management gedung dprd internet network level 3 1 our management dinas perpustakaan local network level 1 12 our public services dispora internet network level 3 1 our management inspektorat pc level 3 4 our management 4. data analysis the multi-objective optimization method based on ratio analysis (moora) is an algorithm that optimizes two or more conflicting attributes simultaneously (sunardi, fadlil, & fitrian pahlevi, 2021) as well as a method used to optimize the ranking of a number of alternatives with several stages based on ratio analysis (akmaludin, sihombing, dewi, rinawati, & arisawati, 2021). the first algorithm is to input the value of the criteria where the value of the criteria in an alternative is the value that will later be processed and the result becomes a decision. the criteria values are then converted into a decision matrix that defines the rows of data. the form of the matrix in question can be seen in equation 1. 𝑋 = 𝑥𝑖𝑗 = [ 𝑥𝑖𝑗 ⋯ 𝑥𝑖𝑛 ⋮ ⋮ ⋮ 𝑥𝑚1 ⋯ 𝑥𝑚𝑛 ] ............................................... (1) in this equation, the data takes the form of rows and columns. in equation (1) 'i' represents the number of rows and 'j' represents the number of columns. 'm' is the alternative and the 'n' is the number of attributes. the next process is normalization in the moora algorithm to unite each element of the matrix so that the elements on the matrix have a uniform value. normalization of the matrix can be seen in equation 2. 𝑋𝑖𝑗 ∗ = 𝑥 √∑ 𝑥𝑖𝑗 2∗ 𝑖=1 ...................................................................... (2) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 510 equation (2) is obtained by dividing alternative values by square roots and alternate quadratic quantities the normalization that has been carried out is then continued by reducing the values of max and min-max to indicate that an attribute is more important multiplied by the corresponding weight, as depicted in equation 3. 𝑌𝑖 ∗ = ∑ 𝑤𝑗𝑋𝑖𝑗 ∗𝑔 𝑗=1 −∑ 𝑤𝑗𝑋𝑖𝑗 ∗𝑛 𝑗=𝑔+1 .................................. (3) equation (3) aims the summary of benefit attribute 'j' to 'g' and then reduces the cost attribute iteratively 'g+1' until 'n' for each alternative 'i'. yi is the preference value and w is the weight. the final value of the calculation uses equation 3 to determine the ranking of the moora calculation results with the highest ranking value being the highest preference value. results and discussion in determining the selection of priorities for information technology improvements, a method was needed to assist in the determination of the regional apparatus whose damage was repaired, a decision support system was needed to find out which regional apparatus was prioritized for repairs. the method used in the improvement priority is the multi-objective optimization based on ratio analysis (moora) method. the algorithm to be used in the process of prioritizing information technology improvements can be seen in figure 1. figure 1. pseudocode calculation moora the process of calculating the moora method began by giving weight to each criterion, then a suitability rating was generated to form a decision matrix and carried out normalization of the decision matrix. after normalization, attribute optimization was performed by including weights. benefit optimization value (max) minus cost optimization value (min). the biggest optimization result showed that the alternative was prioritized. in the moora method, there were criteria as an assessment process to determine priority improvements. the criteria used in the repair priority were the type of damage (c1), the risk of complaints (c2), the time of request (c3), and the type of service (c4). the alternative selection is shown in table 2. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 511 table 2. alternative selection alternative criteria c1 c2 c3 c4 kec. maduran local network level 2 1 day public services kec. sekaran local network level2 10 minute public services alternative criteria c1 c2 c3 c4 gedung pkk internet network level 5 >24 our management kec. pucuk software level 2 2 our public services kec. brondong hardware level 2 1,5 our public services bakesbangpol internet network level 4 24 our management gedung dprd internet network level 3 1 our management dinas perpustakaan local network level 1 12 our public services dispora internet network level 3 1 our management inspektorat pc level 3 4 our management furthermore, the determination of criteria and weights in accordance with predetermined qualifications is indicated in table 3. table 3. criteria and quality criteria description quality type c1 type of damage 0,14 benefit c2 the risk of complain 0,29 benefit c3 demand hour 0,21 benefit c4 type of services 0,36 benefit after knowing the alternative determination, then determine the quantitative value of the criteria on each alternative. the weight of the criteria uses the proposed approach (annisaa, anugrah, & devi, 2022). the criteria used are as follows ; the type of malfunction (c1) is data sourced from the request. with the type of criteria that are of the benefit type, where if the vulnerability or damage is higher, it has a high level of assessment. the rating is in table 4. table 4. value of the risk of damage type of damage description value internet network very high 5 local network high 4 hardware enough 3 pc low 2 software very low 1 the risk of complaint (c2) is the risk of complaints coming from the user. the criteria are of the benefit type, where if the risk of the complaint is high, it has a high level of assessment. the rating is in table 5. table 5. the value of risk complain the risk of complaint description value level 1 very high 5 level 2 high 4 level 3 enough 3 level 4 low 2 level 5 very low 1 the request hour (c3) in this case is the time it takes to make repairs. the criteria are of the benefit type, where if the time required is a lot, the assessment given is high. the rating is in table 6. table 6. the value of demand hour demand hour description value >24 hour very high 5 10 24 hour high 4 3-10 hour enough 3 30 minutes – 3 hours low 2 0 – 30 minutes very low 1 type of service (c4) is a service contained in the regional apparatus with the type of benefit criteria, where if the type of public service there is damaged, the value provided is high from management services. the rating is in table 7. table 7. type of service value demand hour description value public service high 2 management moderate 1 if the value of each criterion has been determined, then create a matching rating table as in table 8. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 512 table 8. the alternate match rating alternative criteria c1 c2 c3 c4 kec. maduran 4 4 5 2 kec. sekaran 4 4 1 2 gedung pkk 5 1 5 1 kec. pucuk 1 4 2 2 kec. brondong 3 4 1 2 bakesbangpol 5 2 4 1 gedung dprd 5 3 2 1 dinas perpustakaan 4 5 4 2 dispora 5 3 2 1 inspektorat 2 3 3 1 furthermore, the application of the moora method was carried out in selecting improvement priorities so as to produce the best alternative that could be chosen and recommended (hendrayana & mahendra, 2019) to prioritize the improvement. after the results of the suitability rating in table 8 are transformed into the x matrix as follows: 𝐗 = ( 𝟒 𝟒 𝟒 𝟒 𝟓 𝟏 𝟏 𝟒 𝟑 𝟒 𝟓 𝟐 𝟓 𝟑 𝟒 𝟓 𝟓 𝟐 𝟑 𝟑 𝟓 𝟐 𝟏 𝟐 𝟓 𝟏 𝟐 𝟐 𝟏 𝟐 𝟒 𝟏 𝟐 𝟏 𝟒 𝟐 𝟐 𝟑 𝟏 𝟏) the approach taken to the moora method in the matrix normalization process is obtained from the denominator, the best choice is the square root of the sum of the squares and each alternative per attribute (agustina & sutinah, 2022), matrix normalization is used to calculate the number of alternatives and the number of criteria (wardani, parlina, & revi, 2018). the calculation of normalization is by dividing each alternative by the root value of the sum of the alternative values for each criterion that has been raised to the first power. the following is an example of calculating matrix normalization: 𝐴11 = 4 √42 + 42 + 52 +12 + 32 + 52 +52 + 42 + 52 +22 = 0,314 𝐴21 = 4 √42 + 42 + 52 +12 + 32 + 52 +52 + 42 + 52 +22 = 0,314 in the same way, do it for all alternative c1 and other criteria so that the results are obtained as in table 9. table 9. normalization results alternative criteria c1 c2 c3 c4 kec. maduran 0,314 0,364 0,488 0,400 kec. sekaran 0,314 0,364 0,098 0,400 gedung pkk 0,393 0,091 0,488 0,200 alternative criteria c1 c2 c3 c4 kec. pucuk 0,079 0,364 0,195 0,400 kec. brondong 0,236 0,364 0,098 0,400 bakesbangpol 0,393 0,182 0,390 0,200 gedung dprd 0,393 0,273 0,195 0,200 dinas perpustakaan 0,314 0,455 0,390 0,400 dispora 0,393 0,273 0,195 0,200 inspektorat 0,157 0,273 0,293 0,200 optimizing the criteria for each alternative is given an importance value, provided that the maximum criteria type weight value is greater than the minimum criteria quality (ferdian & chotijah, 2022). to get the results of the optimization calculations, it was done by means of the results of the matrix normalization multiplied by the weights that had been determined for each criterion (siregar, poningsih, & safii, 2018). the results of optimization calculations can be seen in table 10. table 10. the result of optimization alternative criteria c1 c2 c3 c4 kec. maduran 0,044 0,105 0,102 0,144 kec. sekaran 0,044 0,105 0,020 0,144 gedung pkk 0,055 0,026 0,102 0,072 kec. pucuk 0,011 0,105 0,041 0,144 kec. brondong 0,033 0,105 0,020 0,144 bakesbangpol 0,055 0,053 0,082 0,072 gedung dprd 0,055 0,079 0,041 0,072 dinas perpustakaan 0,044 0,132 0,082 0,144 dispora 0,055 0,079 0,041 0,072 inspektorat 0,022 0,079 0,061 0,072 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.472 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 513 the preference value is obtained by calculating the maximum and minimum values, namely by adding up the value of the benefit and cost criteria. max is the criterion for the type of benefit and min is the criterion for the type of cost (alisia, ginting, & syari, 2021). in this study there are only types of benefit criteria, the calculation results can be seen in table 11. table 11. rankings alternative max min yi kec. maduran 0,396 0 0,396 kec. sekaran 0,314 0 0,314 gedung pkk 0,256 0 0,256 alternative max min yi kec. pucuk 0,301 0 0,301 kec. brondong 0,303 0 0,303 bakesbangpol 0,262 0 0,262 gedung dprd 0,247 0 0,247 dinas perpustakaan 0,402 0 0,402 dispora 0,247 0 0,247 inspektorat 0,235 0 0,235 after calculating the preference value, the result of the highest preference value is the best alternative. the results of the ranking can be seen in table 12. tabel 12. rankings alternative result ranking dinas perpustakaan 0,396 1 kecamatan maduran 0,314 2 kecamatan sekaran 0,256 3 kecamatan brondong 0,301 4 kecamatan pucuk 0,303 5 bakesangpol 0,262 6 gedung pkk 0,247 7 gedung dprd 0,402 8 dispora 0,247 9 inspektorat 0,235 10 based on the analysis that has been carried out using the moora method, the highest value calculation results are shown in table 12, rank 1 is obtained at the library service alternative with the type of damage to the internet network, the level of damage is 1, with a request time of 12 hours, and the type of service is public service. conclusions and suggestions conclusion prioritization of information technology improvement using the moora method can be applied properly and optimally because it produces index values on all alternatives. of the ten regional devices tested by the library service showed the highest priority results with a value of 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(2021). sistem pendukung keputusan seleksi penerimaan bantuan koperasi dengan penerapan metode moora berbasis android pada dinas tenaga kerja, koperasi dan ukm kota gorontalo. prosiding semmau 2021, 186–195. jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 111 implementation of the fuzzy tsukamoto method for office stationery estimation stock system tedy rukmana-1*), hersanto fajri-2, dahlia widhyaestoeti-3 fakultas teknik & sains, program studi teknik informatika, konsetrasi sistem informasi, universitas ibn khaldun bogor, indonesia https://uika-bogor.ac.id/ tedyrukmana1502@gmail.com-1*), hersanto.fajri@gmail.com-2, dahlia@uika-bogor.ac.id-3 (*) corresponding author abstract information technology supports the company's operational activities in recording incoming goods, outgoing goods, and existing inventory. pt. mitraniaga distribusindo is a company engaged in food distribution, which includes supporting aspects for smooth operations, such as stock availability of office stationery. the tsukamoto fuzzy method aims to calculate the estimated stock of office stationery needed to provide recommendations for deciding the estimated stock. the estimated target variable is more numerical, with models built using complete records providing the variable value of the target as a predictive value. testing the accuracy of the calculation results of the office stationery stock estimation system was conducted using the root mean squared error (rmse) to get a value of 5.9 to make the decisions more precise. in application development, using the waterfall model, which provides a sequential or sequential software life flow approach starting from design analysis, coding, and up to application testing using black box testing that is produced, each test form produces an appropriate status, according to the expected results. keywords: fuzzy tsukamoto; root mean squared error; estimation stock system abstrak teknologi informasi mempunyai peranan penting untuk menunjang seluruh aktifitas operasional perusahaan dalam mencatat barang masuk, barang keluar serta persediaan barang yang ada. pt. mitraniaga distribusindo merupakan salah satu perusahan yang bergerak dibidang distribusi makanan yang tentu didalamnya terdapat aspek pendukung untuk kelancaran operasional seperti ketersediaan stok alat tulis kantor. metode fuzzy tsukamoto bertujuan untuk melakukan perhitungan estimasi stok alat tulis kantor yang dibutuhkan mendatang sehingga mampu memberikan rekomendasi dalam memutuskan jumlah estimasi stock alat tulis kantor. variabel target estimasi lebih kearah numerik dengan model yang dibangun menggunakan record lengkap menyediakan nilai variabel dari target sebagai nilai prediksi. uji keakuratan hasil perhitungan sistem estimasi stok alat tulis kantor dilakukan menggunakan root mean squared error (rmse) mendapatkan nilai 5,9 sehingga keputusan yang diperoleh lebih tepat. dalam pengembangan aplikasi menggunakan model waterfall yang menyediakan pendekatan alur hidup perangkat lunak secara sekuensial atau terurut dimulai dari analisis desain , pengkodean dan sampai pengujian aplikasi menggunakan blackbox testing yang dihasilkan setiap form uji menghasilkan status sesuai, sesuai dengan hasil yang di harapkan. kata kunci: fuzzy tsukamoto; root mean squared error; sistem estimasi stok introduction science and technology are currently developing very rapidly, especially in the field of information technology, and the development of information technology is very beneficial for developments, one of which is in the field of trade (haryati, 2012). information technology is important in supporting all company operations to record incoming, outgoing, and inventory of goods such as pt. mitraniaga distribusindo is a company engaged in the food distribution sector, which mailto:tedyrukmana1502@gmail.com-1 mailto:hersanto.fajri@gmail.com p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 112 includes aspects that support smooth operations, such as the availability of stationery stock. the importance of stock availability is one factor that affects the smooth operation of a company in which administrative needs need special attention, as is the case in determining the need for office stationery stock. the estimation system can provide recommendations for deciding the number of stock receipts using the tsukamoto fuzzy method to make decisionsore precise and computerized and prevent subjective decisionmaking (novianti puspitasari, andi tejawati, & friendy prakoso, 2019). tsukamoto's fuzzy logic reasoning provides a way to understand system performance by assessing system input and output from observations. the research (hendi setiawan, 2020) explains that fuzzy logic is a technique/method used to deal with uncertainties in problems with many answers. if you look at fuzzy logic, it is easy to understand, and fuzzy logic uses basic set theory. fuzzy logic is a group set representing a certain state in a variable. an application is needed that can simplify performance and solve problems related to inventory processing, and also the method used is the fuzzy logic method. in research conducted by (prayogi & santoso, 2018), the tsukamoto method is an extension of monotone reasoning. in the tsukamoto method, each consequence of a rule in if-then must be represented by a fuzzy set with a monotonous membership function. as a result, the output of the inference results from each rule is given strictly (crisp) based on the α predicate (fire strength). the final result is obtained by using a weighted average. based on the accuracy test results, the error value obtained from the small forecasting results is 0.0607%. in other research (huda, 2018), controlling raw material stocks is an activity that a company or agency always carries out with the constraint that often occurs in raw material stocks. when the demand for raw material quantities is not correct, it can result in running out of raw materials, impacting cafe operations. the implementation of fuzzy tsukamoto is widely used to predict or predict the future, such as the use of fuzzy tsukamoto in predicting the availability of teak wood based on the variables that affect it. the application of fuzzy tsukamoto in controlling warehouse stock recommends purchasing goods based on consumer needs using the tsukamoto method. office stationery is a supporting factor for smooth operational activities. office stationery is objected that is used up in the daily work of administrative employees. (m. ramaddan julianti, muhammad iqbal dzulhaq, & ahmad subroto, 2019) the waterfall model is a system development model for this research. the waterfall model provides a sequential or sequential software life-flow approach starting from analysis, design, coding, and testing. the tsukamoto fuzzy method aims to calculate the estimated stock needed in the future to provide recommendations for the estimated amount of office stationery stock. the estimated target variable is more numerical, with models built using complete records providing the variable value of the target as a predictive value. building a webbased estimation system helps determine the estimated amount of office stationery stock needed by implementing the tsukamoto fuzzy method. the accuracy of the calculation results of the office stationery stock estimation system is tested using root mean squared error (rmse). in application development, the waterfall model provides a sequential or sequential software life flow approach starting from design analysis and coding until the application testing using black box testing. research (huda, 2018) shows that requests for the wrong amount of raw materials can result in running out of raw materials, impacting operational disruptions. there needs to be an appropriate raw material management system to fulfil stock availability in the warehouse. the system to be designed uses the tsukamoto fuzzy method. in testing the accuracy of the system also uses rmse (root mean square error). the results of the tsukamoto fuzzy test on 25 training data with sales parameters, expiration date, demand, and yield 0.78%. research on implementation of fuzzy tsukamoto method for office stationery estimation stock system. designed to calculate the estimated stock needed for the future so that it can provide advice when determining the estimated amount of office stationery stock. research methods this study uses a qualitative method approach. to obtain data by interviewing parties related to this research. describe the meaning of research facts through the interview and observation stages of participation and explain the facts that occur in the field (muhammad rijal fadli, 2021). the research methodology can be seen in figure 1. jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 113 figure 1. research methods time and place of research this research was conducted at pt. mitraniaga distribusindo starts from august 2022 to october 2022. research target / subject in this research, the target/subject is the admin head, who acts as the person in charge of the operational office stationery stock, and then the interview stages are carried out in the formation of data, variables, and criteria. procedure based on the research method described in figure 1, where the initial stage is analysis. analysis of business processes begins with observation, interview stages, and the application of literature studies resulting in output in the form of variable data, criterion data, and alternative data, which is then carried out by the fuzzy tsukamoto algorithm to produce recommendation values. in the next stage, the results of tsukamoto's fuzzy calculations built a program with the application of object-oriented programming through the unified modeling language (uml) design process to produce use cases, activities, sequences, classes, and deployment diagrams. the next stage is coding in implementing web-based programs using the php programming language, writing code using visual studio, databases using mysql, and web browsers using chrome. furthermore, at the testing stage, testing the results of estimation calculations uses the tsukamoto fuzzy method to find out how accurate the stock inventory estimation is. carried out by root mean squared error (rmse) and black box testing provides test results for the suitability of the application with the function or functional capability of the system. this testing phase is carried out to find out how the results of the system design are to be able to find out deficiencies and what things need to be fixed for further development. data, instruments, and data collection techniques in this study, the data formed will then be processed for tsukamoto fuzzy calculations, for the p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 114 criteria can be seen in table 1. the formation of fuzzy sets can be seen in table 2. table 1. kriteria data no variable statement 1 permintaan office stationery expenditure data 2 stock office stationery inventory data 3 estimasi office stationery stock estimation data table 2 fuzzy function function variable fuzzy set universal set domain input permintaan sedikit 10-80 10 s/d 40 banyak 40 s/d 80 stock sedikit 10-80 10 s/d 40 banyak 40 s/d 80 output estimasi berkurang 10-80 10 s/d 40 bertambah 40 s/d 80 alternative data is office stationery data contained in pt. mitraniaga distribusindo. the instrument is to interview the admin head, who is in charge of office stationery operations. in order to obtain data on the criteria variable for implementing fuzzy through the stages of fuzzification, fuzzy inference, and defuzzification. data analysis technique with fuzzy tsukamoto the tsukamoto fuzzy algorithm then processes the data obtained by forming variables and fuzzy sets beforehand. as an example of the office stationery stock data that will be processed in this case, the variable values obtained consist of requests 60 and stock 47 and the estimated value to be searched for 1. fuzzification after obtaining the next variable is the fuzzification process of forming curves, sets, and membership functions for each variable. a. permintaan there are 2 sets : sedikit and banyak figure 2 permintaan curve figure 2 is a process of fuzzification of the permintaan variable, which consists of sets of sedikit and banyak, showing the degree of membership value resulting from the calculation as follows: the membership function : 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝑆𝑒𝑑𝑖𝑘𝑖𝑡 [𝑥] = { 1, 𝑥 ≤ 10 80−𝑥 70 , 10 ≤ 𝑥 ≤ 80 0, 𝑥 ≥ 80 .................................................................................................. (1) 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝐵𝑎𝑛𝑦𝑎𝑘 [𝑥] = { 0, 𝑥 ≤ 10 𝑋−10 70 , 10 ≤ 𝑥 ≤ 80 1, 𝑥 ≥ 80 .................................................................................................. (2) the value of the degree of membership is obtained : 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝑆𝑒𝑑𝑖𝑘𝑖𝑡 [60] = 80−60 70 = 20 70 = 0,285714286 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝐵𝑎𝑛𝑦𝑎𝑘 [60] = 60−10 70 = 50 70 = 0,714285714 b. stock there are 2 sets : sedikit and banyak figure 3 stock curve jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 115 figure 3 is a process of fuzzification of the stock variable, which consists of sets of sedikit and banyak, showing the degree of membership value resulting from the following calculations : the membership function : 𝜇𝑆𝑡𝑜𝑐𝑘𝑆𝑒𝑑𝑖𝑘𝑖𝑡 [𝑦] = { 1, 𝑦 ≤ 10 80−𝑦 70 , 10 ≤ 𝑦 ≤ 80 0, 𝑦 ≥ 80 (3) 𝜇𝑆𝑡𝑜𝑐𝑘𝐵𝑎𝑛𝑦𝑎𝑘 [𝑦] = { 0, 𝑦 ≤ 10 𝑦−10 70 , 10 ≤ 𝑦 ≤ 80 1, 𝑦 ≥ 80 ...... (4) the value of the degree of membership is obtained : 𝜇𝑆𝑡𝑜𝑐𝑘𝑆𝑒𝑑𝑖𝑘𝑖𝑡 [47] = 80−47 70 = 33 70 = 0,471428571 𝜇𝑆𝑡𝑜𝑐𝑘𝐵𝑎𝑛𝑦𝑎𝑘 [47] = 47−10 70 = 37 70 = 0,528571429 c. estimasi there are 2 sets : berkurang and bertambah figure 4 estimasi curve figure 4 is a process of fuzzification of the estimated variable. the value to be searched for consists of a "berkurang" and "bertambah" set; the final value will show the value that will enter the "berkurang" or "bertambah" set, and the resulting degree of membership value in figure 4 is generated from the following calculations : 𝜇𝐸𝑠𝑡𝑖𝑚𝑎𝑠𝑖𝐵𝑒𝑟𝑘𝑢𝑟𝑎𝑛𝑔 [𝑧] = { 1, 𝑧 ≤ 10 80−𝑧 70 , 10 ≤ 𝑧 ≤ 80 0, 𝑧 ≥ 80 ...................................... (5) 𝜇𝐸𝑠𝑡𝑖𝑚𝑎𝑠𝑖𝐵𝑒𝑟𝑡𝑎𝑚𝑏𝑎ℎ [𝑧] = { 0, 𝑧 ≤ 10 𝑧−10 70 , 10 ≤ 𝑧 ≤ 80 1, 𝑧 ≥ 80 ...................................... (6) 2. fuzzy inference this stage is the process of forming the rules (rules) that will be used, and the following are the rules in this case : a. [r1] if permintaan sedikit and stock banyak then estimasi berkurang. b. [r2] if permintaan sedikit and stock sedikit then estimasi berkurang. c. [r3] if permintaan banyak and stock sedikit then estimasi bertambah. d. [r4] if permintaan banyak and stock banyak then estimasi bertambah. the value obtained in the rule ; [r1] if permintaan sedikit and stock banyak then estimasi berkurang. 𝛼 − 𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡1 = 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝑆𝑒𝑑𝑖𝑘𝑡 ∩ 𝜇𝑆𝑡𝑜𝑐𝑘𝐵𝑎𝑛𝑦𝑎𝑘 = 𝑚𝑖𝑛 (0,285714286; 0,714285714) = (0,285714286) 80 −𝑍 70 = 0,285714 𝑍1 = 60 [r2] if permintaan sedikit and stock sedikit then estimasi berkurang. 𝛼 − 𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡2 = 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝑆𝑒𝑑𝑖𝑘𝑡 ∩ 𝜇𝑆𝑡𝑜𝑐𝑘𝑆𝑒𝑑𝑖𝑘𝑖𝑡 = 𝑚𝑖𝑛 (0,285714286 ; 0,471428571) = (0,285714286) 80 − 𝑍 70 = 0,285714286 𝑍2 = 60 [r3] if permintaan banyak and stock sedikit then estimasi bertambah. 𝛼 − 𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡3 = 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝐵𝑎𝑛𝑦𝑎𝑘 ∩ 𝜇𝑆𝑡𝑜𝑐𝑘𝑆𝑒𝑑𝑖𝑘𝑖𝑡 = 𝑚𝑖𝑛 (0,714285714 ; 0,471428571) = (0,471428571) 𝑍 −10 70 = 0,471428571 𝑍3 = 43 [r4] if permintaan banyak and stock banyak then estimasi bertambah. 𝛼 − 𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡4 = 𝜇𝑃𝑒𝑟𝑚𝑖𝑛𝑡𝑎𝑎𝑛𝐵𝑎𝑛𝑦𝑎𝑘 ∩ 𝜇𝑆𝑡𝑜𝑐𝑘𝐵𝑎𝑛𝑦𝑎𝑘 = 𝑚𝑖𝑛 (0,714285714 ; 0,528571429) = (0,528571429) 𝑍 −10 70 = 0,528571429 𝑍4 = 47 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 116 3. defuzzification defuzzification is the final stage of calculation using a weighted average. so that the following values are obtained : 𝑧 = 𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 1 ∗𝑧1+𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 2 ∗𝑧2+𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 3 ∗𝑧3+𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 4 ∗𝑧4 𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 1 +𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 2 +𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 3 +𝛼𝑝𝑟𝑒𝑑𝑖𝑘𝑎𝑡 4 𝑧 = 79,4 1.57142857 = 50,5272728 the result of the tsukamoto fuzzy calculation is 50.5272728, rounded up to 51 with additional estimation information. next, an accuracy test using the rmse is carried out. the following is a table of the results of the rmse test on the results of accuracy of the results of the calculation of the office stationery stock estimation system using the root mean squared error method. table 3 root mean squared error no nama alternatif manual sistem error square of error n i yi' yi yi'-yi ( yi' -yi)^2 1 amplop coklat f4 50,9615 42,45 8,5 72,4 2 bantex besar 30 30 0,0 0,0 3 bantex kecil 30 30 0,0 0,0 4 binder clip no. 200 47,7344 44,655 3,1 9,5 5 binder clip no. 155 57,7556 70 -12,2 149,9 6 binder clip no. 105 39,0909 33,333 5,8 33,2 7 binder clip no. 107 47,6923 46,667 1,0 1,1 8 binder clip no. 220 49,8983 46,833 3,1 9,4 9 double tape 46,2727 47 -0,7 0,5 10 ballpoint biru 41,1818 39,333 1,8 3,4 11 ballpoint hitam 45,9706 43,515 2,5 6,0 12 ballpoint merah 33,3333 30 3,3 11,1 13 box file (bindex/gema) 16 10 6,0 36,0 14 buku besar 35,6889 36,8 -1,1 1,2 15 buku kecil 37,451 35,385 2,1 4,3 16 buku pencatatan ekspedisi 54,5455 70 -15,5 238,8 17 buku pencatatan km 53,2727 58 -4,7 22,3 18 business file 47,5455 46 1,5 2,4 19 cutter kecil 10 10 0,0 0,0 20 dudukan lakban 28 28 0,0 0,0 21 isi cutter kecil 15 10 5,0 25,0 22 isi cutter besar 30 30 0,0 0,0 23 isi straples kecil 78 70 8,0 64,0 24 isi straples besar 40,0862 35,625 4,5 19,9 25 isolatip 12mm x 72 18,9744 10 9,0 80,5 26 kertas 3 play full 25,1136 10 15,1 228,4 27 kertas 3 play prs 33,1 30,4 2,7 7,3 28 kertas 4 play 48,8871 47,074 1,8 3,3 29 kertas a4 49,8983 46,833 3,1 9,4 30 kertas karbon 38 36 2,0 4,0 total 1043,462465 n 30 rmse 5,897633607 dibulatkan 5,9 the table presents a process for calculating the root mean squared error (rmse) where a comparison is made where manual calculations with system calculations are tested so that the total rmse obtained in the calculation of the office stationery stock estimation system is 5.897633607 rounded up to 5.9. jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 117 results and discussion tsukamoto fuzzy calculations produce a recommendation value from the process that starts with strict numbers as input. fuzzy numbers are formed through fuzzification, and then fuzzy sets are grouped through an inference process. from the inference results, a defuzzification process is to confirm numbers as output. then the rmse accuracy test results get a value of 5.9. rmse can be negatively oriented, where a lower value indicates a better value. the system development resulted in a business process implemented with the tsukamoto fuzzy method, as seen in figure 5. figure 5 business process there is an admin actor in the implementation of office stationery stock estimation with a system that has been built, which starts with inputting stock data, and then the system performs input tasks. then the admin inputs the value to be estimated, and the system performs the task of inputting value data, then the system performs the tsukamoto fuzzy algorithm then all the tasks that have been carried out are recorded in the system database. in the next stage, the admin can print the system calculation results. in the system, development use uml so that class diagram diagrams are formed describing the system's structure in terms of defining the classes that will be made to build the system. the following is a class diagram which can be seen in figure 6. figure 6 class diagram next, the use case diagram is obtained. use case diagrams are used to find out what functions are contained in the system and who has the right to perform these functions. this system has login functions, alternative data management, data management criteria, data management rules, calculations, and data management passwords. the following is a use case for the office stationery stock estimation system depicted in figure 7. figure 7 use case diagram p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 118 in building this system, it is necessary to have a database design to find out how the system processes and records data in the database, the database used is mysql. figure 8 database relationship in figure 8. shows a relational database consisting of the following: 1. table tb_alternatif contains office stationery stock item data consisting of kode_alternatif, nama_alternatif, and keterangan. next, kode_alternatif is the primary key connected to the tb_rel_alternatif table. 2. table tb_rel_alternatif the stock value of office stationery consists of id, kode_alternatif, and kode_kriteria. next, kode_kriteria is the primary key connected to the tb_kriteria table. 3. table tb_kriteria contains variable criteria for office stationery stock that will be used to form a set consisting of kode_kriteria, nama_kriteria, batas_bawah, and batas_atas. next, kode_kriteria is the primary key connected to the tb_aturan table. 4. table tb_aturan contains variable rules for the fuzzy inference process consisting of id_atuan, no_aturan, kode_kriteria, operator, and kode_himpunan. next, kode_himpunan is the primary key connected to the tb_himpunan table. 5. table tb_himpunan contains variable set consisting of kode_himpunan,kode_kriteria,nama_himpunan. 6. table tb_user contains user data variables consisting of user and pass. system implementation the implementation process into program code uses the php programming language. this stage is converting system specifications into an executable system to produce a system view that makes it easier for users to access each feature. 1. dashboard page the page displays the main page after logging in and presents the features of the office stationery stock estimation system. the following is the dashboard display which is seen in figure 9. figure 9. dashboard page 2. nilai page this page functions to add valuable data that will be estimated based on alternative data, criteria data, and rule data used. the following is a display of the value page, which can be seen in figure 10. figure 10. nilai page 3. perhitungan page this page shows the results of estimation calculations using the tsukamoto fuzzy method. the following displays the calculation page, which can be seen in figure 11. figure 11. perhitungan page jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.471 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 119 conclusions and suggestions conclusion based on the author's description regarding the implementation of the tsukamoto fuzzy method of office stationery, a stock estimation system has been prepared. office stationery stock estimation system performs the calculation process using the tsukamoto fuzzy method where through the process of fuzzification stages, fuzzy inference, then defuzzification, testing the accuracy of the calculation results of the office stationery stock estimation system is carried out using the root mean squared error (rmse) to get a value 5,9. if the rmse value is smaller, the predicted value is close to the observed value, and the rmse can range from 0 to ∞. rmse can be negatively oriented, where a lower value indicates a better value. office stationery stock estimation system has been implemented using the waterfall method, which goes through the stages of analysis, uml diagram design, database design, and coding using visual code. the php programming language has been successfully tested using black box testing so that this system makes it easier for the admin head to estimate the office stationery stock that will be needed. suggestion it is suggested that for future research development, it is expected to develop a desktopbased estimation system using other fuzzy methods such as the mamdani and sugeno methods. references agus prayogi, edy santoso, & sutrisno. 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(2021). black box testing equivalence partitions for front-end testing on academic systems sitoda. jurnal ilmiah teknologi infomasi terapan, 7(3), 211–216. https://doi.org/10.33197/jitter.vol7.iss3.202 1.626 https://doi.org/10.24843/mtk.2018.v07.i02.p201 https://doi.org/10.24843/mtk.2018.v07.i02.p201 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 499 implementation of the k-means clustering for teacher performance assessment grouping (pkg) at mi bani hasyim cerme bagus firmansyah-1*, umi chotijah-2 informatics engineering, engineering university of muhammadiyah gresik gresik, indonesia bagusfirman860@gmail.com-1*, umi.chotijah@umg.ac.id-2 (*) correponding author abstract evaluation of teacher performance at mi bani hasyim cerme still uses the manual method. using office applications such as excel and word results in a significant accumulation of data that makes it difficult for school principals to calculate scores and evaluate the results of clustering or teacher performance scores, so it is wasteful of energy, time, and cost. the k-means clustering method is expected to facilitate the clustering process of teacher performance values as a source of information and make it easy for school principals to classify teacher performance results. this research aims to obtain clustering values on teacher performance assessment data and to replace the teacher performance assessment system at mi bani hasyim, which was previously carried out conventionally into a web-based system. the results of this study are the clustering values of teacher performance assessment and a web-based teacher performance appraisal system. it is expected to facilitate the process of evaluating teacher performance in the bani hasyim primary school in the future. keywords: teacher, teacher performance assessment, k-means clustering, mi bani hasyim, web system abstrak penilaian kinerja guru di mi bani hasyim cerme masih menggunakan cara manual. menggunakan aplikasi perkantoran seperti excel dan word yang berakibat penumpukan data yang sangat banyak sehingga menyulitkan kepala sekolah dalam menentukan skor penilaian dan mengevaluasi pengelompokan atau nilai kinerja guru dengan cara yang boros tenaga, waktu dan uang. metode k-means clustering diharapkan dapat mempermudah proses clustering nilai kinerja guru sebagai sumber informasi dan memudahkan kepala sekolah dalam mengklasifikasikan hasil kinerja guru. penelitian ini bertujuan untuk mendapatkan nilai klusterisasi pada data penilaian kinerja guru serta menggantikan sistem penilaian kinerja guru pada mi bani hasyim yang sebelumnya dilakukan secara konvensional menjadi sistem berbasis web. hasil dari penelitian ini adalah nilai clastering penilaian kinerja guru dan sistem penilaian kinerja guru berbasis web sehingga diharapkan dapat mempermudah proses penilaian kinerja guru pada mi bani hasyim kedepannya. kata kunci: guru, penilaian kinerja guru, k-means clustering, mi bani hasyim, sistem web introduction measuring an educational institution's performance is critical. performance measurement is carried out to evaluate and plan future education appropriately, especially on teachers' performance as executors and even as spearheads of education. various types of information are required to ensure that education and learning services are delivered effectively, efficiently, and accountable. improving educational quality must always measure its performance through various information, task control, funding reports, and the, most important, teacher performance reports because teachers play a very strategic role in determining educational quality, which necessitates legal personality and professional ability requirements and can be held accountable (muhiddinur, 2019). data mining is a method of data processing used to discover hidden patterns in data. this data mining method's data processing results can be used to make future decisions. data mining entails, in essence, data collection and selection, data preprocessing, data analysis (including visualization of results), interpretation of findings, and knowledge application. data mining is the process of extracting p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 500 patterns from data using specific algorithms. the process uses detailed analysis, automatically looking for simple patterns in large amounts of data (ndehedehe et al., 2013; ong, 2013; schuh et al., 2019). clustering is the process of grouping objects with similar properties into object classes. k-means is one of the clustering methods that can be used in this problem. as a method of nonhierarchical data clustering that groups data into one or more clusters. data with the same characteristics are grouped in one cluster, while data with different characteristics are grouped in another. this method is used to categorize teachers and employees based on data from student, teacher, and employee questionnaires. this method is used because it is an interactive method that is simple to interpret, apply, and dynamic on scattered data (han et al., 2012; hughes, 2012; ong, 2013). the manhattan distance is commonly used for measurement because it is simple to calculate and understand and more appropriate for some problems, such as calculating the absolute difference between the coordinates of two objects (pribadi et al., 2022; yaniar, 2011). david l. davies and donald w. bouldin invented the davies bouldin index (dbi) in 1979. the davies-bouldin index maximizes inter-cluster distance while attempting to minimize the distance between points within a cluster. if the maximum inter-cluster distance exists, it indicates that the similarities between each cluster have increased slightly, making the differences between clusters more visible. if the minimal intra-cluster distance indicates that each object in the cluster has level similarity, then the characteristics of the high level (bates & kalita, 2016; sartika & jumadi, 2019). teacher performance is still evaluated manually at bani hasyim primary school, using office applications such as excel and word. the results of the performance appraisal instrument generate a large number of documents for each teacher. thus, even during the storage process, teachers and school principals will struggle to determine the results of calculating scores and evaluating the results of clustering or teacher performance scores, wasting time and money (faisal et al., 2020; lopis, 2016). in previous research by (panjaitan et al., 2015) and (sukrianto, 2016), a study was conducted on teacher performance clustering using the kmeans clustering method, which resulted in the classification of teacher performance into five clusters: bad cluster, poor cluster, moderate cluster, poor cluster well, and perfect cluster. in previous research by (imantika et al., 2019), the k-means clustering method has been described as being used to divide teachers and employees into groups based on the value of the questionnaire results. the analytical hierarchy process (ahp) method is then used to prioritize teachers' and employees' choices from various alternatives. related research was also carried out by (nurzahputra et al., 2017). this paper, titled application of the k-means algorithm for lecturer assessment clustering based on the student satisfaction index, used the results of 146 student satisfaction questionnaires for all lecturers in the study program totaling 12 lecturers. the k-means clustering method was used in this study, with good and poor clusters. the total centroid score for the excellent cluster is 17,099 (5 good lecturers), and the total centroid score for the poor cluster is 15,874. (7 bad lecturers). previous research differs from this research in that the authors used the k-means clustering method to classify teacher performance scores at mi bani hasyim over the last five years, then added a graph to monitor the development of teacher performance scores over the last five years so that it can be seen whether the teacher is improving or deteriorating. k-means clustering is expected to facilitate the clustering process of teacher performance values as a source of information and make it easy for school principals to classify teacher performance results (faisal et al., 2020). research methods types of research this research uses a quantitative method that is systematic and uses mathematical models. time and place of research this research was conducted at bani hasyim primary school, and the time of research was from august 2022 to november 2022. research target / subject the target of this research is the performance value of teachers at bani hasyim primary school. procedure 1. problem identification problem identification is the first step in applying the k-means clustering method. problem identification aims to determine the appropriate data be analyzed using the k-means clustering jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 501 method to classify teachers based on performance scores. 2. data, instruments, and data collection techniques the data used in this study is the bani hasyim primary school teachers, the techniques used for data collection include the following: a) field research in field research, researchers directly visit research sites and collect data needed for research. field research was conducted directly by interviewing the bani hasyim primary school principal to obtain the required teacher information. b) literature research literature research is carried out by collecting references from journals or academic books related to the issues discussed. 3. data processing this stage is carried out to create raw data that will be processed into quality data. this is done in order to obtain more accurate results with the use of the k-means clustering method. 4. data analysis this stage is carried out based on the results of observations and data collection carried out. system requirements analysis is carried out to determine the features to be used in the system. results and discussion the k-means algorithm is one of the partitional algorithms since it is based on defining the initial centroid value, allowing the initial number of groups to be determined (madhulatha, 2012). the k-means algorithm uses an iterative procedure to create database clusters. after receiving the desired number of initial clusters as input, it generates the final centroid point as output. the centroid's starting point will be chosen randomly by the k-means method's pattern k. the initial cluster centroid candidates can influence the total number of iterations needed to find the cluster centroid. in order to design the algorithm in a way that will produce higher performance, we must identify the centroid cluster, which can be seen from the high initial data density (eltibi & ashour, 2011; hung et al., 2005; saranya & punithavalli, 2011). the k-means algorithm's final output will be a centroid point, which is what it is intended to do. each dataset object joins a cluster once the kmeans iteration is complete. the cluster value is calculated by looking through all the items to locate the cluster closest to the object. based on the shortest distance, the k-means algorithm will cluster data points in a dataset (bangoria et al., 2013). the distance to all of the data from the original centroid value, which was randomly selected as the starting point, was determined using the euclidean distance calculation. data that are close to the centroid will group. this process is repeated until no change exists in any group(chaturvedi et al., 2013). according to this study, the authors grouped by using four variables shown in table 1. table 1. criteria data code criteria k1 pedagogic k2 personality k3 social k4 professional this calculation uses the performance values of 8 teachers, which are initialized with the letters a to h. then, for the year of performance evaluation, it is initialized with one as 2018 and 5 as 2022. for example, data a1 was mrs. istianah's performance value in 2018, data a2 was mrs. istianah's performance value in 2019, and so on, until data h5. table 2 shows the initialization of the teacher code, and table 3 shows the data for teacher performance scores calculated using the k-means clustering method. table 2. teacher data code teacher name a istianah, s.pd.i b dwi yuniartiningtyas w, s.pd c muslimah, s.pd.i d mar’atus sholihah, s.ag e siti qoniah, s.pd.i f ni’matul karimah, s.pd.i g winanto, s.pd h muhammad irwan, s.pd.i table 3. teacher performance assessment data code k1 k2 k3 k4 a1 3,3 4 3,5 2,5 a2 3,1 3,3 4 3,5 a3 3,3 3 4 3,5 a4 3,4 3 4 3,5 a5 3,6 3,7 3,5 3 … … … … … … … … … … h1 3 4 3,5 1,5 h2 3,1 3 3 3 h3 3,4 3,3 4 3,5 h4 3,4 3,7 3,5 3,5 h5 3,7 3,7 3 3,5 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 502 teacher performance appraisal data is processed using the k-means clustering method, which will then be grouped into 4 clusters, namely "very good," "good," "enough," and "poor," which is shown in table 4. table 4. score score very good good enough poor 1. determine the number of k clusters according to this study, 4 clusters were selected randomly with pedagogic, personality, social, and professional variables. 2. determine the initial value of the midpoint (centroid) randomly based on this, the authors determine that the initial centroid is done randomly, as seen in table 5. table 5. initial centroids initial centroid cluster k1 k2 k3 k4 c1 3,7 3 3 3 c2 3,7 3,3 4 3 c3 3,9 4 4 3,5 c4 2,7 3,7 4 3 3. calculate each data's distance to the centroid with the manhattan distance formula shown in formula 1. 𝑑𝑀𝑎𝑛ℎ𝑎𝑡𝑡𝑎𝑛(𝑥,𝑦) = ∑ |𝑥𝑖 − 𝑦𝑖| 𝑛 𝑖=1 ……………………(1) the following example is calculated from a1 data to 4 centroids with manhattan distance. where data a1 was obtained previously through initialization in table 2 in the calculation becomes x1 and four centroids consisting of c1, c2, c3, and c4. a) calculation of data a1 against centroid 1 𝑑(𝑥1,𝑐1) = ∑|𝑥1𝑖 − 𝑐1𝑖| 𝑟 𝑖=1 = |3,3 − 3,7| + |4 − 3| + |3,5 − 3| + |2,5 − 3| = 2,4 b) calculation of data a1 against centroid 2 𝑑(𝑥1,𝑐2) = ∑|𝑥1𝑖 − 𝑐2𝑖| 𝑟 𝑖=1 = |3,3 − 3,7| + |4 − 3,3| + |3,5 − 4| + |2,5 − 3| = 2,1 c) calculation of data a1 against centroid 3 𝑑(𝑥1,𝑐3) = ∑|𝑥1𝑖 − 𝑐3𝑖| 𝑟 𝑖=1 = |3,3 − 3,9| + |4 − 4| + |3,5 − 4| + |2,5 − 3,5| =2,1 d) calculation of data a1 against centroid 4 𝑑(𝑥1,𝑐4) = ∑|𝑥1𝑖 − 𝑐4𝑖| 𝑟 𝑖=1 = |3,3 − 2,7| + |4 − 3,7| + |3,5 − 4| + |2,5 − 3| =1,9 the calculation of a1 data for the centroid above obtained the lowest value in the calculation of a1 data for the fourth centroid, which is equal to 1.9, so that a1 data will be entered into the fourth cluster, and so on for a2 data to h5 data. 4. assigns each data to the nearest cluster the following is the result of calculating the iteration distance; the shortest distance for each data to the centroid is shown in the table in yellow, and the closest centroid is the cluster that the data follows, which can be seen in table 6. table 6. iteration 1 distance calculation results code iteration 1 c1 c2 c3 c4 a1 2,4 2,1 2,1 1,9 a2 2,43333 1,1 1,46667 1,2 a3 1,9 1,2 1,6 1,8 a4 1,8 1,1 1,5 1,9 a5 1,26667 0,9 1,63333 1,4 … … … … … … … … … … h1 3,7 3,4 3,4 2,6 h2 0,6 1,9 3,3 2,1 h3 2,13333 0,8 1,16667 1,5 h4 1,96667 1,6 1,33333 1,7 h5 1,16667 1,8 1,53333 2,5 5. defining a new centroid the average value of each variable in each cluster can be used to calculate the new centroid shown in table 7. table 7. new centroid new centroid cluster k1 k2 k3 k4 c1 3,48 3,26 3,06 3,06 c2 3,51 3,47 3,79 3,17 c3 3,49 3,85 3,82 3,73 c4 3,11 3,88 3,44 2,69 the objective function change value is still over the threshold in the first iteration. thus the jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 503 calculation will continue until the objective function change value is below the threshold in the following phase, which involves lowering the initial objective function value. the results of the objective function computation and variations in the objective function's value for each completed iteration are displayed in table 8. table 8. objective function change iteration objective function objective function change 1 14,277 985,723 2 10,638 3,639 3 10,638 0 the calculation halts at the third iteration in line with the results' goal function change value in table 8. the third-iteration change in the goal function, which has a value of 0, is significant enough to surpass the threshold. the outcome of the third iteration calculation is shown in table 9. table 9. iteration 3 distance calculation results code iteration 3 c1 c2 c3 c4 a1 2,00 1,77 1,98 0,16 a2 1,70 0,84 1,29 2,45 a3 1,61 0,97 1,42 2,59 a4 1,60 0,87 1,32 2,55 a5 1,36 1,00 1,32 1,09 … … … … … … … … … … h1 3,30 3,07 3,28 1,46 h2 0,64 1,77 2,79 2,17 h3 1,49 0,54 0,99 2,22 h4 1,32 0,94 0,82 1,39 h5 1,12 1,70 1,42 2,07 the cluster center or centroid obtained is the centroid in the last iteration, namely the centroid in the 3rd iteration. the final centroid is shown in table 10. table 10. last centroid last centroid cluster k1 k2 k3 k4 c1 3,36 3,22 3 3,17 c2 3,42 3,4 3,85 3,2 c3 3,5 3,81 3,83 3,75 c4 3,37 3,96 3,44 2,5 in this study, researchers have determined four criteria for evaluating teacher performance, as shown in table 4. the four criteria are initialized into 4 clusters by sorting the average of each cluster on the last centroid shown in table 11, followed by the clustering results in table 12. table 11. score initialization score initialization very good c3 good c2 enough c4 poor c1 table 12. results of teacher performance assessment clustering code cluster score a1 c4 enough a2 c2 good a3 c2 good a4 c2 good a5 c2 good … … … … … … h1 c4 enough h2 c1 poor h3 c2 good h4 c3 very good h5 c1 poor table 12 shows that teachers have very good, good, enough, and poor scores. furthermore, teachers with low scores will be included in the training for improving teacher performance assessments at bani hasyim primary school. davies-bouldin index validity the davies-bouldin index seeks to minimize distances between cluster points while maximizing distances between clusters (dense). the davies-bouldin index's lowest value will indicate the ideal number of clusters, which falls within the range of (0, 1). the distance of each data point from the centroid and the mean value is calculated to provide calculations for the ssw in the first stage. the results of estimating the ssw value using the kmeans computations are shown in table 13. table 13. ssw calculation results cluster ssw c1 0,52 c2 0,52 c3 0,46 c4 0,42 the next step is calculating the ssb (sum of square between cluster) values to gauge how far clusters are from one another apart. to do this, p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 504 measure the distance between a cluster's centroids. the results of estimating the ssb value are shown in table 14. table 14. ssb calculation results ssb cluster 1 2 3 4 1 0,00 0,87 1,18 1,09 2 0,87 0,00 0,69 0,99 3 1,18 0,69 0,00 1,32 4 1,09 0,99 1,32 0,00 evaluation of the ratio (rij), which seeks to determine the dbi value for each cluster, comes next. each cluster's ratio value (dbi) is used to evaluate the dbi of the entire cluster. a good cluster has the smallest density value and the highest possible separation value. the results of estimating the dbi value using the k-means computations are shown in table 15. table 15. dbi calculation result r cluster r max dbi 1 2 3 4 1 0,00 1,19 0,84 0,86 1,19 1,24 2 1,19 0,00 1,42 0,94 1,42 3 0,84 1,42 0,00 0,66 1,42 4 0,86 0,94 0,66 0,00 0,94 the ratio with the most significant value is chosen to find the average, resulting in a dbi value of 1.24235. system implementation 1. system login page when the user enters the system, he or she will see the display shown in figure 1. the user is asked to log in using the email and password previously created. if the user has not registered, he will not be able to enter the system. figure 1. login page 2. teachers data page after the user logs into the system, the teacher data page will appear. users can add, edit and delete teacher data through the teacher data page shown in figure 2. figure 2. teachers data page 3. teachers score page on this teacher's score page shown in figure 3, there are teacher performance scores from year to year for the last five years which include pedagogic, personality, social, and professional. figure 3. teachers score page 4. clustering page furthermore, on the clustering page shown in figure 4, there are several features, such as the range of years that will be calculated with k-means clustering, then the user can choose which centroid will be used to perform the calculation. figure 4. clustering page 5. calculation process page after selecting the year range and centroid, the user will be directed to the calculation process jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.475 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 505 page shown in figure 5. here, the user can see kmeans calculations starting from the first iteration to the last iteration. figure 5. calculation process page 6. clustering results page the clustering results page shown in figure 6 contains the clustering results from each teacher over five years. on this page, the user can get conclusions about which teachers get good grades and which teachers get poor grades so that training and workshops can be conducted for teachers who get poor grades to improve teacher performance appraisal. figure 6. clustering results page 7. monitoring page the monitoring page shown in figure 7 contains a graph of each teacher's performance calculation score in the last five years. here, users can monitor the progress of each teacher's performance. figure 7. monitoring page conclusion and suggestion conclusion the authors' conclusions from the research include classifying teacher performance evaluations at mi bani hasyim based on four assessment categories, pedagogic, personality, social, and professional. teachers' performance assessments are grouped into very good, good, enough, and poor. the iteration process carried out in this study obtained three iterations and the results of the tests that were carried out, then formed teacher group data with excellent ratings consisting of 12 (twelve) teacher data, teacher group data with good ratings consisting of 10 (ten) teacher data, teacher group data with enough assessment consisting of 9 (nine) teacher data, and teacher group data with poor assessment consisting of 9 (nine) teacher data. suggestion the k-means method should also be compared with other approaches to make more accurate clustering. references bangoria, b., mankad, n., & pambhar, v. 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(2011). perbandingan ukuran jarak pada proses pengenalan wajah berbasis principal component analysis ( pca ). proceeding seminar tugas akhir jurusan teknik elektro fti‐its, 1–6. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5958 http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/5958 https://doi.org/https:/doi.org/10.48550/arxiv.1205.1117 https://doi.org/https:/doi.org/10.48550/arxiv.1205.1117 http://repo.iainbukittinggi.ac.id/id/eprint/131 http://repo.iainbukittinggi.ac.id/id/eprint/131 https://doi.org/10.33633/tc.v16i1.1284 https://doi.org/https:/doi.org/10.23917/jiti.v12i1.651 https://doi.org/https:/doi.org/10.23917/jiti.v12i1.651 https://seminar-id.com/semnas-sainteks2019.html https://seminar-id.com/semnas-sainteks2019.html https://doi.org/10.1016/j.procir.2019.03.217 https://doi.org/10.1016/j.procir.2019.03.217 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 521 masked face detection automation system using mask threshold and viola jones method aminurachma aisyah nilatika1, khoerul anwar*2, eka yuniar3 1,2 teknologi informasi, 3 sistem informasi stmik ppkia pradnya paramita malang, indonesia https://stimata.ac.id/ 1aminurachmaaisyahnilatika@gmail.com, 2*)alqhoir@stimata.ac.id, 3eka@stimata.ac.id (*)corresponding author abstract reducing or even breaking the chain of covid-19 virus infections during a pandemic is important. the techniques encouraged are mandatory hand washing, social distancing, and wearing masks. wearing masks is urgent. therefore, requiring people to wear masks is the right policy. this study aims to detect people who use or do not use masks by applying the viola-jones method. this study modified the threshold algorithm by applying a masking threshold to optimize facial segmentation. meanwhile, viola jones was built by combining several concepts of haar feature, integral image, adaboost, and classifier cascade into the main method for detecting objects. the performance of the proposed method for face detection has an accuracy of 95%, a precision of 94.73%, and a recall of 100%. 5. the masked face detection test has an accuracy of 94%, a precision of 100%, and a recall of 90.90% keywords: segmentation; face detection; masks; viola jones;mask tresholder abstrak mengurangi atau bahkan memutus rantai infeksi virus covid-19 dimasa pandemi menjadi hal yang peting. teknik yang digalakkan adalah wajib cuci tangan, social distancing, dan wajib memakai masker. memakai masker menjadi urgen, oleh karena itu mewajibkan masyarakat untuk memakai masker adalah kebijakan yang tepat. penelitian ini bertujuan untuk pendeteksian wajah manusia yang menggunakan masker atau tidak menggunakan masker dengan menerapkan metode mask tresholder dan viola jones. mask tresholder diterapkan pada proses segmentasi wajah untuk mereduksi gangguan pada proses segmentasi wajah. sementara itu viola jones dibangun dengan menggabungkan beberapa konsep fitur haar, citra integral, adaboost, cascade pengklasifikasi menjadi sebuah metode utama untuk mendeteksi objek. kinerja metode yang diusulkan pada deteksi wajah mempunyai akurasi sebesar 95%, presisi 94.73%, dan recall 100%. 5. pada pengujian deteksi wajah bermasker mempunyai akurasi sebesar 94%, presisi 100% , dan recall 90,90% kata kunci: deteksi wajah; segmentatsi;masker;mask tresholder;viola jones introduction the coronavirus that hit various countries began in wuhan, hubei, china in 2019, later called coronavirus disease-2019 (hui et al., 2020)]. the world health organization or world health organization (who) officially declared the coronavirus as a pandemic on march 9, 2020. this means that this virus has spread widely throughout the world. the indonesian government has also issued a disaster emergency status from february 29 to may 29, 2020, regarding this virus pandemic for 91 days. in this regard, until november 1, 2021, the government of the republic of indonesia recorded 4,244,761 people were detected positive for covid-19 while a total of 143,423 deaths (cfr: 3.4%) and a total of 4,089,419 patients had been declared cured from contracting covid. until 2022 this virus has not completely disappeared. therefore, the public's vigilance against it is maintained. the spread of the covid-19 virus is indicated to mostly occur when someone is symptomatic or symptomatic to others using communication at very close distances and not wearing the perfect ppe equipment. moreover, another transmission may occur because the infected person transmits the virus but has not experienced transmission symptoms. this transmission is called presymptomatic transmission. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 522 the government has taken many steps to overcome this pandemic, one of which is by socializing the mandatory hand washing movement, social distancing movement, and the mandatory movement to wear masks to reduce or even break the chain of infection with the covid-19 virus. the mandatory mask movement is a movement that requires all people to wear masks. several masks are recommended by the government to be used. among them are cloth masks, which can be used for four hours and then be washed again. the surgical masks and n-95 masks are only reserved for health workers. in indonesia, many people still ignore warnings and regulations regarding the use of masks whenever they are outside the home, and many are unaware of the importance of carrying out this prevention. most indonesians know that wearing a mask is a positive step to reduce the spread of covid-19, but less than 50% are obedient to follow it. detecting people wearing masks is easy to do with the human eye, but for computer-based intelligent systems, this is a challenge. there are two main problem s, namely how the computer can detect human faces, and the next problem is how the detected faces can be identified using masks or not wearing masks directly without requiring data training but with high accuracy through photo and video images. many studies have been conducted to detect faces using various techniques and algorithms to be implemented on devices with limited resources (wihandika, 2021). research (suharso, 2017) to detect facial images conducted several experiments with pixel value thresholds and obtained the best value of 70. then this image was used to obtain haar features and obtained face detection accuracy of 90.90%. it is different from (kirana & isnanto, 2016), which uses pca for image segmentation to obtain haar features, and the result of face detection is 90.90%. likewise (suhery & ruslianto, 2017) use pca for facial image segmentation, and the accuracy of the face detection model is 90%. in addition to the several methods that have been presented, the face detection method with haar cascade is quite popular, including those used by (budiman, 2021); formatting citation; nono heryana, rini mayasari, 2020; abidin, 2018; utaminingrum et al., 2017; javed mehedi shamrat et al., 2021; padilla et al., 2012; minu et al., 2020; poorvi taunk, g. vani jayasri, padma priya j, 2020; braganza et al., 2020). almost all of these studies use binary images obtained by applying a certain threshold value with an accuracy rate of 90% (kirana & isnanto, 2016; suharso, 2017; suhery & ruslianto, 2017). research on masked faces has been carried out using various methods, including using cnn (sakshi et al., 2021; vu et al., 2022), deep learning (monica m, 2021; yadav, 2020),(mufid naufal baay, astria nur irfansyah, 2021) with an accuracy of 82%, while (öztürk et al., 2021) with an accuracy of 85%. several studies used the jones viola (suharso, 2017) with 88% accuracy researchers (putri et al., 2019) with 90% accuracy. another researcher who used viola jones for face detection was (hassan & dawood, 2022)(florestiyanto et al., 2020; pratama, 2022; suharso, 2017) for real-time with 67.6% accuracy. research by (fendi et al., 2020) has 90.9% accuracy (karim sujon et al., 2022). therefore, it is possible to develop a threshold method that can detect faces to increase the accuracy of face detection this study aims to detect human faces with masks or not. this article applies the jones viola and threshold mask. mask threshold to reduce interference from segmentation results. meanwhile, viola jones is built to detect masked faces. research methods the research framework developed to achieve the objectives consists of three stages. the first stage is preprocessing the input image by converting the color and size dimensions. the flow of the research framework is shown in figure 1. figure 1. workflow of the masked face detection system the output of the process is a grey-level image. the second stage is face detection by applying the haar feature, integral image, adaboost, and cascade jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 523 classifier methods. the output of the second stage is the face-detected image. the third or final stage is detecting the face image with a mask or not. the development of the proposed threshold is discussed at this stage. preprocessing the input data is obtained from two sources, namely from the webcam and downloaded from the internet. data in the form of images or videos. at this stage, two initial treatments are carried out on the image: color conversion and image size changes. the color conversion process is carried out from three rgb color spaces into one grey-level space. the grey level was chosen because it supports the next process. the grey level has one value component, making it easier to perform pixel computations (suhery & ruslianto, 2017). three color spaces, r (red), g (green), and b (blue), into the grey level image that is accommodated in the variable s, then the conversion takes the average of r, g, and b as written in equation 1 𝑠 = ( 𝑟+𝑔+𝑏 3 ) ........................................................................ (1) image size change is a change in image resolution. the process is done by reducing the image's dimensions for the number of pixels to be less by sampling distanced pixels. this process is carried out because the images obtained from the webcam used have a resolution of 480x640 pixels, and the images downloaded from the internet have different resolutions, affecting the speed of calculating the next process. therefore, uniform image dimensions are needed. at this stage, the process of changing the size to 340x240 pixels for the image obtained from the webcam, while the image obtained from the process file changes the size to 50% of the original resolution. data processing the first step is to determine the unique haar features as the key to the image. haar has the advantage of a fast computing process. the haar features (poorvi taunk,g. vani jayasri,padma priya j, 2020; vaibhav, 2020)used are shown in figure 2. figure 2. features of haar this can happen because the computation is calculated based on the features contained in the grid frame and not for all pixels in an image. in order to obtain the desired haar features, several main parts of the human face are selected. the parts that are haar features in this article are eye features, nose features, and mouth features. these three sections were chosen because they indicated that each face has a different geometry. the feature selection process can be seen in figure 3. figure 3. eye, nose, and mouth features the process after feature selection is continued in the image integration process. in this step, the pixels in the area read as haar features are summed. this method uses four specific sub-areas of a larger whole area. therefore, this method is considered capable of using features efficiently. the pixel values of the integral image are shown as subimages, as shown in figure 4 figure 4. integral image section l1, l2, l3, and l4 are sub-images of the input. the number of pixels for a is calculated(jones, 2004) using equation 2. l1, l2, l3, and l4 are sub-images of the input. the number of pixels for a is calculated[20] using equation 2. 𝐴 = 𝐿4 + 𝐿1 − (𝐿2 + 𝐿3) ..................................... (2) meanwhile, the change in value from the input image to the integral image is calculated using equation 3. 𝑠(𝑥, 𝑦) = 𝑖(𝑥, 𝑦) + 𝑠(𝑥, 𝑦 − 1) + 𝑠(𝑥 − 1, 𝑦) − 𝑠(𝑥 − 1, 𝑦 − 1) .................................................... (3) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 524 for s(x,y) is the cumulative sum for the integral image. the next process is to determine certain features used to set the threshold value using the adaboost method. when managing a strong classifier, weight is added to the weak classifier, which is then integrated into one to analyze whether the image contains an object. the weak classifier is the prediction result with an inaccurate level of truth. meanwhile, the steps taken by adaboost to establish a strong classifier are: normalizing the weights to obtain a probability distribution value or a candidate for a weak classifier, evaluating each candidate for a weak classifier, selecting a candidate for a weak classifier with the smallest error rate, and then selecting it as a weak classifier. classify all training data using the previously obtained weak classifier and re-weight the data. increase the weight of each misclassified data, and then reduce the weight (return to the initial weight) of all data appropriately. it is hoped that any misclassification can be monitored and corrected by the weak classifier at the next stage. the final classifier is obtained by combining all weak classifiers from each step increase. graded classification using the cascade classifier method. the cascade classifier has three levels of classification (suharso, 2017). the process flow to determine the presence or absence of facial features in the image is shown in figure 5. figure 5. graded classification flow segmentation the process begins with an edge detection process at the end of the color gradation that limits two homogeneous images that show different brightnesses. this study was conducted to determine the difference in intensity between the background and the face area for the entire specified digital image. what is desired from this process is to obtain a clear boundary line between one area and another in the image (anwar & setyowibowo, 2021). then proceed with assigning a threshold value to emphasize the image of objects and non-objects by changing the grey-level image into two colors, black and white. in this research, the threshold value process is done by filtering the pixel value. values that are less than the benchmark will be presented in black. at the same time, the pixel value above the benchmark is presented in a light or white color. the threshold value is calculated on the average value of the sum of the maximum value of fmask and the minimum value of fmin, and the calculation formula is used based on equation 4. 𝑇 = 𝑓𝑚𝑎𝑘𝑠+𝑓𝑚𝑖𝑛 2 ......................................................... (4) the explanation of the variables in equation (4)t is the threshold value, fmax is the highest pixel value, and fmin is the minimum pixel value. this segmentation process is a type of operation that aims to break up an image into several segments with certain criteria (anwar et al., 2021). this type of operation is closely related to pattern identification. in this study, segmentation is carried out based on the facial texture area because, in the initial image processing, we only want to focus on the core facial composition from the eyes to the chin, as well as the location of the mask that should be. the results of facial image segmentation identify whether there are faces with masks. the system works to separate images identified as having the characteristics of the eyes, nose, and mouth objects inserted into a marker box frame. the pixel value of this rectangular box is stored in a variable used to disguise this segmentation. however, the segmentation results have problems, especially when operated on video. the disturbance occurs in the background of the object. there are many white spots. therefore, this study improved the segmentation results by applying convolution to the background. convolution in this study is used to reduce the disturbances after object segmentation. the essence of the convolution method is to multiply two matrices. in this article, the two matrices used are binary image matrices from segments with thresholding using equation 4, while the second matrix is the rgb image matrix. the method used is a segmented binary image edge detected as a mask. the process is done by multiplying each rgb component with a mask image in f(x,y) binary format. the process formulation is written in equation 5 (anwar et al., 2021). the r(), g(), and b() matrices have pixel values from 0 to 255, (xi,yj) = 0…255. at the same time, f(xi,yj) is a binary image with value [0 1]. rf(xi,yj) = r (xi,yj) * f (xi,yj) gf(xi,yj) = g (xi,yj) * f (xi,yj) ........................................... (5) bf(xi,yj) = b (xi,yj) * f (xi,yj) jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 525 the flow of the segmentation on the face without a mask can be seen in figure 6, and the segmentation on the face with a mask can be seen in figure 7. figure 6. unmasked face image method figure 7. masked face image method dataset retrieval of image data was used to test the study results with 20 images obtained consisting of 10 real-time webcam images and ten downloaded images. the image specifications are: webcam images with a resolution of 480 x 640 pixels, images from uploaded files with different resolutions placed in the program folder, images containing more than one face using masks, images containing more than one face not wearing masks, the image contains a face that uses a cloth mask, a medical mask and does not use a mask, an image contains a face using a face shield. using the diverse nature of the data, it is hoped that information about the proposed method's ability can be obtained properly. data analysis technique the output of this process is to display the detected image as a face or part of the face detected with rgb color and the background to be black. tests on the proposed method are mapped using a confusion matrix in table 1, consisting of precision, recall, and system accuracy in detecting the input image. each calculation for each parameter is 1. 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 (𝑇𝑃+𝐹𝑃) .................................................... (6) 2. 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 (𝑇𝑃+𝐹𝑁) ............................................................. (7) 3. 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 (𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁) ................................ (8) results and discussion face detection the stages of facial segmentation with the proposed method are shown in figure 8. the character of the image from the face segmentation is shown in figure 8.a. is the input image without a binary mask using equation 4, which shows the segmentation results are not perfect in figure 8. b. in this study, the mask threshold method was added by applying equation 5 to obtain better results with the background. the method has been tested with five real-time images (webcam) and five downloaded images. the proposed method can segment 10 test images correctly (100%). the results obtained are shown in figure 8. c. figure 8. face detection application of the algorithm used in the masked image segmentation process, as shown in figure 9 figure 9. masked face image the result of masked image segmentation is shown in figure 9. b. binary image segmentation using equation 4 showed that the desired object still has much interference. to get better results, the mask transcoder convolution method is applied. the proposed method has been tested with five real-time images (webcams) and five downloaded images. the method can correctly segment 10 test images. examples of performance, image 9a is the input image, image 9b is the result of the old method, and image 9c is the result of the proposed method. tests on the face detection method with the viola-jones and mask tresholder methods were conducted on real-time webcam images and offline images (upload files). an example of correct detection is shown in table 1. table 1. the results of the masked face detection test p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.470 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 526 the webcam image comprises a single unmasked image and an unmasked multiple-face image with 5 test images. meanwhile, the system was tested with webcam images for face detection with masks for single and multiple-face images. tests for face detection without masks with real-time images with two single images and multiple face images totaling five tests. the system performance detected five tests correctly. examples of performance as shown in table 1 no. 1 and no. 2. testing with two uploaded images consisting of a single face image and multiple face images. the system performance can detect two tests correctly. in the uploaded image in table 1 no. 4, there is a face mask, but it does not cover the haar features (mouth, nose, and eyes) used in the proposed system, but the mask is attached to the neck. therefore the system detects it as an unmasked face. tests for masked face detection were carried out with four real-time images. the performance of the proposed system can detect four tests correctly. an example of system performance in detecting masked faces is shown in table 1, no 3. then the system is tested with six uploaded images. the test results showed that five images were correctly detected as a masked face and 1 test image failed to be detected correctly. the system's failure in detecting this is caused by the inability to segment the facial area. testing the model with a real-time image containing both masked and unmasked faces. in testing with this image data type, the performance of the proposed system can detect faces with masks on the test image correctly. however, the system did not properly detect the unmasked face because it recognized it as a masked face. the results of the system performance as shown in table 1 no. 4. conclusions and suggestions conclusion based on the test results of the experiments, it can be concluded that the masking threshold and viola jones methods can be implemented properly to detect facial images. the threshold mask applied for segmentation can reduce the disruption of the segmentation process. the proposed model has an accuracy of 95%. while the accuracy obtained for detecting masked faces is 94%. this threshold mask method can be applied for various purposes of color image segmentation. suggestion the object in this article is a stationary person. therefore it can be developed for masked face detection in moving humans in complex open spaces or indoors. references abidin, s. 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(2020). deep learning based safe social distancing and face mask detection in public areas for covid-19 safety guidelines adherence. international journal for research in applied science and engineering technology, 8(7), 1368–1375. https://doi.org/10.22214/ijraset.2020.3056 0 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 439 prediction of rainfall and water discharge in the jagir river surabaya with long-short-term memory (lstm) retzi yosia lewu-1, slamet-2, sri wulandari-3, widdi djatmiko-4, kusrini-5, mulia sulistiyono-6*) magister of informatics engineering universitas amikom yogyakarta yogyakarta, indonesia 1retzi.lewu@students.amikom.ac.id, 2slametmieno@students.amikom.ac.id, 3sriwulandari@students.amikom.ac.id, 4widdi.dj@students.amikom.ac.id, 5kusrini@amikom.ac.id, 6*) muliasulistiyono@amikom.ac.id (*) corresponding author abstract floods can occur at any time if the amount of river water discharge and rainfall intensity tends to be high, so preparations and ways of handling are needed to anticipate flooding quickly, precisely, and accurately for the surabaya city public works service. one of the steps to predict and analyze the status of the flood disaster alert level is to calculate predictions based on rainfall and the amount of river water discharge. this study uses the long-short term memory (lstm) algorithm to predict using a time series dataset of rainfall and river water discharge in the jagir river, surabaya. this data is used to make predictions with the proportion of 70% training data and 30% testing data. data normalization is performed in intervals of 0 and 1 using a min-max scaler and activated using relu (rectified linear unit) and adam optimizer. the process continues by repeating the process to enter iterations, or epochs, until it reaches the specified epoch (n). the data is then normalized to their original values and visualized. the model was evaluated and produced acceptable performance evaluation results for the rainfall variable, namely at epoch (n) = 75 for training data, namely a score of 0.054 for mae and 0.099 for rmse. in contrast, data testing was given a score of 0.041 for mae and 0.091 for rmse. as for the water discharge variable, the performance evaluation shows the difference between the training and testing data. results of training data mae = 11.10 and rmse=18rmse =18.61.61 at epoch (n) = 150. results of data testing mae = 11.37 and rmse = 21.08 at epoch (n) = 100. these results indicate an anomaly that needs to be discussed in further research. keywords: rainfall; water discharge; prediction; flood; long short term memory (lstm) abstrak banjir dapat terjadi sewaktu-waktu apabila faktor jumlah debit air sungai dan intensitas curah hujan cenderung tinggi, sehingga diperlukan persiapan dan cara penanganan untuk mengantisipasi banjir secara cepat, tepat, dan akurat bagi dinas pekerjaan umum kota surabaya. salah satu langkah untuk memprediksi dan menganalisis status tingkat siaga bencana banjir adalah dengan menghitung prediksi berdasarkan curah hujan dan jumlah debit air sungai. penelitian ini menggunakan algoritma long-short term memory (lstm) untuk memprediksi dengan menggunakan dataset time series curah hujan dan debit air sungai di sungai jagir surabaya. data ini digunakan untuk membuat prediksi dengan proporsi 70% data training dan 30% data testing. normalisasi data dilakukan dalam interval 0 dan 1 menggunakan minmax scaler dan diaktifkan menggunakan relu (rectified linear unit) dan adam optimizer. proses dilanjutkan dengan mengulang proses untuk memasukkan iterasi, atau epoch, hingga mencapai epoch (n) yang ditentukan. data kemudian didenormalisasi ke nilai aslinya dan divisualisasikan. model dievaluasi dan menghasilkan nilai hasil evaluasi kinerja yang dapat diterima untuk variabel curah hujan yaitu pada epoch (n) = 75 untuk data training yaitu skor 0,054 untuk mae dan skor 0,099 untuk rmse, seta data testing diberi skor 0,041 untuk mae dan 0,091 untuk rmse. sedangkan untuk variabel debit air, evaluasi kinerja menunjukkan perbedaan antara data training dan data testing. hasil data training mae = 11.10 dan rmse = 18.61 pada epoch (n) = 150. hasil data testing mae = 11.37 dan rmse = 21.08 pada epoch (n) = 100. hasil ini menunjukkan adanya anomali sehingga perlu dibahas pada penelitian selanjutnya. kata kunci: curah hujan; debit air; prediksi; banjir; long short term memory (lstm) mailto:2xxxxxxxx@students.amikom.ac.id p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 440 introduction as an archipelagic country close to the equator, indonesia has an excellent opportunity to experience flooding. the monitoring results of the national disaster management agency (bnpb) stated that since 2018 floods have become a disaster with the most significant impact, according to the available data (https://bnpb.go.id/infographics). the flood disaster occurred evenly in indonesia, including in surabaya. there are several large rivers in surabaya, one of them is the jagir river, which needs to be examined for its flood alert status, considering that the river is an artificial river located in a densely populated area. several factors, including rainfall and water discharge, can cause floods. these two factors can be used to determine flood alert status. in hydrology, it is explained that river water discharge is a measure of the amount of water flowing out of a watershed (das) in volume units per second. the river water discharge unit is cubic meters per second (m3/second) (asdak, 2023). every river in surabaya has an essential role in accommodating and storing water which will then flow into the major rivers in surabaya and empty into the sea. an excessive river water discharge will result in a flood disaster that can damage or cause property loss and even claim lives. flood disasters can indeed occur when there is instability in the river's flow, and it comes relatively quickly. so preparation and handling methods are also needed to quickly, precisely, and accurately anticipate floods for the dinas pekerjaan umum pengairan provinsi jawa timur upt pengelolaan sumber daya air surabaya. one of the steps to anticipate a flood disaster is calculating the predicted amount of river water discharge. the term prediction is similar to classification and estimation, in which prediction results lie in the future (larose, 2005). predictions can be made using several algorithms, including machine learning, artificial neural networks (ann), and lstm (long et al.). the lstm algorithm was first introduced in 1997 by hochreiter and schmidhuber (hochreiter & schmidhuber, 1997). lstm consists of several layers that can be repeated and has several basic variable calculation processes, including addition, multiplication, and other mathematical functions. so in this study, the prediction will be held by using the existing periodic time series data of the amount of river water discharge in recent years, and a predictive result of the river water discharge will be obtained for some time to come using lstm as a method. therefore, it will explain the use of lstm for predicting rainfall and water discharge by analyzing data obtained from the past to obtain projections of future data. furthermore, to determine the performance of the lstm algorithm model, a testing process will be carried out using mae (mean absolute error) (bouktif, fiaz, ouni, & serhani, 2018) and mean squared error (mse) (shetty, padmashree, sagar, & cauvery, 2021), in this case, rmse (root mean squared error) (elizabeth michael, mishra, hasan, & al-durra, 2022; kouadri, pande, panneerselvam, moharir, & elbeltagi., 2022), to test the prediction results on actual data. mae is the absolute change between the original and prediction values (wang & lu, 2018) and the average for all the values. in contrast, it is explained as the square root of mse (mean square error), which is the square of change between the original and prediction values and the average for all the values (navlan, fandango, & idris, 2021). using the lstm algorithm model, this study is expected to produce an acceptable score (near zero) for both mae and rmse. it is an understandable reason so that it can provide knowledge to increase information for upt pengelolaan sumber daya air surabaya in anticipating/managing floods in the surabaya area, especially those caused by the jagir river. research methods types of research this study uses a quantitative approach. using the method of literature study and observation is as follows: literature study much research has been conducted on flood prediction using lstm (long short term memory) and other methods. literature study related to lstm: rizki et al., in 2022, researched rainfall prediction for the city of malang and found that the application successfully processed rainfall predictions for malang with rainfall parameters (rizki, basuki, & azhar, 2020). the number of hidden layer neurons with the most optimal results is 256 hidden layer neurons. this is because the 256 hidden layer neurons have the lowest error rate, 12,247 on the train data and 11,481 on the test data. the number of epochs with the most optimal results is 150 epochs. this is because the number of 150 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 441 epochs has the lowest error rate, namely on the train data of 12,079 and the test data of 11,288. the composition of data train and data test with the most optimal results is the composition of 50% train data and 50% test data. this is because the composition of 50% train data and 50% test data has the lowest error rate; namely, the train data is 12,079, and the test data is 11,288. this research is considered not too significant because it only uses one variable, namely rainfall; devi et al., in 2022, conducted a dasarian rainfall prediction using the vanilla rnn and lstm methods to determine the beginning of the rainy and dry seasons. they obtained the best features: humidity, pressure, and visibility (devi, bayupati, & wirdiani, 2022). models with features that have been selected using the backward elimination method obtain more optimal performance compared to models that use all data features. each model using the vanilla rnn and lstm methods obtained poor results at a learning rate of 0.0001. this study's learning rate of 0.0001 requires a more significant epoch to obtain optimal results. the best model is obtained by the vanilla rnn method with feature selection. the rmse obtained was 28.4308, and r2 was 0.6139. the r2 value of 0.6139 is included in the strong category, where this model is suitable for predicting primary rainfall data. the information obtained from the results of the 2021 rainfall prediction is that june will enter the dry season in june, and 1 december will enter the rainy season. kardhana et al. 2022 improved the flood prediction method using the lstm-rnn and sadewa satellite data (kardhana, valerian, rohmat, & kusuma, 2022). the lstm-rnn is used to predict the water level (sudriani, ridwansyah, & a rustini, 2019) in the katulampa dam using sadewa satellite data. the results show that the model can accurately predict the katulampa water level and provides a potential for implementing and improving lead time for flood mitigation. using the lstm-rnn, the model can accurately predict the water level in katulampa with repeated data t − 24 hours, with r2 above 0.82. the model can maintain r2 above 0.80 for the next 24 hours in the prediction. literature study related flood prediction using other methods: supatmi et al. 2019 proposed a hybrid approach based on a neural network and a fuzzy inference system for flood vulnerability, namely the hybrid neuro-fuzzy inference system (hn-fis). hnfis is a model that can automatically learn and obtain output that can present the essence of fuzzy logic 2 computational intelligence and neuroscience (supatmi, hou, & sumitra, 2019). the system is implemented in 31 districts in the city of bandung. flood prediction relies on several variable inputs: population density, area elevation, and rainfall in a time series from 2008 to 2012. the main contribution of this paper is to provide a hybrid prediction for flood susceptibility based on neural networks and a fuzzy inference system for accurate flood prediction. it used data variables that utilized the bandung database for flood hazard prediction and developed a practical hybrid prediction approach for flood susceptibility with higher accuracy. noymanee & theeramunkong conducted research in which machine learning techniques were developed to predict errors in rainfall simulations. a hybrid model based on mike11 and machine learning techniques will provide better predictive results than only one mike11 model (noymanee & theeramunkong, 2019). using the variant inflation factor, sampurno et al. conducted a statistical analysis to analyze the multicollinearity between the predictor variables (sampurno, vallaeys, ardianto, & hanert, 2022). the researcher tested four kernels, namely linear, polynomial, radial basis, and sigmoid, and found that the radial kernel had the best performance in the svm algorithm. b. observation observations were made on the data available at the dinas pekerjaan umum pengairan provinsi jawa timur upt pengelolaan sumber daya air surabaya. the observation results in the form of a dataset are then processed in the following manner: figure 1. research flowchart p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 442 time and place of research the research was conducted from 29 may 2023 to 10 july 2023 with the details as shown in figure 2 below: figure 2. research schedule research took place at dinas pekerjaan umum pengairan provinsi jawa timur upt pengelolaan sumber daya air surabaya. research target/subject the subject is dinas pekerjaan umum pengairan provinsi jawa timur upt pengelolaan sumber daya air surabaya, where we derive the population of data. the data population is a dataset of rainfall and water discharge, and the data samples are those captured from 2020 to 2022, with as many as 1096 rows. the data sample uses rainfall and water discharge as they are being used as the variables within the research. data, instruments, and data collection techniques the data used in this study is a dataset from the irrigation public works office of east java province upt water resources management surabaya captured the raw data using provided devices : rainfall data is recorded based on the output of a device called the automatic rainfall recorder (arr) through the wonokromo station, and water discharge data is recorded based on the output of a device called awlr (automatic water level recorder) through the jagir river floodgates in surabaya. these data will be used for future prediction calculations using the lstm method, focusing on the following rainfall and water discharge as research variables. data analysis technique the dataset is analyzed using some steps, as shown in figure 1. they are: 1. wrangling and preprocessing, in which the attributes are checked whether each variable column has the potential to have anomalous attributes or columns with the potential to have no value (null). 2. splitting the data into training and testing data with a composition of 70:30. 3. lstm modelling. this is the primary process of the study. python is being used to model the prediction. each variable is analyzed using lstm by processing into several layers during some iterations (named epoch) through these actions: a) normalization. scaling is applied for the data in a specific interval of 0 and 1. so it is said that the value on the dataset is normalized into ≤1 using the min-max scaler. b) activation. this study uses relu (rectified linear unit) to activate the output. the output of the activation function is expressed as 0 (zero) if the input is negative. however, if the input is positive, the output will equal the input value of the activation function (szandala, 2021). adam optimizer is also used to iteratively update the weighted network based on training data in this step. c) input epoch. this is the part to input how many iterations through the codes. epoch is defined from a certain number of iterations (n) during several basic variable calculation processes, including addition, multiplication, and other mathematical functions regards to lstm until it is completed (reach the defined epoch). d) denormalization is when a scaler puts the result back into a normal form. inverse e) data visualization is visualized into a plot diagram for each variable. 4. evaluation is the next step, where the model is evaluated using some formula to measure the performance of each result. in this study, mae (mean absolute error) and rmse (root mean square error) are used to show the performance of each variable. 5. the last step is to conclude the result and derive recommendations and suggestions for future works. results and discussion the dataset which is collected from the dinas pekerjaan umum pengairan provinsi jawa timur upt pengelolaan sumber daya air surabaya will be used for future prediction calculations using the lstm method, focusing on the following data variables: jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 443 1. rainfall data is recorded based on the automatic rainfall recorder (arr) output through the wonokromo station. there are guidelines for determining average level status, namely a rainfall value of less than 100 mm. in contrast, the alert status will apply if the rainfall value exceeds 100 mm. the data used is from january 2020 to december 2022. the sample rainfall dataset is listed in table 1. table 1. rainfall datasets. date rainfall 01/01/2020 85 02/01/2020 0 03/01/2020 0 … … 30/12/2022 1 31/12/2022 1.4 2. water discharge data is recorded based on the output of a device called awlr (automatic water level recorder) through the jagir river floodgates in surabaya. the data used is from january 2020 to december 2022. there are guidelines for determining the status of the green level if the water debit value is more than or equal to 180 m3/second and the yellow level if the water debit value is more than or equal to 200 m3/second. the level was red if the water debit value was more than or equal to 220m3/second. the data used is from january 2020 to december 2022. the sample rainfall dataset is listed in table 2. table 2. water discharge dataset date water discharge 01/01/2020 44.04 02/01/2020 26.18 03/01/2020 19.63 … … 30/12/2022 117.8 31/12/2022 125.4 preprocessing the number of datasets collected from january 2020 to december 2022 is 1,096 data consisting of date, rainfall, and water discharge variables. the data will then go through an analysis process before making predictions by selecting data and checking the attributes of each variable column with the potential to have anomalous attributes and columns with the potential to have no value (null). to be useful for data mining, the databases must undergo preprocessing in the form of data cleaning and data transformation(larose, 2005). data splitting preprocessing will then be divided into two parts, with a ratio of 70% as training data and 30% as testing data. this split data process aims to train past data to predict future data. based on the data sharing ratio above, out of 1074 data, 756 training data were obtained and 318 testing data. the datasharing process in python can be seen more clearly in figure 3. figure 3. splitting data lstm modelling the modelling process steps are: 1) normalization. scaling is applied for the data in a certain interval of 0 and 1. so it is said that the value on the dataset is normalized into ≤1 using the mimmaxscaler function as figure 4, and 5 follows. figure 4. normalization for rainfall variable figure 5. normalization for water discharge variable 2) activation. this study uses relu (rectified linear unit) to activate the output. the output of p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 jurnal riset informatika vol. 5, no. 3. june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 444 the activation function is expressed as 0 (zero) if the input is negative. however, if the input is positive, the output will equal the input value of the activation function (szandała, 2020). the activation process for each variable is shown by the codes below: model = sequential() model.add(convlstm2d(filters=64, kernel_size=(1,1), activation='relu', input_shape=(1, 1, 1, seq_size))) model.add(flatten()) model.add(dense(32)) model.add(dense(1)) model.compile(optimizer='adam', loss='mean_squared_error') model.summary() note that the adam optimizer is also used to optimize, to update the weighted network based on training data iteratively. the codes yield: figure 6. activation result input epoch. epoch is defined from a certain number of iterations (n) during several basic variable calculation processes, including addition, multiplication, and other mathematical functions regards to lstm until it is completed (reach the defined epoch). several epochs, namely 10, 50, 75, 100, and 150, were run in this study. the variations will also occur for each n (50, 75, 100 and 150) provided as input. figure 6. the result is different for each ten iterations. 3) denormalization. this is a step in which a scaler returns the result to a standard form.inverse function: 4) data visualization is the last step in which the model is visualized into a plot diagram in which each variable is presented. note that the visualization may vary for each epoch and variable. a. rainfall data visualization calculations using the adam optimization model on the rainfall variable with a variation of 10 epoch values are presented in the graphical visualization in figure 7. figure 7. adam epoch rainfall graph epoch 10 b. water discharge data visualization calculations using the adam optimization model on the water discharge variable with a variation of 75 epoch values are presented in the graphical visualization in figure 8. figure 8. adam epoch water discharge graph epoch 75 performance evaluation based on the calculation of the epoch variations, an evaluation will be carried out using the mean absolute error (mae) and root mean square error ( rmse). jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.558 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 445 the formula for each of them is as follows: mae(𝑦,𝑦 ^ ) = ∑ |𝑁−1𝑖=0 𝑦𝑖−𝑦 ^ 𝑖| 𝑁 ................................................. (1) rmse(𝑦,𝑦 ^ ) = √∑ (𝑦𝑖−𝑦 ^ 𝑖) 2 𝑁−1 𝑖=0 𝑁 .................................... (2) the documentation on both training data and testing data for the rainfall variable results of the mean absolute error (mae) and the root mean square error (rmse) for each epoch is presented in table 3 below: table 3. rainfall evaluation results no epoch training testing mae rmse mae rmse 1 10 5.73 9.40 5.95 11.12 2 50 7.31 11.72 6.05 10.62 3 75 0.054 0.099 0.041 0.091 4 100 7.73 10.77 7.65 11.10 5 150 6.43 9.56 7.00 12.11 table 3 shows the acceptable value of performance evaluation results for the rainfall variable on epoch = 75 for both training, which scored 0.054 for mae, 0.099 for rmse, and testing data, 0.041 for mae and 0.091 for rmse. in the same way as the previous variable, the following documentation on both training data and testing data for the water discharge variable results of the mean absolute error (mae) and the root mean square error (rmse) for each epoch is presented: table 4. water discharge evaluation results no epoch training testing mae rmse mae rmse 1 10 15.96 25.13 13.95 22.06 2 50 13,11 22.85 11.91 21.33 3 75 13.93 21.53 12.87 21.08 4 100 12.43 21.82 11.37 21.08 5 150 11.10 18.61 12.07 21.28 the result shows that the minor performance and water discharge evaluation scores differ for training and testing data. training data results mae = 11.10 and rmse = 18.61 in epoch (n) = 150. testing data results mae = 11.37 and rmse = 21.08 in epoch (n) = 100. this result shows two anomalies: a) for both training and testing data results, a high value of mae and rmse, which are far from 0 (zero); b) the lowest score of both mae and rmse in training and testing data lies on different epochs. training data is on epoch (n) = 150, while testing data is on epoch (n) = 100. the dataset shows no zero value for the water discharge column (which means it is impossible to find the river dry). conclusions and suggestions conclusion implementing the lstm method on the variables of rainfall and water discharge in certain epochs variations result in calculations of future data projections with certain conditions. based on the research results, it can be concluded that the rainfall variable reached an acceptable accuracy on epoch 75 with a mean absolute error (mae) of 0.054 and the root mean square error (rmse) of 0.099 for the training data. also, it has acceptable accuracy on epoch 75 with a mean absolute error (mae) of 0.041 and the root mean square error (rmse) of 0.091 for the testing data. the water discharge variable had anomalies, as the minor score was too far from the acceptable training and testing data score. training data results mae = 11.10 and rmse = 18.61 in epoch (n) = 150, while testing data results mae = 11.37 and rmse = 21.08 in epoch (n) 100. future work and recommendation since this study only compares two variables, namely rainfall and water discharge, it is recommended for further research to use more variables or other neural network methods (algorithms) and a comparative analysis process using several methods at once so that it can be seen that the performance results can be better than this study. the anomalies found in water discharge performance evaluation should be verified from another perspective as several reasons may cause the high score on mae and rmse. it is the modelling scheme that might not support non-zero datasets. references asdak, c. 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(2018). analysis of the mean absolute error (mae) and the root mean square error (rmse) in assessing rounding model. iop conference series: materials science and engineering, 324(1). https://doi.org/10.1088/1757899x/324/1/012049 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 481 classification of batu bara songket using gray-level co-occurrence matrix and support vector machine sriani-1, muhammad siddik hasibuan-2, rizkika ananda-3 department of computer science, faculty of science and technology universitas islam negeri (uin) sumatera utara medan, indonesia https://ilkomp.uinsu.ac.id/ sriani@uinsu.ac.id, muhammadsiddik@uinsu.ac.id, rizkika.ananda@uinsu.ac.id (*) corresponding author abstract songket is a traditional woven cloth from the malay and minangkabau tribes. songket can also be classified from the brocade woven family and woven with gold or silver thread. songket cloth's beauty is the indonesian people's wealth and preservation. batu bara regency is one of indonesia's regions with several songket motifs characteristics. public knowledge of batu bara songket motifs is still minimal, and the differences between one motif and another are still unknown. this research provides information about the variety of songket fabrics by classifying six types of batu bara songket motifs, namely the bunga tanjung motif, pucuk betikam motif, pucuk cempaka motif, pucuk pandan motif, tampuk manggis motif and tolab berantai motif based on the extraction of the gray level texture feature. the co-occurrence matrix includes four parameters: contrast, correlation, energy, and homogeneity, as well as a classification method with a support vector machine. the feature extraction values process as input for classification using a support vector machine. the highest accuracy achieved in this study was 57%, using 60 training data and 30 test data. keywords: classification; batu bara songket motif; gray level co-occurrence matri; support vector machine abstrak songket merupakan jenis kain tenunan tradisional yang berasal dari suku melayu dan minangkabau. songket juga dapat digolongkan dari keluarga tenunan brokat dan dapat ditenun dengan benang emas dan perak. keindahan kain songket merupakan kekayaan masyarakat indonesia yang harus terus dilestarikan. kabupaten batu bara merupakan salah satu wilayah di indonesia yang memiliki beberapa ciri khas motif kain songket. pengetahuan masyarakat akan motif-motif songket batu bara masih minim dan perbedaan antara motif yang satu dengan motif yang lain masih belum diketahui. penelitian ini dibuat dengan tujuan untuk memberikan informasi tentang ragam kain songket dengan mengklasifikasi enam jenis motif songket batu bara yakni motif bunga tanjung, motif pucuk betikam, motif pucuk cempaka, motif pucuk pandan, motif tampuk manggis dan motif tolab berantai berdasarkan ekstraksi ciri tekstur grey level co-occurrence matrix meliputi empat parameter yakni contrast, correlation, energy, dan homogeneity, serta metode klasifikasi dengan support vector machine. nilai ektraksi ciri tersebut selanjutnya akan diproses menjadi masukan untuk klasifikasi menggunakan support vector machine. akurasi tertinggi yang dicapai dalam penelitian ini sebesar 57 %, dengan menggunakan 60 data latih dan 30 data uji. kata kunci: klasifikasi; motif songket batu bara; grey level co-occurrence; support vector machine introduction indonesia is a country that is rich in unique and distinctive cultural heritage diversity. every nation or tribe has a culture (tahrir et al., 2017). a diverse cultural heritage can become essential for indonesia, and its preservation is mandatory. one manifestation of the results of this cultural process is the creation of works of art that all indonesian ethnic groups own. indonesia's diverse traditional fabrics result from cultural processes, geographical differences, flora, fauna, lifestyle differences, and livelihoods producing various traditional fabrics. indonesian traditional fabrics are in great demand p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 482 in national and international markets (johan wahyudi & ihdahubbi maulida, 2019). one of the ethnic cultures in indonesia is malay, especially in north sumatra (rigitta, 2021). songket is a traditional malay and minangkabau woven fabric in indonesia, malaysia, and brunei (nurhalimah et al., 2020). songket belongs to the brocade woven family. weaving songket cloth by hand using gold and silver threads is generally worn on formal occasions. the beauty of songket can attract domestic and foreign tourists who like indonesian traditional cloth art (salamah & kusumanto, 2017). batu bara regency has a type of songket with its characteristics (abdiansyah, 2018), public knowledge of the batu bara songket motifs is still minimal, and the difference between one motif and another is still unknown. the lack of automated data collection is the cause of this, and no application can analyze the types of batu bara songket motifs, which can help the community to provide knowledge to the public about the batu bara songket motif and is no longer wrong in recognizing the batu bara songket motif. several studies regarding the classification of songket motifs based on texture have several times in previous studies, such as the feature extraction study using the grey-level co-occurrence matrix (glcm) method. the gabor filter for image classification of pekalongan batik (surya et al., 2017), other studies on the classification of songket cloth in lombok use glcm and moment invariant as well as linear discriminant analysis (lda) (nurhalimah, 2020), feature extraction of songket images based on texture using the grey level cooccurrence matrix (glcm) method (amalia, 2018), application of a speeded-up robust feature on the random forest for classification of palembang songket motifs (yohannes et al., 2020). this study has advantages over previous research, namely using grounded theory through qualitative analysis using songket cloth objects that are observed and interacted with based on the participants' views. this research was made to provide information about the various types of coal songket cloth that previous researchers have not studied by classifying six types of batu bara songket motifs, namely six types of batu bara songket motifs namely bunga tanjung motif, pucuk betikam motif, pucuk cempaka motif, pucuk pandan motif, tampuk manggis motif, and tolab berantai motif. gray level co-occurrence matrix (glcm) method is a method for extracting image textures. texture extraction to retrieve essential information from an image before it is used for the following process, using feature extraction methods considered optimal in research (ramadhani & bethaningtyas dyah, 2018). the gray level cooccurrence matrix (glcm) method is an adequate texture descriptor and has better accuracy and computation time than other texture extraction methods (widodo et al., 2018). the support vector machine (svm) method is a machine learning technique. they learn by using a pair of input and output data as the desired target. it is called supervised learning, and the advantages of the support vector machine (svm) method are in recognizing and classifying an object (anggraini, 2017). support vector machine (svm) is a classification method with high generalizability and input space dimensions (neneng et al., 2016). research methods types of research the author's research is a type of grounded theory through the analysis of qualitative and quantitative methods. time and place of research this research was conducted precisely at the "yusra" craftsmen of batu bara woven cloth. padang genting village no. 6 district talawi and the place for system design in the computer laboratory of the faculty of science and technology, state islamic university of north sumatra. research time in november 2021 to march 2022. research target / subject the research target in this study is the batu bara community, who do not know much about the types of batu bara songket motifs. procedure the process carried out to research the classification of batu bara songket motifs based on texture with the gray level co-occurrence matrix (glcm) and support vector machine (svm) methods, namely through several stages of designing the analytical method. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 483 songket motif rgb image da ta shar ing training data test data greysca le greysca le glcm feature extraction glcm feature extraction classification result analysis svm learnin g outcomes svm learnin g hyperplane search training figure 1. songket motif classification system planning diagram figure 1 shows that the diagram uses input as an rgb songket motif image. the image is divided into two parts: the training and test data images. then the training and test data images are changed from rgb to grayscale. after that, the glcm feature extraction is sought, which consists of contrast, entropy, energy, and homogeneity features. for training data, multiclass svm learning is used in the hyperplane separator of the six types of songket motifs. the last stage is to test the system on the test data images and analyze the results of image classification on the hyperplane function that has been obtained. the steps taken in the glcm calculation are as follows the formation of the initial glcm matrix from pairs of two parallel pixels corresponding to the directions 0°, 45°, 90°, and 135°. the following form a symmetrical matrix by adding the initial matrix glcm with its transpose values, normalizing the glcm matrix by dividing each matrix element by the number of pixel pairs, and then feature extraction, namely contrast, homogeneity, energy, correlation (widodo et al., 2018). contrast = ∑ ∑ (𝑖1 − 𝑖2) 2𝑝(𝑖1, 𝑖2)𝑖2𝑖1 ................................ (1) homogeneity = ∑ ∑ 𝑝(𝑖1,𝑖2) 1+|𝑖1−𝑖2| 𝑖2𝑖1 ..................................................... (2) energy = ∑ ∑ 𝑝2(𝑖1, 𝑖2)𝑖2𝑖1 ....................................... (3) correlation = ∑ ∑ (𝑖−𝜇𝑖)(𝑗−𝜇𝑗)𝑝(𝑖,𝑗) 𝜎𝑖 𝜎𝑗𝑗𝑖 ......................................... (4) in the process of classifying songket motifs using svm, in research, in this case, the svm multiclass approach that it uses is a classification method “one against all.” in this method, k binary svm models are built, with k being multiple classes. each classification of the model it wants to use uses total data to find solutions to problems. svm classifies two classes between one class and others seen as one class. the class for a data sample is directly determined by this method. when the data sample is not included in the group containing the set class but in a specific class, then that class is a class from the sample data in question (pitoyo, 2020). table 1. svm classification with one-agains-all method yi = 1 yi=-1 kernel hypothesis class 1 not class 1 f1(x)=(w1)x+b1 class 2 not class 2 f2(x)=(w2)x+b2 .................................................................................................. (5) table 1 shows test results on test data that produce a decision function with the maximum value given a value of y_i=1 (true), while other decision functions give a value of y_i=-1 (false). accuracy : accuracy = 𝐶𝑜𝑟𝑟𝑒𝑐𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑛𝑢𝑚𝑏𝑒𝑟 𝐴𝑚𝑜𝑢𝑛𝑡 𝑜𝑓 𝑑𝑎𝑡𝑎 × 100 % ................. (6) data, instruments, and data collection techniques songket motif image data collection is divided into training and test data. the training data consists of 60 samples of songket motif image data, and the test data consists of 30 samples of songket motifs. the total sample data is 90 images of the songket motif. the data collection technique used in this study is : 1. interview figure 2. is an interview technique that was conducted by seeking information and knowledge sourced from experts engaged in fields related to this research, namely with mrs. hj. ratna, one of the craftsmen who also opened a songket business in the batubara district, so the author gets relevant data references and knows the names of the batu bara songket motifs. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 484 figure 2. interview with mrs. hj. ratna 2. observation the technique is an observation of data collection techniques by observing directly an object to seek information and knowledge related to research. image motif bunga tanjung pucuk betikam pucuk cempaka image motif pucuk pandan tampuk manggis tolab berantai figure 3. sample image of batu bara songket in figure 3. it is an example of a sample of batu bara songket images, where each songket sample shown in figure 3. has 30 image data. the six types of songket motifs are tanjung flowers, betikam shoots, cempaka shoots, pandan shoots, tampuk manggis, and tolab berantai. 3. library studies, namely in this study also used literature studies taken from scientific articles, books, and others. data analysis technique this analysis is needed to determine what kind of software will be produced. the needs analysis in this study is as follows: start rgb image input rgb image input image transformation extraction value extraction metho d glcm end figure 4. glcm feature extraction flowchart from the flowchart figure 4. the above explains the stages in the glcm feature extraction method, namely as follows : a. input the rgb image of the batu bara songket motif. b. then the image is changed from rgb to grayscale. c. after that, look for the glcm feature extraction, which consists of contrast, homogeneity, energy, and entropy features. d. generates image extraction values from the glcm method. start extraction value data normalization results data normalization results of determining output value y determination of output value y end determination of w eight (w) and bias (b) values result values ​​(w) and (b) finding h yperplane as a classification function classification results figure 5. svm classification method flowchart jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 485 from the flowchart figure 5. above explains the stages in the support vector machine (svm) classification method, namely as follows: a. after getting the value from feature extraction from the glcm method, input the glcm feature data. b. data normalization was using the equation formula attached to the svm theory. c. after normalization, can pay, pay output value (y). d. then the value of weight (w) and bias (b). e. after all, can be seen, the system looks for hyperplane as a decision function. f. so the classification uses the svm method. output in the batu bara songket motif classification system based on texture using the gray level co-occurrence matrix (glcm) method and support vector machine (svm), namely the results of the classification of songket motif types through a feature extraction process using the gray level co-occurrence method matrix (glcm) and the classification process uses the support vector machine (svm) method. results and discussion testing based on the existing image samples, a testing process on these images. at the testing stage, the digital image is in (*.jpg) format with a size of 512x512 pixels. the system testing process using the matlab application see in the process below: 1. application initial screen figure 6. initial display form figure 6. is the initial form, which is the main page for running the program to be worked on. 2. image input display figure 7. image input display form figure 7. in this form, the image input is by pressing the image input button, and then the system will direct it to select the data to be tested, and then the system will automatically display the inputted image and the image file name. 3. grayscale display figure 8. rgb display form to grayscale in figure 8. this form will be processed using a grayscale. by pressing the grayscale button, the system will process the rgb file to grayscale and display the resulting grayscale image on axes2. 4. gray level co-occurrence matrix (glcm) feature extraction display figure 9. gray level co-occurrence matrix feature extract display form figure 9. this form will perform feature extraction on the image by pressing the feature extract button. the table will display the feature extraction value of the gray level co-occurrence matrix (glcm). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 486 5. classification result display figure 10. image classification results display form of batu bara songket motifs example 10 the supplied image's classification results show on this form's display. clicking the classification button will cause the system to display the supplied image's classification findings automatically. system test results based on test data on the image of the type of songket batu bara motif that has been, if there is, in this case, a testing process will be carried out on the motif image songket batu bara with format (*.jpg). in the process of testing the motif classification songket below, there are 30 test data with 5 data from each type of songket motif, 5 test data for bunga tanjung, 5 test data for pucuk betikam, 5 test data for pucuk cempaka, 5 test data for pucuk pandan, 5 test data for tampuk manggis, and 5 test data for tolab berantai. from the result testing of as many as 30 test data. the following are the results of system testing of each image of the songket motif tested to obtain a classification of the type of coal songket motif, see table 2 below: table 2. data testing no. decision function svm multiclass one against all score yi types of batu bara songket classification result information 1. f1(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 bunga tanjung 'motif bunga tanjung' true 2. f1(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 bunga tanjung 'motif bunga tanjung' true 3. f1(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 bunga tanjung 'motif bunga tanjung' true 4. f1(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 bunga tanjung 'motif bunga tanjung' true 5. f1(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 bunga tanjung 'motif bunga tanjung' true 6. f2(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) -1 pucuk betikam 'motif pucuk pandan' false 7. f2(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 pucuk betikam 'motif pucuk betikam' true 8. f2(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 pucuk betikam 'motif pucuk betikam' true …. …………… …. ……….. ……… 30. f6(x)=sign(w1.x1+ w2.x2+ w3.x3+ w4.x4+b) 1 tolab berantai 'motif tolab berantai' true jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 487 table 2 shows 30 test data with 5 data from each type of songket motif. each test data has a one against all svm decision functions according to each class. entering the yi value shows the classification results and the types of songket motifs. if the yi value is one, then the classification results are declared correct by the type of songket motif being tested. conversely, if the yi value is -1, then the classification results are declared wrong and do not produce output that matches the type of songket motif in table 3. table 3. classification test results no image file classification result average extraction result 1. bunga tanjung 01.jpg ‘motif bunga tanjung’ contrast = 0.055182 correlation = 0.89292 energy = 0.80746 homogeneity = 0.97932 2. bunga tanjung 02.jpg ‘motif bunga tanjung’ contrast = 0.054825 correlation = 0.90229 energy = 0.89758 homogeneity = 0.98303 3. bunga tanjung 03.jpg 'motif bunga tanjung' contrast = 0.044537 correlation = 0.9417 energy = 0.79726 homogeneity = 0.98128 4. bunga tanjung 04.jpg 'motif bunga tanjung' contrast = 0.081439 correlation = 0.81279 energy = 0.91401 homogeneity = 0.98792 5. bunga tanjung 05.jpg 'motif bunga tanjung' contrast = 0.029251 correlation = 0.91165 energy = 0.76756 homogeneity = 0.98585 6. pucuk betikam 01 .jpg 'motif pucuk pandan' contrast = 0.59997 correlation = 0.82928 energy = 0.091888 homogeneity = 0.77182 7. pucuk betikam 02.jpg 'motif pucuk betikam' contrast = 0.45013 correlation = 0.75828 energy = 0.14618 homogeneity = 0.81243 8. pucuk betikam 03.jpg 'motif pucuk betikam' contrast = 1.4175 correlation = 0.67485 energy = 0.097793 homogeneity = 0.70073 9. pucuk betikam 04.jpg 'motif pucuk pandan' contrast = 0.4908 correlation = 0.91867 energy = 0.10273 homogeneity = 0.80891 10. pucuk betikam 05.jpg 'motif pucuk pandan' contrast = 0.40894 correlation = 0.85346 energy = 0.11702 homogeneity = 0.82458 11. pucuk cempaka 01.jpg 'motif pucuk pandan' contrast = 0.27883 correlation = 0.92845 energy = 0.12754 homogeneity = 0.86776 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 488 continue table 3. classification test results no image file classification result average extraction result 12. pucuk cempaka 02.jpg 'motif pucuk pandan' contrast = 0.53642 correlation = 0.92068 energy = 0.070588 homogeneity = 0.79759 13. pucuk cempaka 03.jpg 'motif pucuk pandan' contrast = 0.47518 correlation = 0.93722 energy = 0.087429 homogeneity = 0.81312 14. pucuk cempaka 04.jpg 'motif pucuk betikam' contrast = 2.06 correlation = 0.78755 energy = 0.060153 homogeneity = 0.64077 15. pucuk cempaka 05.jpg 'motif pucuk pandan' contrast = 0.26947 correlation = 0.9625 energy = 0.13427 homogeneity = 0.89281 16. pucuk pandan 01.jpg 'motif pucuk pandan' contrast = 0.20786 correlation = 0.98435 energy = 0.20333 homogeneity = 0.91465 17. pucuk pandan 02.jpg 'motif tolab berantai' contrast = 0.7327 correlation = 0.9424 energy = 0.32664 homogeneity = 0.85992 18. pucuk pandan 03.jpg 'motif pucuk pandan' contrast = 0.2666 correlation = 0.97432 energy = 0.30293 homogeneity = 0.90128 19. pucuk pandan 04.jpg 'motif pucuk pandan' contrast = 0.27675 correlation = 0.97278 energy = 0.19923 homogeneity = 0.89126 20. pucuk pandan 05.jpg 'motif pucuk pandan' contrast = 0.21481 correlation = 0.98068 energy = 0.25459 homogeneity = 0.91031 21. tampuk manggis 01.jpg 'motif tampuk manggis' contrast = 0.52105 correlation = 0.94653 energy = 0.14224 homogeneity = 0.85553 22. tampuk manggis 02.jpg 'motif pucuk pandan' contrast = 0.29603 correlation = 0.95872 energy = 0.13533 homogeneity = 0.88979 23. tampuk manggis 03.jpg 'motif pucuk pandan' contrast = 0.56491 correlation = 0.9307 energy = 0.28442 homogeneity = 0.87347 24. tampuk manggis 04.jpg 'motif pucuk pandan' contrast = 0.81957 correlation = 0.88644 energy = 0.085617 homogeneity = 0.7813 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 489 continue table 3. classification test results table 3 shows 30 test data with 5 data from each type of songket motif. there is a songket image file with the file name according to the name of the songket motif. the classification results are found in the system according to the class of songket motifs tested. the system's average classification results are obtained according to the class of songket motifs tested. from the test results of all 30 test data, there are 13 types of songket motifs that are wrong in the placement of songket motifs according to their class, so from the results of the classification of songket motifs, the accuracy results are obtained with a value of 57% with a description of 17 test data that are correct for class placement and 13 incorrect test data in class placement. conclusions and suggestions conclusion based on the results of tests carried out by classifying the batu bara songket motif based on the image of the songket motif using the gray level co-occurrence matrix (glcm) and support vector machine (svm) methods, the following conclusions were drawn: in the manual calculation of the support vector machine (svm) classification using the one againstall decision function equation with class = sign(f(x)). the function sign is used to check the results of the calculations performed on the test data. the test results on the test data produce the decision with the maximum value given a value of y_i=1, while the other decision functions are given a value of y_i=-1. from the results of testing all test data, which are 30 test data, from the results of the classification of the type of songket motif, the accuracy results with a value of 57% with a description of 17 test data that is correct in class placement and 13 test data are wrong in class placement. extraction of gray level co-occurrence matrix (glcm) features of batu bara songket motif images used to classify types of batu bara songket motifs. the distance of neighboring pixels (distance) is one and in the direction of 0°, 45°, 90°, and 135°. the resulting feature of the gray level cooccurrence matrix (glcm) represents the texture value of the songket motif image. so that these values are used to classify the types of batu bara no image file classification result average extraction result 25. tampuk manggis 05.jpg 'motif pucuk cempaka' contrast = 1.7677 correlation = 0.86206 energy = 0.064323 homogeneity = 0.69035 26. tolab berantai 01.jpg 'motif tolab berantai' contrast = 0.33832 correlation = 0.96599 energy = 0.46026 homogeneity = 0.90777 27. tolab berantai 02.jpg 'motif tolab berantai' contrast = 0.82393 correlation = 0.88715 energy = 0.46915 homogeneity = 0.86199 28. tolab berantai 03.jpg 'motif tolab berantai' contrast = 0.62157 correlation = 0.88225 energy = 0.46947 homogeneity = 0.90785 29. tolab berantai 04.jpg 'motif tolab berantai' contrast = 0.24126 correlation = 0.96976 energy = 0.47912 homogeneity = 0.92227 30. tolab berantai 05.jpg 'motif tolab berantai' contrast = 0.37501 correlation = 0.97167 energy = 0.46261 homogeneity = 0.90785 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.469 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 490 songket motifs using the support vector machine (svm) method, conclusions should be in the form of paragraphs that answer the research objectives. it tells how the researcher’s work can advance current knowledge but does not seem to discuss it. suggestion the addition of the type of songket motif studied is universal. the use of different methods as a comparison of this study. it expanded using other feature extraction methods, such as color or shape extraction. image capture of songket motifs is idealized in terms of lighting. moreover, it can be developed into a mobile-based application or website so the wider community can use it. references abdiansyah, m. (2018). peran dinas pendidikan dan kebudayaan dalam mempromosikan budaya kain tenun songket di desa padang genting kabupaten batubara. 82. amalia, i. (2018). ekstraksi fitur citra songket berdasarkan tekstur menggunakan metode gray level co-occurrence matrix (glcm). jurnal infomedia, 3(2), 64–68. https://doi.org/10.30811/jim.v3i2.715 anggraini, r. (2017). klasifikasi jenis kualitas keju dengan menggunakan metode gray level cooccurrence matrix (glcm) dan support vector machine (svm) pada citra digital. eproceeding of engineering, 4(2), 2035–2042. johan wahyudi, & ihdahubbi maulida. (2019). pengenalan pola citra kain tradisional menggunakan glcm dan knn. jurnal teknologi informasi universitas lambung mangkurat (jtiulm), 4(2), 43–48. https://doi.org/10.20527/jtiulm.v4i2.37 neneng, n., adi, k., & isnanto, r. 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(2018). pemanfaatan ciri gray level co-occurrence matrix (glcm) citra buah jeruk keprok (citrus reticulata blanco) untuk klasifikasi mutu. jurnal pengembangan teknologi informasi dan ilmu komputer, 2(11), 5769– 5776. https://j-ptiik.ub.ac.id/index.php/jptiik/article/view/3420 yohannes, y., devella, s., & pandrean, a. h. (2020). penerapan speeded-up robust feature pada random forest untuk klasifikasi motif songket palembang. jurnal teknik informatika dan sistem informasi, 5(3), 360– 369. https://doi.org/10.28932/jutisi.v5i3.1978 jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 17 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. analisis kepuasan pelanggan terhadap kualitas pelayanan jasa logistik pt. cipta krida bahari maria sitorus sistem informasi stmik nusa mandiri jakarta maria.sitorus8@gmail.com abstract the number of ckb logistics customers is 2133 in the year. this company sometimes can not be served perfectly, such as the relative consumers who feel that the quality of service provided by ckb logistics does not match the price paid (junedi, 2014. some consumers experience shipments that are sent often arrive not on time. purpose of analysis review this is to identify the level of ckb logistics service quality for customer satisfaction.testing the dimensions of service quality that affect customer satisfaction.determining the dimensions of service quality that are more dominant in influencing customer satisfaction.the results obtained are known that the csi value is based on the importance of 0.86 where the customer is "very satisfied "with ckb service and csi value based on the performance level of 0.74, which means customers feel" satisfied "with the service performance carried out by the ckb. keywords: logistics services, important performance analysis methods, cronbach's apha method. abstrak jumlah pelanggan ckb logistics sebanyak 2133 pada tahun. hal ini perusahaan terkadang tidak dapat terlayani dengan sempurna, seperti relatif konsumen yang merasa bahwa kualitas pelayanan yang diberikan oleh ckb logistics tidak sesuai dengan harga yang dibayarkan (junedi, 2014. sebagian konsumen mengalami barang kiriman yang dikirimkan sering tiba tidak tepat waktu. tujuan kajian analisis ini untuk mengidentifikasi tingkat kualitas pelayanan ckb logistics untuk kepuasan pelanggan. menguji dimensi kualitas pelayanan yang mempengaruhi kepuasan pelanggan. menentukan dimensi kualitas pelayanan yang lebih dominan dalam mempengaruhi kepuasan pelanggan. hasil yang didapat diketahui bahwa nilai csi berdasarkan tingkat kepentingan sebesar 0.86 dimana pelanggan "sangat puas" dengan pelayanan ckb dan nilai csi berdasarkan tingkat kinerja sebesar 0.74 yang berarti pelanggan merasa “puas” terhadap kinerja pelayanan yang dilakukan oleh ckb. kata kunci: jasa logistik, metode important performance analysis, metode apha cronbach. pendahuluan masyarakat yang bertempat tinggal di kota-kota besar, kebutuhan akan jasa akan semakin meningkat, seperti kebutuhan akan sarana angkutan, fast food café, dan sarana tempat hiburan akhir pekan. hal ini sejalan dengan dalil ekonomi, bahwa trend kebutuhan terhadap ragam dan mutu jasa adalah searah dengan perkembangan kualitas hidup dan pendapatan masyarakat. adanya kecenderungan ini maka masyarakat akan lebih menuntut pelayanan yang lebih baik (supriyanto & soesanto, 2012) dan sesuai dengan kebutuhannya, sehingga mereka lebih teliti dan kritis (wijayanti & seminari, 2013)dalam memilih segala sesuatu yang dapat digunakan untuk memenuhi kebutuhannya. sebagian besar sektor publik menyadari pentingnya kepuasan konsumen dan mempunyai batasan anggaran untuk pelayanan konsumen. meskipun terdapat kendala, pemimpin dalam organisasi sektor publik harus secara terusmenerus meningkatkan kualitas pelayanan. hal ini juga dilakukan oleh pt. cipta krida bahari (ckb) sebagai salah satu penyedia jasa logistik terpadu yang berfokus pada jasa pengiriman barang. faktor objektifnya adalah sejumlah keunggulan kompetentif yang dimiliki oleh ckb logistics, antara lain: layanan logistik terpadu, proyek logistik, manajemen gudang, manajemen shorebase, pengiriman industri, dan http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 18 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. logistik batubara serta berfokus untuk mendukung industri energi terkait seperti minyak dan gas, pertambangan, alat berat, pembangkit listrik, dan konstruksi. perusahaan ini menawarkan solusi logistik terbaik dengan keahlian dan jaringan di lebih 35 gateway di seluruh indonesia, dipimpin oleh oleh tim manajemen yang berpengalaman dan professional dengan pengalaman bertahuntahun dalam bidang logistik, terhubung dengan sistem online sehingga pelanggan akan mendapatkan informasi yang akurat dan dapat di andalkan tentang pengiriman mereka kapan saja, dimana saja. jumlah pelanggan ckb logistics sebanyak 2133 pada tahun. hal ini perusahaan terkadang tidak dapat terlayani dengan sempurna, seperti relatif konsumen yang merasa bahwa kualitas pelayanan yang diberikan oleh ckb logistics tidak sesuai dengan harga yang dibayarkan (junedi, 2014. sebagian konsumen mengalami barang kiriman yang dikirimkan sering tiba tidak tepat waktu (sari, 2014) seperti yang dijanjikan pada jasa pelayanan yang dipakai, walaupun saat sudah berusaha memberikan yang terbaik kepada pelanggan. demi terwujudnya maksud tersebut, maka ckb logistics diharapkan mampu menjaga perilaku yang ramah, efsien dan efektif dalam menyajikan produk dan jasanya, sehingga dapat menumbuhkan kepercayaan dan kenyamanan dari pihak pelanggan. ckb logistics di harapkan dapat lebih mengetahui faktor-faktor apa sajakah yang dapat meningkatkan kepuasan pelanggan dan juga mengevaluasi dan memperbaiki faktor-faktor yang dapat menurunkan tingkat kepuasan pelanggan. kinerja dari ckb logistics akan menjadi penilaian pelanggan dalam mempersepsikan pelayanan yang diberikan. persepsi pelanggan sendiri dipengaruhi oleh kinerja serta kualitas produk dan jasa (kurniasih, 2015) yang seharusnya diberikan secara maksimal kepada pelanggan, karena pelanggan merupakan prioritas utama perusahaan dalam mempertahankan kelangsungan hidup perusahaan. secara tidak langsung kritikan dari pelanggan yang ditujukan kepada pelayanan ckb logistics merupakan suatu masukan yang berguna untuk meningkatkan kinerja ckb logistics. tujuan penelitian ini untuk mengidentifikasi tingkat kualitas pelayanan ckb logistics untuk kepuasan pelanggan. menguji dimensi kualitas pelayanan yang mempengaruhi kepuasan pelanggan. menentukan dimensi kualitas pelayanan yang lebih dominan dalam mempengaruhi kepuasan pelanggan. bahan dan metode a. metode pengumpulan data, populasi dan sample penelitian teknik pengambilan data dilakukan dengan cara observasi langsung, wawancara, penyebaran kuesioner untuk mendapatkan data primer, sedangkan untuk data sekunder berasal dari studi pustaka. metode pemilihan sampel yang digunakan yaitu metode non probability sampling dengan menggunakan teknik convenience sampling. responden tersebut adalah pelanggan ckb. jumlah responden dalam penelitian ini ditentukan dengan rumus slovin (n. p. a. p. sari, 2014) yaitu: 𝑛 = 𝑁 (1+𝑁∙𝑒 2) ………………………………………….. (1) n = jumalah sampel n = ukuran populasi e = persentase kelongaran ketidak telitian karena kesalahan sampel yang masih bias di tolelir 10%. diketahui jumlah pelanggan ckb sebanyak 2133 pada tahun 2013 pelanggan , sehingga di peroleh sampel sejumlah : 𝑛 = 2133 (1 + 2133 ∙ 10%2) 𝑛 = 2133 (22,33) = 96 b. metode analisis data 1. uji validitas penggujian validitas dimaksudkan untuk mengetahui sejauh mana alat pengukur (instrument) mengukur apa yang ingin diukur. uji validitas digunakan untuk menghitung nilai korelasi (r) antara data masing-masing pertanyaan dengan skor total. teknik yang dipakai untuk menguji validitas kuesioner adalah teknik korelasi product moment person. uji validitas dalam penelitian ini dilakukan dengan menggunakan bantuan microsoft excel 2007 dan spss versi 22.0. 2. uji reliabilitas reliabilitas adalah nilai yang menunjukkan konsistensi suatu alat ukur didalam mengukur gejala yang sama. uji reliabilitas dalam penelitian ini menggunakan teknik apha cronbach. hasil uji yang dilakukan dengan menggunakan bantuan software spss versi 22.0 for windows menunjukkan bahwa peubah tersebut bersifat sangat reliabel. 3. important performace analysis http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 19 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. metode important performance analysis digunakan untuk mendapatkan informasi tentang tingkat kepuasan pelanggan terhadap suatu pelayanan dengan cara mengukur tingakat kepentingan dan tingakat pelaksanaannya. tingkat kepentingan dari kualitas pelayanan adalah seberapa penting suatu peubah pelayanan bagi pelanggan terhadap kinerja pelayanan. skala likert 4 tingkat digunakan untuk mengukur tingkat kepentingan yaitu sangat penting, penting, kurang penting, kurang penting, sangat tidak penting. ke empat tingkat tersebut diberi skor sebagai berikut: a. jawban sangat penting diberi skor 4 b. jawaban penting diberi skor 3 c. jawaban kurang penting diberi skor 2 d. jawaban sangat tidak penting diberi skor 1 tingkat pelaksanaan adalah kinerja aktual dari mutu pelayanan diberikan oleh ckb logistics, yang dirasakan oleh pelanggannya. skala likert 4 digunakan untuk mengukur tingkat pelaksanaan yaitu sangat penting, penting, kurang penting, sangat tidak penting. ke empat tingkat tersebut diberi skor sebagai berikut : a. jawban sangat penting diberi skor 4 b. jawaban penting diberi skor 3 c. jawaban kurang penting diberi skor 2 d. jawaban sangat tidak penting diberi skor 1 dalam analisis data ini terdapat dua buah variabel yang diwakili oleh huruf x dan y, dimana x adalah tingkat kinerja suatu produk konsumen sementara y adalah tingkat kepentingan konsumen (supranto, 1998) 𝑇𝐾𝑖 = 𝑋𝑖 𝑌𝑖 𝑥 100% ……………………………………….. (2) tki : tingkat kesesuain responden xi : bobot penilaian pelanggan terhadap kinerja pt.ckb yi : bobot penilaian pelanggan terhadap tingkat kepentingan atribut pt. ckb bobot penilaian kinerja atribut produk adalah bobot tangapan atau penilaian responden terhadap kinerja atribut-atribut yang telah dilakukan atau dirasakan oleh responden. kinerja pt. ckb dianggap telah memenuhi kepuasan pelanggan jika tki >100%. dan sebaliknya, jika besar tki<100% maka kinerja pt. ckb dianggap belum dapat memenuhi kepuasan pelanggan. setelah diketahui tingkat kepentingan dan tingkat pelaksanaan setiap peubah untuk seluruh responden, selanjutnya adalah memetakan hasil perhitungan yang telah didapat kedalam diagram kartesius. masing-masing atribut diposisikan dalam sebuah diagram, dimana skor rata-rata penilaian terhadap tingkat kinerja ( x ) menunjukkan posisi suatu atribut pada sumbu x, sementara posisi atribut pada sumbu y, ditunjukkan oleh skor rata-rata tingkat kepentingan terhadap atribut ( y ). (supranto, 1998) 𝑋1̅̅ ̅̅ = ∑ 𝑋𝑖 𝑛 ………………………………………………. (3) 𝑌1̅̅̅̅ = ∑ 𝑌𝑖 𝑛 …………..…………………………….…….. (4) 𝑋1̅̅ ̅̅ : skor rataan setiap peubah i pada tingkat kinerja 𝑌1̅̅̅̅ : skor rataan setiap peubah i pada tingkat kepentingan ∑ 𝑋𝑖 : total skor setiap peubah i pada tingkat pelaksanaan dari seluruh responden ∑ 𝑌𝑖 : total sekor setiap peubah i pada tingkat kepentingan dari seluruh responden. n = total responden diagram kartesius adalah diagram yang terdiri dari empat bagian yang dibatasi oleh dua buah bagian garis yang berpotongan tegak lurus pada titik x dan y, dimana x adalah rata-rata dari bobot tingkat kinerja atribut produk, sedangkan y merupakan rata-rata dari tingkat kepentingan seluruh faktor yang mempengaruhi kepuasan pelanggan, rumusnya adalah (supranto, 1998) �̅� = ∑ 𝑛=1 𝑥𝑖𝑖 𝑘 ………………………………………………… (5) �̅� = ∑ 𝑛=1 𝑦𝑖𝑖 𝑘 ………………………………………………… (6) �̅� : rataan dari total rataan bobot tingkat pelaksanaan �̅� : rataan dari total rataan bobot tingkat kepentingan k = jumlah peubah yang ditetapkan nilai x dan nilai y digunakan sebagai pasangan koordinat titik-titik atribut yang memposisikan suatu atribut terletak dimana pada diagram kartesius. penjabarannya dari diagram kartesius adalah sebagai berikut: http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 20 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sumber: (supranto, 1998) gambar 1. diagram tingkat kinerja dan tingkat kepentingan a. kuadran a ( prioritas utama ) kuadaran ini merupakan wilayah yang membuat peubah dengan tingkat kepentingan tertinggi, tetapi memiliki tingkat kinerja rendah. peubah –peubah yang masuk pada kuadran ini harus ditingkatkan kinerjanya. perusahaan harus secara terus –menerus melaksanakan perbaikan. b. kuadran b ( pertahankan prestasi ) faktor-faktor yang dianggap penting oleh pelanggan dari faktor-faktor yang dianggap pelanggan telah sesuai dengan apa yang dirasakannya, sehingga tingkat kepuasannya relatif lebih tinggi. peubah –peubah yang masuk pada kuadaran ini harus tetap dipertahankan dan harus terus dikelola dengan baik, hal ini dikarenakan semua peubah ini menjadikan produk atau jasa tersebut unggul dimata pelanggan. c. kuadran c ( prioritas rendah ) kuadaran ini merupakan wilayah yang membuat peubah dengan tingkat kepentingan dan tingkat kinerja rendah. peubah – peubah mutu pelayanan yang termasuk dalam kuadran ini dirasakan tidak terlalu penting oleh pelanggan dan pihak perusahaan hanya melaksanakan dengan biasa saja. pihak perusahaan belum merasa terlalu perlu mengalokasikan biaya dan investasi untuk memperbaiki kinerjanya ( prioritas rendah ). namun perusahaan juga perlu mewaspadai, mencermati, dan mengontrol setiap peubah pada kuadran ini, karena tingkat kepentingan pelanggan dapat berubah seiring meningkatnya kebutuhan. d. kuadran d ( berlebihan ) faktor-faktor yang dianggap kurang penting oleh pelanggan dan dirasakan terlalu berlebihan. peubah-peubah yang termasuk dalam kuadran ini dapat dikurangi agar perusahaan dapat menghemat biaya. c. indeks kepuasan pelanggan ( customer satisfaction index ) pengukuran terhadap indeks kepuasan pelanggan (customer satisfaction index) di perlukan karena hasil dari pengukuran tersebut dapat digunakan sebagai acuan untuk menentukan sasaran–sasaran ditahun –tahun mendatang. tanpa adanya indeks kepuasan pelanggan tidak mungkin top management dapat menentukan target dalam peningkatan kepuasan pelanggan.selain itu indeks juga diperlukan karena proses pengukuran kepuasan pelanggan bersifat kontinu. cara menghitung indeks kepuasan pelanggan adalah: 1. menghitung weighting factors (wf) yaitu fungsi dari median importance score atau skor median tingkat kepentingan masingmasing atribut dalam 10% dari total median importance score atau skor median tingkat kepentingan untuk seluruh atribut yang diuji. 2. menghitung weighted score (ws) yaitu fungsi dari median satisfaction score atau skor median tingkat kepuasan masing-masing atribut dikalikan dengan weighting factors (wf) masing-masing atribut. 3. menghitung wighted median total (wmt) yaitu total dari nilai weighted score (ws) keseluruhan 4. indeks kepuasan pelanggan yaitu perhitungan dari weighted median total (wmt) dibagi skala maksimum atau hightest scale dikali 100%. tingkat kepuasan responden secara menyeluruh dapat dilihat dari kriteria tingkat kepuasan pelanggan atau konsumen, dengan kriteria sebagai berikut: a. 0,81 – 1,00 sangat puas b. 0,66 – 0,80 puas c. 0,51 – 0,65 cukup puas d. 0,00 – 0,35 tidak puas hasil dan pembahasan a. perhitungan uji vadilitas dan uji reliabiltas uji validitas dalam penelitian ini menggunakan metode product moment, sedangkan uji reliabilitas menggunakan cronbach alpha dengan alat bantu statistikprogram spss versi 22.0. 1. uji vadilitas a. dasar pengambilan keputusan berdasarkan tingkat kinerja http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 21 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. rhitung > rtabel = valid rhitung< rtabel = tidak valid rtabel = n= 100 = 0,195 (berdasarkan tabel distribusi nilai 5%) berdasarkan hasil uji vadilitas berdasarkan tingkat kepentingan dibawah maka dapat disimpulkan rhitung 0,452 dan rtabel 0,195 maka data dapat dikatakan valid. b. dasar pengambilan keputusan berdasarkan tingkat kepentingan rhitung > rtabel = valid rhitung< rtabel = tidak valid rtabel = n= 100 = 0,195 ( berdasarkan tabel distribusi nilai 5% ) berdasarkanhasil uji vadilitas berdasarkan tingkat kepentingan dibawah maka dapat disimpulkan rhitung 0,524 dan rtabel 0,195 maka data dapat dikatakan valid. tabel 1. case processing summary n % cases valid 100 100.0 excludeda 0 .0 total 100 100.0 a. listwise deletion based on all variables in the procedure. sumber: (sitorus, 2014) tabel 2. reliability statistics cronbach's alpha n of items .929 15 sumber: (sitorus, 2014) 2. uji reliabilitas a. dasar pengambilan keputusan berdasarkan tingkat kinerja alpha > rtabel = reliabel alpha < rtabel = tidak reliabel rtabel = n= 100 = 0,195 ( berdasarkan tabel distribusi nilai 5% ) berdasarkan hasil uji vadilitas berdasarkan tingkat kepentingan dibawah maka dapat disimpulkan alpha 0,929 dan rtabel 0,195 maka data dapat dikatakan reliabel. b. dasar pengambilan keputusan berdasarkan tingkat kepentingan alpha > rtabel = reliabel alpha < rtabel = tidak reliabel rtabel = n= 100 = 0,195 ( berdasarkan tabel distribusi nilai 5% ) berdasarkan hasil uji vadilitas berdasarkan tingkat kepentingan dibawah maka dapat disimpulkan alpha 0,946 dan rtabel 0,195 maka data dapat dikatakan reliabel. case processing summary n % cases valid 100 100.0 excludeda 0 .0 total 100 100.0 a. listwise deletion based on all variables in the procedure. sumber: (sitorus, 2014) reliability statistics cronbach's alpha n of items .946 15 sumber: (sitorus, 2014) b. perhitungan customer satisfaction index (csi) pengukuran customer satisfaction index (csi) dilakukan untuk mengetahui kepuasan pelanggan dan dijadikan acuan dalam menentukan sasaran – sasaran di masa yang akan datang. tanpa adanya csi, kecil kemungkinan top manager dapat menetukan goal dalam peningkatan kepuasan pelanggan. hasil perhitungan csi dapat dilihat pada tabel 1, dibawah ini: berikut adalah perhitungan csi berdasarkan tingkat kepentingan: a. skor median di dapat dari total perhitungan jawaban kuesioner b. average di dapat dari skor median dibagi dengan jumlah responden ( 355/ 100= 3.55). c. index didapat dari average dibagi skala likert dimana dalam penelitian ini skala yang dipakai empat dikali 10 ( 3.55/4) x 10 = 8.875 menjadi 8.88). d. importance weighting factor atau di sebut juga wf didapat dari average dibagi total average (3.55/ 51.67 = 0.068 menjadi 0.07 ). e. importance weighting score atau di sebut juga ws didapat dari wf dikali average ( 0.07 x 3.55 = 0.24 ). f. customer satisfaction index( csi ) di dapat dari total ws dibagi dengan skala likert yaitu 4 ( 3.45/4 = 0.86 ). berikut adalah perhitungan csi berdasarkan tingkat kinerja: a. skor median di dapat dari total perhitungan jawaban kuesioner b. average di dapat dari skor median dibagi dengan jumlah responden ( 302/ 100=3.02). http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 22 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. c. index didapat dari average dibagi skala likert dimana dalam penelitian ini skala yang dipakai empat dikali 10 ( 3.02/4) x 10 = 87.55 ). d. importance weightingfactor atau di sebut juga wf didapat dari average dibagi total average ( 3.02/ 44.40 = 0.068 menjadi 0.07 ). e. importance weighting score atau di sebut juga ws didapat dari wf dikali average ( 0.07 x 3.02 = 0.21 ). f. customer satisfaction index ( csi ) di dapat dari total ws dibagi dengan skala likert yaitu 4 ( 2.97/4 = 0.74 ). hasil diatas menunjukkan nilai csi berdasarkan tingkat kepentingan dan tingkat kinerja sebesar 0,86 dan 0,74, yang mana hasil dari perhitungan tersebut di dapat bahwa tingkat kepentingan lebih tinggi nilainya dibandingkan dengan tingkat kinerja, ini menunjukkan bahwa tingkat kinerja dari pt. ckb belum maksimal diberikan. jika dilihat dari intervalnya berdasarkan tingkat kepentingan 0.81 – 1.00 berarti pelanggan “sangat puas” sedangkan berdasarkan tingkat kinerja 0.66 – 0.80 berarti pelanggan “puas”. dengan ini pt. ckb di harapkan harus bisa memberikan pelayanan lebih baik khususnya dari segi kinerja agar kedepannya pelanggan akan sangat puas dengan pelayanan yang berikan. c. analisa strategi untuk meningkatkan kepuasan pelanggan dengan menggunakan importance performance analysis pengukuran ipa dijabarkan kedalam diagram kartesius yang tersaji pada gambar menunjukkan bahwa sumbu x dan y . sumbu x merupakan nilai rataan tingkat kinerja dan sumbu y merupakan nilai rataan kepentingan. untuk mengetahui secara jelas penempatan dari 15 atribut kualitas jasa yang telah dianalisa tersebut , maka 15 atribut tersebut akan dikelompokan menjadi empat kuadran. sumber: (sitorus, 2014) gambar 2. diagram kartesius importance performance analysis keterangan : 1. kuadran i a. ketersediaan armada yang sudah uji emisi ( atribut 15 ) b. kecepatan dan kesiapan dalam merespon permintaan pelanggan (atribut 12 ) c. pemahaman karyawan terhadap kebutuhan logistik pelanggan (atribut 7) d. kompensasi yang ditawarkan jika terjadi kerusakan atau kehilangan barang (atribut 10) e. pengiriman tepat waktu dan barang diterima dengan lengkap (atribut 4) 2. 2. kuadran ii 1. pengetahuan karyawan mengenai produk layanan logistik (atribut 1) 2. kemudahan dalam pengurusan dokumen pengiriman barang (atribut 2) 3. kemudahan dalam memperoleh informasi status pengiriman barang di situs ckb logistics (atribut 3) 4. ketersediaan jenis produk layanan dan cakupan wilayah layanan (atribut11) 5. kebersihan gudang yang disewakan (atribut 14) 3. c. kuadran iii a. perlakuan dan kecepatan dalam penyelesaian complain atau klaim (atribut 5 ) 4. kuadran iv a. harga yang diberikan dapat diterima oleh pelanggan (atribut 6) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 23 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. b. kualitas layanan yang diberikan sesuai dengan uang yang dikeluarkan oleh pelanggan (atribut 8) c. sikap dan tingkah laku karyawan terhadap pelanggan (atribut 9) d. proaktif menginformasikan status pengiriman kepada pelanggan (atribut 13) kuadran pertama terletak di sebelah kiri atas, kuadran kedua berada di sebelah kanan atas, kuadran tiga berada di sebelah kiri bawah , dan kuadran ke empat berada di sebelah kanan bawah. posisi masing –masing atribut pada keempat kuadran tersebut dijadikan sebagai alternatif strategi untuk meningkatkan kepuasan pelanggan ckb. gambar juga menunjukkan posisi masing– masing atribut yang mempengaruhi kepuasan pelanggan ckb dalam kuadrannya masing–masing. interprestasi dari importance and performance analysis dapat dilihat pada kuadran–kuadran dibawah ini : 1. kuadran i ( prioritas pertama ) atribut – atribut yang berada dalam kuadran ini harus lebih di prioritaskan dan di perbaiki, sehingga tingkat kinerja semakin meningkat dan menjadi lebih baik lagi, karena atribut-atribut ini memiliki nilai kepentingan yang tinggi bagi konsumen , namun kinerjanya masih kurang memuaskan. atribut–atribut yang berada pada kuadran ini adalah: a. pengiriman tepat waktu dan barang diterima dengan lengkap ( atribut 4 ) ketepatan antara jadwal yang telah diberikan dengan kenyataan dilapangan merupakan atribut yang memiliki nilai tingkat kepentingan paling tinggi dan juga menjadi atribut yang memiliki nilai kinerja paling rendah. hal tersebut menunjukkan adanya kesenjangan atau gap yang cukup besar antara harapan dan kinerja yang dirasakan oleh pelanggan ckb. pt. ckb sebagai penyelenggaran tunggal pelayanan jasa logistik dalam hal ini ckb perlu melakukan evaluasi sehingga dapat diketahui faktor –faktor yang menyebabkan rendahnya tingkat kinerja yang dimiliki oleh atribut diatas sehingga dapat ditemukan atau diambil langkah penyelesaian dan perbaikan yang tepat dan cepat. evaluasi secara rutin atau berkala adalah langkah baik yang penting untuk dilakukan oleh perusahaan untuk segera mengetahui dan melakukan tindakan penyelesaian terhadap permasalahan yang muncul. mengingat ketepatan jadwal dalam hal ini menyangkut tepat waktu, konsisten dan kecepatan pelayanan merupakan hal penting dalam pelayanan jasa logistik. b. pemahaman karyawan terhadap kebutuhan logistik pelanggan (atribut 7) pemahaman karyawan dalam memberikan informasi yang dibutuhkan pelanggan merupakan salah satu bentuk pelayanan yang penting untuk dilakukan , karena sangat membantu pelanggan dalam mengambil keputusan terutama dalam kondisi darurat. c. kompensasi yang ditawarkan jika terjadi kerusakan atau kehilangan barang (atribut 10) respon yang cepat terhadap terjadinya kerusakan dan kehilangan barang yang disampaikan oleh pelanggan menunjukkan adanya tanggung jawab yang besar dari perusahaan kepada pelanggannya. setiap permasalahan yang diantisipasi dengan baik dan cepat oleh perusahaan dapat memberikan suatu kesan yang baik kepada pelanggan ,dan pelanggan tidak akan menjadi terlalu kecewa dan terus mengingat kejadian itu. e. kecepatan dan kesiapan dalam merespon permintaan pelanggan (atribut 12) kecepatan dan kesiapan dalam merespon permintaan pelanggan dapat dilihat dari pelaksanaan pelayanan yang dilakukan kepada pelanggan. jika pelanggan merasa kurang puas dengan pelayanan yang didapat maka dapat diartikan bahwa kinerja petugas atau kemampuan karyawan dalam melaksanakan pekerjaannya belum maksimal. karyawan –karyawan yang bertugas memberikan pelayanan langsung kepada pelanggan memiliki tanggung jawab yang besar terhadap kepuasan pelanggan itu sendiri, karena karyawan – karyawan inilah yang berhadapan langsung dengan pelanggan. dengan kata lain baik atau buruknya citra dari pelayanan perusahaan sebagian besar berada di tangan mereka. oleh karena itu manajemen perusahaan dalam hal ini ckb perlu mengambil langkah – langkah perbaikan guna peningkatan kinerja dimasa yang akandatang. langkah-langkah tersebut antara lainsebagai berikut: 1) evaluasi kinerja internal dimana evaluasi ini dilakukan dengan melibatkan seluruh karyawan terutama yang bertugas dilapangan secara rutin. 2) pelatihan dan pengarahan diberikan kepada petugas dan berkaitan dengan tugas serta tanggung jawab masing – masing. f. ketersediaan armada yang memadai ( atribut 15 ) ketersedian armada akan sangat berpengaruh terhadap ketepatan pengiriman barang, dikarenakan jika armada tidak memandai http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 24 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. akan menimbulkan barang – barang yang seharusnya dikirimkan secara tepat waktu menjadi terhalang. dengan demikian ketersedian armada sangat penting dan sangat berpengaruh dalam kegiatan proses pengambilan dan pengiriman barang. 2. kuadran ii ( pertahankan prestasi ) atribut –atribut yang temasuk ke dalam kuadran ini merupakan atribut –atribut yang dianggap penting oleh pelanggan dan kinerja dari pihak perusahaan pada atribut ini juga sudah sangat baik, sehingga pelanggan merasa puas. atribut –atribut yang termasuk kedalam kuadran kedua adalah: a. pengetahuan karyawan mengenai produk layanan logistik (atribut 1) setiap karyawan sudah dibekali pengetahuan produk layanan logistik yang ada di perusahaan sehingga karyawan sudah mengerti akan produk layanan yang ditawarkan. b. kemudahan dalam pengurusan dokumen pengiriman barang (atribut 2) dalam pengurusan dokumen pengiriman barang tidak sulit dikarenakan ckb sudah mempunyai sertifikat dan pengakuan baik pengiriman melalui udara, darat maupun laut. c. kemudahan dalam memperoleh informasi status pengiriman barang di situs ckb logistics ( atribut 3) pelanggan tidak akan merasa bingung akan status barang mereka, dikarenakan setiap pelanggan dapat melihat status barangnya dengan cara mengakses situs ckb dan status setiap pengiriman akan selalu terupdate. d. ketersediaan jenis produk layanan dan cakupan wilayah layanan ( atribut 11 ) jenis produk layanan dan cakupan wilayah sangat luas, dimana jenis layanan produk yang ditawarkan bisa melalui udara, darat, laut maupun penyewaan gudang. sedangkan untuk cakupan wilayah layanan tidak terbatas. 5. kebersihan gudang yang disewakan ( atribut 14 ) gudang yang ditawarkan sangat bersih dikarenakan barang –barang yang disimpan di dalam gudang harus terjaga , baik dari sirkulasi udara, rak –rak penyimpanan barang, dan kebersihan gudang dari debu. 3. kuadran iii (prioritas rendah) atribut yang masuk ke dalam kuadran ini merupakan atribut –atribut yang dianggap kurang penting oleh pelanggan dan kinerjanya pada atribut ini juga kurang begitu diperhatikan karena atribut –atribut pada kuadran tiga merupakan atribut –atribut yang kurang berpengaruh terhadap kepuasan pelanggan.atribut –atribut yang termasuk kedalam kuadran tiga adalah: 1. perlakuan dan kecepatan dalam penyelesaian complain atau klaim (atribut 5) walaupun keberadaan atribut –atribut diatas dianggap kurang berpengaruh terhadap pelanggan namun dalam pelaksanaan dilapangan harus tetap di perhatikan sesuai dengan tingkat kebutuhan dan kepentingan dari ketersediaan pelayanan itu sndiri. karena jika tidak diperhatikan dan kinerjanya menjadi memburuk maka akan dapat berpengaruh pada keseluruhan pelayanan yang diberikan kepada pelanggan. 4. kuadran iv ( berlebihan ) atribut ini menunjukkan atribut yang dirasa kurang penting oleh pelanggan, tetapi kinerjanya dilakukan dengan baik sehingga pelanggan menilai kinerja tersebut dirasakan berlebihan. atribut – atribut yang termasuk kuadran empat adalah : a. harga yang diberikan dapat diterima oleh pelanggan ( atribut 6 ) b. kualitas layanan yang diberikan sesuai dengan uang yang dikeluarkan oleh pelanggan (atribut 8 ) c. sikap dan tingkah laku karyawan terhadap pelanggan (atribut 9) d. proaktif menginformasikan status pengiriman kepada pelanggan (atribut 13) sebagian besar pelanggan menilai bahwa atribut ini sudah dilaksanakan dengan baik oleh ckb , namun karena atribut dianggap kurang penting oleh pelanggan, maka pelayanan yang berlebihan sebaiknya dikurangi agar lebih hemat biaya atau dapat juga dialokasikan pada atribut – atribut lain yang dianggap lebih penting oleh pelanggan tanpa menghilangkan atribut –atribut diatas. atribut –atribut diatas tetap dilaksanakan pelayanannya namun harus dilihat dan sesuai dengan porsi dan tingkat kepentingannya sehingga tidak menimbulkan efek berlebihan. secara umum penelitian ini menujukkan hasil yang memuaskan. hasil analisis menggunakan importance performance analysis dan customer satisfaction index menunjukkan bahwa variable-variabel yang berpengaruh pada kepuasan pelanggan secara umum cukup baik. hal tersebut ditunjukkan dari tingginya tanggapan puas dari responden terhadap kondisi dari masing-masing variable penelitian. dari hasil analisis dengan importance performance analysis dan customer satisfaction index menunjukkan menunjukkan bahwa variable-variabel kualitas layanan memiliki pengaruh yang signifikan terhadap kepuasan pelanggan. hal ini http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 25 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. menunjukkan bahwa peningkatan kepuasan pelanggan akan terkait dengan kualitas layanan. responsiveness berpengaruh terhadap kepuasan pelanggan artinya apabila responsiveness yang dipersepsikan dengan perlakuan dan kecepatan dalam penyelesaian komplain, harga yang diberikan dapat diterima oleh pelanggan, dan pemahaman karyawan terhadap kebutuhan logistik pelanggan, maka kepuasan pelanggan akan meningkat. reliability berpengaruh terhadap kepuasan pelanggan artinya apabila reliability yang dipersepsikan dengan pengetahuan karyawan mengenai produk layanan logistik, kemudahan dalam pengurusan dokumen pengiriman barang, kemudahan dalam memperoleh informasi status pengiriman barang di situs ckb logistics, dan pengiriman tepat waktu dan barang diterima dengan lengkap maka kepuasan pelanggan akan meningkat. reliability merupakan kemampuan memberikan layanan yang diharapkan dengan segera, akurat dan memuaskan sehingga masalah yang dilaporkan cepat diatasi dan selesai pada waktu yang sesuai dengan harapan pelanggan. apabila perusahan memiliki kualitas layanan yang handal, maka kepuasan pelanggan akan semakin meningkat. sebaliknya apabila layanan semakin tidak handal, maka kepuasan pelanggan akan semakin menurun. tangible berpengaruh terhadap kepuasan pelanggan apabila fasilitas yang diberikan perusahaan sesuai dengan dengan harapan pelanggan, maka kepuasan pelanggan akan meningkat. assurance berpengaruh terhadap kepuasan pelanggan apabila karyawan memiliki pengetahuan dan kemampuan yang baik tentang produk jasa yang dijualnya, maka pelanggan akan merasa puas. sebaliknya apabila karyawan tidak memiliki pengetahuan yang baik tentang prosuk jasa yang dijualnya, pelanggan tidak mendapat rasa aman sesuai ekspektasinya dari suatu produk jasa yang di terimanya. harapan yang tidak tercapai akan menimbulkan kekecewaan dan ketidak puasan pelanggan. emphaty berpengaruh terhadap kepuasan pelanggan jika karyawan dalam melakukan layanan perusahaan kurang perhatian terhadap pelanggan seperti tidak melakukan komunikasi dan perhatian kepada pelanggan, tidak memahami kebutuhan dari pelangga, maka konsumen akan merasa kecewa dan merasa tidak puas atas layanan yang telah diberikan. kesimpulan berdasarkan hasil analisi data pembahasan pada bagian sebelumnya yang dilakukan mengenai faktor-faktor yang mempengaruhi kepuasan, maka dapat ditarik kesimpulan sebagai berikut: 1) variabel bukti fisik (tangible) terbukti mempunyai pengaruh terhadap kepuasan pelanggan ckb. 2) variabel keandalan (reliability) terbukti mempunyai pengaruh terhadap kepuasan pelanggan ckb. 3) variabel daya tanggap (responsiveness) terbukti mempunyai pengaruh terhadap kepuasan pelanggan ckb. 4) variabel jaminan (assurance) terbukti mempunyai pengaruh terhadap kepuasan pelanggan ckb. 5) variabel empati (emphaty) terbukti mempunyai pengaruh terhadap kepuasan pelanggan ckb. kinerja ckb terhadap atribut kualitas pelayanan yang menentukan kepuasan pelanggan ckb masih kurang memuaskan. hal ini berdasarkan hasil dari importance performance analysis yang menunjukkan bahwa terdapat lima atribut yang berada dalam kuadran pertama (prioritas pertama) yang mana atribut –atribut yang terdapat pada kuadran ini merupakan atributatribut yang memiliki nilai kepentingan yang tinggi bagi konsumen, namun kinerjanya masih kurang memuaskan. berdasarkan customer satisfaction indeks , diketahui bahwa nilai csi berdasarkan tingkat kepentingan sebesar 0.86 dimana pelanggan "sangat puas" dengan pelayanan ckb dan nilai csi berdasarkan tingkat kinerja sebesar 0.74 yang berarti pelanggan merasa “puas” terhadap kinerja pelayanan yang dilakukan oleh ckb. bisa dikatakan bahwa berdasarkan kepentingan dengan kinerja mengalami perbedaan dimana kepentingan lebih besar dibandingkan dengan kinerja. dengan ini ckb harus bisa melakukan perubahan yang lebih baik lagi khususnya di tingkat kinerja. referensi kurniasih, i. d. (2015). pengaruh harga dan kualitas pelayanan terhadap loyalitas pelanggan melalui variabel kepuasan (studi pada bengkel ahass 0002-astra motor siliwangi semarang). jurnal administrasi bisnis, 1(1), 37–45. retrieved from https://ejournal.undip.ac.id/index.php/janis /article/view/4316 sari, n. p. a. p. (2014). faktor – faktor yang memengaruhi struktur modal pada perusahaan non keuangan yang terdaftar di bursa efek indonesia tahun 2008-2012. e-jurnal akuntansi, 7(1), http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 26 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 33–47. retrieved from https://ojs.unud.ac.id/index.php/akuntansi /article/view/8639 sari, y. u. (2014). penanganan export import cargo maskapai garuda indonesia di pt gapura angkasa bandar udara ahmad yani semarang. jurnal ground handling, 1(2). retrieved from http://jurnal.sttkd.ac.id/index.php/jgh/arti cle/view/125 sitorus, m. (2014). laporan akhir penelitian analisis kepuasan pelanggan terhadap kualitas pelayanan jasa logistik pt. cipta krida bahari. jakarta. supranto, j. (1998). statistik teori dan aplikasi (1st ed.). jakarta: erlangga. supriyanto, y., & soesanto, h. (2012). analisis pengaruh kualitaspelayanan, harga, dan fasilitas terhadap kepuasan pasien rawat jalandi rumah sakit kariadi semarang. universitas diponegoro. retrieved from http://eprints.undip.ac.id/37113/ wijayanti, i. a. h., & seminari, n. k. (2013). pengaruh gaya hidup terhadap perilaku pembelian handphone blackberry dengan merek sebagai pemoderasi. jurnal manajemen, 2(6), 639– 653. retrieved from https://ojs.unud.ac.id/index.php/manajeme n/article/view/5219 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 285 website evaluation of the faculty of industrial technology universitas islam indonesia using the system usability scale method rafi arribaath alfaresy-1, chanifah indah ratnasari-2*) informatika universitas islam indonesia yogyakarta, indonesia 19523160@students.uii.ac.id, chanifah.indah@uii.ac.id (*) corresponding author abstract to maintain and improve the quality of the website of the faculty of industrial technology (fti), universitas islam indonesia (uii), usability testing is performed on the website using the system usability scale (sus). this study aims to evaluate usability and analyze the user experience on the fti uii website so that the faculty can follow up on it. respondents consisted of 41 active fti uii students. respondents were asked to complete scenarios on the fti website while being watched by examiners. they then filled out the sus questionnaire with ten statements and a likert scale for answers. using the sus method, the test scores were 69.32. based on these results, the acceptability of the fti web is in the marginal high range, the adjective rating is at an ok level close to good, the grade scale is in class c, and the net promoter score (nps) could be passive on website users. based on these results, it can be concluded that the usability of the fti uii website is acceptable to users but has not yet attained a maximum score; therefore, a user has not yet recommended the site to other users. this confirms that the fti website requires additional enhancements. keywords: usability; user experience; website; system usability scale; sus abstrak dalam rangka menjaga dan meningkatkan kualitas website fakultas teknologi industri (fti), universitas islam indonesia (uii), perlu dilakukan usability testing terhadap website tersebut menggunakan system usability scale (sus). tujuan dari penelitian ini adalah untuk mengevaluasi usability dan menganalisis user experience pada website fti uii, sehingga dapat dilakukan tindak lanjut oleh pihak fakultas. responden terdiri dari 41 mahasiswa aktif fti uii. pengujian dilakukan dengan meminta responden untuk melakukan skenario pada website fti yang diamati langsung oleh penguji, kemudian responden mengisi kuesioner yang berisi 10 pernyataan dengan skala likert untuk jawabannya. hasil testing dengan me withetende sus diperoleh hasil sebesar 69,32. berdasarkan hasil tersebut, acceptability web fti berada pada rentang marginal high, adjective rating pada tingkat ok mendekati good, grade scale pada kelas c, dan net promoter score (nps) berpotensi pasif pada penguna website. berdasarkan hasil tersebut dapat disimpulkan bahwa website fti uii memiliki usability yang sudah dapat diterima pengguna, namun belum mencapai skor maksimal, sehingga pada kondisi ini seorang pengguna belum merekomendasikannya kepada pengguna lain. hal ini menegaskan bahwa pada website fti masih perlu dilakukan perbaikan dan peningkatan lebih lanjut. kata kunci: usabilitas; pengalaman pengguna; website; system usability scale; sus introduction the internet plays an essential role in the rapid development of technology (ferdiansyah et al., 2022). utilization of the website is something that is commonly used in the internet era as it is today. the website is used as a medium for delivering information to visitors. smartphone users, computers, or laptops connected to the internet network can surf (browse) to find the desired information (setiawan & widyanto, 2018). in 2011, the directorate general of informatics applications, ministry of communication and informatics of the republic of indonesia, stated that the website is one of the most visited information services by internet users in the world (mz, 2016). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 286 so many uses of the website, one of which is in the field of education. the website is used in colleges, schools, non-formal educational institutions, and other institutions. the purpose of its use also varies, ranging from a medium for conveying institutional profiles, supporting academic activities, promotional media, and many more. the faculty of industrial technology (fti), universitas islam indonesia (uii), is an educational institution that also uses the website to convey information to the general public, the academic community, and prospective new students. the faculty website with the address https://fit.uii.ac.id/ conveys academic information, news, services, facilities, study programs offered, accreditation, and much more. websites must have good usability so that interactions between visitors and the website can be as spontaneous and natural as possible (ananda yul et al., 2020). a website must also have service standards that ensure smooth access and easy search for information users need (aji & dpa, s.t, m.kom, 2020). an evaluation of the website is required to maintain and improve the quality of the fti uii website. in this study, an evaluation was carried out from the usability side of the fti uii website. according to nielson (2003), the definition of usability is a quality indicator that measures how easy the interface is to use (nielson, 2003). a wellstructured system design creates an interface that users can easily interact with. a good interface can also make visitors return to the site and increase visitor satisfaction. conversely, if the user interface is not designed correctly, it can cause visitor dissatisfaction and frustration and make as many as 40% of visitors reluctant to return (s.minocha, 2005), (aprilia et al., 2015). evaluation of a website can use several methods, such as user experience questionnaire (ueq), system usability scale (sus), and heuristic evaluation (he). the evaluation of sus has been done a lot before. like the evaluation of pondok pesantren qodratullah website, banyu asin regency, south sumatra, involving ten respondents with heterogeneous criteria from the level of devotees, sus score 88-grade scale is b, with excellent adjective ratings as well as acceptability including acceptable (purwaningtias & ependi, 2020). meanwhile, intyanto et al. (2021) conducted a test using the sus method on the campus website of the pacitan state community academy, with 34 respondents scoring 60.75, with a grade d position, an adjective ok rating, and a marginally low acceptance level. a similar test on the usability of the time excelindo website on 40 respondents was conducted by ramadhan et al. (2019). the test obtained a sus score of 70.13 before the improvement recommendation. however, after receiving the sus score recommendation, it increased by 80.3, so the grade b category, adjective ratings are excellent, and the acceptability is acceptable. the sus method was used in this study, and the respondents were current students at fti uii. the goal of this study is to figure out how usable the fti uii website is so that its level of usefulness can be determined and follow-up can be done in the future to make the fti uii website even better. research methods the study is done in six stages, as shown in figure 1. the first stage is a literature review that continues determining respondents and conducting scenario testing. the next stage is testing the website, the questionnaire spread, processing and analyzing data, and then making conclusions and suggestions. figure 1. research stage https://fit.uii.ac.id/ jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 287 literature review a literature review is conducted to gather various previous research that has been done and various sources of scientific theory as a basis for evaluating user experience (ux) uii fti at the website. a literature review collected the relevant usability testing, user experience testing, system usability scale, and other literature studies. determination of respondents respondents in this study were active students of the faculty of industrial technology at the indonesian islamic university. according to roscoe in sugiyono's research (sugiyono, n.d.), the number of samples suitable (required) for research is 30-500. in this study, it was determined that the number of respondents required was 30-50 people. the criteria for respondents in this study are: a. active student of fti uii, b. i have used the website https://fit.uii.ac.id/. testing scenario at this stage, scenarios will be tested on respondents regarding several things or features found on the fti uii website. the test scenario is as follows: • how do i download the academic calendar on the fti uii website? (results shown in figure 2) • how to access/download uts/uas schedule? (results shown in figure 3) • how to make a letter of good behavior from the faculty? (results shown in figure 4) respondents were asked to find out about these things on the fti uii website by being directly observed by the examiner to see whether the respondent could work on these scenarios smoothly or if there were any problems/confusion. figure 2. the page showing the academic calendar of uii the page displayed in figure 2 shows one of the pages available on the fti uii website where users can access or download the 2022-2023 academic calendar. if the user can access or download the academic calendar without anyone's assistance, it is considered that he is already fluent in working on scenario 1. figure 3. uts information page the page displayed in figure 3 shows one of the other pages available on the fti uii website in the form of information about midterm or semester exams that will take place in the odd or even semester of the current school year. if users can access or download the info menu without anyone's help, they are fluent in working on scenario two. figure 4. siso page for writing letters of good behavior meanwhile, the system display in figure 4 shows one of the systems used in fti uii, where users can access and create letters online and independently. if the user can access and use the available system without anyone's help, they are p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 288 fluent in working on scenario three and exploring information on the fti website. questionnaire testing and filling measuring usability on a website or application with the sus method is based on the subjective point of view of the user/respondent. the use of sus has several advantages, including:  test results from sus are expressed in the form of a scale with a score range of 0-100 so that this can be applied easily (brooke, 1996), (brooke, 2013), (bangor et al., 2009).  ependi says the sus method calculation process is easy to understand and not complicated d (ependi et al., 2019).  according to gardner, sus can be used at no additional cost and is also free of charge (macklin, chris, 2020).  according to john brooke, sus is generally used with a small respondent/sample size but has proven valid, consistent, or reliable (brooke, 2013) research on usability testing/ evaluation using the system usability scale method is often used because it has characteristics/properties that differ from other questionnaires, especially those that have been validated and tested with small respondent scores (brooke, 2013). questionnaires with the sus method still provide satisfactory results after considering the use of time, costs, and even in small samples. the test scenario and sus questionnaire in this study are shown in figure 5. this questionnaire consists of 10 statements adapted from john brooke's research (brooke, 2013). each statement has 5 likert scale options to measure respondents' responses, (1) strongly disagree, (2) disagree, (3) normal, (4) agree, (5) strongly agree. processing and analysis of data the completed questionnaire was converted into a number from 1 to 100 using the sus method. this number will determine whether the product, in this case, the fti uii website, is appropriate or not for use (dusea et al., 2015), (pudjoatmodjo & wijaya, 2016). the bigger the number generated, the better the usability. the method for assessing sus is as follows: statement on odd numbers: (𝑛 − 1) ................................................................................ (1) statement on even numbers: (5 − 𝑛) ............................................................................... (2) where n is the value of each question given by the respondent. the results of these calculations are then added up, then multiplied by the result with a value of 2.5 (dusea et al., 2015). the calculation formula above is shown in equation (3) (pudjoatmodjo & wijaya, 2016). score s = (((n1 – 1) + (5 – n2) + (n3 – 1) + (5 – n4) + (n5 – 1) + (5 – n6) + (n7 – 1) + (5 – n8) + (n9 – 1) + (5 – n10) ∗ 2.5) ........ (3) information: score s = total score of all respondents, n1 s.d. n10 = statement likert value from 1 to 10 given by respondents next, the average calculation is carried out, whose formula is shown in equation (4). average score of sus = ∑ score𝑛 𝑛 0 .......................... (4) based on the average score obtained, there are three perspectives or points of view in sus when formulating evaluation results (ependi et al., 2019), which are also shown in figure 5: • acceptability consists of three levels: unacceptable, marginal (low and high), and acceptable. acceptability is used to check the level of user acceptance of the website. • grade scale (rating scale) consisting of a, b, c, d, and f is used to determine the level (grade) of the website/application. • adjective rating is a hierarchy (level) of best imaginable, good, ok, poor, and worst imaginable. adjective rating is used to determine the ranking of the website. figure 5. sus score (ependi et al., 2019) in addition, another perspective or point of view in formulating the sus evaluation results is the sus score percentage (sus score percentile rank). this score contains conditions (ependi et al., 2019): jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 289 a) grade a = score >= 80,4 b) grade b = score >= 74 and < 80,4 c) grade c = score >= 68 and < 74 d) grade d = score >= 51 and < 68 e) grade f = score < 51 results and discussion the fti uii website was tested with test scenarios and the sus method from march 14 to april 4, 2023. respondents who carried out the test were a total of 41 people, who were active students of fti uii consisting of 6 majors, as shown in table 1. table 1. respondents per departement no departement number of responden 1 industrial engineering 8 industrial engineering (ip) 5 2 chemical engineering 12 chemical engineering (ip) 0 3 informatics 9 informatics(ip) 0 4 electrical engineering 5 5 machine engineering 1 6 textile engineering 1 total 41 based on testing using scenarios where respondents were asked to carry out commands in that scenario, a summary of the results is shown in table 2. meanwhile, the results of usability testing using sus are shown in table 3. table 2. recap test results with scenarios results of examiners' observation of scenario work by respondents scenario s1 s2 s3 smoothly 13 28 7 be constrained 11 5 10 controlled but able to reach the goal 6 3 4 controlled and unable to reach the goal 11 5 20 total 41 41 41 based on table 2, the scenarios in which respondents could not reach their goals were sorted from the highest in scenario 3, 1, and the lowest in scenario 2. while the sequence of scenarios, both smooth and constrained but respondents still able to achieve their goals, is sorted from the highest is scenario 2 (as many as 36 respondents managed to achieve the goal), the following sequence is scenario 1 (as many as 30 respondents), and the lowest is scenario 3 (as many as 21 respondents). table 3. fti uii website sus score respondent score sus respondent score sus r1 55 r22 70 r2 57,5 r23 70 r3 67,5 r24 80 r4 75 r25 67,5 r5 77,5 r26 70 r6 57,5 r27 77,5 r7 67,5 r28 70 r8 75 r29 87,5 r9 77,5 r30 67,5 r10 67,5 r31 87,5 r11 82,5 r32 65 r12 62,5 r33 80 r13 75 r34 67,5 r14 60 r35 72,5 r15 87,5 r36 77,5 r16 45 r37 45 r17 70 r38 77,5 r18 70 r39 50 r19 65 r40 52,5 r20 67,5 r41 70 r21 75 average score 69,32 based on table 3, it can be seen that the sus calculation obtains an average score of 69.32. these results are then formulated for the level of acceptability, grade scale, and adjective rating and correlate with the net promoter score (nps), as shown in figure 7. figure 6. fti website sus score (nps, acceptability, adjective, grade scale) based on the results of the formulation of the sus score from the point of view of p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 290 acceptability, adjective rating, grade scale, and also nps, it can be concluded that the fti uii website for acceptability is in the marginal high range for adjective ratings is at the ok level (close to good), and for the grade scale is at level c. meanwhile, based on nps, it can be passive on website users. the results of the formulation of the sus score are shown in table 4. table 4. sus test results description result sus total score 69,32 grade scale c adjective rating ok (approaching good) acceptability marginal high nps passive to get a grade "a" from a website, the sus score must be >= 80.4, while for the grade below, namely b, the sus score is >= 74 and < 80.4. while the fti website's sus score based on the usability testing results obtained a score of 69.32, indicating it is in grade c. the fti website score has usability that is acceptable to users but has yet to reach a maximum score, so in this condition, a user has not recommended it to other users. this confirms that the fti website still needs further improvement and improvement. an analysis is carried out on each statement point on the sus questionnaire to know the steps that can be taken. this is intended to produce recommendations to improve the sus score. in the sus questionnaire, odd statements are positive statements. this means that respondents give opinions that agree or strongly agree if they support the statement. odd statements 1, 3, 5, 7, and 9 are shown in figures 7, 8, 9, 10, and 11. figure 7. results of statement 1 based on figure 7, as many as 51% of respondents agree, and 17% strongly agree, that respondents are familiar with the information and features on the fti website. figure 8. results of statement 3 figure 8 shows that most respondents feel familiar with the information and features on the fti uii web. not much different: in statement 3, shown in figure 8, as many as 59% of respondents agree, and 2% strongly agree that the fti web is easy to navigate. figure 9. results of statement 5 figure 9 shows that 46% agree and 41% strongly agree that the fti website is helpful for respondents. this figure differs quite drastically from the two previous statements (statements 1 and 3), which agree and strongly agree at around 60%; statement 5 is in the 80s. 0 5 10 15 20 25 0 1 2 3 4 0 6 7 21 7 0% 15% 17% 51% 17% i'm familiar with the info and also the features on the fti web site. 0 20 40 0 1 2 3 4 0 2 14 24 10% 5% 34% 59% 2% i think the fti web is easy to explore. 0 5 10 15 20 0 1 2 3 4 0 0 5 19 17 0% 0% 12% 46% 41% i think this fti website is useful to me 0 10 20 0 1 2 3 4 0 0 10 19 12 0% 0% 24% 46% 29% i assess the functionality or features provided on this website are well designed and prepared jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 291 figure 10. results of statement 7 the results of statement 7 are shown in figure 10. as many as 46% of respondents agree, and 29% strongly agree, that the functions or features on the fti web are well-designed and prepared. figure 11. results of statement 9 figure 11 shows the results of statement 9. as many as 44% of respondents agreed, and 15% strongly agreed, that the fti web interface was attractive and user-friendly. in contrast to odd statements, which indicate positive statements, even numbered statements indicate negative statements. this means that if the respondent gives an opinion that agrees or strongly agrees, this is a negative value or something that needs to be fixed from the fti website. even statements 2, 4, 6, 8, and 10 are shown in figures 12, 13, 14, 15, and 16. figure 12. results of statement 2 the results of statement 2 are shown in figure 12, which shows that 66% of respondents agree and 10% strongly agree that respondents find it challenging to search for the desired information on the fti web. figure 13. results of statement 4 figure 13, the result of statement 4, shows that as many as 66% of respondents agree and 20% strongly agree that respondents need technical assistance to use or browse the fti web. figure 14. results of statement 6 figure 14 shows the results of statement 6, where 61% agree and 7% strongly agree that the fti web is too complex. figure 15. results of statement 8 0 5 10 15 20 0 1 2 3 4 0 0 17 18 6 0% 0% 41% 44% 15% i find this website interesting and user-friendly. 0 5 10 15 20 25 30 0 1 2 3 4 0 0 11 27 4 0% 0% 27% 66% 10% i find it difficult to do information retrieval on the fti web 0 5 10 15 20 25 30 0 1 2 3 4 0 2 4 27 8 0% 5% 10% 66% 20% i need technical assistance to use or browse the fti website 0 5 10 15 20 25 0 1 2 3 4 1 1 11 25 3 2% 2% 27% 61% 7% i think this website is too complex (loading a lot of unnecessary things) 0 5 10 15 20 25 0 1 2 3 4 0 8 7 22 4 0% 20% 17% 54% 10% i need to learn a lot before i can browse the fti website well. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 292 the results of statement 8 are shown in figure 15, with 54% of respondents agreeing and 10% strongly agreeing that respondents exploring this website must learn many things first. figure 16. results of statement 10 figure 16 shows the results of statement 10, where 51% of respondents agree and 2% strongly agree that the fti website needs to be improved to make it even better. based on the ten statements in the sus questionnaire, if sorted by statements with the most positive and most negative results are shown in table 5 and table 6. table 5. statement sequence with most positive results sequence statement a b total 1 5 46% 41% 87% 2 7 46% 29% 75% 3 1 51% 17% 68% 4 3 59% 2% 61% 5 9 44% 15% 59% 6 10 20% 5% 25% 7 8 20% 0% 20% 8 4 5% 0% 5% 9 6 2% 2% 4% 10 2 0% 0% 0% information: a = agree (for negative statements), disagree (for positive statements) b = strongly agree (for negative statements), strongly disagree (for positive statements) table 6. statement sequence with most negative results sequence statement a b total 1 4 66% 20% 86% 2 2 66% 10% 76% 3 6 61% 7% 68% 4 8 54% 10% 64% 5 10 51% 2% 53% 6 1 15% 0% 15% 7 3 5% 0% 5% 8 5 0% 0% 0% 9 7 0% 0% 0% 10 9 0% 0% 0% information: a = agree (for negative statements), disagree (for positive statements) b = strongly agree (for negative statements), strongly disagree (for positive statements) when seen from table 6, the order of the top five points that need special attention is that respondents need technical assistance to browse the website, have difficulty searching for information, the website is too complex, need to learn many things before browsing the website, and the web needs improvement. based on the results of this study, the uii faculty of industrial technology can decide which points are the priority for improvement. conclusions and suggestions conclusion the results of the usability/usability evaluation on the fti uii website using the system usability scale (sus) questionnaire method obtained a score of 69.32. this figure is from the view of acceptability, adjective rating, grade scale, and net promoter score (nps). it is known that the fti website for acceptability is in the marginal high range, the adjective rating is close to good, the grade scale is in class c, and the nps has the potential to be passive to website users. this result shows that the usability of the fti website is still acceptable, but for the usability of the fti website to be even better, periodic improvements are still needed. suggestion to maintain quality and improve the website's usability to make it even better, the fti website should make periodic improvements, significantly reducing or eliminating unnecessary parts of the content. in addition, it is necessary to carry out further similar research using different 0 5 10 15 20 25 0 1 2 3 4 2 8 9 21 1 5% 20% 22% 51% 2% i feel like this website needs to be fixed. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.542 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 293 methods such as; usability testing, heuristic evaluation, ueq (user experience questionnaire), and others. further research is also recommended using a more significant number of respondents to obtain primary data that is accurate, valid, and reliable. references aji, h. p., & dpa, , s.t, m.kom, n. r. 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(2014). metode penelitian kuantitatif kualitatif dan r dan d. perpustakaan uin sultan syarif kasim riau. jurnal riset informatika vol. 4, no. 2. march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 165 implementation of the trend moment method in estimating bread sales wahyuni sirgar1*), arridah zikra syah2, indra ramadona harahap3 sistem informasi1,2 , manajemen informatika3 sekolah tinggi manajemen informatika dan komputer royal kisaran, indonesia1,2,3 www.stmikroyal.ac.id siregarwahyuni834@gmail.com1*), azsyra@gmail.com2, ir.harahapma@gmail.com3 (*) corresponding author abstrak metode trend moment adalah suatu peramalan untuk menghasilkan angka perkiraan persediaan roti pada masa yang akan datang, sehingga tidak terjadinya kelebihan maupun kekurangan stok persediaan roti di bulan yang akan datang. di dalam penelitian ini menggunakan data penjualan roti setiap bulan, dari mulai bulan januari sampai desember 2021. catatan penjualan pada setiap bulan tersebut berguna untuk melihat gambaran apakah mengalami kenaikan atau mengalami penurunan. adapun hasil dari penelitian ini yaitu terciptanya sistem yang sudah terkomputerisasi dan mampu menghasilkan angka perkiraan dalam memprediksi penjualan untuk bulan berikutnya. sehingga mempermudah dalam mengetahui berapa banyak roti yang akan terjual dan mempertimbangkan stok barang dan berapa banyak yang akan diproduksi pada bulan berikutnya sehingga tidak mengalami kekurangan ataupun kelebihan stok roti. hasil dari prediksi penjualan selama 12 bulan pada tahun 2021, menghasilkan prediksi pada tahun 2022 bulan januari, pada periode ke-13 dengan hasil mad (mean absolute deviation) 40,08% dan tingkat mse (mean squared error) 27,64%. kata kunci: peramalan, prediksi, penjualan roti, trend moment. abstract the second strategy's pattern is a gauging technique for delivering an expected number of future bread supplies so there is no abundance or lack of stock in the upcoming month. information on bread deals is utilized in this concentration consistently from january to december 2021. every month's deals records are valuable for deciding if sales have expanded or diminished. the review's discoveries incorporate the improvement of an electronic framework that can create rough numbers in anticipating bargains for the next month, simplifying it to decide how much bread will be sold while considering the supply of products and how much will be delivered before very long. the following month with the objective that there is no need or overflow of bread stock. the consequences of deals forecasts for a very long time in 2021 produce expectations in january 2022, in the thirteenth period with mad (mean absolute deviation) aftereffects of 40.08% and mse (mean squared error) paces of 27.64%. keywords: forecasting, prediction, bread sales, trend moment. introduction one methodology in computerized reasoning that can be used for expectation is the pattern second strategy (trend et al., 2021). determining procedures with the pattern second is utilized to determine the number of future sales using sales data for one year or equal to 12 months (yulian, anggraeni, & aini, 2020). the trend moment utilizes quantifiable, numerical assessment procedures to track down the limit of a straight line rather than a turbulent line moulded by ongoing corporate information (andriano frans, orisa, & adi wibowo, 2020). estimating is the work and investigation of expecting future open doors (sohrabpour et al., 2021). a production head makes bread production data per month manually and then submitted to the admin section for approval by the manager. then after it is approved, the admin section will print it. after the bread production data is published, the production head will distribute it to the respective section heads. the month-to-month bread creation information utilized as a source of perspective only mailto:siregarwahyuni834@gmail.com1* mailto:azsyra@gmail.com mailto:ir.harahapma@gmail.com p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 jurnal riset informatika vol. 4, no. 2 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 166 uses uncertain estimates or just guessing, and the process of calculating the data on the amount of bread per month is still manual and does not use a computerized system. in this condition, the company will suffer losses (sutriadi, 2021). in determining a forecast, it is better to use specific forecasting methods so that the influence of subjective elements in choosing a forecasting decision. many forecasting methods can be utilized to decide figures, one of which is the trend moment strategy (iqbal & nugroho, 2021). in general, the problem of lack of stock of goods often arises in the trading business sector where calculations or estimates that are still being carried out do not use forecasting methods, so actions without planning are considered ineffective, difficulties in predicting the stock that must be prepared for businesses engaged in that field. the mining sector often overwhelms corporate actors if an order is not available with all the demands, which can threaten business actors. (heriansa & supratman, 2021). an improvement that will by and large form (create) or (decline) over the long haul is acquired from ordinary changes over the long haul, and the worth is exceptionally level or (smooth). at last, a period series is said to have an example assuming its not unexpected worth changes after some time, with the goal that it very well may be depended on to increment or diminish over the period for an optimal gauge. the powers that can impact designs are populace change, cost, advancement, and convenience (konsumsi et al., 2021). the structure comes from latin (systema), and greek (sustema) is a unit that includes parts associated together to work with the movement of information, matter, and energy. this term is routinely used to portray the presence of components that communicate with one another in another sense. the framework is characterized as an assortment or set of features, parts, or factors that are coordinated, associated, related to one another generally, a framework is a bunch of substances (equipment, brainware, programming) that interface, participate and team up to accomplish specific objectives (tahmasebi, borin, jatowt, xu, & hengchen, 2021). a framework is many interrelated parts that cooperate to accomplish some objective. moreover, one more comprehension of the framework comprises components and information (input), (handling), and result (yield) accordingly, in a straightforward framework can be deciphered as an assortment or set of components or components that work together and are coordinated. what's more, rely upon one another. the framework is intended to improve or upgrade data handling (sudiatmo, 2021). from the data on the number of bread sales for 12 months starting from january to september in 2021, then with the trend moment prediction of the optimal sales of bread production in hoya for january to december 2021 can be seen in figure 1. figure 1. bread sales data based on the sales data table above, for 12 months, the number of sales in january-december 2021 has a monthly recap from the sales results will be analyzed appropriately to predict future sales to increase profits and minimize losses or production shortages due to lack of forecasting or preparation of goods for the next month. in deciding on a check, it should be tackled utilizing specific assessment strategies so the effect of theoretical parts in determining a decision of a review can be avoided by the different estimation procedures that can be used to select, one of which is the pattern second technique. research methods research framework prepare for this research, and it is necessary to have a clear research framework structure in stages. the system is the means that will be taken in taking care of the issue, so the last objective in anticipating the deals of hoya can be executed. the exploration structure utilized is: there should be a defined course of action for arranging this exploration to evaluate the stages' architecture. the system is the means that will be done in settling the issue, with the goal that the last objective in foreseeing the offer of hoya can be executed. the exploration system utilized is seen in figure 2. jan febr mare apri mai juni juli agus sep okto nove dese total roti-1000 blueberry 5500 6500 4500 5000 2300 4400 3200 6500 3200 2000 4500 3200 50800 roti-1000 coklat 6800 5200 5000 4500 7000 6500 2000 5000 5500 7000 4500 6500 65500 roti-1000 durian 4000 5500 4500 8000 5600 2800 3900 4800 6000 3500 9000 4500 62100 roti-1000 kelapa 4000 3500 4000 2500 2500 3000 3500 4800 2800 4000 3900 3500 42000 roti-1000 pandan 4800 2000 5500 6800 4800 3000 3000 5500 4200 3600 1200 4800 49200 roti-1000 mocca 1500 5900 4500 3000 3000 3300 5600 2800 5900 3200 2000 2500 43200 roti-1000 srikaya 3200 3300 4000 2000 3300 1500 4000 3500 4000 3800 1800 3000 37400 roti-1000 strawberry 5200 4000 3500 3000 4700 1800 3600 4500 5500 4000 3500 1200 44500 roti isi 8 manis kosong 800 650 480 880 500 500 800 680 580 700 550 400 7520 roti tawar tawar 1500 950 1500 900 1500 1500 1500 950 950 880 800 150 13080 roti kasur kasur mises 700 700 700 600 650 500 500 650 650 700 700 700 7750 roti sobek sobek coklat 500 500 500 500 400 400 350 400 400 350 350 350 5000 penjualan roti bulan januari desember 2021 jumlah penjualan/bulan nama jenis jurnal riset informatika vol. 4, no. 2. march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 167 figure 2. research framework forecasting systems determining frameworks are a significant device for settling on informed business choices. despite organization size and profile, estimating assists authoritative administration by demonstrating assumptions in procedures fundamental exchanging presumptions exchanging or purchaser conduct. estimating is a significant resource yet requires extraordinary abilities and the correct information. a method for forming informed expectations that use factual knowledge to determine future heading patterns. businesses use gauging for various purposes, including forecasting future expenditures and selecting how to allocate their spending plans. method trend moment pattern second is a development that will, in general, build (growth) or decline (decrease) in the long haul acquired from the average change after some time, and the worth is very level or (smooth). a period series is said to have a pattern assuming its regular worth changes every once in a while, so it is customary to increment or decline during the period for the ideal conjecture. the powers that can impact patterns are changes in the populace, costs, innovation, and usefulness. the second pattern is one of the strategies in estimating, which has one advantage: how to know the projected benefit and loss of deals of an object of merchandise that will happen in the next year (ilyas, marisa, & purnomo, 2018). the trend moment uses mathematical and statistical calculations to determine a straight line function such as a line dotted formed by a company's historical data. only in this way can the influence factors be avoided. the trend moment method is a technical pattern that varies from strategies, determining that information x verifiable should even be odd or even because the worth of x generally begins with 0 request first the design. the system has a blend of measurable as examination techniques, practices, and time. in utilizing the pattern technique, this should be possible utilizing the authentic information of the variable while the recipe is used to plan. condition 1 is used to ascertain the pattern or change in esteem. the condition is used to work out the incline or coefficient of a trend line. condition 3 is utilized to work out the consistent (fernández-naranjo et al., 2021). procedure there is a mix of factual investigation right now as pattern examination and second techniques. in the use of the pattern second strategy, it tends to be finished utilizing chronicled information from one variable, while the recipe utilized in the arrangement of the pattern condition method with the moment method is as follows, description: y = value to be predicted. a = constant number. b = slope or coefficient of inclination of the trend line. x = time index, starting from 0, 1, 2, and so on. to find the values of a and b from the above formula, you can use the substitution and elimination method mathematically, namely: description: y = total sales data x = total time xy = total sales data times time n = total data after getting the value from the forecast obtained from the aftereffects of anticipating the pattern, the second strategy will be corrected aided by the consequences of estimating with the pattern second technique will be adjusted again against the effect of using seasonal sales. the calculation of the seasonal sales index is: to get the final forecast results after being influenced by the sales index, the following calculation will be used: y* = sales index × y p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 jurnal riset informatika vol. 4, no. 2 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 168 where: y* = the forecast results using the trend moment method are influenced by the season index. y* = prediction results using the trending moment. the accuracy of the forecasting measurement results measures the error level of the difference between the forecasting results and the actual demand. three commonly used steps are referred to in (yulian et al., 2020), namely: 1. average absolute deviation (mean absolute deviation = mad) mean absolute deviation (mad) is a method for testing or evaluating forecasting methods that use the total number of errors. mad is the first stage of the overall model estimation error. the formula used to calculate mad is: mad = σ(absolut dari errors) n .............................................. (1) where: xt = actual demand in t-period. ft = forecasting demand in period t. n = number of forecasting periods involved. 2. msemean square error (mse) is a method with another technique to test the error rate of the forecasting method. each error is squared. this approach technique determines the significant forecast error due to the squared error. mse is the second way to measure the magnitude of the overall forecast error. mse is the average squared differences between the predicted and actual values. the formula used to calculate mse is: mse = σ(xt − ft)² n where: xt = actual demand in t-period. f = forecasting demand in period t. n = number of forecasting periods involved. 3. average absolute percentage error (mean absolute percentage error = mape) mean absolute percentage error (mape) measures the error in the forecasting method with the absolute error technique in each period divided by the real observed value for that period. then the result is calculated as the average value of the fundamental percentage error. mape is an error test that looks for the percentage difference between the actual and estimated data. the formula used to calculate mape is: mape = (100/n) xt ft/at where: xt = actual demand in t-period. ft = forecast of demand (forecast) in period-t. n = number of forecasting periods involved. bread sales prediction the forecast is a course of deliberately assessing something probably going to occur later on in light of various data, with the goal that blunders are limited. while the strategy used to quantify a variable in what's to come depends on instinctual reflection from the past time (iqbal & nugroho, 2021). 1. emotional estimation depends on the individual's instincts that shape it. 2. forecasting objective will anticipate depending on important information in the past by utilizing procedures and strategies in checking information (sari, 2017). 3. long-term guess. 4. medium-term guess. 5. provisional estimates. system development live cycle (sdlc) as per krismiaji, sdlc (systems development life cycle) is one of the most widely recognized when a new evolution is completed by experts and software engineers to fabricate data frameworks. this is an image of sdlc (muhammad & raharja, 2022). sdlc (systems development life cycle) is software design, processes development, and trying. this technique depicts the general programming advancement cycle to create quality programming and measure up to clients' assumptions. (hidayat, 2021). system analysis and planning aids. there must be a system design tool that the author has implemented to achieve optimal analysis results in designing information systems. the creator's information system design tools are uml (unified modeling language), movement charts, class graphs, arrangement outlines, use case graphs, and flowcharts. jurnal riset informatika vol. 4, no. 2. march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 169 figure 3. unified modeling language figure 3 explains several activities that run this system, namely the login process, item data, sales data, transaction data, calculate predictions, and sales transactions to predict sales. a. scenario use case login name use case : login actor : admin purpose : gaining access to use the system description : use case is used for the login process b. scenario use case goods data name use : goods data actor : admin purpose : entering goods data and attributes predicting sales description : admin uses this use case to input goods data and goods attribute data for the process of predicting sales. c. scenario use case sales data name use : sales data actor : admin purpose : enter sales data and predict sales attributes. description : the admin uses the case to input sales data and sales data attributes to predict sales. d. scenarios use case transaction data name use case : transaction data actor : admin purpose : entering transaction data and attributes predicting sales description : case is used by admin to input data and attributes of transaction data to predict sales. e. scenario use case data calculate prediction name use case : prediction count data actor : admin purpose : entering predictive count data and predicting sales attribute description : case is used by admin to input predictive count data and attribute data for the sales predicting process. uml (unified modeling language) is a visual language for displaying and imparting a framework by utilizing graphs and supporting texts that function to perform modelling. uml (unified modeling language) can picture, characterize, develop, and record a product's concentrated framework's antiquities (abdalazeim and meziane, 2021). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 jurnal riset informatika vol. 4, no. 2 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 170 results and discussion this stage is the stage for analyzing the data needed in system design, as for the data required for input, namely the data on the number of sales of bread each week, even though production is weekly, but the stock carried out by the hoya factory is monthly, so it is done sales predictions perpetual, as for the number of sales of bread for a week, among others: table 1. sales data month weekly sales october 9625 november 9675 december 9670 january 9420 february 9062 march 7300 april 7987 mey 10020 june 9920 july 8432 agusty 8200 september 7700 analysis of the calculation process of the trend moment method the second strategy's pattern is one of the techniques utilized in determining deals. in making deals gauging utilizing the pattern second strategy, we take the example of bread forecasting using sales data for the last year, namely sales data for 12 months in 2021 from january to december. the calculation should be possible with the accompanying advances: 1. calculating the total sales data (y) in 2021 for 12 months which we call y. y = 428050. 2. determine the value of the parameter (y), where (x) is a time index starting from zero (0), so the sum of the time index (∑ x) is 66 3. determine the value (xy) and x², step. it is necessary to determine the value of "a" and "b" used in the trend moment equation. 4. determining the value (xy) is done by multiplying the historical data (y) and the time index (x). then xy = 2280170 5. the subsequent stage is to decide the worth of x² by squaring the time index (x). x² is 506 6. the results of calculating the values of y, x, xy, x² can be seen in table 4.2, entering the step of determining the importance of "a" and "b" in the following table. table 2. criteria month year sales x x² x*y october 2021 38500 0 0 38500 november 2021 38700 1 1 38700 december 2021 38680 2 4 77360 january 2021 37680 3 9 113040 february 2021 36250 4 16 145000 march 2021 29200 5 25 146000 april 2021 31950 6 36 191700 mey 2021 40080 7 49 280560 june 2021 39680 8 64 317440 july 2021 33730 9 81 303570 agusty 2021 32800 10 100 328000 september 2021 30800 11 121 338800 total 428050 66 506 2280170 1. the formula used in the preparation of this method. to find the values of a and b from the above formula, use a mathematical way with the solution using the substitution method and the elimination. the equations are. based on the data in calculation of trend moment, using the following equation: multiplied elimination y = na + b.∑ x 428050 = 12. a + b. 66 x 66 428050 = 12 a + 66 b x 12 xy = a. x + b. x² 2280170 = a. 66 + b.506 2280170 = 66 a + 506 b solution: method substitution and elimination: 28251300 = 428458 a + 396 b 27362040 = 428458 a + 27363 b 889260 = 26967 b b = 889260/26967 b = 32,97585 the equation describes the elimination to get value b. it is known that y = 428050, then n is the number of sales data, namely 12 data, and ex = 66 of the total number of time indexes. likewise with the value of y xy = 2280170 which is the sum of the sales data times the time index. after knowing the values of the equations, then the elimination by multiplying 66 in equation one and multiplying by 12 in the second equation. the result of the elimination is to get a b value of 32,97585, which can then be used to obtain the a value with equation one changing the b value to 32,97585 as follows: y = na + b.∑x 428050 = 12 a + 66 b 428050 = 12 a + 66 (32,97585) 428050 = 12 a + 105.84 12 a = 1176.40 a = 98.03 jurnal riset informatika vol. 4, no. 2. march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 171 2. after the values of a and b are known, the next step is to enter the process of determining the value of y or trend with the equation: y = a + bx. in the equation below, the sales forecasting calculation process will be carried out in januarydecember 2021 using the results of previous calculations. it is known that the value of a=98.03 and b=32.97585, and the value of x=25 (september), which is the time index. y = a + bx y = 98.03+ 32.97585 (x) y = 98.03+ 32.97585 (25) y = 98.03+ 824,39625 y = 922.42 then the forecasting results for january 2022 will increase by 922.42. 3. calculate errors using the mse (mean squared error) method. the actual data in january 2022 was a 922.42 sales increase, then the results of the forecast using the trending moment to get the mse value, the difference between the actual data and the forecast data was calculated, then the results were divided by the existing data and multiplied by 100%. the calculation using mse is as follows: mse = (actual data – forecast data) / actual data| *100% = ((30800 922.42)/30800│* 100% = 0.97005 * 100% = 97.005% where mse is absolute accuracy = 100% error = 100% 97.005% = 2.995% after obtaining the value of the trending moment above, it will be calculated using the season index. based on the season index formula, the following calculation results will be obtained: season index = average demand for a particular month. average demand per month note: average demand for a particular month = 35670.83 average demand per month = 190014,167 season index = 1 after being influenced by the season index, the final forecast's outcomes will use the following calculation: y = season index x y y* = 1068.3 as a result of the sample test stage forecasting, the number of bread sales forecasted in january 2022 was 1068.3 or equivalent to 1068.3. if the amount of bread inventory is less than the forecast number, then hoya's party must re-produce so that there is no shortage of stock of bread to be distributed to consumers algorithm (trend moment) the flowchart in figure 4 describes the flow in the system. this flowchart also understands how the system can direct the system. the following is an explanation: 1. admin and manager start 2. log in to the trend moment application home 3. admin/manager login enter username and password 4. bread sales data recap per month 5. of sales input sales data that you want to forecast 6. input data to be predicted in the next three months 7. conduct three sales forecasts next month 8. see the results of the forecast, then print a report 9. count again if you want to make predictions 10. admin log out when you have finished forecasting sales figure 4. algorithm trend moment where : y = trend value or variable to be predicted a = constant number b = slope of trendline coefficient x = time index (starting from 0.1,2,….n) y = the number of sales data x = the number of time periods xy = the number of sales data multiplied by the time period n = the number of data p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 jurnal riset informatika vol. 4, no. 2 march 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 172 system result the trend's second strategy to anticipate hoya bread sales consists of displaying the login form, main form, profile form, item data form, sales data form, prediction calculation form, prediction result data form, change password form, and report form. login form display, coming up next is the administrator login structure shown from the trend second strategy to foresee bread sales: figure 5. login form figure 5 shows that structure login is the underlying presentation that seems when the administrator runs the application framework. if you desire to enter the framework, the administrator should enter your username and secret phrase. in this framework, just the administrator approaches privileges to have the option to run the application framework. if successful, you will enter the system, as shown in figures 6, 7, and 8. figure 6. view form main figure 7. prediction calculation results figure 8. prediction result data coming up next, figure 9 is a presentation structure from the use of the trend second to foresee deals of hoya bread: figure 9. sales data report figure 10. prediction result data report jurnal riset informatika vol. 4, no. 2. march 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i2.349 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 173 figure 10 displays the form of the sales prediction data reports the output of the prediction calculations performed by the admin. this report displays the predicted number of future sales based on the acquisition of goods sold from 2019 to 2020. conclusions and suggestions conclusion by predicting bread sales using the trend moment, it is known that the stock of bread that must be provided in january 2022 is 296, with mad (mean absolute deviation) 40.08% and mse (mean squared error) 27.64%. the based forecasting system is designed website where the system can simplify and expedite parties in predicting bread sales for the following month. estimating frameworks can function admirably in giving answers for keeping away from hoarding or overselling bread to be distributed to customers/buyers. this system can be used to make predictions about bread sales. suggestion to improve system performance in the future to be accessed online. users' access rights are still generated, so a more significant difference in access rights is needed. the level of security needs to 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(2020). penerapan metode trend moment dalam forecasting penjualan produk cv. rabbani asyisa. jurteksi (jurnal teknologi dan sistem informasi), 6(2), 193–200. jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 219 the work is distributed under the creative commons attribution-noncommercial 4.0 international license mobile based student presence system using haar cascade and eigenface facial recognition methods suherman achmad1, nazori a z2, achmad solichin3 faculty of information technology universitas budi luhur jakarta, indonesia 1)1811600202@student.budiluhur.ac.id, 2)nazori@budiluhur.ac.id, 3)achmad.solichin@budiluhur.ac.id (*) corresponding author abstract using biometric technology for recording attendance in the school environment is still not widely done by researchers. in this study, a solution was proposed to the problems that occurred in the school environment where parents/guardians could not monitor the presence of their children in school. the solution offered is a student attendance recording system based on facial recognition algorithms (face recognition). the built system can record the presence of students when entering the classroom and when returning home or out of class. proposed methods for identifying student attendance are the haar cascade and eigenface algorithms. the system can also provide notice of attendance or absence of students in real time to parents/guardians via email that has been registered. based on the test results, the method can provide accurate and fast facial recognition results. the presence system developed based on mobile can recognize faces up to a distance of 200-300 cm with low and moderate light intensity. keywords: presence system, haar cascade classifier, eigenface abstrak pemanfaatan teknologi biometrik untuk pencatatan kehadiran di lingkungan sekolah masih belum banyak dilakukan oleh peneliti sebelumnya. dalam penelitian ini, diusulkan sebuah solusi atas permasalahan yang terjadi di lingkungan sekolah yang mana orang tua/wali tidak dapat melakukan monitoring terhadap kehadiran anaknya di sekolah. solusi yang ditawarkan berupa sistem pencatatan kehadiran siswa berbasis algoritma pengenalan wajah (face recognition). sistem yang dibangun dapat mencatat kehadiran siswa saat masuk ke kelas, maupun saat pulang atau keluar dari kelas. metode yang diusulkan untuk mengidentifikasi kehadiran siswa adalah algoritma haar cascade dan eigenface. sistem juga dapat memberikan pemberitahuan kehadiran maupun ketidakhadiran siswa secara realtime ke orang tua/wali melalui email yang telah terdaftar. berdasarkan hasil pengujian, metode tersebut mampu memberikan hasil pengenalan wajah yang akurat dan cepat. sistem presensi yang dikembangkan berbasis mobile mampu mengenali wajah hingga jarak 200-300 cm dengan intensitas cahaya rendah dan sedang. kata kunci: sistem presensi, haar cascade classifier, eigenface introduction the speed of access to information is currently the most fundamental need for managing and transferring data. the speed of access to information is currently the most fundamental need for managing and transferring data. existing and rapidly developing technology is now expected to build a system to provide solutions for disciplining students and providing benefits for schools. a series of evaluations conducted by administrative bureaus and administrative staff found several weaknesses related to the presence of learners. in this case, the system of recording attendance in schools is still manually done with a signature recording system, which is considered easy to manipulate, so the lack of information received by parents/guardians on the presence of their children in school. therefore, a computerized system can manage information quickly and accurately to help smooth activity and become one of the influential factors in improving the discipline of learners. facial recognition technology is growing and widely used for the identification process. facial recognition is one of the technologies that has now been applied to many applications in the field of presence (rijal & ariefianto, 2008; satwikayana et al., 2021; septyanto et al., 2020). among others, supporting the identification system in schools to become a tool to find out data collection such as names, nis, and majors, in addition to facial recognition conditions http://creativecommons.org/licenses/by-nc/4.0/ mailto:1811600202@student.budiluhur.ac.id1 mailto:achmad.solichin@budiluhur.ac.id3 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 220 the work is distributed under the creative commons attribution-noncommercial 4.0 international license that are input (input) system, is also a significant problem. related to this study, there are several similar previous studies. simaremare and kurniawan compare lbph and eigenface for recognizing three faces at once in a real-time situation (simaremare & kurniawan, 2016). the researchers tested the accuracy of both methods in recognizing three faces at once. the test was conducted on 300 samples of facial imagery with four lighting conditions, namely indoor and outdoor daylight. the results of this test showed that the accuracy rate of lbph is better than eigenface, with the average accuracy of lbph being 93.54% and eigenface being 63.54%. the false rejection rate (frr) in the lbph method is lower than that of the eigenface method, with the average frr lbph being 0.24% and frr eigenface being 6.38%. this study only compares the two methods and is not a merger of the two methods, so the results obtained are not maximal. another researcher compared eigenface and fisherface methods for face recognition (firasari et al., 2022). then, septyanto et al. proposed a facial recognition presence application using haar cascade algorithm (septyanto et al., 2020). this test was conducted on 13 starcross store employees, each conducting 30 presidency trials. successful absentees had success scores of 87%, and 13% failed from 390 attempts. some absenteeism that fails occurs because several factors can affect absenteeism, such as high lighting, jacked head position, and the use of attributes (hats, glasses, etc.). the result of this study is that the system can identify faces with a reasonable degree of accuracy, but has limitations in knowing the face if the lack of lighting, conversely if the lighting is high or too bright, then the face cannot be identified, for it requires additional methods to overcome such weaknesses. many studies have also used haar cascade (anarki et al., 2021; behera, 2020; sulistiyo et al., 2014). in another research, the fisherface method supports the academic system (amri & rahmata, 2016; firasari et al., 2022). the system is built using primary visual programming languages and databases using mysql. in this test, the results obtained differed from one face to another, and the results came out in the name of the class of study program majors. facial recognition processing in absenteeism can work well if the data in the database is not too much and at the same lighting, so the level of face search approaching in the database can be better. based on testing, the percentage of facial recognition success reaches 80%. the disadvantage of this panel is that the detection results are sometimes not maximal if the distance of the face with the webcam is far and the position is not by the webcam. another weakness is that the system cannot identify the face if the light is too bright or dark. mobile-based face recognition technology is a rapidly developing field with significant implications for security, law enforcement, and mobile applications (arisandi et al., 2018; samet & tanriverdi, 2017). researchers are currently investigating the accuracy and performance of mobile-based face recognition systems compared to traditional desktop-based systems (abuzar et al., 2020; alburaiki et al., 2021; rodavia et al., 2017). there is growing concern about the privacy implications of mobile-based face recognition and how it can be used to identify individuals without their consent or knowledge (ahmed khan et al., 2021). studies are being conducted to explore ways to improve the accuracy and performance of mobile-based face recognition systems. moreover, researchers are also studying the performance of mobile-based face recognition systems on diverse faces, including different races, ages, and genders. studies have found that some systems perform better than others on diverse faces and that there are significant disparities in performance depending on the demographic group. overall, ongoing studies in mobile-based face recognition focus on developing new algorithms and techniques to improve the technology's accuracy and performance, addressing privacy concerns, and improving performance on diverse faces. this android-based attendance identification system aims to comfort parents/guardians, improve student discipline, and connect information online. the formula of the problem in this study is how the process of identifying the presence of learners entering and exiting the classroom can be detected early, accurately, and on target. based on previous research mentioned above, it is necessary to build further research by combining two different methods to produce a model of the student presence system using android-based facial recognition patterns (samet & tanriverdi, 2017). research methodology this research method is used as a guideline for researchers to implement research so that the results achieved do not deviate from the goals set. in this case, the stages of research conducted are as follows: data collection stages http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 221 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the data collection stages in this study are as follows: 1. literature studies or study review. this technique begins with collecting data by studying the necessary materials, concepts, and theories from several written sources (books, magazines, tutorials, etc.), and the necessary understanding will be used as a reference for the preparation of research. 2. direct observation. this technique will be held direct observation of the main symptoms of what is being studied. observations made in actual situations are necessary for particular purposes. 3. designing student presence identification applications to support the proposed student presence identification system to provide information to parents/guardians via email using android-based facial recognition. 4. prepare sample data as input in research. system development stages the stages of application system development of student presence using the waterfall model, where the model consists of analysis, design, programming (coding), testing, and maintenance. the stages of the process use the waterfall model as follows: 1. analysis at the analysis stage, collecting needs is complete, observed, defined in detail, and presented as a specification system. 2. design at this stage, the system design process is carried out by requirement on hardware and software to form the overall system architecture. software design will identify the basic abstraction of software systems and their relationships. 3. coding at the coding stage, software design is realized as a program. furthermore, the program that has been realized will be tested by verifying whether each unit meets its initial specifications. 4. testing at this stage, the program that has been tested is confirmed to have fulfilled the software. requirement. when fulfilled, the user can implement and use the software system. 5. maintenance this stage is the maintenance part of the system, which carries out the program's operation, such as changes or improvements of the user's needs due to adaptation to the actual situation. the waterfall model is seen in the image below. design and implementation 1. system planning starting from entering the classroom and then doing the presence automatically to enter as a student. an image of the activity diagram identification of the student's presence can be seen in figure 1. figure 1. activity diagram identifies student presence the flow activity diagram in figure 1 is described as follows: a. learners perform presence activities. if true, then the identification process of the presence is successful; if wrong, then facial matching is not successfully identified, and learners are asked to return to presence. b. after successful identification, the student's face will be read on the system. c. next, the system will store the student's face on the server. 2. design of functions and infrastructure the infrastructure on the system will be built based on mutually agreed needs; outside of unified modeling language (uml) design, there is also application design for the user interface. this system will be described as infrastructurally by students connected to the presence system server and parents/guardians as receiving information. to be more clear can be seen in figure 2 system network infrastructure. figure 2. design function and infrastructure http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 222 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the flow of the function and system infrastructure design in figure 2 is the stage in running the presence system. here is an explanation of the flow of system and infrastructure function design that can be done, including: a) the first step, learners make a presence using facial recognition through a webcam or smartphone camera b) step two, the system connected to the internet will verify the face of the learner and whether the face of the participant can perform facial matching on the system. c) in step three, the learner's facial data will be stored on the system storage media, where nis, name, class, and email parents/guardians are stored using facial recognition when making a facial list. d) step four, student data stored on storage media will automatically connect to the cloud or internet network. e) in the last step, parents will receive a message about their child's presence through an online email address. 3. system component each component in figure 2 has different functions. the function of each component will be explained as follows: a. the webcam component is hardware that serves as a tool to take the image of the face of learners. this webcam will be a facial matching tool when conducting presence checks and taking images of the learner's face. b. the interface component is software on a desktop computer that serves as a tool to design and create a system that can connect with the web to take images of students' faces. smartphone components are multi-function devices used to install android-based presence applications so that applications can be used easily and quickly. c. the facial image component performs facial recognition mechanisms with the help of a webcam or smartphone camera, and this face capture is helpful for storage in the database and as input at the time of the presence. d. internet components function as an intermediary between the user and the system. the design is simple to make it easier for users to access information. facial steps with haar algorithm the process of facial shortness is needed in several stages. in the haar method, the process of facial shortness looks as in figure 3. figure 3. facial shortness process from figure 3 above, it is determined in advance whether the area is detected whether there is an object or not. the next process is to detect objects using haar cascade classifier, with steps to be described as follows. a. calculate feature samples with the haar algorithm. figure 4. rectangular feature haar cascade figure 4 is the result of camera detection, where there is a blue box which is a frame with values y = 480 and x = 640. in the blue box is the exposed face. figure 5 is the result of facial detection that has been recorded, so it has a size of 240 x 320. an original image converted to grayscale is shown in figure 9 figure 5. face detection with haar-like feature figure 6. the difference in the original image with grayscale http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 223 the work is distributed under the creative commons attribution-noncommercial 4.0 international license from the results of figure 6, it will be converted into matrix values so that haar-like squares in the input image are obtained as follows in table 1. table 1. square haar-like image input 46 45 44 45 44 44 46 44 41 43 44 44 46 43 40 40 43 44 45 40 40 40 44 45 45 41 39 40 44 45 46 41 40 41 45 46 the process of calculating the dark value and the light value, i.e. feature value = number of pixel values (dark value) – number of pixel values (light value), thus generating the feature value haar = 213. to calculate the haar value feature using the summed area table, known as the integral image, first formed an integral value matrix image. here is the matrix of integral values obtained from the input image, as seen in table 2. table 2. the integral value of the image from the input image 46 91 135 180 224 268 92 181 266 354 442 530 138 270 395 523 654 786 183 355 520 688 863 1040 228 441 645 853 1072 1294 274 528 772 1021 1285 1553 the haar feature value of the matrix area above is calculated using the following formula: i(x',y')=s(a)+s(d)+s(b)-s(c). so that the haar value feature is obtained = (528+46-274-91) (772+91-528135) + (1021+135-772-180) = 213 the haar = 213 value feature is then compared to the threshold determined as object detection. if haar's feature value is higher than the threshold, it can be said that the area meets the haar filter. this process will continue to retest the area with other haar filters, and if all haar filters are met, then it is said that there are observed objects in that area. results and discussions facial matching steps with eigenface algorithm facial recognition algorithms begin by creating a matrix of columns from faces inputted into a database. a column matrix's average vector image (mean) is calculated by dividing it by the number of images stored in the database. in the eigenface algorithm, the first step before determining the eigenface value first arranges a flat vector image matrix. the process flow can be seen in figure 7. figure 7. facial recognition process facial recognition algorithms are performed through several stages. 1) the first step is arranging an s matrix set of all training images. here is a training image of two facial data, as seen in figure 8 and figure 9, each of which has a matrix value. 𝐶1 = [ 1 0 2 1 2 1 0 2 2 ] figure 8. training image image of face image 1 𝐶1 = [ 1 1 2 0 2 1 1 2 4 ] figure 9. training image image http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 224 the work is distributed under the creative commons attribution-noncommercial 4.0 international license 2) the second step is arranging the entire training image into one matrix. [ 𝑎 𝑏 𝑐 𝑥 𝑦 𝑧 ] → [𝑎 𝑏 𝑐 𝑥 𝑦 𝑧] 𝐶1 = [ 1 0 2 0 2 1 0 2 2 ] → [1 0 2 0 2 1 0 2 2] 𝐶2 = [ 1 2 2 0 2 1 0 2 4 ] → [1 1 2 0 2 1 0 2 4] 3) here is a count of flat vector averages. sum up the entire row from the flat vector obtained so that a matrix measuring 1 x (h x w) will be obtained. 𝐶1 + 𝐶2 = [ 1 0 2 0 2 1 0 2 2 1 1 2 0 2 1 0 2 4 ] 𝐶1 + 𝐶2 = [2 1 4 0 4 2 0 4 6] next, divide the matrix result by the number of images n to get the flat vector average value. [ 2 1 4 0 4 2 0 4 6 2 ] = [1 1 2 0 2 1 0 2 3] 4) the following flat vector average value will be used to calculate the eigenface value of the facial image in the training image. using the flat vector average value above, the eigenface can be calculated. how to reduce the rows on the flat vector matrix with flat vector average values. if the result is below zero, the value is replaced with zero. 𝐶1 = 1 0 2 0 2 1 0 2 2 1 1 2 0 2 1 0 2 3 0 0 0 0 0 0 0 0 0 𝐶1 = 1 1 2 0 2 1 0 2 3 1 1 2 0 2 1 0 2 4 0 0 0 0 0 1 0 0 1 5) here is the identified process. the identification process calculates the eigenface value of the test face matrix to determine the eigenface and flat vector values. the results can be seen in figure 10.. 𝐶𝑡 = [ 2 2 4 1 2 2 2 4 4 ] figure 10. test image (testface) 𝐶𝑡 = [ 2 2 4 1 2 2 2 4 4 ] → [2 2 4 1 2 2 2 4 4] 𝐶𝑡 = [ 2 2 4 1 2 2 2 4 4 1 1 2 1 0 0 0 2 3 1 1 2 1 0 2 2 2 1 ] eigenface value of test image 𝐶𝑡 = [1 1 2 1 0 2 2 2 1] 6) once the eigenface value is obtained, it can be identified by determining the shortest distance with the eigenface of the eigenvector training image. here determines the smallest eigenface value of the two image faces that are already known eigenface values. the results can be calculated as follows. eigenface value c1 = (0 0 0 0 0 0 0 0 0 0 0) 𝐶1 = 0 0 0 0 0 0 0 0 0 1 1 2 1 0 2 2 2 1 −1 −1 −2 −1 0 −2 −2 −2 −1 c1 = 1+1+2+1+0+2+2+2+1 = 12 eigenface value c2 = (0 0 0 0 0 1 0 0 1) 𝐶1 = 0 0 0 0 0 1 0 0 1 1 1 2 1 0 2 2 2 1 −1 −1 −2 −1 0 −1 −2 −2 0 c2 = 1+1+2+1+0+1+2+2+0 = 10 the smallest eigenface value of the two image faces above obtained from the distance of the image of face one has the smallest value of 10. the identification results concluded that the test face was more similar to face two than face one. application testing once this pretension identification software using facial recognition is built, the next stage is the display trial stage. this trial stage includes testing from the beginning of student data entry to the face-matching process when doing the presence of entry and home. the details will be described as follows. 1. test the app's entry window the use of the application is initiated by the user who must log in first (figure 11), if the login is successful then the user can enter the system. there are menus that can be selected for activities in the http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 225 the work is distributed under the creative commons attribution-noncommercial 4.0 international license system according to the desired function (figure 12). figure 11. app login menu view figure 12. app main menu view 2. test the filling of data on the menu of the list of learners. before testing, you can perform the face detection display (figure 13). the detected face will show a blue focus box furthermore after the face is detected, data input on the face list (figure 14). if it has been saved, then if the face is detected, a green focus box will appear with a caption with the name according to the detected face. figure 16 shows the faces stored on list data. figure 13. face detection view figure 14. data input on the face list figure 15. faces of registered learners figure 16. faces stored on list data 3. test the filling of data on the class entry menu. on this menu, students will do the presence of entering the class by using their faces. the step is that the learner faces the application, which is approximately 30cm away, then the face will be detected in the blue box and then face matching, then the face and name of the learner and the duration of the face matching process will be seen on the green box, after which automatically the student data will be stored and sent to the parent/guardian email, like the figure 17, 18, and 19 below. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 226 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 17. face entry detection figure 18. face entry match figure 19. the process saves incoming data and messages sent to the email 4. test data are filled on the out-of-class menu. in this menu, students will do the presence of exiting the class / going home using the face. the step is that the learner faces the application, which is approximately 30cm away, then the face will be detected in the blue box and then face matching, then the face and name of the learner and the duration of the face matching process will be seen on the green box, after which automatically the student data will be stored and sent to the parent/guardian email, like the figure 20, 21, and 22 below. figure 20. face entry detection figure 21. face entry matc figure 22 process save data home and messages sent to the email 2.1. system testing based on distance and intensity of light. this test intends to determine the objects detected or undetected that can present learners in the classroom by matching faces at different distances/radii and light intensity. here is a test by matching faces at a distance/ radius and intensity of light. figure 23. face matching distance 30cm figure 23 is a facial matching test from 0cm to 30cm with low light intensity. gambar 24. face matching distance 50cm figure 24 is a facial matching test from 0cm to 50cm with low light intensity. figure 25. face matching distance 100cm http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.340 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 227 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 25 is a facial matching test at a distance of 0cm to 100cm with low light intensity. figure 26. face matching distance 150cm figure 26 is a facial matching test from 0cm to 150cm with low light intensity. figure 27. face matching distance 200cm figure 27 is a facial matching test from 0cm to 200cm with low light intensity. figure 28. face matching distance 250cm figure 28 is a facial matching test from 0cm to 250cm with low light intensity. figure 29. 300cm distance face matching figure 29 is a facial matching test from 0cm to 250cm with low light intensity. based on the facematching image that has gone through the application test process, the following summary of the results of the face-matching trial can be seen in table 3 below. table 3. presence test results with distance coverage and light intensity no detection object distance light intensity detecti on 1 figure iv30 0cm until 30cm low detected 2 figure iv31 0cm until 50cm low detected 3 figure iv32 0cm until 100cm low detected 4 figure iv33 0cm until 150cm low detected 5 figure iv34 0cm until 200cm low detected 6 figure iv35 0cm until 250cm low detected 7 figure iv36 0cm until 300cm low detected conclusion as for the conclusion of this study, where applications can be run automatically to read and analyze the presence of learners, applications can be run on android smartphones version 5.0 lollipop, 6.0 marshmallow, 7.0 nougat, 8.0 oreo, 9.0 pie, and android 10, in addition, this study can write the results of detection done either verbose when executed or written in true-false values entered in the processed database. this study has found the results of testing with the merger of two algorithms or methods that can recognize faces up to a distance of 300cm and obtain an average percentage of facial recognition system success reaches 90%, so that the application in this study can provide information to parents/guardians quickly, precisely and accurately. this research still has some limitations. therefore it still needs to be developed to increase effectiveness, efficiency, and the addition of features to support the right target presence. for that, it is necessary to develop subsequent systems, such as software and hardware, that must be met for this application to work correctly. this study is limited to locating learners and needs to be developed before it can be applied to other fields. references abuzar, m., ahmad, a. bin, & ahmad, a. a. bin. 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(2021). sistem presensi mahasiswa otomatis pada zoom meeting menggunakan face recognition dengan metode convulitional neural network berbasis web. jati (jurnal mahasiswa teknik informatika), 5(2), 785– 793. https://doi.org/10.36040/jati.v5i2.3762 septyanto, m. w., sofyan, h., jayadianti, h., simanjuntak, o. s., & dessyanto, b. p. (2020). aplikasi presensi pengenalan wajah dengan menggunakan algoritma haar cascade classifier. telematika : jurnal informatika dan teknologi informasi, 16(2), 87–96. https://doi.org/10.31315/telematika.v16 i2.3182 simaremare, h., & kurniawan, a. (2016). perbandingan akurasi pengenalan wajah menggunakan metode lbph dan eigenface dalam mengenali tiga wajah sekaligus secara real-time. sitekin: jurnal sains, teknologi dan industri, 14(1), 66–71. https://doi.org/10.24014/sitekin.v14i1.27 03 sulistiyo, w., suyanto, b., hestiningsih, i., mardiyono, m., & sukamto, s. (2014). rancang bangun prototipe aplikasi pengenalan wajah untuk sistem absensi alternatif dengan metode haar like feature dan eigenface. jtet (jurnal teknik elektro terapan), 3(2), 93–98. https://doi.org/10.32497/jtet.v3i2.180 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 139 decision support system for supplier selection on time concept with ahp and saw method warih dwi cahyo-1*), wahyu ari wibowo-2, suwarno-3, rizki ripai-4 1,2,3program studi ilmu komputer, fakultas teknologi informasi, universitas budi luhur, jakarta 1wassreh@gmail.com, 2wahyuariwibowo60@gmail.com, 3suwarmo663@gmail.com 4rekayasa keamanan siber, politeknik piksi input serang rizkiripai@piksiinputserang.ac.id (*) corresponding author abstract seeing the rapid development of global business causes companies to compete as the best to meet global market demands. in the current era of globalization, technological development is beneficial for human life. all human activities today can be done quickly and easily using a computer. decision support system is a computer-based system that assists decision-making in utilizing specific data and models to solve various unstructured problems. decision makers in selecting the best supplier for time concept are still having difficulties, and this is because there are no appropriate criteria and weights. making a decision support system is expected to help solve the problems in time concept. moreover, it can provide benefits or convenience for time concept when selecting the best supplier. the author uses the method of analytical hierarchy process (ahp) and simple additive weighting (saw). according to the system test results, the consistency ratio (cr) calculation value is 0.0752. the comparison assessment is considered consistent if the consistency ratio (cr) value is not greater than 0.1000. so that the comparison of the criteria does not need to be recalculated because it is consistent. keywords: ahp method; saw method; supplier selection; decision support system. abstrak melihat perkembangan global bisnis yang sedemikian cepatnya menyebabkan perusahaan berlombalomba sebagai yang terbaik untuk memenuhi permintaan pasar global. di era globalisasi seperti saat ini teknologi yang sudah berkembang sangat bermanfaat bagi kehidupan manusia. segala macam kegiatan manusia saat ini dapat dikerjakan dengan cepat dan mudah menggunakan komputer. sistem penunjang keputusan adalah suatu sistem berbasis komputer yang ditujukan untuk membantu pengambilan keputusan dalam memanfaatkan data dan model tertentu untuk memecahkan berbagai persoalan yang tidak terstruktur. pengambil keputusan dalam pemilihan supplier terbaik pada time concept masih mengalami kesulitan, hal ini disebabkan karena belum adanya kriteria dan bobot yang tepat. dengan dibuatnya sebuah sistem penunjang keputusan ini diharapkan dapat membantu memecahkan masalah yang ada pada time concept. serta dapat memberikan manfaat atau kemudahan bagi time concept pada saat melakukan pemilihan supplier terbaik. penulis menggunakan metode analytical hierarchy process (ahp) dan simple additive weighting (saw).hasil dari pengujian sistem maka didapatlah nilai perhitungan consistency rati o (cr) yaitu sebesar 0,0752. penilaian perbandingan dikatakan konsisten jika nilai consistency ratio (cr) tidak lebih besar dari 0,1000. sehingga penilaian perbandingan kriteria tidak perlu dilakukan perhitungan ulang karena sudah konsisten kata kunci: metode ahp; metode saw; pemilihan supplier; sistem penunjang keputusan. introduction seeing the rapid development of global business causes companies to compete as the best to meet global market demands. in the current era of globalization, technological development benefits human life. all human activities today can be done quickly and easily using a computer. according to hati and fitri in 2017, supplier selection is one of a company's essential activities because purchasing raw material components represents 40 to 80 percent of the total product cost and impacts https://www.cermati.com/artikel/decision-support-system-dss p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 140 company performance. companies have different criteria for choosing suppliers as business partners(hati & fitri, 2017) (purwitasari & pribadi, 2015). depending on the company's goals for smooth production and operations. many companies make fatal mistakes in choosing suppliers, resulting in company losses. assessment of suppliers requires various criteria that can describe the supplier's overall performance—the number of suppliers engaged in the sale of watches. the time concept company has several alternative suppliers, each with advantages and disadvantages. this requires foresight from time concept to analyze which supplier is suitable and worthy to be prioritized as a business partner. supplier selection is a long process. first, to evaluate suppliers based on several criteria, such as service, discount, payment duration, delivery time, and delivery amount. at the time of evaluation of supplier selection, trade-offs often occur, such as suppliers who provide fast service but late delivery, which can result in empty watch stocks. the more criteria time concept wants in the supplier selection, the more complicated the problem. therefore, it needs a decision-making technique. so the research can use the ahp method and the saw method to complete the decision support system that will be made, where the value of the weighting of the criteria for the ahp method will be the input for the simple additive weight (saw) method in ranking the best supplier selection alternatives. researchers also attach previous research (bagaspati & irawan, 2020). the decision support system uses the ahp and smart methods to select the best supplier. the results of research at pt. muria karya sentosa produces a weight of each criterion, namely quality criteria with a weight of 36.67%, delivery time criteria with a weight of 25.94%, price criteria with a weight of 7.61%, service criteria with a weight of 20.53%, and conformity criteria with weight 9.25%. the results of calculating the weight of the criteria have been tested with a consistency index (ci) value of 0.0945 and a consistency ratio (cr) value of 0.0844. the cr value is consistent because it is less than or equal to 0.1. calculations with the smart method ranked suppliers with the best value owned by pt. mighty lightning bow of 0.7627 (auddie & mahdiana, 2019). martoyo, anang, and fajar mahardika. "the effect of promotion and distribution on customer satisfaction at pt tiga serangkai internasional bandung branch." the method used is the promotion effect method with variable three obtained. satisfactory promotion and distribution for consumers can increase company improvement(martoyo & mahardika, 2020). setiyawan, siswanti and hasbi, 2020. analytical hierarchy process method and simple multiattribute rating technique as support for supplier selection decisions. ahp and smart methods. based on the analysis of validity testing using ten samples and determining the performance of dss, there is a similarity level of 20% or a difference of 80%. it is due to the difference in calculating the weight of each criterion between the manual process and the application. where the calculation of the application is not only calculated from the value of each criterion but also multiplied by the weight of the criteria. (setiyawan et al., 2020). putra, habibie and handayani, 2020. decision support system for supplier selection in tb.nameene with the simple additive weighting method. the results of this test are done by comparing the output or output of the system with the results of manual calculations against several formulas in the saw method. application testing is done by looking at the overall output given as a result of the analysis of the application with the actual conditions. moreover, after the coding is complete, a testing process will be carried out on the resulting application to find out whether the resulting application is to find out whether designed application is running correctly and by design carried out (putra et al., 2020). implementation of the waterfall method in the analog image digitization process. with the image processing method of negative images in matlab. with the results of the research and discussion, it can be concluded that this application can take pictures using a laptop or notebook webcam and can be used for digital image processing that comes from negative images (photo clichés) with inverted images to accurate color images. the results of the application image processing can be stored in jpg/jpeg (joint photographic expert group) format. the testing results show that this application is easy to use and is likely to be developed in a better and more complex direction (mahardika et al., 2017). mahardika, f., setiawan, e., & saputra, d. i. s. (2019). application of color segmentation to images on social media with the fuzzy k-means cluster algorithm. this is the basis for conducting this research to find image segmentation in social media images. researchers conducted research by combining image segmentation using the fuzzy kmeans cluster algorithm. the existence of this merger is expected to get maximum results. the research results on social media show the distribution of images for each area that has similarities and also find out the grouping of pixels jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 141 based on the proximity of the distance between pixels (mahardika et al., 2019). implementation segmentation of color image with detection of color to detect object (mahardika et al., 2017). with the image segmentation method in matlab. the results of object detection in 2-dimensional images are a somewhat complex process to do. object detection requires a computer vision approach for the desired part of the object to be accurately recognized by the computer (mahardika & saputra, 2017). apriastika, prima, and lusi fajarita. "decision support system for determining the best teacher at sd strada santa maria using the ahp (analytical hierarchy process) and saw (simple additive weighting) methods. this study discusses the decision support system in determining the best teacher at strada santa maria elementary school. strada santa maria elementary school is a superior, caring educational community with a spirit of service. currently, the school has conducted assessment activities to determine the best teacher, but this assessment has not produced maximum results because at strada santa maria elementary school, all teachers are said to be good, all are equal, and no one distinguishes degrees. strada santa maria elementary school requires an objective and structured decision support system so that the results obtained are by expectations and can improve the quality and quality of teachers so that they are well achieved. the system that will be created uses the ahp (analytical hierarchy process) method to determine the weight of the existing criteria, namely spiritualism, professionalism, leadership, and solidarity, which will later be compared with other criteria. moreover, the saw (simple additive weighting) method is used in selecting alternative rankings (apriastika & fajarita, 2019). setiyawan, bayu aji, sri siswanti, and muhammad hasbi. "analytical hierarchy process method and simple multi-attribute rating technique as supporting supplier selection decisions."the process of choosing a supplier in sukoharjo has not used the application program in making the decision but still uses the manual way of writing. in addition, in conducting the selection of suppliers conducted by the hrd section is still subjective, so the results obtained in the selection of suppliers are less valid because in selecting hrd suppliers only choose based on price criteria, where if there is a supplier that offers the lowest price then the supplier will be chosen as a supplier in sukoharjo. the research aims to build and implement a decision support system helpful in selecting suppliers in sukoharjo using the analytical hierarchy process (ahp) and simple multiattribute rating technique (smart). the ahp method calculates the weight of the criteria, and the smart method calculates the supplier's alignment. with testing, the black box system is already running according to the function, and for the results of the validity, test get, the test value results in the category very good with a percentage of 80% (setiyawan et al., 2020). gholamian, k., vakilifard, h., talebnia, g., & hejazi, r. (2020). conceptual design of sustainable outsourcing with balanced scorecard using analytic hierarchy process: a case study for fajr jam gas refining company. our findings indicated that a sustainable outsourcing model was successfully designed using a balanced scorecard. economic, social, and environmental sustainability was considered in each of the balanced scorecard faces used in the model. this work's primary objectives were a sustainable domestic business, customer satisfaction, and sustainable learning and growth. finally, a balanced scorecard with 26 strategic objectives was designed and implemented. to this end, paired comparisons were performed to compute the importance of each strategic goal in every phase and make prioritization accordingly (gholamian et al., 2020). gholamian, k., vakilifard, h., talebnia, g., & hejazi, r. (2019). identification and prioritization of environmental criteria of sustainable outsourcing model in fajr jam gas refining company with analytic hierarchy process method. the findings indicated that designing a sustainable outsourcing model using a balanced scorecard. seven strategic objectives in the environmental dimension had to be considered. two objectives in the learning and growth aspect, two in the internal business processes aspect, two in the customer satisfaction aspect, and a strategic environmental objective in the financial performance aspect were identified. finally, using a paired comparisons questionnaire, the importance of each strategic objective was calculated and prioritized (gholamian et al., 2019). yahya, s., & kingsman, b. (2017). vendor rating for an entrepreneur development program: a case study using the analytic hierarchy process method. this paper describes a case study into vendor rating for a government-sponsored entrepreneur development program in malaysia. the paper reviews current methods for vendor rating and finds them wanting. it illustrates a new approach based on saaty's analytic hierarchy process method, developed to assist in multicriteria decision problems. the new method overcomes the difficulties associated with the categorical and simple linear weighted average p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 142 criteria ranking methods. it provides a more systematic way of deriving the weights to be used and scoring vendor performance (yahya & kingsman, 2017). gap or gap analysis is defined as comparing actual performance with potential or expected performance. gap analysis evaluates the business by comparing the company's current performance with previously targeted performance and determining what steps need to be taken to reduce the gap and achieve the desired state in the future. this analysis model is based on consumer assumptions by comparing company performance with specific standards or consumer expectations (stolzer & goglia, 2016). gap analysis is used as a business evaluation tool focused on performance gaps. this analysis is used to determine the gap between consumer perceptions and expectations and identify the actions needed to enable them to reduce the gap and achieve the expected performance in the future. therefore, the company wants to know the gap between consumers' perceptions and expectations of quality service, price, and quality of watch products at time concept. supplier selection is a long process. first, the supplier is evaluated based on several criteria: service, price discounts, payment duration, delivery time, and the number of shipments. when evaluating supplier selection, there is often a tradeoff, such as a supplier who provides fast service but delivers late, which can result in an empty watch stock. the more criteria time concept wants to select suppliers, the more complicated the problem is. therefore, a decision-making technique is needed. this research aims to create a decision support system to help the supplier selection process correctly and accurately. research methods 1. research methods research methodology is a scientific process or method to obtain data for research purposes, such as data collection methods and research methodologies used as research guidelines so that the results achieved do not deviate from their objectives. this study examines the selection of the best suppliers using the analytical hierarchy process (ahp) and simple additive weight (saw) methods on the time concept(laurentinus & rinaldi, 2019). 2. sample selection method the sample selected or used in this study is supplier data on time concept and the criteria that have been determined, among others: a. service this criterion relates to supplier services to company requests with a fast response. the assessment of these criteria uses an ordinal scale based on the following table 1: table 1. ordinal service scale target value information 1 very bad 2 bad 3 normal 4 very nice 5 very good b. discounts this criterion relates to the discount given by the supplier to time concept. discounts are obtained from questions asked by the purchasing department when they want to order or from supplier invoices received by time concept. the assessment of this criterion is seen from the amount of discount given by the supplier. c. payment duration this criterion relates to the repayment period of the watch. these criteria are obtained from questions asked by the purchasing department when they want to order or from supplier invoices received by time concept. the assessment of this criterion is seen from the length of time for repayment. the longer the payment period, the better. d. delivery time this criterion relates to the delivery time of the watch. these criteria are obtained from questions the purchasing department asks when they want to order or from supplier invoices that time concept has received. 27 assessment of this criterion is seen from the time the order arrives. the sooner it comes, the better. e. number of send this criterion relates to the number of watch orders received. these criteria are obtained from questions the purchasing department asks when they want to order or from supplier invoices that time concept has received. the assessment of these criteria uses an ordinal scale based on table 2. table 2. ordinal scale number of sends target value information 1 tidak sesuai 2 sesuai in addition to the criteria, there are several alternative suppliers, as seen in table 3. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 143 table 3. alternative no alternative alternative code 1 pt. radiance sp001 2 pt. time line sp002 3 pt. vip watch sp003 4 pt. luminoux sp004 5 pt. alva jaya mandiri sp005 6 pt. swiss watch sp006 7 pt. central watch sp007 3. data collection method in this study, to collect data. the author uses observation, interviews, document analysis, questionnaires, and literature studies. 4. research instruments research instruments are tools that are needed or used to collect data. the instrument used in collecting data in this study is to provide questionnaires to respondents. the respondent of this research is the general manager of brand activation, which can be seen in appendix-a of the questionnaire based on the following table 4. table 4. alternatives respondents amount alternative 1 5. data analysis techniques data analysis techniques used to analyze the data in this study are descriptive analysis, analytical hierarchy process (ahp), and simple additive weighting (saw). descriptive analysis aims to draw conclusions and provide an overview of current business processes. at the same time, the analytical hierarchy process (ahp) is used to determine the weight of each predetermined criterion. because time concept has not given weighting criteria. simple additive weighting (saw) is used to sort the alternatives after the criteria are processed and to determine the supplier rankings sorted from the most significant value to the smallest value to obtain results to determine the best supplier candidate(hati & fitri, 2017). 6. research stages the following are the stages of the research which can be seen in figure 1: figure 1. research stages 1. formulation problems at this research stage, the problems to be discussed are formulated based on the observations' results. 2. interview the interview is the process of collecting data on the time concept by asking questions directly to assist in making a decision support system. the questions asked included issues related to supplier selection and the criteria for supplier selection. 3. library studies library studies are carried out by looking for journals, books, and information from the internet related to the problem under study. 4. observation then observations are made by visiting time concept to conduct interviews with time concept decision-makers to obtain information regarding problems and the ongoing process of selecting suppliers. 5. data collection then carry out the data collection stage by collecting documents from time concept. 6. questionnaire design on the time concept side, the criteria used in the supplier selection process are determined so that the writer can directly design a questionnaire based on the specified criteria and, at the same time, carry it back to the data collection stage when filling out the questionnaire by the brand activation general manager as an expert respondent. 7. data analysis after the data collection process and questionnaire design, the next step is to analyze the data using the analytical hierarchy process (ahp) and simple additive weighting (saw) methods which will then obtain the level of importance of p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 144 each criterion, and the general manager brand activation makes the decision maker. 8. model design and system design then the next stage is designing the model and system design as a description, planning, and sketching or arrangement of several separate elements into a unified whole and function. 9. testing the decision support system application for supplier selection is tested to determine whether the system built is correct and uses the black box testing method. 10. report preparation after the test runs according to the required system, then there will be a report preparation process results and discussion 1. system analysis in analyzing the problems in time concept, namely determining the best supplier, the researchers in this chapter discuss the data analysis process. the data analysis technique is based on data collected through interviews, observations, questionnaires, and literature studies to determine the system requirements for solving the right problem. the data is processed in detail and precisely. then the data collected in detail will be developed with the selected methods to help design the desired system. 2. ahp method a. calculation process ahp figure 2. hierarchical criteria b. criteria used first, in this study, the general manager brand activation will give the importance and attributes of each criterion. criteria data is data regarding the criteria of decision-making. the following criteria table example contains several columns, namely the criteria name and attributes for the weight calculation using ahp weighting. the weight of the criteria determines how critical the criteria are. the criteria attribute consists of benefits or costs, where benefit means that the greater the value, the better, while the smaller the cost, the better the value. the following is the level of importance used in table 5: table 5 criteria level criteria name attribute service benefit potongan harga benefit durasi pembayaran benefit waktu pengiriman cost jumlah kirim benefit c. weighting stage ahp in this study, the head of brand activation will first be given a questionnaire to find the level of importance of the existing criteria, then the ahp method of comparison of criteria will be used to determine the weight of each criterion that will be jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 145 used as a benchmark for the assessment as follows table 6: 1) value of interest comparison between criteria table 6. pairwise comparison matrix criteria service discounts payment duration delivery time send amount service 1 2 1/2 1/3 2 discounts ½ 1 1 ½ 3 payment duration 2 1/1 1 ½ 3 delivery time 3 2 2 1 2 send amount ½ 1/3 1/3 ½ 1 2) convert fraction matrix to decimal table 7. pairwise comparison matrix criteria service discounts payment duration delivery time send amount service 1,0000 2,0000 0,5000 0,3333 2,0000 discounts 0,5000 1,0000 1,0000 0,5000 3,0000 payment duration 2,0000 1,0000 1,0000 0,5000 3,0000 delivery time 3,0000 2,0000 2,0000 1,0000 2,0000 send amount 0,5000 0,3333 0,3333 0,5000 1,0000 3) multiplying paired matrices (square of paired matrices) [ 1,0000 2,0000 0,5000 0,5000 1,0000 1,0000 2,0000 1,0000 1,0000 0,3333 2,0000 0,5000 3,0000 0,5000 3,0000 3,0000 2,0000 2,0000 0,5000 0,3333 0,3333 1,0000 2,0000 0,5000 1,0000] 𝑥 [ 1,0000 2,0000 0,5000 0,5000 1,0000 1,0000 2,0000 1,0000 1,0000 0,3333 2,0000 0,5000 3,0000 0,5000 3,0000 3,0000 2,0000 2,0000 0,5000 0,3333 0,3333 1,0000 2,0000 0,5000 1,0000] 4) matrix multiplication results [ 4,9999 5,8332 4,3332 6,0000 4,9999 4,2499 7,5000 7,9999 4,9999 2,9166 12,1666 3,1667 11,0000 3,6666 14,0000 12,0000 12,6666 8,1666 3,3333 2,9999 2,2499 4,9999 22,0000 1,5000 4,9998 ] this result is obtained from = ((1,000*1,0000)+(2,0000*0,5000)+(0,5000*2,000 0)+(0,3333*3,0000)+( 2,0000*0,5000) =(1,0000+1,0000+1,00000+0,9999+1,0000) =4,9999 5) add up each row of the result of matrix multiplication [ 4,9999 5,8332 4,3332 6,0000 4,9999 4,2499 7,5000 7,9999 4,9999 2,9166 12,1666 3,1667 11,0000 3,6666 14,0000 12,0000 12,6666 8,1666 3,3333 2,9999 2,2499 4,9999 22,0000 1,5000 4,9998 ] = 30,2495 29,4165 38,1664 59,8331 15,0829 172,7484 6) normalize each number of rows in the matrix with the total rows, which will produce an eigenvector 30,2495 29,4165 38,1664 59,8331 15,0829 172,7484 𝐸𝑖𝑔𝑒𝑛 𝑉𝑒𝑐𝑡𝑜𝑟 → 0,1751 0,1703 0,2209 0,3464 0,0873 1 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 146 the eigenvector results are obtained from = 30,2495/172,7484 =0,1751 7) determine the weight of each criterion taken from the eigenvector table 8 criteria and weights criteria name weight service 0,1751 discounts 0,1703 payment duration 0,2209 delivery time 0,3464 send amount 0,0873 8) multiply the decimal number value of each criterion matrix by the weight/eigenvector. table 9 vector counting table criteria service discounts payment duration delivery time send amount vector service 1,0000 2,0000 0,5000 0,3333 2,0000 0,9162 discounts 0,5000 1,0000 1,0000 0,5000 3,0000 0,9139 payment duration 2,0000 1,0000 1,0000 0,5000 3,0000 1,1765 delivery time 3,0000 2,0000 2,0000 1,0000 2,0000 1,8287 send amount 0,5000 0,3333 0,3333 0,5000 1,0000 0,4784 weight 0,1751 0,1703 0,2209 0,3464 0,0873 this result is obtained from: = (1,0000*0,1751)+( 2,0000*)+(0,5000)+(0,33330,3464) +( 2,0000*0,0873) = 0,1751+0,3406+0,1105+0,1154+0,1746 = 0,9162 9) the result of the vector is divided by the weight or eigenvector in table 10. table 10 results table𝜆 criteria vector weight results service 0,9162 0,1751 5,2324 discounts 0,9139 0,1703 5,3664 payment duration 1,1765 0,2209 5,3259 delivery time 1,8287 0,3464 5,2792 send amount 0,4784 0,0873 5,4800 this result is obtained from 𝜆 = 0,9162/ 0,1751= 5,2324 10) calculate the sum of each result (λ) from each criterion and then divide by the number of elements as in the equation below: 𝜆𝑀𝑎𝑘𝑠 = ∑ 𝜆 /𝑛 λmaks = (5,2324+ 5,3664+ 5,3259+ 5,2792+ 5,4800)/5 λmaks = 26,6839 / 5 = 5,3368 11) calculate the consistency index (ci) shown like this equation: ci = (5,3368– 5) / (5-1) ci = 0,3368 / 4 = 0,0842 12) calculating consistency ratio(cr) the consistency ratio (cr) value is obtained by dividing consistency index (ci) and ratio index (ri). for the consistency ratio test, if the cr result is < 0.1, then the data is considered consistent and does not need to be recalculated, but if the cr value is > 0.1, then a recount is required. cr = 0.0842/1.12 = 0.0752 cr value < 0.1, then the data is declared consistent. with consistent testing, the weighting is no longer needed for recalculation. 3. simple additive weighting (saw) method the simple additive weighting (saw) method is used to determine the final alternative value in selecting the best supplier, which will produce the output as the best supplier based on the highest ranking value. a. saw calculation process based on the number of suppliers in the time concept, the simple additive weighting (saw) method is applied in determining suppliers. the supplier has the following data in table 11: jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 147 table 11 saw calculation process alternative name criteria service discounts payment duration delivery time send amount pt. radiance 3 250000 64 14 2 pt. time line 4 400000 64 14 1 pt. vip watch 5 500000 32 14 2 pt. luminoux 4 300000 32 14 2 pt. alva jaya mandiri 3 400000 64 14 1 pt. swiss watch 4 300000 32 14 1 pt. central watch 4 300000 32 14 1 1) first, normalization is carried out into an r matrix to calculate the value of each criterion, calculating based on the profit criteria or the cost criteria with the following equation in table 12: table 12 classification of criteria criteria name attribute service benefit discounts benefit payment duration benefit delivery time cost service benefit a) pt. radiance 𝑟1 = 0,6; 𝑟2 = 0,5; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 1 b) pt. time line 𝑟1 = 0,8; 𝑟2 = 0,8; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 0,5 c) pt. vip watch 𝑟1 = 1; 𝑟2 = 1; 𝑟3 = 0,5; 𝑟4 = 1; 𝑟5 = 1 d) pt. luminoux 𝑟1 = 0,8; 𝑟2 = 0,6; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 1 e) pt.alva jaya mandiri 𝑟1 = 0,6; 𝑟2 = 0,8; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 0,5 f) pt. swiss watch 𝑟1 = 0,8; 𝑟2 = 0,6; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 0,5 g) pt. central watch 𝑟1 = 0,8 ; 𝑟2 = 0,6 ; 𝑟3 = 1; 𝑟4 = 1; 𝑟5 = 0,5 from the results of the calculation matrix above, get the value of the matrix r: 𝑅 = [ 0,6000 0,5000 1,0000 0,8000 0,8000 1,0000 1,0000 1,0000 0,5000 1,0000 1,0000 1,0000 11,0000 1,0000 1,0000 0,8000 0,6000 0,5000 0,6000 0,8000 1,0000 0,8000 0,6000 0,5000 0,8000 0,6000 1,0000 1,0000 1,0000 1,0000 0,5000 1,0000 0,5000 1,0000 0,5000 ] 2) alternate ranking after getting the r-value from the r matrix and the weight value (w), the next step is the preferred process (vi) using the formula: 𝑅 = [ 0,6000 0,5000 1,0000 0,8000 0,8000 1,0000 1,0000 1,0000 0,5000 1,0000 1,0000 1,0000 11,0000 1,0000 1,0000 0,8000 0,6000 0,5000 0,6000 0,8000 1,0000 0,8000 0,6000 0,5000 0,8000 0,6000 1,0000 1,0000 1,0000 1,0000 0,5000 1,0000 0,5000 1,0000 0,5000 ] a) pt. radiance get a score of 0,8448 b) pt. time line gets a score of 0,8873 c) pt. vip watch get a score of 0,84896 d) pt. luminoux get a score of 0,7862 e) pt. alva jaya mandiri get a score of 0,8523 f) pt. swiss watch get a score of 0,7428 g) pt. central watch get a score of 0,8532 after doing the above calculations, that pt. vip watch is the best supplier, with a value of 0.8896. conclusions and suggestions conclusion based on the research results, several conclusions were drawn, including decision support systems with the ahp and saw methods can be used to assess and select the best supplier, so there is no doubt in the selection. of course, several criteria and weights play a significant role in determining the ranking results. the results of the system test by inputting data from the questionnaire, the value of the consistency ratio (cr) calculation is 0.0752. suppose the consistency ratio (cr) value is less than or equal to 0.1000. in that case, the comparison evaluation is considered consistent to avoid the necessity of doing a new calculation in the comparative assessment of the criteria since it is consistent. suggestion the following is a suggestion for future system development: companies to train users who will use this decision support system to operate properly and correctly. subsequent research uses other variables to support changes in decision p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.460 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 148 support systems according to future company needs. references apriastika, p., & fajarita, l. 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(2017). vendor rating for an entrepreneur development programme: a case study using the analytic hierarchy process method. https://doi.org/10.1057/palgrave.jors.26007 97, 50(9), 916–930. https://doi.org/10.1057/palgrave.jors.26 00797 13 3 jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 133 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem pendukung keputusan pemberian beras untuk keluarga miskin dengan metode simple additive weighting jamal abdul nasir sistem informasi stmik nusa mandiri jakarta www.nusamandiri.ac.id jamalabdul66@gmail.com abstract to improve indonesia's economic stability, the government has tried various methods. one of the policies taken by the government is to issue a raskin (rice for the poor) policy. in the distribution of raskin there are often obstacles or problems, one of which is the leveling of the amount of raskin received by all recipients and the misdirected distribution of rice. decision support systems are part of overcoming these problems so a study was made to develop a computer application that helps make a decision. the subject of this research was the application of the decision support system for selection of raskin acceptance using the simple additive weighting (saw) method. the purpose of this study as an alternative to assist the rt management in determining decisions related to the provision of raskin rice for residents in accordance with the requirements and criteria of the rt 04 rw.01 sukmajaya depok administration. keywords: simple additive weighting, raskin, decision support system abstrak untuk memperbaiki stabilitas ekonomi indonesia, pemerintah mengupayakan berbagai cara. salah satu kebijakan yang diambil pemerintah yaitu dengan mengeluarkan kebijakan raskin (beras untuk masyarakat miskin). dalam pendistribusian raskin sering kali ditemui kendala atau permasalahan, salah satunya adanya penyamarataan jumlah raskin yang diterima oleh semua penerima dan pembagian beras yang salah sasaran. sistem pendukung keputusan merupakan bagian untuk mengatasi masalah tersebut maka dibuat sebuah penelitian untuk mengembangkan suatu aplikasi komputer yang membantu mengambil sebuah keputusan. subjek pada penelitian ini adalah aplikasi sistem pendukung keputusan seleksi penerimaan raskin menggunakan metode simple additive weighting (saw). tujuan penelitian ini sebagai salah satu alternatif untuk membantu kepengurusan rt dalam menentukan keputusan terkait pemberian beras raskin bagi warga sesuai dengan syarat dan kriteria pengurus rt 04 rw.01 kelurahan sukmajaya depok. kata kunci: simple additive weighting, raskin, sistem pendukung keputusan pendahuluan indonesia merupakan negara agraris, ratarata penghasilan didapat dari pertanian. salah satu bidang pertanian yang paling maju adalah padi, yang menghasilkan beras sebagai makanan pokok. banyaknya warga negara indonesia, menyebabkan hasil panen beras dalam negeri tidak cukup untuk memenuhi kebutuhan warganya (rini & soyusiawaty, 2014), sehingga memerlukan tambahan pasokan dari luar negeri. hal ini menyebabkan terjadinya kekurangan bahan pangan (widiarsih, 1974) terutama pada keluarga tidak mampu. untuk memperbaiki stabilitas ekonomi indonesia, pemerintah mengupayakan berbagai cara. salah satu kebijakan yang diambil pemerintah yaitu dengan mengeluarkan kebijakan raskin (beras untuk masyarakat miskin). dalam pendistribusian raskin sering kali ditemui kendala atau permasalahan, salah satunya adanya penyamarataan jumlah raskin yang diterima oleh semua penerima raskin (angrawati, angrawati, yamin, & ransi, 2016). masih terjadi kecurangan dalam pemilihan masyarakat calon penerima raskin (handayani, 2016). sementara ada derajat sosial yang berbeda antara sesama penerima raskin tersebut. derajat sosial ini terutama dipengaruhi oleh tingkat pendapatan dan jenis profesi atau sumber mata pencaharian para penerima raskin. dilingkungan rt.04 rw.01 kelurahan sukmajaya merupakan sebuah pemerintahan level paling bawah pada suatu sistem pemerintahan yang terletak di kecamatan sukmajaya depok. warga di rt.04 rw.01 ini berasal dari berbagai daerah di indonesia. dengan jumlah kepala keluarga sebanyak 250 kepala keluarga (kk). dalam pengambilan keputusan untuk menentukan kriteria keluarga miskin dibutuhkan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 134 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sebuah sistem informasi yang dapat membantu mengatasi kecurangan yang dilakukan oleh pihak tertentu dalam menentukan calon penerima raskin. sistem pendukung keputusan merupakan bagian dari sistem informasi berbasis komputer yang mengatasi masalah ini. sistem ini dapat mendukung pengambilan keputusan calon penerima raskin berdasarkan kriteria-kriteria yang telah ditentukan. cara kerja sistem ini mencakup seluruh tahap pengambilan masalah, memilih data yang relevan dan menentukan pendekatan yang digunakan dalam proses pengambilan keputusan sampai pemecahan dan solusi masalah. beras untuk keluarga miskin atau raskin merupakan program pemerintah dalam menanggulangi kemiskinan. program raskin ini bertujuan untuk membantu kelompok miskin dan rentan miskin mendapat cukup pangan dan nutrisi karbohidrat tanpa terkendala (marlaeni & satria, 2017). namun dalam pelaksanaan raskin ini banyak masalah, diantara masalah tersebut adalah pembagian beras yang salah sasaran (septian, bahri, & makmur, 2013), untuk mengatasi masalah tersebut maka dibuat sebuah penelitian untuk mengembangkan suatu aplikasi komputer yang membantu mengambil sebuah keputusan. subjek pada penelitian ini adalah aplikasi sistem pendukung keputusan seleksi penerimaan raskin menggunakan metode simple additive weighting (suryeni, agustin, & nurfitria, 2015). metode penggumpulan data dengan literatur, dokumentasi, wawancara. tahap pengembangan aplikasi meliputi perancangan interface, analisis, pembuatan diagram konteks, diagram alir data, entity realationship diagram, mapping table, rancangan tabel, perancangan menu dan antarmuka, implementasi dan pengujian, metode yang digunakan adalah metode simple additive weighting dan pengujian sistem menggunakan blackbox test dan alpha test. dari penelitian yang menghasilkan sebuah perangkat lunak sistem pendukung keputusan seleksi penerimaan raskin menggunakan metode simple additive weighting dengan kemampuan dapat membantu menyeleksi warga berdasarkan kriteria-kriteria kondisi rumah, pekerjaan, penghasilan, jumlah tanggungan, asset pribadi. informasi yang dihasilkan adalah warga layak dan tidak layak menerima beras. hasil uji coba menunjukkan bahwa aplikasi ini layak dan dapat digunakan. (rini dan soyusiawaty 2014:2) tujuan penelitian ini sebagai salah satu alternatif untuk membantu kepengurusan rt dalam menentukan keputusan terkait pemberian beras raskin bagi warga sesuai dengan syarat dan kriteria pengurus rt 04 rw.01 kelurahan sukmajaya depok. melakukan penilaian dari setiap kriteria untuk pemberian beras raskin. menerapakan metode simple additive weighting (saw) dalam menentukan pemberian beras raskin untuk warga rt.04 rw.01 kelurahan sukmajaya depok. merancang suatu sitem pendukung keputusan pemberian beras raskin yang memenuhi syarat dan kriteria dengan cepat dengan kebutuhan pengurus rt.04 rw.01 kelurahan sukmajaya depok. metode penelitian a. tahapan penelitian dalam hal ini akan diuraikan mengenai langkah langkah yang akan dilakukan untuk mendapatkan metodologi penlitian yang merupakan suatu tahapan yang harus diterapkan agar penilitian dapat dilakukan dengan terarah dan memudahkan dalam melakukan analisa terhadap permasalahan yang ada. tahapan penelitian tentang sistem pendukung keputusan pemberian beras untuk keluarga miskin dengan metode simple additive weighting (saw) dijelaskan secara umum sebagai berikut: 1. survey literatur dalam tahap ini peneliti melakukan pengumpulan bahan literatur dan informasi terkait. 2. identifikasi masalah mengidentifikasi masalah yang akan dibahas, berkaitan dengan sistem pendukung keputusan pemberian beras untuk keluarga miskin dengan metode simple additive weighting (saw) sesuai dengan literature dan informasi yang diperoleh. 3. studi pustaka peneliti mempelajari buku-buku, jurnal penelitian, dan e-book teori tentang sistem pendukung keputusan dan metode simple addictive weighting (saw) yang akan digunakan sebagai kajian teori dalam penelitian. 4. hipotesis peneliti memiliki hipotesis awal, yaitu diduga dalam pengambilan keputusan dengan menggunakan simple additive weighting (saw) akan memberikan alternatif keputusan yang baik bagi pengurus rt.04/01 sehingga pengambilan keputusan tepat pada sasaran. 5. menentukan kriteria dan sumber data peneliti menentukan kriteria-kriteria dari sistem pendukung keputusan metode menggunakan metode simple additive weighting http://creativecommons.org/licenses/by-nc/4.0/ 13 5 jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 135 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional (saw) dalam menetukan pemberian raskin, diantaranya kepemilikan kartu keluarga sehat (kks), penghasilan, tempat tinggal, pendidikan, pola makan, dan kemampuan berobat. kemudian menentukan data-data yang dibutuhkan berdasarkan populasi, sampel dan cara pengambilan sampel. kemudian menentukan objek penelitian dan mempersiapkan alat pengumpulan data dengan menentukan dan menyusun alat ukur atau instrument penelitian yang akan digunakan dalam penelitian ini yaitu berupa skala model guttman. 6. observasi lapangan dan perijinan peneliti secara langsung datang ke salah satu lingkungan rukun tetangga, yaitu rt.04 rw.01 di depok dan meminta ijin kepada pihak-pihak terkait dan berwenang. 7. mengumpulkan data peneliti mengumpulkan data-data dan melakukan wawancara mengenai halhal yang berkaitan dengan penlitian. 8. analisis data peneliti menganalisa dan mengolah data quesioner, serta menentukan bobotbobot dari masing-masing kriteria. 9. menarik kesimpulan peneliti mengambil suatu kesimpulan berdasarkan analisis data-data yang terdapat pada bab-bab sebelumnya dan diperiksa apakah kesimpulan sesuai dengan hipotesis, maksud dan tujuan penelitian. selain itu juga memberikan saran yang dapat digunakan sebagai masukan bagi pengurus rt terkait untuk dapat dimanfaatkan lebih lanjut. b. instrument penelitian adapun jenis instrument yang digunakan dalam penilitian ini, yaitu ; a. observasi metode ini diterapkan dengan mendatangi obyek lingkungan rukun tetangga, yaitu salah satu pengurus rt.04/01 di depok untuk mendapatkan data-data yang dibutuhkan. b. wawancara pengumpulan data dengan cara wawancara adalah suatu usaha untuk mengumpulkan informasi dengan mengajukan beberapa pertanyaan secara lisan kepada pengurus rt.04/01. c. studi pustaka metode ini dilaksanakan dengan melakukan studi kepustakaan melalui membaca buku-buku, jurnal penelitian sejenis e-book yang dapat mendukung penulisan skripsi ini, yaitu yang menjelaskan tetntang sistem pendukung keputusan (spk) dengan menggunakan metode simple additive weighting (saw). d. quesioner pengumpulan data dengan cara mengumpulkan angket yang telah diisi oleh narasumber yang bertujuan untuk mendapatkan data yang akan digunakan dalam penerapan metode simple additive weghting. c. metode pengumpulan data, populasi dan sampel penelitian 1. metode pengumpulan data metode pengumpulan data yang dilakukan peneliti terbagi menjadi 2 cara, yaitu: a. dengan melakukan observasi langsug, wawancara, dan questioner untuk mendapatkan data primer. b. data sekunder berasal dari mengumpulkan dan mengidentifikasi serta mengolah data tertulis berbentuk buku-buku dan jurnal yang berkaitan dengan penilitian. 2. populasi dan sampel penelitian menurut sugiyono (2008: 80) “populasi adalah wilayah generalisasi yang terdiri: obyek/subyek yang mempunyai kualitas dan karakteristik tertentu yang ditetapkan oleh penenliti untuk dipelajari dan kemudian ditarik kesimpulannya.” menurut sugiyono (2008: 81) “teknik sampling adalah teknik pengambilan sampel. untuk menentukan sampel yang akan digunakan dalam penelitian.” dalam penelitian ini, peneliti melakukan observasi dan wawancara langsung kepada pengurus rt.04 rw.01 di depok. populasi yang diambil adalah warga sebanyak 50 orang. dari populasi tersebut akan diambil 42 sampel. hasil penelitian dan pembahasan tabel dibawah merupakan isi kuisioner dimana setiap item instrumen nilai tersebut diambil dari skala rating. tabel 1. variabel skala kriteria no data c1 c2 c3 c4 c5 1 aryana sb sb sb tb sb 2 henandar tb sb sb tb sb http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 136 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional no data c1 c2 c3 c4 c5 3 karmadi sb sb sb tb sb 4 miftahudin tb sb sb tb sb 5 malik tb sb sb sb sb 6 rendi tb sb sb sb sb 7 kartija tb sb sb tb sb 8 delfi efendi sb sb sb tb sb 9 deni tb sb sb sb sb 10 gunarso tb sb sb tb sb 11 sunandar tb sb sb tb sb 12 bram sb sb sb tb sb 13 deden sb sb tb tb sb 14 agus tb tb tb tb sb 15 munawar sb sb tb tb sb 16 abdul majid tb tb sb sb sb 17 suryani sb sb tb tb sb 18 sairin tb tb sb tb sb 19 heriansyah tb sb sb sb sb 20 ayat tb sb tb tb sb 21 jaka wahyudin tb sb tb tb sb 22 amran tb tb tb tb sb 23 subeki tb sb sb sb sb 24 syahroni tb tb sb sb sb 25 m idris tb sb tb sb sb 26 sukriyah sb sb sb sb sb 27 sarinah sb sb sb sb sb 28 imbron tb tb tb sb sb 29 dimas sb tb sb tb sb 30 zarkoni sb sb sb tb sb 31 bambang s r sb sb tb tb sb 32 ridwan sb sb sb tb sb 33 adi gunawan sb sb tb tb sb 34 marhadi sb sb sb tb sb 35 nenih sb sb sb tb sb 36 udin sb sb tb tb sb 37 mat amin sb sb tb tb sb 38 homsyah sb sb sb tb sb 39 yati aryati sb sb sb tb sb 40 dian jauhari sb tb tb tb sb 41 oktorizal sb sb tb sb sb 42 toriq sb tb tb tb sb data matrik x tabel dibawah ini merupakan data matriks dimana setiap kriteria didapat dari rekap kuisioner diubah ke bilangan fuzzy berdasarkan nilai bobot yang telah ditentukan. tabel 2. matriks persamaan no data matriks bobot fuzzy c1 c2 c3 c4 c5 1 aryana 1 1 1 0.25 1 2 henandar 0,25 1 1 0.25 1 3 karmadi 1 1 1 0.25 1 4 miftahudin 0.25 1 1 0.25 1 5 malik 0.25 1 1 1 1 6 rendi 0.25 1 1 1 1 7 kartija 0.25 1 1 0.25 1 8 delfi efendi 1 1 1 0.25 1 9 deni 0.25 1 1 1 1 10 gunarso 0.25 1 1 0.25 1 11 sunandar 0.25 1 1 0.25 1 12 bram 1 1 1 0.25 1 13 deden 1 1 0.25 0.25 1 14 agus 0.25 0.25 0.25 0.25 1 no data matriks bobot fuzzy c1 c2 c3 c4 c5 15 munawar 1 1 0.25 0.25 1 16 abdul majid 0.25 0.25 1 1 1 17 suryani 1 1 0.25 0.25 1 18 sairin 0.25 0.25 1 0.25 1 19 heriansyah 0.25 1 1 1 1 20 ayat 0.25 1 0.25 0.25 1 21 jaka wahyudin 0.25 1 0.25 0.25 1 22 amran 0.25 0.25 0.25 0.25 1 23 subeki 0.25 1 1 1 1 24 syahroni 0.25 0.25 1 1 1 25 m idris 0.25 1 0.25 1 1 26 sukriyah 1 1 1 1 1 27 sarinah 1 1 1 1 1 28 imbron 0.25 0.25 0.25 1 1 29 dimas 1 0.25 1 0.25 1 30 zarkoni 1 1 1 0.25 1 31 bambang s r 1 1 0.25 0.25 1 32 ridwan 1 1 1 0.25 1 33 adi gunawan 1 1 0.25 0.25 1 34 marhadi 1 1 1 0.25 1 35 nenih 1 1 1 0.25 1 36 udin 1 1 0.25 0.25 1 37 mat amin 1 1 0.25 0.25 1 38 homsyah 1 1 1 0.25 1 39 yati aryati 1 1 1 0.25 1 40 dian jauhari 1 0.25 0.25 0.25 1 41 oktorizal 1 1 0.25 1 1 42 toriq 1 0.25 0.25 0.25 1 perhitungan ai 𝑟𝑖 = { 𝑋𝑖𝑗 𝑀𝑎𝑥 𝑋𝑖𝑗 𝑗𝑖𝑘𝑎 𝑗 𝑎𝑑𝑎𝑙𝑎ℎ 𝑎𝑡𝑟𝑖𝑏𝑢𝑡 𝑘𝑒𝑢𝑛𝑡𝑢𝑛𝑔𝑎𝑛 (𝑏𝑒𝑛𝑒𝑓𝑖𝑡) 𝑀𝑖𝑛 𝑋𝑖𝑗 𝑋𝑖𝑗 𝑗𝑖𝑘𝑎 𝑗 𝑎𝑑𝑎𝑙𝑎ℎ 𝑎𝑡𝑟𝑖𝑏𝑢𝑡 𝑏𝑖𝑎𝑦𝑎 (𝑐𝑜𝑠𝑡) 𝑋𝑖𝑗 ..................... (1) dimana dengan rij adalah rating kinerja ternormalisasi dari alternatif ai pada atribut cj : i = 1,2…,m dan j = 1,2…,n keterangan : max xij = nilai terbesar dari setiap kriteria i min xij = nilai terbesar dari setiap kriteria i. x ij = nilai atribut yang dimiliki dari setiap kriteria. benefit = jika nilai terbesar adalah terbaik. cost = jik nilai terkecil adalah terbaik. http://creativecommons.org/licenses/by-nc/4.0/ 13 7 jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 137 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional r ( 1 0.25 1 0.25 0.25 1 1 1 1 1 1 1 1 1 1 0.25 1 1 0.25 1 0.25 0.25 0.25 1 1 0.25 1 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 1 1 0.25 1 1 1 1 1 1 1 0.25 1 0.25 1 0.25 1 0.25 1 1 1 0.25 1 0.25 1 1 1 0.25 0.25 1 1 1 1 1 1 1 0.25 0.25 0.25 1 0.25 1 1 0.25 0.25 0.25 1 1 0.25 1 1 0.25 1 1 0.25 0.25 0.25 0.25 1 1 0.25 0.25 1 0.25 1 1 1 1 1 1 1 1 1 1 0.25 1 0.25 0.25 0.25 0.25 1 0.25 0.25 1 0.25 0.25 0.25 1 1 1 1 1 1 0.25 0.25 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1) normalisasi r r = ( 1 1 1 1 1 1 1 1 1 1 0.25 1 0.25 1 1 1 1 0.25 1 1 1 1 1 1 1 1 1 0.25 1 0.25 0.25 1 1 0.25 0.25 0.25 0.25 0.25 0.25 1 0.25 0.25 0.25 0.25 0.25 0.25 1 1 1 1 1 1 1 1 1 1 1 1 0.25 1) hasil dari normalisasi diatas selanjutnya dikalikan dengan bobot kriteria sesuai jenjangnya : bobot vektor = [1 : 0,75 : 0,5 : 0,25 : 0,25] hasil vektor 𝑉𝑖 = ∑ 𝑤𝑗𝑟𝑖𝑗 𝑛 𝑗=1 ..................................................................... (2) nilai preferensi untuk setiap alternatif (vi) diberikan rumus sebagai berikut, dimana : vi = rangking untuk setiap alternatif. wj = nilai bobot rangking (dari setiap kriteria). rij = nilai rating kinerja ternormalisasi. berdasarkan hipotesis pengambilan keputusan dengan menggunakan simple additive weighting (saw) akan memberikan alternative keputusan yang baik bagi pengurus rt.04 rw.01 sehingga pengambilan keputusan tepat pada sasaran. hasil penelitian dari metode simple additive weighting (saw) yang telah diperhitungkan dapat disimpulkan bahwa pemberian raskin diberikan kepada dengan hasil tabel 3. tabel rangking id nama rangking id nama rangking 101 aryana 2.56 122 amran 0.87 102 henandar 1.81 123 subeki 2 103 karmadi 2.56 124 syahroni 1.43 104 miftahudin 1.81 125 m idris 1.52 105 malik 2 126 sukriyah 2.75 106 rendi 2 127 sarinah 2.75 107 kartija 1.81 128 imbron 1.06 108 delfi efendi 2.56 129 dimas 2 109 deni 2 130 zarkoni 2.56 110 gunarso 1.81 131 bambang s r 2.18 111 sunandar 1.81 132 ridwan 2.56 112 bram 2 133 adi gunawan 2.18 113 deden 2.18 134 marhadi 2.75 114 agus 0.875 135 nenih 2.56 115 munawar 2.18 136 udin 2.18 116 abdul majid 1.43 137 mat amin 2.18 117 suryani 2.18 138 homsyah 2.56 118 sairin 1 139 yati aryati 2.56 119 heriansyah 2 140 dian jauhari 1.62 120 ayat 1.43 141 oktorizal 2.37 121 jaka wahyudin 1.43 142 toriq 1.62 tabel 4. tabel rangking terbaik id no nama rangking id no nama rangking 126 1 sukriyah 2.75 136 16 udin 2.18 127 2 sarinah 2.75 137 17 mat amin 2.18 134 3 marhadi 2.75 105 18 malik 2 101 4 aryana 2.56 106 19 rendi 2 103 5 karmadi 2.56 109 20 deni 2 108 6 delfi efendi 2.56 112 21 bram 2 130 7 zarkoni 2.56 119 22 heriansyah 2 132 8 ridwan 2.56 123 23 subeki 2 138 9 homsyah 2.56 129 24 dimas 2 139 10 yati aryati 2.56 102 25 henandar 1.81 141 11 oktorizal 2.37 104 26 miftahudin 1.81 113 12 deden 2.18 110 27 gunarso 1.81 117 13 suryani 2.18 111 28 sunandar 1.81 131 14 bambang s r 2.18 140 29 dian jauhari 1.62 133 15 adi gunawan 2.18 142 30 toriq 1.62 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 138 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional kesimpulan berdasarkan hasil penelitian dan pembahasan dapat diambil kesimpulan bahwa sistem pendukung keputusan ini dibangun utuk membantu dalam pemberian raskin dilingkungan rt.04 rw.01 sukmajaya depok dengan menggunakan metode, yaitu metode simple additive weighting (saw) yang dapat mempercepat proses menentukan pemberian raskin dengan perhitungan yang akurat. hasil penelitian dari metode simple additive weighting (saw) yang telah diperhitungkan dapat disimpulkan bahwa pemberian raskin diberikan kepada sukriyah dengan hasil 2.75. penelitian dapat dikembangkan lebih lanjut dengan krtiria-kriteria yang berbeda sesuai dengan kriteria dan bobot yang ditentukan untuk menyelesaikan permasalan-permasalahan sosial lainnya. referensi angrawati, d., angrawati, d., yamin, m., & ransi, n. (2016). sistem pendukung keputusan menentukan jumlah beras miskin menggunakan metode simple additive weight (saw). semantik, 2(1). retrieved from http://ojs.uho.ac.id/index.php/semantik/arti cle/view/712 handayani, h. (2016). sistem pendukung keputusan untuk penerimaan raskin (beras miskin) di desa tanggul kundung menggunakan metode saw. kediri. retrieved from http://simki.unpkediri.ac.id/mahasiswa/file_ artikel/2016/11.1.03.03.0117.pdf marlaeni, n. u., & satria, f. (2017). sistem pendukung keputusan untuk menentukan penerima raskin (beras untuk rakyat miskin) menggunakan metode saw (studi kasus: desa cabang empat kec. abung selatan kab. lampung utara). prociding kmsi, 5(1). retrieved from http://ojs.stmikpringsewu.ac.id/index.php/p rocidingkmsi/article/view/450 rini, a. s., & soyusiawaty, d. (2014). sistem pendukung keputusan seleksi penerimaan beras untuk keluarga miskin dengan metode simple additive weighting. jstie (jurnal sarjana teknik informatika) (e-journal), 2(2), 121– 130. https://doi.org/10.12928/jstie.v2i2.2728 septian, m. d., bahri, t. s., & makmur, t. (2013). analisis efektivitas dan efisiensi distribusi beras miskin (raskin) di kecamatan trienggadeng kabupaten pidie jaya. jurnal agrisep, 14(1), 70–78. retrieved from http://www.jurnal.unsyiah.ac.id/agrisep/art icle/view/910 suryeni, e., agustin, y. h., & nurfitria, y. (2015). sistem pendukung keputusan kelayakan penerimaan bantuan beras miskin dengan metode weighted product di kelurahan karikil kecamatan mangkubumi kota tasikmalaya. proceedings konferensi nasional sistem dan informatika (kns&i), 0(0). retrieved from http://ejournal.stikombali.ac.id/index.php/knsi/article/view/488 widiarsih, d. (1974). pengaruh sektor komoditi beras terhadap inflasi bahan makanan. jurnal sosial ekonomi pembangunan, 2(6), 244–256. retrieved from https://ejournal.unri.ac.id/index.php/jsep/a rticle/view/863 http://creativecommons.org/licenses/by-nc/4.0/ journal of informatics research vol. 4, no. 4, september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 391 iot-based home automation using nodemcu esp8266 paul k.a windesi-1*),mingsep rante sampebua-2, remuz mb kmurawak-3 123sistem infromasi, fakultas matematika dan ilmu pengetahuan alam universitas cenderawasih papua, indonesia https://www.uncen.ac.id 1*)remuzbertho3@gmail.com, 2mingsep75@gmail.com, 3remuzbertho3@gmail.com (*) corresponding author abstrak home automation didefinisikan sebagai teknologi otomatisasi yang berfokus pada majemen rangkaian serta peralatan elektronik di rumah, kantor dan lainnya. home automation merupakan bentuk perkembangan dari internet of things (iot) yang memungkinkan komunikasi serta kontrol melalui perangkat selama terhubung ke internet. penelitian ini bertujuan untuk merancang prototype home automation pada perangkat penerangan seperti lampu, sensor cahaya untuk mengaktifkan lampu, dan beberapa lampu yang dikontrol menggunakan perangkat mobile. metode penelitian menggunakan metode prototype, dimana pengembangan sistem difokuskan pada hasil masukan dari pelanggal yang akan dievaluasi untuk pengembanagan perangkat lunak. adapun tahapan dalam penelitian ini dimulai dengan menganalisis kebutuhan perangkat, studi literatur, perancangan sistem, perancangan hardware, perancangan antarmuka pengguna pengujian dan sampai pada hasil. output dari penelitian ini akan dibuat dalam bentuk prototype dimana semua komponen akan tempatkan berdasarkan layout yang sudah digambarkan pada perancangan. sistem ini dapat menolong pengguna dalam mengontrol peralatan yang ada di dalam rumah dari mana saja dan kapan saja,termasuk memanfaatkan sensor cahaya untuk memberikan inputan agar menyalakan atau mematikan lampu tersebut.. kata kunci: home automation, prototype, iot, arduino, nodemcu8266 abstract home automation is an automation technology that manages circuits and electronic equipment in homes, offices, and others. home automation is a form of internet of things (iot) development that allows communication and control through devices connected to the internet. this study aims to design a home automation prototype on lighting devices such as lamps, light sensors to activate lights, and several lights controlled using mobile devices. the research method uses the prototype method, where system development is focused on the results of input from customers who will be evaluated for software development. the stages in this research begin with analyzing device requirements, literature study, system design, hardware design, user interface design testing, and arriving at the results. this research output will be made in the form of a prototype, where all components will be placed based on the layout described in the design. this system can help users control the equipment in the house from anywhere and anytime, including using light sensors to provide input to turn the lights on or off. keywords: home automation, prototype, iot, arduino, nodemcu8266 introduction the internet has become an essential part of various life activities. (lasera & wahyudi, 2020). one of the impacts of the rapid development of the internet is the internet of things (iot) (malik et al., 2019). iot can be defined as a computational scheme interconnected with digital devices, a mechanism of transmitting data over a defined network without human involvement at any level (muktiawan & nurfiana, 2018; singh et al., 2020). iot allows users to manage and optimize electronic devices by using the internet. (junaidi, 2015). iot technology offers connectivity of system devices and services in various fields, such as agriculture, building management, health, energy, transportation, and even home management, often known as home automation. (jain et al., 2019) home automation is an automation technology that manages electronic devices and p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 journal of informatics research vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 392 equipment in the home, office, and others. (santoso et al., 2021; soleh & susilo, 2016). home automation allows for the management of lights (lighting), temperature (temperature), washing machines, etc. (mowad et al., 2014). this procedure is possible through the presence of electronic sensors such as temperature sensors, humidity, gas concentration, etc. (david et al., 2015) the existence of covid-19 has caused the role of technology to be increasingly important. social distancing, which requires many business processes and activities, is done with the internet. (komalasari, 2020) home automation can help facilitate human tasks by utilizing the internet. previous research on home automation (harahap, 2018) proposed an automatic clothesline prototype using water sensors and ldr sensors based on arduino microcontrollers to overcome weather uncertainty by applying the fuzzy logic controller (flc) method. (gitakarma, 2018) created a home automation system (has) to control bluetooth-based electrical devices using an android application to make it easier for elderly and physically disabled people in the family (santoso et al., 2021) and implemented nodemcu in home automation with the blynk control system using dht11 sensors to monitor temperature and humidity and mq2 sensors to detect smoke or gases that could potentially cause fires as well as relays to control electronic devices from anywhere. based on the author's search results, there is very little research on iot in papua due to very limited internet infrastructure constraints (wayangkau et al., 2020). this research has urgency as one of the pioneers in implementing such research in papua. this research can provide an overview of how to maximize the existing infrastructure to implement iot and home automation and help people use the internet to facilitate routines. based on this background, the author designed a prototype of home automation where lighting devices such as lights outside the house can automatically turn on when conditions are dark and turn off when conditions are bright using light sensors. the author added several components, such as a relay to control the lights in the house and a camera module for monitoring. for the user interface, the author uses the blynk application. the author also added a notification feature to the user interface so that when the light is dark or bright, the notification will appear to notify that the light will be turned on or off. the system is expected to make it easier to control the equipment at home from where we are and at any time. research methods the internet of things (iot) is a concept/scenario in which an object can transmit data over a network without requiring human-to-human or human-tocomputer interaction. iot also provides efficient data storage and exchange by connecting physical devices through electronic sensors and the internet. iot is closely related to machine-to-machine (m2m) communication in the manufacturing and electricity, oil, and gas industries. products designed with m2m communication are often considered intelligent or "smart" systems. (muktiawan & nurfiana, 2018). (wadhwani et al., 2018) figure 1. iot scheme for home automation home automation is an automation technology that manages electronic devices and equipment in the home, office, and others. (santoso et al., 2021; soleh & susilo, 2016). home automation allows the management of lamps (lighting), temperature, washing machines, etc., as shown in figure 1 (mowad et al., 2014). this procedure is possible using electronic sensors such as temperature, humidity, gas concentration, etc. (david et al., 2015). home automation consists of two components: hardware as output input and software to monitor and control hardware. (kholmatov & darvishev, 2020) nodemcu esp8266 is an integrated chip connecting the microcontroller to the internet via wifi. it offers a complete and standalone wifi journal of informatics research vol. 4, no. 4, september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 393 network solution, which allows it to be hosted or as a wifi client. the esp8266 has powerful onboard processing and storage capabilities, allowing it to be integrated with sensors and other specialized device applications through gpios with easy development and minimal loading times. (hidayat et al., 2018) nodemcu has also equipped a usb to serial communication chip to program. it only requires a micro usb data cable. the blynk application is straightforward (artiyasa et al., 2020). to connect blynk on boards such as arduino, raspberry pi, wemos, and nodemcu only need to enter the auth token code sent via email when creating a blynk account, and then the auth code is entered in the program code and upload it on the board. from this application, we can control anything remotely wherever we are with a record of being connected to the internet. (santoso et al., 2021) in conducting this study, the authors applied several research methods, such as collecting data, where the research was carried out, and how to design the desired miniature. the research methodology applied by the author is as follows in figure 2. figure 2. research methods block diagram the design of this system is first planned by creating a block diagram. a block diagram is used at the beginning of the design to be an initial illustration of how the system works. the block diagram as a whole can be seen in figure 3 figure 3. hardware block diagram based on the block diagram image above, it can be explained that this system works because when nodemcu, esp32-cam, light sensors, and relays get a power source from the adapter, the devices will turn on. furthermore, according to the ssid, nodemcu and esp32-cam will automatically search for ssid via wifi or the nearest hotspot, which will later be entered into the program code. after the esp32-cam gets power, the device will automatically get an ip from wifi; then, the ip will forward using scraping software on the laptop so that the public can access the device. furthermore, the light sensor, the light sensor itself can directly trigger the relay to turn the light on and off when it is dark or bright without a program code. however, in this system, a notification program code will be generated on nodemcu so the blynk application can pop notifications when the sensor detects light or darkness. after creating a block diagram and knowing the functions of each component used, the next stage is hardware design. there are several processes in hardware design, including small design, circuit schemes, and wiring. figure 4 shows the appearance of the small layout design for home automation figure 4. miniature layout p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 journal of informatics research vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 394 information: x: lights y: camera z: sensors the development of the user interface on the blynk application serves as an intermediary to monitor the system that has been designed. figure 5 illustrates the user interface design that has been created. figure 5. user interface design this research uses a quantitative approach, just an example of this research. results and discussion connection test in this prototype connection test, connection testing is carried out using two different networks. it aims to prove that this prototype system can be accessed anywhere (accessible by the public). the connection testing is carried out by testing the connection between the blynk application and the system prototype via a smartphone using the internet network. the blynk application can be accessed publicly, but the public cannot access the esp32-cam camera module on the prototype. to be accessible to the public, the author uses ngrok software to forward the ip on the camera module to the public. here is a picture of the test results. figure 6. application notification when the device is not connected based on the picture above, it can be seen that figure 6 is a condition where the user interface device display has not been connected to the internet, and figure 7 is a condition where the user interface device display is connected to the internet. figure 7. when the device is connected user interfaces control test in the testing phase, the user interface display will be tested on the blynk application via smartphone, on the guest l_ruang button, l_kamar1, l_kamar2, l_rsantai, notification icon, and camera. this test aims to find out what is by functions have been designed and whether there is a delay on each device. journal of informatics research vol. 4, no. 4, september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 395 table 11. user interface display testing no. input output button status delay (sec) information 1 l_rtamu lights on on 1 match lights off off 1 match 2 l_kamar1 lights on on 1 match lights off off 1 match 3 l_kamar2 lights on on 1 match lights off off 1 match 4 l_rsantai lights on on 1 match lights off off 1 match 5 notification appear 2 match 6 camera appear 3 match from table 1, every button on the user interface displayed during testing by pressing the button to turn off and turn on the lights on the prototype was found to be a delay of 1 second. then in the test, the notification that appears on the user interface when the sensor detects the presence of light and the rate of light is found to be a delay of 2 seconds. furthermore, in the camera test to display the image on the user interface display, a delay of 3 seconds was found so that the image could appear on the screen. the delay found in this user interface test is due to the quality of the internet network used. if the quality of the internet network used is not good, the delay found will be greater; on the contrary, if the quality of the internet network used is good, the delay found is minor. figure 8. condition when prototype in the off state figure 9. condition when prototype is on figure 10. when the button is off and the notification display when the sensor is dark 10 based on the picture above, it can be seen that figure 8 is a condition when the button has not been pressed and is in the off condition, and figure 9 is a condition when the button has been pressed and is in the state. then figure 10b is an image of the notification display when the resistance of the light sensor increases due to dark conditions, and figure 10b is a notification image when the resistance of the light sensor decreases due to bright conditions. conclusions and suggestions conclusion after testing, it is known that an iot-based home automation prototype has been successfully created and controlled via the internet, using the blynk application on a smartphone that can be accessed anywhere and anytime. some of the devices used are light sensors, lights, relays and buttons, which are connected to each other. in addition, the design of an iot-based home automation prototype has worked according to the design. suggestion some improvements that can be made for home automation are not only using lights and cameras but can be improved for other devices, such as motors, electric pumps, and other sensors. such as temperature sensors, gas sensors, motion sensors and so on. in addition, it can also replace power from electricity to solar cells to provide a more optimal power source. the author can also add security features such as better monitoring quality with multi cameras that can be recorded in real-time. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.431 journal of informatics research vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 396 references artiyasa, m., nita rostini, a., edwinanto, e., & pradifta junfithrana, a. 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(2020). utilization of iot for soil moisture and temperature monitoring system for onion growth. emerging science journal, 4(special issue), 102–115. https://doi.org/10.28991/esj-2021-sp1-07 jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.453 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 445 implementation of machine learning algorithms for early detection of cervical cancer based on behavioral determinants duwi cahya putri buani-1*, indah suryani-2 informatika universitas nusa mandiri jakarta, indonesia duwi.dcp@nusamandiri.ac.id, indah.ihy@nusamandiri.ac.id (*) corresponding author abstract cervical cancer is a disease that affects women and has the highest mortality rate after breast cancer. early detection of cervical cancer is critical at this time, so cervical cancer patients are decreasing. many women, especially in indonesia, are less concerned about the dangers of cervical cancer, even though if detected earlier, this disease will be easier to treat. one alternative for early detection can use machine learning algorithms. the machine learning algorithms used in this study are naïve bayes (nb), logistic regression (lr), decision tree (dt), svm, and random forest. in this study, a random under-sampling method was employed, which had no uses in any prior research. this technique makes the accuracy of the five algorithms even better. the research results show that nb has an accuracy rate of 91.67%, lr has an accuracy rate of 87.5%, dt has an accuracy rate of 81.81%, svm has an accuracy rate of 75%, and rf has the highest accuracy rate of 94.45%. this research shows that the best model is rf or random forest. keywords: cervical cancer; machine learning; random forest abstract kanker servik merupakan penyakit yang diidap oleh wanita memiliki tingkat kematikan terbesar di dunia setelah kanker payudara. deteksi dini kanker serviks sangat penting untuk saat ini, agar pasien kanker serviks semakin berkurang. banyak wanita terutama di indonesia kurang peduli dengan bahayanya kanker serviks, padahal jika dideteksi lebih dini penyakit ini akan lebih mudah untuk ditangani. salah satu alternatif untuk melakukan deteksi dini dapat menggunakan algortima machine learning. algortima machine learning yang digunakan dalam penelitian ini adalah naïve bayes (nb), logistic regerson (lr), decision tree (dt), svm dan random forest. dalam penelitian ini juga menggunakan teknik random under sampler yang pada penelitian sebelumnya tidak digunakan, teknik ini menjadikan akurasi dari ke-lima algortima menjadi semakin baik. dari hasil penelitian yang dilakukan menunjukan bahwa nb memiliki tingkat akurasi 91.67%, lr memiliki tingkat akurasi 87.5%, dt memiliki tingkat akurasi 81.81%, svm memiliki tingkat akurasi 75% dan rf memiliki tingkat akurasi yang paling tinggi yaitu 94.45%. dari penelitian ini menunjukan bahwa model yang paling baik adalah rf atau random forest. kata kunci: kanker servik; machine learning; random forest introduction globocan (global cancer observatory) stated that asian countries, including indonesia, contribute most significantly to cancer cases worldwide. data sourced from darmais hospital in 2018 showed that the most cancer cases were breast cancer at 19.18%, cervical cancer at 10.69%, and lung cancer at 9.89% (agustyawati, fauzi, & pratondo, 2021; pangribowo, 2019; wongkar, angka, & angeline, 2022). the who (world health organization) states that cervical cancer is a deadly disease that ranks second only to breast cancer. about 50,000 women have diagnosed with cervical cancer annually (sobar, machmud, & wijaya, 2016)(setyani, 2018). the high number of cervical cancer patients is influenced by the lack of knowledge among the public, especially women, to carry out early detection before cancer spreads (aisah, hafiyusholeh, & ulinnuha, 2022; winarni & suratih, 2020). this data shows that cervical cancer is one of the most common cases of cancer in indonesia, so it needs to be detected early (arifin, siregar, ratna, & mudzakir, 2021; hidayah, cholissodin, & adikara, 2019). to perform early detection using machine learning. machine learning p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.453 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 446 is used as a classifier to detect the probability of cervical cancer risk based on its behavior and determinants (feblian & daihani, 2017). previous research using the naïve bayes (nb) and logistic regression (lr) algorithms showed the following results in table 1: table 1. previous research algorithm accuracy auc nb 91.67% 0.96 lr 87.5% 0.97 source: (sobar et al., 2016) table 1 shows previous studies using the same data but only two popular algorithms in 2016: nb and lr. in this study, the authors will compare nb and lr algorithms with other algorithms to find the best model for the early detection of cervical cancer. previous research conducted by sober used the same data but only made comparisons with two algorithms, nb and lr, which were prevalent then. this study compares three additional algorithms: decision tree (dt), svm, and random forest (rf). these three additional algorithms have their respective advantages that can cover the weaknesses of the naïve bayes algorithm, which in previous studies had the highest level of accuracy, namely 91.67%. in addition to adding algorithms for comparison, this study also uses the random under sampler technique to overcome class imbalance, although this method had not been employed in earlier investigations. previous research using the same data using the svm algorithm has an accuracy rate of 87%, this research uses a sample data of 59 data and four attributes without using the random under sampler technique and data processing using python (arifin et al., 2021). previous research using cervical cancer risk classification data with feature selection based on expert interviews used the extreme learning machine algorithm to classify and measure using the confusion matrix curve, resulting in an accuracy of 91.76% (hidayah et al., 2019). previous research used the decision trees algorithm. the results of the accuracy error in the study were 0% using 19 attributes, and the data was hospital patient data. dr. wahidin sudirohusodo makassa, in this study, used symptoms and signs to determine the stage of cervical cancer suffered by patients (irmayani1, 2017). the medical lens is typically used in cervical cancer studies, not a lifestyle perspective. in this study, the authors used data from habits carried out in everyday life. of course, the information was collected from both people with and without cervical cancer. research methods in this study, the crisp-dm (cross industry standard process for data mining) model was used, which consisted of six stages, namely business understanding, data understanding, data preparation, modeling, evaluation, and deployment (firqiani, kustyo, & giri, 2008; hasanah, soim, & handayani, 2021; matovani & hadiono, 2018). a. stages of business understanding based on data from the uci machine learning repository with a total of 72 respondents, 22 were cancer patients, and 50 were cancer survivors. all respondents were residents of a city in jakarta, indonesia. examining sufferers must be done so that the disease can be detected early to reduce the risk of cervical cancer. using data mining with classification algorithms with a high level of prediction and accuracy can help overcome these problems so that the diagnostic results obtained are accurate. this study used algorithm comparisons to obtain high accuracies, such as logistic regression, naïve bayes, svm, decision trees, and random forests. b. data understanding stage the data used are secondary data obtained from the survey results of cervical cancer patients, and the data comes from a questionnaire distributed to 72 respondents, of which 22 are cancer patients and 50 are not cancer survivors. all respondents are residents of cities in jakarta, indonesia, which can be accessed publicly through uci machine learning repositories. data consists of 19 attributes and one attribute as a class. source: (buani & suryani, 2022) figure 1. visualization of ca cervix variables the ca cervix variable is a label variable used to classify having cervical or no cervical cancer. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.453 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 447 source: (buani & suryani, 2022) figure 2. visualization of eating behavior data the eating behavior variable is a variable that describes the consumption of food. the food consumed is very influential on the health of the body. source: (buani & suryani, 2022) figure 3. visualization of behavioral sexual risk variable behavioral sexual is a variable that most likely determines whether a person has cervical cancer. c. stages of data preparation the total data in this study was 72, which already has a label where respondents have a risk of cancer and respondents who do not. however, this data still contains duplicate data, outliers, and anomalous or inconsistent data. therefore, this stage is necessary to obtain quality data to produce a more effective and efficient model. an example of outlier data can be seen in figure 4 below: source: (buani & suryani, 2022) figure 4. eating behavior variable figure 4 shows that the behavior eating variable has no outlier data, so no data is far from observation. source: (buani & suryani, 2022) figure 5. distribution of ca-cervix data in figure 5, if blue is data labeled no cervical cancer and the orange bar is cervical cancer. from the figure, the data must equate first. here the author uses the random under sampler technique. then the result is as in figure 6. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.453 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 448 source: (buani & suryani, 2022) figure 6. data distribution after random under sampler figure 6 shows the use of the random under sampler technique data distribution. the random under sampler deals with classes/labels that are not identical in number. in the random under sampler process, data is the division into testing data and training data, and the data division is 70% for training data and 30% for testing data. after the random under sampler, perform techniques and data in the same class. the next thing to do is look at the correlation between attributes or variables using heatmap, seen in figure 7. source: (buani & suryani, 2022) figure 7. heatmap correlation between variables figure 7 shows that the darker the heatmap color, the more attributes or variables have a stronger association with data classes or labels if and only if. figure 3 shows that the variables/attributes are behavioral sexual risk, commitment of intentions, aggregation of intentions, norm significant people, appreciative social support, self-hygiene behavior, perceptual susceptibility, and fulfillment of norms. eight variables/attributes are the attributes that most influence the class/label of cervical cancer. a. dataset the dataset used in this study is public data from the uci machine learning repository with a total of 72 data, data consisting of 20 attributes, and one attribute is a label, which sees in table 2: table 2. data description variable behavior_sexualrisk behavior_eating behavior_personalhygine intention_aggregation intention_commitment attitude_consistency attitude_spontaneity norm_significantperson norm_fulfillment perception_vulnerability perception_severity motivation_strength motivation_willingness socialsupport_emotionality socialsupport_appreciation socialsupport_instrumental empowerment_knowledge empowerment_abilities empowerment_desires ca_cervix (this is a class attribute, 1=have cervical cancer, 0=no cervical cancer) source: (sobar et al., 2016) table 2 shows the variables or attributes used in the study. these attributes include behavioral sexual risk, behavior eating, behavior personal hygiene, aggregation of intentions, commitment of intentions, attitude consistency, attitude spontaneity, norm significant people, norm fulfillment, perceptual vulnerability, perceived severity, motivation strength, volitional motivation, emotional, social support, appreciative social support, instrumental social support, empowerment knowledge, empowerment ability, empowerment desire and ca_cervix (these are class attributes, 1=cervical cancer, 0=no cervical cancer). b. research methods the model used in this study is the application of machine learning algorithms for the early detection of cervical cancer. the algorithms used include decision tree (dt), svm, random forest (rf), and two algorithms from previous studies, nb and lr. then from the five algorithms selected the jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.453 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 449 best model, the results are accurate. figure 1 shows the flow chart of this study. source: (buani & suryani, 2022) figure 8. research flowchart figure 8 shows research from data preparation, such as checking blank data, duplicate data, and imbalance classes. results and discussion this study compared five algorithms: logistic regression, naïve bayes, svm, decision tree, and random forest. for lr and nb, the accuracy results come from previous studies. svm is a classification algorithm whose level of accuracy in a model depends on the kernel functions and parameters used, and the advantage of svm is that it can classify and overcome regression with linear and non-linear (dasmasela, tomasouw, & leleury, 2021)(parapat, furqon, & sutrisno, 2018). decision trees are prediction model techniques that use for task classification and prediction (bahri & lubis, 2020)(wijaya, bahtiar, kaslani, & r, 2021)(wuryani & agustiani, 2021)(schonlau & zou, 2020). the results of the study can be seen in table 3. table 3. predicted results algorithm accuracy auc nb 91.67% 0.96 lr 87.5% 0.97 dt 81.81% 0.81 rf 94.45% 0.75 svm 75% 0.75 source: (buani & suryani, 2022) table 3 is the result of the research conducted in this study. table 3 describes the accuracy results after conducting experiments where naïve bayes (nb) has an accuracy of 91.67%, logistic regression (lr) of 87.5%, decision tree (dt) of 81.81%, svm 75%, and random forest (rf) 94.45%, from the table. it shows that the highest accuracy in this study is rf. source: (buani & suryani, 2022) figure 9. graph of prediction results figure 9 is a visualization of the prediction results made by nm, lr, dt, rf, and svm from the image showing that random forest is the best algorithm in making predictions with a result of 94.45% conclusions and suggestions the results of this research using the random under sampler technique show that the model using the svm algorithm is 75%, while the results of the model using the decision tree algorithm are 82%. for the model with the random forest algorithm, 94% of the results indicate that the random forest is a random forest model. the best method for early detection of cervical cancer in behavioral determinants. references agustyawati, d. n., fauzi, h., & pratondo, a. 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(2021). random forest classifier untuk deteksi penderita covid-19 berbasis citra ct scan. jurnal teknik komputer, 7(2), 187–193. https://doi.org/10.31294/jtk.v4i2 227 expert system development to identify employee personality types using dempster shafer theory julia fajaryanti1*), rogayah2 informatika, fakultas teknologi industri universitas gunadarma www.gunadarma.ac.id julia@staff.gunadarma.ac.id, rogayah@staff.gunadarma.ac.id (*) corresponding author abstrak sumber daya manuisa menjadi aset penting bagi perusahaan untuk berkembang dan mewujudkan cita -cita perusahaan. salah satu usaha untuk mengoptimalkan kapasitas karyawan adalah dengan mengetahui kepribadiannya. kepribadian merupakan bentuk yang dimiliki seseorang individu dalam bertingkahlaku dan segala watak yang membedakan antara seorang individu yang satu dengan seorang individu lainnya. mengetahui kepribadian karyawan menjadi suatu hal yang penting bagi perusahaan dan karyawan itu sendiri. karena dengan mengetahui kepribadian seseorang perusahaan dapat dapat memaksimalkan potensi karyawan dan dapat menepatkan posisi tertentu yang sesuai dengan kepribadian karyawan. penelitian ini bertujuan untuk mengimplementasikani dempster-shafer theory pada mesin inferensi dalam membangun sistem pakar untuk mengidentifikasi tipe kepribadian karyawan. dempster-shafer theory dapat melakukan perhitungan probabilitas sehingga dapat dilakukan pembuktian berdasarkan tingkat kepercayaan dan penalaran yang logis. sistem yang dikembangkan mampu mengidentifikasi tipe kepribadian karyawan melalui sifat atau gejala yang ada pada diri karyawan. selain itu, sistem dapat menampilkan hasil diagnosis dengan penjelasan tentang tipe kepribadian, sifatnya dalam pekerjaan dan pekerjaan atau posisi yang cocok untuk tipe kepribadian tersebut. berdasarkan hasil uji akurasi yang diperoleh dari hasil perbandingan diagnosa sistem pakar dengan analisis seorang pakar menunjukkan nilai akurasi mencapai 85%. kata kunci: dempster-shafer theory; sistem pakar; mesin inferensi; tipe kepribadian abstract human resources are an essential asset for the company to develop and realize the company's goals. one of the efforts to optimize the capacity of employees is to know their personality. personality is the form an individual possesses in behaving and all the characteristics that distinguish one individual from another. understanding employees' personality is essential for the company and the employees themselves. because by knowing a person's personality, the company can maximize the potential of employees and place certain positions that suit the employee's personality. this study aims to implement the dempster-shafer theory on an inference engine in building an expert system to identify employee personality types. dempstershafer's approach can perform probability calculations so that evidence can be carried out based on confidence and logical reasoning. the system developed can identify the employee's personality type through the nature or symptoms that exist in the employee. in addition, the system can display the diagnosis results with an explanation of the personality type, its character in work and occupations or positions suitable for that personality type. based on the results of the accuracy-test obtained from the comparison of expert system diagnoses with the analysis of an expert, the accuracy value reaches 85%. keywords: dempster-shafer theory; expert system; inference engine; personality type introduction human resources are one of the most important elements for a company or organization to run well. where human resources have a significant influence on the success of achieving goals in order to realize the company's vision and mission. today, many companies have viewed human resources not only as resources but rather as valuable capital or assets that must be cared for and maintained for development (zulkarnaen, 2018). one of the developments of human resources is knowing each employee's personality. the reason is that each employee has various 228 psychological behaviours that must be processed to achieve company goals. personality is the form an individual possesses in behaving and all the characteristics that distinguish one individual from another (sya'baniah et al., 2019). personality is something that someone needs to know so that everyone can develop the potential that exists in each individual (darmansah et al., 2021). human personality types have been studied and summarized into 4 (four) types. the four types are included in the proto-psychological theory. this theory was first discovered in the century bc by hippocrates, then by galen was developed into a medical approach. according to this theory, human personality is grouped into four categories: choleric, sanguine, phlegmatic, and melancholic. knowing employees' personalities is essential for the company and the employees themselves. because by knowing a person's personality, the company can place certain positions that are appropriate and can maximize the potential of employees. to find out a person's personality type, you can use technology such as an expert system. expert system is a sub-field of artificial intelligence that can manage and draw conclusions based on specific rules obtained from knowledge (borman et al., 2020). expert systems are also called knowledge-based systems, and this is because the expert system provides a collection of knowledge obtained from an expert and the required knowledge sources (putri, 2018). the purpose of developing an expert system is to build a system that can ease human work, especially those related to the use of the ability and experience of an expert (sucipto et al., 2019). one of the inference engine methods in expert systems that can overcome uncertainty is the dempster-shafer theory. the dempster-shafer idea comes up with an approach to calculating probabilities so that proof can be done based on the level of belief and logical reasoning so that it can be used in combining information (evidence) (rahmanita et al., 2019). several studies have shown that the dempster-shafer theory can reasonably produce expert systems. research the expert system used to diagnose gastric disease using the dumpster-shafer algorithm (ardiansyah et al., 2019). in this study, the dempster shafer produced a system based on the confidence function with an accuracy of 95%. further research, developing a system that can diagnose human skin diseases using the dempstershafer algorithm (mz et al., 2020). in this study, the expert system developed for each symptom has a confidence value used to calculate the density that results in conclusions. based on the accuracy test, it produces a discount of 90%. meanwhile, the mean opinion score (mos) test resulted in a mos size of 4.35, which means the system has a good feasibility level. furthermore, research on developing an expert system for diagnosing oral cancer shows that the dempstershafer algorithm can overcome the uncertainty in constructing an inference engine. it is indicated by the results of the accuracy test of 86.6% (napianto et al., 2018). this study aims to implement the dempster-shafer theory on the inference engine in building an expert system to identify the employee personality type. an expert system is built based on a website to make it easier for users to use the system. the system can recognize personality types based on symptoms or a person's character. the system also includes an explanation of the personality type, its nature in the job, and the job or position that is suitable for that personality type. research methods research needs to be arranged in stages so that the research carried out is by the objectives to be achieved. the locations of research carried out in this study are presented in figure 1. figure 1. research stages 229 identification of problems the first step is identifying the main problem to get the right solution. the output of this stage is a statement of the problem to be solved. the situation in the world of work that is often experienced is the inaccuracy of a person's position or job with his personality. it results in not working optimally. so we need a system that can identify a person's personality type to suit his career. knowledge acquisition expert systems are also known as knowledge-based systems, so they cannot be separated from the collection of knowledge from experts or experts. to get an understanding from an expert, a knowledge acquisition stage is needed. knowledge acquisition is the process of extracting, structuring, and organizing knowledge from knowledge sources, so that knowledge can become a knowledge base that is the basis for decisions in expert systems (anita et al., 2019). the output of this stage is knowledge in the form of symptom data, personality type data, and diagnostic rules. this study was obtained through interviews and gathering sources of knowledge through books to get the required data, such as data on symptoms, personality types, and the level of confidence for each sign and personality type. the data was obtained from the results of consultations with experts or experts, namely a psychologist. from the results of interviews with psychologists and collecting data from books, he got 4 (four) personality types with 30 characters or symptoms. the personality type used is based on hippocratic theory and developed by galen. according to this theory, human personality is grouped into four categories: choleric, sanguine, phlegmatic, and melancholic. knowledge representation the next stage is knowledge representation, where the results obtained in knowledge acquisition will be organized regularly to encode expert knowledge into appropriate media forms. knowledge representation is vital in developing expert systems because a good solution will also depend on a good word. if the knowledge representation is not made correctly, the impact will affect the next stage, and the resulting system is not as desired (nasution & khairuna, 2017). from the knowledge acquisition process, knowledge is obtained that will be used as a knowledge base, then knowledge representation is carried out using a decision table, and rules are made based on the understanding that has been obtained from experts, which will later be used to build an inference engine. inference engine the inference engine can be said to be part of an expert system that carries out a reasoning function that utilizes rules with specific patterns (annisa, 2018). the inference engine will perform a search based on the rules in the knowledge base, which results from the conclusion in the form of a solution that suits your needs. in this study, the inference engine used is the dempster-shafer theory. dempster-shafer's approach performs probability calculations to obtain evidence based on the level of belief and logical reasoning because later, it will be used to combine evidence to get firm conclusions. dempster-shafer's theory generally uses the [belief, plausibility] format. bel (belief) is a measure of evidence's ability to support a condition (rahmanita et al., 2019). if belief has a value of 0, then the indication is that there is no evidence. on the contrary, there is a certainty if it has a value of 1. the belief function can be denoted by equation (1). )()( ymxbel x .................................................................. (1) where bel (x) is a belief of (x), while m (y) is a mass function of (y), plausibility (pls) is denoted in equation (2) below. )'()'()( xmxbelxpls n xy  11 .................................. (2) bel(x) is a belief of (x), while m is a mass function. pls(x) is the plausibility of (x). plausibility can have a value between 0 to 1, if there is belief in x' then belief (x') = 1 which results in the result pls(x) = 0. table 1 below is the possibilities that occur between belief and plausibility. table 1. possible bell and pls ranges certainty description [1, 1] [0, 0] [0, 1] [bel, 1] where 0< bel<1 [0, pls] where 0<pls<1 [bel, pls] where 0< bel≤pls<1 all true all wrong uncertainty tendency of support tendency to reject the tendency to support and rejection in fact, in the expert system, some elements describe the possibility of the user's answers, but adjustments are needed. this possibility in dempster-shafer theory is called a power set denoted p, so it becomes equation (3) below. 230 )(pm  ................................................................................... (3) based on equation (3), m is the mass function, and p( ) is the power set. mass function (m) in dempster-shafer theory is the level of confidence in the number of correct answers (evidence) or known as evidence measure with the notation (m) if there is hypothetical information so that m{ } = 1(m) and if there is no information, then m{ } = 1.0. in an expert system, it is possible to have a large amount of evidence that can be used in the uncertainty factor to make a decision. for this evidence to be resolved, the dempster-shafer theory uses rules with the term dempster's rule of combination. the combination between m1 and m2 can be denoted by equation (4) below.   zyx ymxmzmm )()()( 2121 ....................................... (4) where )(zmm 21  is the mass function of the evidence, )( xm1 is the mass function of the evidence (x), )(ym2 is the mass function of the evidence (y), and  is the direct sum operator. the following equation (5) is to calculate the dempster's rule of combination. k ymxm zmm zyx     1 21 21 )()( )( ....................................... (5) from this equation, k is the number of evidential conflicts, the value of toral k can use equation (6) below.   yx ymxmk )()( 21 ......................................................... (6) 21 mm  is a combination of m1 and m2, so it becomes m3. so, if the formulation in formula (5) is entered into formulation (6), then for m3 it can be formulated by equation (7) below.      yx zyx ymxm ymxm zm )()( )()( )( 21 21 3 1 ........................................... (7) implementasi sistem this stage is the stage of making the system through coding to be realized into an expert system (monica & borman, 2017). at this stage the inference engine that has been built using the dempster-shafer algorithm is then implemented into a website-based system using php through a text editor, namely sublime text 3 and a mysql database.. testing the next stage is the testing stage, where the system will be tested for its performance. at this stage, the accuracy of the dempster-shafer approach will be tested on an expert system for diagnosing mental disorders. at this stage the success rate of the developed method is tested using an accuracy test. the level of accuracy is used in order to know the closeness of the test results or the average test results to the actual value (herdiansah et al., 2022). so, the output of this stage is in the form of accuracy test results. the calculation of diagnostic accuracy is obtained from comparing the results of the system diagnosis and the diagnosis of an expert with random cases and the number of cases that have been determined. the following equation (8) is the formula used to calculate the accuracy: % datatest total diagnosiscorrect ofnumber 100accuracy ................ (8) results and discussion to build an expert system for identifying employee personality types with the dempstershafer theory algorithm. dempster-shafer theory performs probability calculations to obtain evidence based on the function of belief and reasoning, which then functions to combine information (evidence). the following is a case study of calculations using the dempster-shafer theory method of diagnosing mental disorders with symptoms including: 1) likes work to be done privately or alone. 2) have an innovative and creative mind. 3) have a strong will and always succeed in achieving the predetermined target. then the solution is as follows: symptom 1 (g1): likes work to be done personally or alone. the density value for g1 which has been determined by the expert is 0.8. g1 is a symptom of melancholic personality type (p1). m1 { p1} = 0.8 m1 { }= 1-0.8 = 0.2 symptom 2 (g2): have an innovative and creative mind. the density value for g2 which has been determined by the expert is 0.4. g2 is a melancholic and sanguine personality type (p1, p2). 231 m2 { p1, p2} = 0.4 m2 { }= 1-0,4 = 0.6 from the results of the density values obtained from g1 and g2, it can be calculated the combined density value of the two, through the table of the density combination rules using equation (7) which was previously discussed. the results of the combination are then used as the basis for calculating the value of new symptoms. table 2 below is a combination of m1 and m2 into m3. table 2. combination rules for m3 {p1, p2} (0.4)  (0.6) {p1} (0.8) {p1} (0.32) {p1} (0.48)  (0.2) {p1, p2} (0.08)  (0.12) berdasarkan persamaan (7) maka kombinasi dari m3adalah: m3 {p1,p2} = 01 080  . = 0.08 m3 {p1} = 01 480320   .. = 0.6 m3 {𝜃} = 01 120  . = 0.12 symptom 3 (g3): have a strong will and always succeed in achieving the predetermined targets. the density value for g3 which has been determined by the expert is 0.6. g2 is a symptom of melancholic, sanguine and choleric personality types (p1, p2, p3). m1 { p1, p2, p3} = 0.6 m1 { }= 1-0.8 = 0.4 from the results of these combinations, then it becomes the basis for calculating the value of new symptoms. the following table 3 is a combination of m3 and m4 into m5. table 3. combination rules for m5 {p1, p2, p3} (0.6)  (0.4) {p1,p2} (0.08) {p1, p2} (0.048) {p1, p2} (0.032) {p1} (0.6) {p1} (0.36) {p1} (0.24)  (0.12) {p1, p2, p3} (0.072)  (0.048) based on equation (7) the combination of m5 is: m5 { p1,p2 } = 01 03200480   .. = 0.08 m5 {p1,p2,p3} = 01 0720  . = 0,072 m5 {p1} = 01 240360   .. = 0,6 m5 { } = 01 0480  , = 0,048 because there are no more new symptoms, it can be concluded that the highest score is the melancholic personality type (p1) with a value of 0.6 or if it is converted into a percentage, it becomes 60%. the next step after the inference engine is built, then realized in the form of an expert system. at this stage, the coding will be based on the results of analysis and design using a particular programming language in order to produce a system that can be used by users. the expert system application that is built has functional requirements, including: the system can manage symptom data, personality type data, knowledge representation, diagnose personality types and see results and explanations of personality types. figure 2 below is the interface of the main menu of the employee personality type identification system. figure 2. main menu interface in this application, the user can choose the symptoms or character of the employee whose personality will be identified. figure 3 below is an interface for diagnosing employee personality types. 232 figure 3. diagnostic interface after selecting the symptom or character of a person whose personality will be identified, the next system will display the results of the diagnosis and the density value generated by the dempstershafer algorithm. after that, the user will be shown an explanation of the personality type of the diagnosis. figure 4 below is the result of the diagnosis and explanation. figure 4. diagnostic results interface equipped with explanations furthermore, after the application is implemented, testing is carried out on the application. the test uses an accuracy test, through a comparison of the results of an expert system diagnosis with the results of an expert's analysis. the test data used were 20 test cases with random symptoms, then diagnosed by experts and expert systems. of the 20 test cases, the system was able to correctly diagnose 17 cases. if these results are included in equation (8), then the resulting accuracy rate is 85%. the graph of the accuracy test results can be seen in figure 5. figure 5. chart of accuracy test results furthermore, the accuracy results are converted using accuracy criteria guided by the following criteria: good, namely 76% to 100%; enough, that is 56% to 75%; less good, which is 40% to 55%, and less good, which is less than 40% (mayatopani et al., 2021). the accuracy of the dempster-shafer theory algorithm is 85%. so that the accuracy generated in the developed model is in the good category. based on the test results and analysis of these results, this is due to the workings of the dempster-shafer theory method on the inference engine, namely by calculating probability so that evidence can be carried out based on the level of confidence and logical reasoning. however, based on the results of the accuracy test, the average error value reaches 15%. this is caused by several factors, including several personality types have the same symptoms. in addition, the dempster-shafer works based on the mass function and the combination of variables, this will affect the diagnostic results. thus, the value of plausability and its combination greatly affects the accuracy results obtained. conclusions and suggestions conclusion this study developed an expert system to identify the personality type of employees using the dhempster shafer method. the dempster-shafer algorithm is used as an inference engine and is able to overcome uncertainty by combining evidence from several sources and providing a level of confidence from the available evidence. the system developed is able to identify the type of employee personality through the nature or symptoms that exist in the employee. in addition, the system can display the results of the diagnosis with an explanation of the personality type, its nature in work and work or positions that are suitable for that personality type. based on the results of the accuracy test obtained from the results of the comparison of expert system diagnoses with the 85% 15% correct diagnosis incorrect diagnosis 233 analysis of an expert, it shows the accuracy value reaches 85%. the accuracy value is influenced by the plausability value and its combination. suggestion to improve future research, there are several things that can be considered, including making comparisons with other methods, in order to get a method that has the maximum accuracy. in addition, the system can be developed based on android so that users do not have to open a browser when using the system via a smartphone. references anita, a., ningsih, s., & solin, d. 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internet provider complaints related to network quality, package prices, user satisfaction, and others. this study aims to classify twitter users' tweets against internet service providers in indonesia by analyzing the sentiments of 3 internet service providers, namely with the keywords biznet, first media, and indihome, using the naïve bayes algorithm and optimization with particle swarm optimization. this research is also helpful in helping to become a measure where prospective new users will see the quality of an internet service provider in indonesia through tweets and then divide the opinion into positive and negative. the results of biznet's research using naïve bayes produce an accuracy of 77.94%, and after optimization, it becomes 81.62%. first media using naïve bayes produces 91.39% accuracy, and after optimization, it becomes 92.88%. indihome using naïve bayes produces an accuracy of 85.78%, and after optimization, it becomes 87.48%. it can be concluded that the naïve bayes algorithm is a good algorithm for classification, and optimization using particle swarm optimization has an effect on increasing accuracy results. keywords: sentiment analysis; internet service provider; naïve bayes; particle swarm optimization abstrak twitter yaitu aplikasi media sosial yang ramai digunakan dimana sebanyak 18,45 juta jiwa penggunanya di indonesia, pengguna twitter dapat mengirim dan membaca pesan maksimal 280 karakter yang ditampilkan. banyak opini serta review yang diunggah pengguna melalui tweet pada media sosial tersebut yang dialami dalam kehidupan sehari-hari, belakangan ini komentar tentang penyedia layanan internet pada pandemi covid-19 banyak diulas oleh para pengguna twitter. permasalahan tentang provider internet melalui komentar yang sering diunggah diantaranya tentang keluhan provider internet yang berkaitan dengan kualitas jaringan, harga paket, kepuasan pengguna dan yang lainnya. penelitian ini bertujuan mengklasifikasi tweet pengguna twitter terhadap penyedia layanan internet di indonesia dengan menganalisis sentimen 3 penyedia layanan internet yaitu dengan keyword biznet, first media dan indihome dengan menggunakan metode algoritma naïve bayes dan dilakukan optimasi dengan particle swarm optimization. penelitian ini juga bermanfaaat membantu menjadi suatu ukuran dimana calon pengguna baru akan melihat kualitas suatu layanan provider internet yang ada di indonesia melalui tweet kemudian membagi opini tersebut menjadi positif dan negatif. hasil penelitian biznet menggunakan naïve bayes menghasilkan akurasi 77,94% dan setelah dioptimasi menjadi 81,62%. first media menggunakan naïve bayes menghasilkan akurasi 91,39% dan setelah doptimasi menjadi 92,88%. indihome menggunakan naïve bayes menghasilkan akurasi 85,78% dan setelah dioptimasi menjadi 87,48%. dapat disimpulkan algoritma naïve bayes merupakan algoritma yang baik untuk klasifikasi, dan optimasi menggunakan particle swarm optimization berpengaruh terhadap peningkatan hasil akurasi. kata kunci: analisis sentimen; penyedia layanan internet; naïve bayes; particle swarm optimization mailto:anugrahanugrah27@wastukancana.ac.id mailto:teguhiman@wastukancana.ac.id mailto:ismi@wastukancana.ac.id3 372 introduction in early march 2020, the president of the republic of indonesia, joko widodo, announced that indonesians work from home or study from home. problems that occur when people decide to choose the best to get internet service. this policy was emphasized by the emergence of large-scale social restrictions (psbb) instructions to suppress the spread of the covid-19 virus (christianto, 2020). by doing all the activities at home, we are also required to stay connected, whether doing work or learning. it also impacts increasing internet users in indonesia during the covid-19 pandemic. based on (apjii, 2019) the internet survey report of the association of internet service providers in indonesia (apjii) 2019-2020 (q2), internet user penetration reached 73.7%, namely 196.71 million people out of a total population of 266.91 million people in indonesia. one of the uses of the internet is social media. several social media that internet users in indonesia often use are facebook, twitter, instagram, linkedin, and others. as a medium for communicating and seeking information, twitter is one of the most popular social media networks, and twitter is often used as a means of promoting products, advertisements, political campaigns, and as a means to express opinions related to criticism, suggestions, issues, and public opinion (sudiantoro et al., 2018). sentiment analysis is the understanding, extracting, and processing of textual data to obtain information. there are many benefits of sentiment analysis from various points of view, including it can be used to get an overview of public perceptions of service quality, monitoring a product, sales predictions, politics, and investor decision-making (ipmawati et al., 2017). in 2018, sentiment analysis was carried out for optimized furniture using particle swarm optimization on naïve bayes. the collection of data taken through the amazon website has as many as 200 reviews, divided into 100 positive and 100 negative. experimental results can reach 93.50%, where optimization affects increasing attributes (aulianita & rifai, 2018). another research (hayuningtyas & sari, 2019) conducted a sentiment analysis for tmii tourism using naïve bayes and carried out particle swarm optimization. data collection through travel websites with 100 reviews, 50 positive and 50 negatives. the results show that the accuracy using only naïve bayes is 70%, and after optimization using pso, it increases to 94.02% the review on the traffic application using the algorithm used is naive bayes with the use of particle swarm optimization. the results of the first experiment on the test using naive bayes got an accuracy of 69.50% and an auc graph of 0.488. then the second test after the naive bayes test was optimized using pso, where the results increased by 76.24%, and the auc graph became 0.636 (er & solecha, 2021). a review of distance learning on twitter was conducted by sentiment analysis in 2021 using the naive bayes algorithm. they were collecting data through twitter tweets using the twitter api as much as 600 data. the accuracy using 3-fold cross validation results reached 93% (khairina & fitriati, 2021). therefore, this study aims to implement particle swarm optimization for sentiment analysis of internet service providers' tweets using naive bayes. research methods the method uses the naïve bayes algorithm and particle swarm optimization to improve optimization to analyze twitter users' tweets against internet service providers. the design for the plot in this research is shown through the research framework in figure 1. below: figure 1. research framework 373 crawling data data crawling is the stage that aims to collect or download data from a database. data collection from this analysis is taken through the twitter server with the help of the twitter api (ramadhan & setiawan, 2019). the data crawling uses the orange tool and is carried out three times because it will look for three tweet documents from the internet provider or isp, carried out on 04-18 april 2022. labeling adding sentiment labeling to each tweet document is obtained into two parts: the positive sentiment for twitter users' tweet documents with a positive tone and the negative sentiment for negative twitter users' tweet documents. because it will be divided into positive and negative only, tweet documents that are neutral, which are not opinions, and from official accounts related to keywords will be deleted. data preprocessing preprocessing is used for helpful sentiment analysis, making data management easy. preprocessing is also carried out before the classification process. this process is needed to clean or remove unnecessary characters or words. this stage aims to optimize the data in the following procedure: classification (muttaqien, 2016). a. transform cases because it is inconsistent in words in the tweeted document, do a lowercase which aims to change all text to lowercase, because the text data or content contained from the crawling data has different letters, namely capital and lowercase letters (harjanta, 2015). b. cleansing tweets found that several things will interfere with the analysis process, so in this cleansing stage, some deletions are carried out using the replace operator in rapidminer tools, such as removing duplicates to remove the same tweet document so that the tweeted document is neater before the labeling process is carried out. furthermore, several deletions are carried out, such as removing url, removing rt (retweet), removing hashtag (#), removing mention (@), and regexp or regular expression. this stage is done to remove certain characters or words with regular expressions set to remove punctuation marks (rustiana & rahayu, 2017). c. tokenization document tweets will be tokenized or split the text or separate word by word, or it can be called sentence fragmentation. another goal of the tokenization process is to make each word's weight value easier. this process uses the tokenize operator in the document from the data process in the rapidminer tools (harjanta, 2015). d. filtering at this stage, the filtering process is carried out to remove words that are not used, namely the stopwords process, to remove or eliminate connecting words such as "and," "which," "in," "is," and others (rustiana & rahayu, 2017). naïve bayes naive bayes is one of the algorithms used for classification. the inventor is a scientist from great britain or england named thomas bayes, and the classification assumes that one attribute or class has no characteristics with other features (bustami et al., 2013). the method used is probability and statistics. naive bayes will carry out a classification that aims to study data patterns so that they can make decisions for data that have not been previously processed and generate sentiment for tweet documents that have not been labeled with a sentiment class, which is positive or negative. in this process, naive bayes will learn from training data using a formula to find the highest probability or opportunity from previous experience or tweet documents. in this case, it is test data. particle swarm optimization particle swarm optimization (pso) was first formulated in 1995 by r. eberhart and j. kennedy and is an algorithm of feature selection that functions to solve an optimization problem. described or the thought process behind this algorithm is inspired by the social behavior of animals, such as flocks of birds or herds of fish in search of food or survival (haupt & haupt, 2004). optimization for increasing attribute weight uses particle swarm optimization (pso) using the optimize weight operator on the rapidminer tools, where previously the naive bayes algorithm was implemented and then added the pso process to find a more optimal value. the application of naive bayes and pso is for several attributes of inertia weight (inertial weight), local best weight (best local weight), and global best weight (best global weight). the number of particles determines the population size (number of population) in pso. evaluation the evaluation in this study uses a confusion matrix on cross-validation for the calculation of accuracy, precision & recall. the analysis starts from the initial stage of using nb and 374 then compares it with the addition of optimized weight or nb pso from the three internet provider keywords. a confusion matrix is a matrix in the form of two times two that can display results in the form of a percentage of the classification performance, which is carried out with several formulas for measuring the performance (andika et al., 2019). visualization at the visualization stage, researchers use microsoft power bi tools to visualize data with several charts, namely stacked column chart, pie chart, clustered column chart, and wordcloud. plus, an explanation for the results of the analysis carried out. power bi is software from microsoft to make it easier to visualize the results of processing data with a dashboard. the resulting report can be displayed via a web or mobile device, this software provides many charts, and you can get templates that support the software through the store by downloading it for free (hanifah, 2020). this software also includes business intelligent software, which can connect many data with several extensions that support it. results and discussion crawling data the data obtained is carried out through a data crawling process on the twitter social media page with the help of the twitter api (application programming interface), which is obtained through a request using a twitter account on twitter developer. figure 2 below shows the crawling data process carried out using orange tools. figure 2. crawling data this crawling process is carried out three times because it will search for three tweet data of internet service providers (isp) in indonesia with the keywords "biznet," "first media," and "indihome." the total tweet documents obtained from this crawling process are 1,050 tweets. the amount of data crawling from each searched keyword is shown in table 1. table 1. amount of data crawling keyword amount of data biznet 136 first media 267 indihome 647 labeling the next stage is labeling. at this stage, the researcher marks the excel file obtained through the crawling process by adding a sentiment column. in the excel file, the researcher separates several columns that are only needed, namely the text or tweet data content and the sentiment column. the labeling process is done manually, with the given classes being positive and negative. table 2 displays the amount of data labeled as a consequence of each keyword search and an example of labeling data that has been assigned a sentiment. table 2. labeling example text sentiment keren filmnya keren berlangganan dengan menggunakan indihome positif marah sama indihome jelek negatif the amount of data labeling results from each searched keyword is shown in table 3 below: table 3. amount of data labeling results keyword amount of data positive sentiment negative sentiment biznet 136 40 96 first media 267 22 245 indihome 647 113 534 data preprocessing a. transform cases convert all letters in the tweeted document to lowercase. an example of the transform cases in a tweet document can be seen in table 4 below: 375 table 4. transformation process before transform cases after transform cases kecewa dengan indihome yang mempersulit pda (perpindahan alamat) https://t.co/j7zkxs9b0 t cc @indihome @indihomecare #indihome kecewa dengan indihome yang mempersulit pda (perpindahan alamat) https://t.co/j7zkxs9b 0t cc @indihome @indihomecare #indihome b. cleansing several deletions are carried out at this stage, such as punctuation, url, rt, #, and @. an example of the cleansing process in a tweet document can be seen in table 5 below: table 5. cleansing process before cleansing after cleansing kecewa dengan indihome yang mempersulit pda (perpindahan alamat) https://t.co/j7zkxs9b0t cc @indihome @indihomecare #indihome kecewa dengan indihome yang mempersulit pda perpindahan alamat c. tokenization break up text or words, or it can be called sentence beheading. an example of the tokenization process in a tweet document can be seen in table 6 below: table 6. tokenization process before tokenization kecewa dengan indihome yang mempersulit pda perpindahan alamat after tokenization kecewa dengan indihome mempersulit pda perpindahan alamat d. filtering at this stage, the conjunction is removed or commonly called stopwords removal. an example of the filtering process in a tweet document can be seen in table 7 below: table 7. filtering process before filtering after filtering kecewa dengan indihome yang mempersulit pda perpindahan alamat kecewa indihome mempersulit pda perpindahan alamat naïve bayes after data preprocessing is done, at this stage, several steps are carried out with several operators used, for the algorithm validation stage uses a cross-validation operator where the operator is divided into two parts of the process, namely training and testing. for the training process, a classification algorithm is used, namely naive bayes, while in the testing process, a model is applied that is connected to performance and to run the naive bayes algorithm or operator in the previous training process. the application process in rapidminer can be seen in figure 3. as follows: figure 3. naïve bayes process in rapidminer furthermore, the connecting process begins with creating a document, processing the paper, and applying the model to predict the testing data. particle swarm optimization in the previous stage, a classification algorithm using naive bayes was carried out at this stage adding an optimized weights operator for particle swarm optimization to optimize the increase in attributes. there is a cross-validation operator for naive bayes in the optimize weights operator because it will optimize the increase in the optimization algorithm. the process in rapidminer can be seen in figure 4. below: 376 figure 4. naïve bayes and particle swarm optimization process in rapidminer evaluation in the previous classification results, researchers will analyze and calculate the performance of the nave bayes algorithm and naive bayes pso using a confusion matrix to calculate accuracy, precision, and recall using rapidminer tools. a. biznet the results of several measurements for evaluating biznet keywords can be seen in table 8 below: table 8. biznet evaluation results measurement nb nb + pso accuracy 77,94% 81,62% precision 62,50% 65,31% recall 62,50% 80,00% b. first media the results of several measurements for evaluating first media keywords can be seen in table 9 below: table 9. first media evaluation results measurement nb nb + pso accuracy 91,39% 92,88% precision 93,70% 95,20% recall 97,14% 97,14% c. indihome the results of several measurements for evaluating indihome keywords can be seen in table 10 below: table 10. indihome evaluation results pengukuran nb nb + pso accuracy 85,78% 87,48% precision 93,29% 95,55% recall 89,08% 88,89% visualization at this stage, visualization is carried out to display a chart for the data results using microsoft power bi: stacked column chart, pie chart, clustered column chart, and wordcloud. a. stacked column chart figure 5 shows a stacked column chart used to visualize the count of tweet documents for each internet service provider using a bar chart. figure 5. tweet document count chart b. pie chart figure 6 shows the pie chart used to visualize the count of sentiment attributes across all tweet documents. figure 6. sentiment attribute count chart c. clustered column chart figure 7 shows a chart used to visualize the number of sentiment attributes for each isp. 377 figure 7. amount sentiment attribute chart of each isp figure 8 shows the confusion-matrix evaluation results chart for each isp. figure 8. confusion-matrix evaluation results chart d. wordcloud figure 9 shows a list of words that often appear. figure 8. wordcloud visualization the accuracy value as a classification result is used as a benchmark in determining the sentiment of a comment or tweet carried out in the confusion matrix test, coupled with optimization using particle swarm optimization to improve accuracy. but in some cases, other classification algorithms are better at testing. so it is necessary to make classification algorithm comparisons to get better accuracy values, such as naive bayes and support vector machines (fikri et al., 2020). conclusions and suggestions conclusion it is concluded from the research that has been done comparing the results of the naive bayes algorithm and optimization using particle swarm optimization on three internet service providers in indonesia. the results of the naive bayes classification accuracy are 77.94%, and after pso optimization has increased by 3.68% to 81.62%. the results of the naive bayes classification accuracy are 91.39%, and after pso optimization has risen by 1.49% to 92.88%. the first keyword is biznet. the second keyword is the first media. the third keyword is indihome, the results of the naive bayes classification accuracy are 85.78%, and after pso optimization is done, it increases by 1.70% to 87.48%. from the results of the three keywords above, it can be concluded that naive bayes is a reasonable classification algorithm, and optimization using particle swarm optimization has an effect on increasing accuracy results. suggestion this research only classifies three internet service providers in indonesia. further research is expected to classify all internet service providers in indonesia and compare them with other classification algorithms 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(2018). analisis sentimen twitter menggunakan text mining dengan algoritma naive bayes classifier. dinamika informatika, 10(2), 398–401. jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 291 comparison of classification algorithms for analysis sentiment of formula e implementation in indonesia fachri amsury1, nanang ruhyana2, tati mardiana3*) sistem informasi1, sains data2,3 universitas nusa mandiri fachri.fcy@nusamandiri.ac.id1, nanang.ngy@nusamandiri.ac.id2, tati.ttm@nusamandiri.ac.id3*) (*) corresponding author abstract the formula e racing series has become one of the world's most prestigious competitions. in 2022, indonesia hosted the famous formula e race. the event possesses the potential for economic benefits for indonesia worth 78 million euros through the arrival of 35,000 spectators. indonesians are enthusiastic about formula e since it allows their nation to encourage tourists and gain international prominence. however, some people do not support this event. since they regard that amid the covid-19 pandemic, it is preferable for the government to focus on people affected by the pandemic rather than support a formula e event. this study compares the support vector machine and naive bayes algorithms in classifying public opinion in the formula e race. this study gets its information from user comments on social media platforms, especially twitter. the stages start with text preprocessing and include cleaning, case folding, tokenization, filtering, and stemming. proceed with weighting using the tf-idf approach. data testing uses a confusion matrix to evaluate the classification results by testing accuracy, precision, and recall. categorizing public opinion using the svm algorithm has an accuracy of 82 percent, a precision of 97.86 percent, and a recall of 77.90 percent. on the other hand, the accuracy of the naive bayes technique is more limited, at 87.54 percent. society's opinion on twitter shows positive sentiment towards implementing formula e. keywords: formula e; tweet; sentiment analysis; support vector machine; naive bayes abstrak seri balap formula e telah menjadi salah satu kompetisi paling bergengsi di dunia. pada tahun 2022, indonesia menjadi tuan rumah balapan formula e yang terkenal. ajang tersebut memiliki potensi manfaat ekonomi bagi indonesia senilai 78 juta euro melalui kedatangan 35.000 penonton. orang indonesia antusias dengan formula e karena memberikan kemungkinan bagi bangsa mereka untuk mendorong wisatawan dan mendapatkan keunggulan internasional. namun, ada pihak yang tidak mendukung acara ini. karena mereka menilai di tengah pandemi covid-19, lebih baik pemerintah fokus pada masyarakat yang terdampak pandemi daripada mendukung ajang formula e. penelitian ini bertujuan untuk membandingkan algoritma support vector machine dan naive bayes dalam klasifikasi opini publik pada balapan formula e. penelitian ini mendapatkan informasi dari komentar pengguna pada platform media sosial, khususnya twitter. tahapannya dimulai dengan text preprocessing dan meliputi cleaning, case folding, tokenization, filtering, dan stemming. selanjutnya dilanjutkan dengan pembobotan menggunakan pendekatan tf-idf. untuk mengevaluasi hasil klasifikasi, pengujian data menggunakan matriks konfusi dengan menguji akurasi, presisi, dan recall. dalam hal pengkategorian opini publik, algoritma svm memiliki akurasi 82 persen, presisi 97,86 persen, dan recall 77,90 persen. di sisi lain, akurasi teknik naive bayes lebih terbatas, yaitu 87,54 persen. opini masyarakat di twitter menunjukkan sentimen positif terhadap pelaksanaan formula e. kata kunci: formula e; tweet; analisis sentimen; support vector machine; naive bayes introduction formula e, at first glance, looks the same as formula 1, but formula e uses completely electric energy as its fuel. the formula e event delivers a mission to promote environmentally friendly electric vehicles, along with many negative criticisms about motorsports, excessive consumption of fuel oil that contributes to changes global environment from emissions produced (robeers, 2019) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 292 formula e is a new racing series dedicated to electric racing vehicles (fatemi, ionel, popescu, & demerdash, 2016). the formula e uses an electric battery to operate and regulate the motor, utilizing the electric energy that is pushed at all global racing events (hall, 2017). in modern motorsport, a crucial problem that often occurs is energy management in racing events, and technical regulations impose limits on the power and energy used in formula e. the limit is the limitation of output power and various rechargeable energy storage systems (ress) for cars. depending on the type of event (liu, fotouhi, & auger, 2020). the formula e racing series has become one of the world's most prestigious competitions. in 2022, indonesia hosted the famous formula e race. the event possesses the potential for economic benefits for indonesia worth 78 million euros through the arrival of 35,000 spectators. the governor of dki jakarta, mr. anis baswedan, issued a regulation through the instructions of the governor of the special capital region of jakarta number 49 of 2021 regarding the settlement of regional priority issues for 2021-2022, which was set on august 4, 2021, by mr. anis baswedan. formula e is one of the resolutions of priority issues, then the target for completion is implementing the formula e race (tanjung, 2021). contains a list of achievements in resolving regional priority issues in 2021-2022. the instructions from the governor of dki jakarta regarding the implementation of formula e raise pros and cons among the public because this activity will be held amid the covid-19 pandemic currently engulfing this country, especially because of the issue of electric cars will cost much money in its implementation. the most crucial problem is the method of funding which is considered to violate existing regulations due to the commitment fee installments, which will last until 2023, which means it will exceed the term of office of the governor of dki, which ends in 2022 (tanjung, 2021) pt. jakarta propertindo (jakpro) was appointed by the governor of dki jakarta, mr. anis baswedan, to hold an international formula e racing event in 2020. however, this implementation has caused pro and contra reactions among politicians and the public regarding the dki provincial government's plan to use apbd funds with a value of 1.6 trillion to carry out the formula e racing event, and there were also protests regarding the place that would become the planned formula e circuit at monas. the support and criticism of the formula e event have attracted attention and warm discussion among the public (kartinawati & wiyawan, 2021). related research on the topic of analysis of public sentiment for twitter to analyze public opinion from tweets posted with analysis using the nave bayes method has an accuracy of 87.34%, a sensitivity of 93.43%, and a specificity of 71.76%, proving a fairly good result in his research (putu et al., 2021). research on sentiment analysis of television broadcasts based on public opinion on twitter with the k-nn method with a total of 400 tweets data produces an accuracy rate using textual weighting is 82.50% with a value of k = 3, giving good results (berliana, shaufiah, s.t., & siti sa'adah, s.t., 2018). research on the classification of posting tweets regarding government policies by applying the nave bayes algorithm by extracting the unigram feature with tweet data of 578 data produces an accuracy rate of 80% (berliana et al., 2018). research on complaint classification using the svm algorithm with a total dataset of 1040 complaints data using rapid miner tools produces an accuracy rate of 95.67%(fatmawati & affandes, 2018). this study aims to see the public response to the formula e event, whether to support or reject this event based on community comments taken from one of the social media platforms, namely twitter, by applying data mining techniques to classify tweets that support the formula e event using a technical approach. smote and support vector machine and naïve bayes algorithms. research methods research using the concept of knowledge discovery in database (tsytsarau & palpanas, 2012). to classify tweets containing support for organizing a formula e event in jakarta. types of research this study uses qualitative data sourced from twitter in the form of tweet data with the query formula e research target / subject the data population is sourced from twitter, namely tweet data taken from may 2020 to june 2022 with a formula e query. the data structure of the results of crawling twitter data using the rapidminer application consists of several columns, namely created-at, from-user, from user-id, to-user, to-user-id, language, source, text, geo-location-latitude, geo-locationlongitude, retweet-count and id. the tweet data generated from this process amounted to 917 tweets. jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 293 procedure the following is a framework that is carried out based on the steps and procedures of this research process 10 fold cross validation dataset preprocessing feature weight smote learning methode classification algorithms svm nb testing data evaluation: confution matrix (auc, precision, recall) f1 measure t-test training data transform cases filter tokens (by length) indonesian stop word removal filter stopwords (dictionary) indonesian stemming tokenize (regexp) tokenize figure 1. research method figure 1 explains that the research model carried out is the classification of tweets containing support for implementing the formula e event in indonesia, precisely in jakarta. the text mining technique and the support vector machine (svm) and naïve bayes methods are used by applying the smote technique to balance the data (gata et al., 2019). 1. data the data is sourced from twitter which is a tweet posted by the general public, tweet data retrieval using the twitter data crawling technique using the rapidminer application, then labeling the tweet data to classify tweets that support the formula e event in jakarta and tweets that do not support the formula e event. 2. data selection & cleaning it aims to select and clean tweet data originating from twitter, eliminating noise and duplication of data. 3. data transformation tweet data selected and cleaned from duplication and noise is then transformed into xls format to facilitate the data mining. 4. data mining tweet data that has been transformed into a dataset, the next stage is to approach the right algorithm by comparing the two algorithms, namely svm and naïve bayes, to see the performance generated from the two algorithms and measure the level of accuracy generated. 5. evaluation the dataset modeled using the svm and naïve bayes algorithms are then visualized to simplify the process of comparing the performance and accuracy of the two algorithms. data, instruments, and data collection techniques the data population is sourced from twitter, namely tweet data taken from may 2020 to june 2022 with a formula e query. the data structure of the results of crawling twitter data using the rapidminer application consists of several columns, namely created-at, from-user, from user-id, to-user, to-user-id, language, source, text, geo-location-latitude, geo-locationlongitude, retweet-count and id. the tweet data generated from this process amounted to 917 tweets. the attribute used in this data is the text column which contains tweets regarding support for the formula e event in jakarta. data analysis technique this study uses tweet data taken from twitter using the rapid miner tweet data tool from may 2020 to june 2022 with a formula e query that will be used in the text mining process. the data structure of the twitter data crawl process consists of several columns, namely created-at, from-user, from-user-id, to-user, to-user-id, language, source, text, geo-location-latitude, geo -locationlongitude, retweet-count and id. the next phase is adding a new field, namely the label used as a class. twitter has much information about tweets that support and don't support the implementation of formula e in jakarta. results and discussion the following is a sample of the data used in the study. table 1. sample twitter data with a label text label @krmtroysuryo2 jegal kiri kanan depan belakang jalan terus formula e harumkan indonesia pada dunia ?? support @krmtroysuryo2 mau dijegal sana-sini, dinyinyirin sana-sini, kalo emang sudah takdirnya acara support p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 294 text label sukses, bakal sukses, turut senang yg diundang pada hadir, dari pak presiden, bu ketua mpr, pak ketua dpr, kepala bpkp, menpora, sampai menteri pariwisata, kapolri, dll https://t.co/0wk7tjab10 event formula e selesai dan tuntas.. terimakaih semua yang sudah menyukseskan… baik dr tenaga kerja sampai presiden @jokowi yg sdh hadir.. semua berjalan dg lancar dan tdk sampai hujan.. lupakan evoria formula e.. waktunya kita sambut hajatan dki jkt nanti.. https://t.co/1wzjjthxks support @alisyarief gilu le ndro!!!!! ajuin proposal sponsorship formula e ke bumn ala ala ormas preman malak buat 17 agustusan . https://t.co/wsjqipeddr notsupport @msaid_didu hanya orang tolol yang mau sponsorin formula e, makanya ditangan mu bumn banyak lubang buat lahan korupsi! karna yang ngurus tolol notsupport @kangmasprabu165 @erickthohir makanya kalau mau ngadain event internasional itu jangan dadakan ngajuin proposal, ini kok kayak jualan tahu bulet... dadakan???? not support table 1 is a sample of research tweet data consisting of a text column containing tweets and a label column is a class of tweet labeling results using the crowdsourced labeling technique divided into two classes, namely support and not support classes. teknik smote the smote (synthetic minority oversampling technique) technique treats class imbalances (amsury, ruhyana, saputra, & sulistyowati, 2020). this technique synthesizes new sample data from the minority class to balance the dataset by creating new instances from the minority class and forming a convex combination of adjacent instances. the training data set consists of smin minority data points and smaj majority data points. for each (xi,yi) ∈ smin , most data points set smaj. for each (xi, yi) ∈ smin, smote generates a new minority data point along a line segment joining xi and one of the k closest neighbors chosen randomly. 𝜒𝑛𝑒𝑤 = 𝜒𝑖 + (𝜒𝑗 − 𝜒𝑖 ) × 𝛿, …………………….…..…(1) where is δ a random number between [0, 1]. the xnew class label is set to +1 (ynew), the minority class label. the newly generated data point {(xnew, ynew)} is then added to the original set s and used to train the classifier. from the above formulation, it is clear that smote informs oversampling to generalize the minority class by creating a larger and less specific decision region rather than a smaller and more specific original region support vector machine support vector machine (svm) is a supervised learning technique with many desirable qualities making it a popular algorithm (mathew, luo, pang, & chan, 2015). it has a solid theoretical foundation and performs classification more accurately than most other application algorithms (windasari, uzzi, & satoto, 2018). many researchers have reported that svm may be an accurate method for text classification (demidova & klyueva, 2017). it is also widely used in sentiment classification. the svm formulation for solving binary classification problems is briefly described in this section. it should be noted that binary svm can easily be extended to multi-class classification using methods such as one-on-many and one-on-one. figure 2 shows the svm algorithm modeling validation process to get the accuracy value. figure 2. svm algorithm validation process jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 295 figure 3. the results of the accuracy of the svm algorithm figure 3 explains the results of the svm algorithm accuracy is 88.11%, with class precision, prediction support results of 97.86%, class precision prediction do not support 81.65%, and class recalls true support results 77.90%, class recall true not support 98.30%. the tweet data results show that the classification for true support tweets with predicted support is 275 data, and tweet data is not supported with 6 data support predictions. true support tweet data by not supporting prediction to much as 78 data, and does not support tweet data to 347 data as not supporting the prediction. naïve bayes naive bayes is a learning algorithm based on bayes theory using strong assumptions (naive)(barfian, iswanto, & isa, 2017). bayes's theory is about finding a probability of something based on pre-existing data. this method can also classify opinions based on previously trained data. the essence of naive bayes is finding the data's highest probability. bayes formula can be written as follows: 𝑃𝑐𝑑 = 𝑃(𝑐)𝑋 𝑃𝑑𝑐 𝑃(𝑑) …………...................................... (2) information: pcd: probability class c after d is entered into class c p(c): the probability of class c before pdc: probability d in class c pd: probability d figure 4 shows the validation process using the naïve bayes algorithm modeling to get the accuracy value. figure 4. nave bayes algorithm validation process figure 5. the results of the accuracy of the naïve bayes algorithm figure 5 explains that the accuracy of the naïve bayes algorithm results is 87.54%, with p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 296 results to support the precision class prediction of 96.17%, class precision prediction does not help 81.62%, and class recalls actual support results 78.19%, class recall true not supporting 96.88%. based on the results of real support tweet data with support predictions of 276 data and true not support tweet data with 11 data support predictions. since numerous nations worldwide take part in the formula e. true support tweet data with predictions of not keeping with as 77 data, and tweet data of not help with forecasts of not investing with 342 data event. conclusions and suggestions conclusion the classification results from this study using the svm algorithm get an accuracy value of 88.11%, with precision class prediction is support results of 97.86%, class precision prediction does not support 81.65%, and class recalls real support results 77.90%, class recall true not supporting 98.30%. while the results of the accuracy of the naïve bayes algorithm are 87.54%, with the results of class precision prediction support of 96.17%, class precision prediction of not supporting 81.62%, and the effects of class recalling true support being 78.19%, class remembering being true not being helping 96.88%. based on the results obtained, the accuracy level using the svm algorithm has a higher level of accuracy compared to the accuracy results generated by the naïve bayes algorithm. a high level of precision so that you can see how many tweets from the indonesian people are intended to support implementing an international standard formula e event in indonesia. suggestion suggestions for further research are expected to add features such as n-gram and apply optimization techniques. then explore various data, for example, from comments on instagram, youtube, or other social media, adding classes with more than two classes, and adding the number of data samples so that research results become more and more accurate, especially in classifying twitter data. references amsury, f., ruhyana, n., saputra, i., & sulistyowati, d. n. 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(2018). sentiment analysis on twitter posts: an analysis of positive or negative opinion on gojek. proceedings 2017 4th international conference on information technology, computer, and electrical engineering, icitacee 2017, 2018-janua, 266–269. https://doi.org/10.1109/icitacee.2017.825 7715 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 298 347 decision support system for wi-fi modem selection using multiple attribute decision making with topsis nur sucahyo-1*), ahmad fitriansyah-2, yogasetya suhanda-3 1,3 sistem informasi, 2teknik informatika institut teknologi dan bisnis swadharma banten, indonesia https://www.swadharma.ac.id/ 1*)n.sucahyo12@gmail.com, 2yogaswa@gmail.com, 3hafaskom@gmail.com (*) corresponding author abstract the modem is a device that can connect mobile phones, pcs, or laptops to the internet network. the modem popular today is mifi or wi-fi modem, a portable modem that makes it easier for users to carry the device anywhere. many wi-fi modems are circulating in the market with various brands with different specifications and capabilities. for this reason, people must be observant in choosing the right wi-fi modem for their needs. selection of the right wi-fi modem will maximize the internet network's performance to facilitate internet use activities. this study aims to develop a decision support system by implementing the multiple attribute decision making (madm) approach using the topsis method for selecting a wi-fi modem to facilitate the selection of the best wi-fi modem according to user needs. based on black-box testing, it shows that the system built can run well. in addition, the calculation results of the topsis method generated by the system with manual calculations produce the same value. keywords: decision support system; wi-fi modem; madm; topsis abstrak modem merupakan perangkat yang memiliki kemampuan untuk menghubungkan handpone, pc maupun laptop ke jaringan internet. jenis modem yang populer saat ini adalah mifi atau modem wi-fi, yang merupakan modem portabel sehingga dapat memudahkan pengguna untuk membawa perangkat di mana saja. saat ini modem wi-fi telah banyak beredar dipasaran dengan berbagai merek dan brand yang memiliki sepesifikasi dan kemampuan yang berbeda-beda. untuk itu, masyarakat harus jeli untuk memilih modem wifi yang tepat dengan kebutuhannya. pemilihan modem wi-fi yang tepat akan memaksimalkan kinerja jaringan internet sehingga memperlancar aktivitas dalam penggunaan internet. tujuan dari penelitian ini adalah agar dapat mengembangkan sistem pendukung keputusan dengan mengimplementasikan pendekatan multiple attribute decision making (madm) menggunakan metode topsis untuk pemilihan modem wi-fi, agar dapat mempermudah pemiihan modem wi-fi yang terbaik dan sesuai kebutuhan pengguna. berdasarkan black-box testing memperlihatkan bahwasanya sistem yang dibangun dapat berjalan dengan baik. selain itu, hasil perhitungan metode topsis yang dihasilkan oleh sistem dengan perhitungan manual menghasilkan nilai yang sama. kata kunci: sistem pendukung keputusan; modem wi-f; madm; topsis introduction the internet has now become a necessity for humans in carrying out their activities. the internet is useful in almost all aspects of human life because it can connect and communicate, get information and knowledge, and learn, making it easier in business and entertainment (walidaini & muhammad arifin, 2018). connect the internet, and a device requires an internet network, one of which is a modem. the type of modem that is popular today is mifi or modem wi-fi, where this device is a portable modem that connects several devices to the internet at once (badriansyah et al., 2021). because this modem is portable, it makes it easier for users to carry this device everywhere. many wi-fi modems are circulating in the market with various brands with different specifications and capabilities. for this reason, people must be observant in choosing the right wi-fi modem for their needs. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.388 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 348 the selection of the right wi-fi modem will maximize the performance of the internet network to facilitate one's internet use activities. so, a solution is needed to solve the problem of selecting the right wi-fi modem device. the system that can provide recommendations in decision-making is a decision support system. a decision support system, abbreviated as dss, is a knowledge-based system that can assist in making decisions. dss can help decision-makers solve semi-structured problems by recommending the best alternative or solution (borman & fauzi, 2018). in the case of the selection of a wi-fi modem, it will involve several criteria and several options with subjective assessments. therefore, the problem was solved using the multiple attribute decision making (madm) approach. madm is an approach to getting the best alternative from several alternatives based on several criteria (jaya et al., 2020). one madm approach that can be implemented is the technique for order preference by similarity to ideal solution (topsis). topsis is a method in which the best solution is obtained based on the shortest distance from the positive ideal solution and the farthest distance from the perfect negative solution to find the optimal solution (nurelasari & purwaningsih, 2020). the topsis method has advantages. namely, the method is practical, simple, and efficient in computation (hertyana, 2019). previous studies that applied the topsis method had results with high accuracy, including research on the application of topsis for the selection of tourist attractions (paypas et al., 2019), which produces an accuracy value of 83.3%. another study regarding the appointment of coffee beans with export quality using topsis with an accuracy value of 84% (borman, megawaty, et al., 2020). furthermore, research on applying topsis to determine ukt waivers obtained an accuracy value of 87% (ulloh et al., 2021). this study aims to develop a decision support system through the multiple attribute decision making (madm) approach using the topsis method for selecting a wi-fi modem to assist in choosing a wi-fi modem that suits the needs and desires of the user. the difference between this study and previous research is that this research focuses on selecting a wi-fi modem. in this study, the criteria were obtained from an expert whose sources were taken from the website (faynilla, 2022). the system was developed based on a website to make it easier for users to use it anywhere and anytime. the criteria for selecting a wi-fi modem are price, battery capacity, type of sale, number of users, and speed. research methods this research develops a decision support system to choose the right wi-fi modem. for this reason, the research stages refer to the system development method. the extreme programming (xp) system development approach is used in developing the system. this approach is a system development method that can be applied to build software on a small to medium scale, and this method can develop systems with changing needs (borman, priandika, et al., 2020). figure 1 below is the stages of the research carried out. figure 1. research stages planning planning is the stage where the process of identifying problems and determining needs is carried out (borman, priandika, et al., 2020). the main problem in this study is to determine the wi-fi modem according to the needs and criteria set by the user. it is an essential device for connecting to the internet. so, we need a system that can recommend the right wi-fi modem. the system to be built requires functions such as managing data on criteria, alternatives, and weights and seeing the best alternative results from the system. design after determining the problems and system requirements, the next step is to design the system to simplify the implementation process. the design is based on user needs, and a framework is made for its implementation (arifin et al., 2021). the plan will provide an overview of the system developed as a reference in the coding or implementation phase. coding the next stage is coding, where the results of the design in the previous stage will be implemented into the system through coding with a certain programming language (rahmanto et al., 2021). in this study, a website-based decision support system will be built so that for coding, the php programming language is used with the text editor notepad++ and uses mysql as the database. 349 testing in the final stage, testing of the developed system is carried out. the testing phase ensures that the system developed can run well and is free from errors (saputra & borman, 2020). the testing method applied is black box testing. this testing technique is a test that focuses on the functionality of the system, whether it is to the specified requirements (herdiansah et al., 2021). results and discussion start developing a decision support system for selecting a wi-fi modem, and the first step is to determine the criteria. wi-fi modem selection criteria used in this study include: 1. price the price criteria are based on the wi-fi modem device's price, an alternative the user has determined. 2. battery capacity the wi-fi modem is equipped with a battery to be used anywhere and anytime. so, battery capacity is a consideration for choosing a wi-fi modem because a modem with a large battery capacity does not require repeated charging, especially when traveling. 3. sales type in indonesia, there are two types of wi-fi modem sales: bundling and non-bundling. bundling is a wi-fi modem sale with internet packages from certain cellular operators. while the non-bundling is the sale of wi-fi modems that only sells the device, users can buy their internet packages. 4. number of users each wi-fi modem has several user capacities. the user capacity offered by the device is the maximum number of users that the wi-fi modem can connect to. 5. speed speed shows the maximum internet speed capacity that the wi-fi modem can provide. after the criteria have been determined, it is continued by assessing the value of the requirements and the conversion value. there are two types of criteria characteristics, namely benefits and costs. the alternative will be better if the value is higher for the benefit criteria type. on the contrary, for the cost criteria class, if the value is lower, the better. from the predetermined criteria, it is identified that the price criterion is a cost criterion, while the criteria for battery capacity, type of sale, number of users, and speed are benefit criteria. table 1 below shows the requirements and value conversions for each standard. table 1. wi-fi modem selection criteria criteria criteria value value conversion price (c1) < 500,000 5 500,000 1,000,000 4 1,000,000 1,500,000 3 1,500,000 2,000,000 2 > 2,000,000 1 battery capacity (c2) < 1.500 mah 1 1.500 2.000 mah 2 2.000 2.500 mah 3 > 2.500 mah 4 sales type (c3) bundling 1 non bundling 2 number of users (c4) < 10 1 10 20 2 20 30 3 > 30 4 speed (c5) < 100 mbps 1 100 200 mbps 2 200 300 mbps 3 > 300 mbps 4 furthermore, the decision maker will assess each criterion's weight or level of importance. the level of importance or weight value will be given a value including not important with a value of 1; enough with a value of 2; fairly important with a score of 3; important with a value of 4; very important with a value of 4. as a case study, in table 2, the following is the weight value of the decision maker. table 2. weight of each criterion c1 c2 c3 c4 c5 4 3 2 2 4 as a case study, for the selection of a wi-fi modem using the alternative topsis method used, including prolink smart 4g lte (a1); alcatel mifi 4g lte (a2); moviemax mobile wi-fi (a3); bolt modem mifi juno (a4). each alternative will be assessed based on product specifications against predetermined criteria. alternative assessments whose values have been converted are presented in table 3. table 3. value of each alternative that has been converted alternative c1 c2 c3 c4 c5 prolink smart 4g lte (a1) 4 2 2 3 2 alcatel mifi 4g lte (a2) 3 3 2 2 3 moviemax mobile wi-fi (a3) 4 4 1 2 2 bolt modem mifi juno (a4) 5 3 1 2 2 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.388 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 350 based on the case study above, it can be solved using the topsis method with the following stages. 1. determine the normalized decision matrix the normalized decision matrix is used to determine the performance of each alternative. the normalized decision matrix for each criterion is denoted by xij, which is obtained from the sum of all alternatives, and each criterion will be divided by the total number of criteria. the calculation to get a normalized decision matrix is through equation (1) below.    m i ij ij ij x x r 1 2 .....................................................................(1) where:: rij = result of normalized decision matrix (matrix r) i = 1, 2, 3, …, m and j = 1, 2, 3, …, n based on the case study above, solving problems using the topsis method begins with determining the normalized decision matrix using equation (1). the normalization decision matrix can be calculated as follows: 4924.0 1240.8 4 5434 4 2222 11   r 3693.0 1240.8 3 5434 3 2222 21   r 4924.0 1240.8 4 5434 4 2222 11   r 6155.0 1240.8 5 5434 5 2222 11   r the calculation is carried out continuously until all alternative values in each criterion have been normalized. the calculation results for the entire rij value are then entered in the r matrix.              4364.00.43643162.04867.06155.0 4364.00.43643162.06489.04294.0 6547.00.43646325.04867.03693.0 4364.00.65476325.03244.04924.0 r 2. determine the weighted normalized decision matrix before the weighted normalized matrix is used, the weight w = (w1,w2,…wn), with equation (2) below: ijjij rwy  ......................................................................(2) where: yij = the results of the weighted normalized decision matrix (matrix y) i = 1, 2, 3, …, m and j = 1, 2, 3, …, n based on table 2, the weight of the criteria w = (4, 3, 2, 2, 4), the weighted normalized matrix can be calculated using equation (2). the following is the calculation process: 9695.14924.0411  rwy j 4771.13693.0421  rwy j 9695.14924.0431  rwy j 4618.26155.0441  rwy j the calculation is carried out continuously until all the values in the r matrix have been multiplied by the weights. then, the results of the normalized decision matrix (y) are obtained as follows:              7457.10.87296325.04600.14618.2 7457.10.87296325.09467.19695.1 6186.20.87292649.14600.14771.1 7457.11.30932649.19773.09695.1 y 3. determine the positive ideal solution matrix and negative ideal solution the positive and negative ideal solution matrices are obtained from calculations using equations (3) and (4).   nyyyya ,...,,, 321 .................................................... (3)   nyyyya ,...,,, 321 ...................................................... (4) with,            attributecost a is j if, ij ymin attributebenefit a is j if, ij y max i i y j ................. (5)          attributebenefit a is j if,y max attributecost a is j if,ymin ij ij i i jy ................. (6) the following is the calculation process for the positive ideal solution.   4771.1;2.4618771;1.96951.9695;1.4min1   y   9467.1;1.4600600;1.94670.9733;1.4max2   y 351   1.2649;0.6325649;0.63251.2649;1.2max3   y   1.3093;0.8729729;0.87291.3093;0.8max4   y   2.6186;1.7457186;1.74571.7457;2.6max5   y  6186;1.3093;2.467;1.26491.4771;1.9a calculating the negative ideal solution can be seen in the following calculations.   4618.2;2.4618771;1.96951.9695;1.4max1   y   9733.0;1.4600600;1.94670.9733;1.4min2   y   0.6325;0.6325649;0.63251.2649;1.2min3   y   0.8729;0.8729729;0.87291.3093;0.8min4   y   1.7457;1.7457186;1.74571.7457;2.6min5   y  7457;0.8729;1.733;0,63252.4618;0.9a 4. determine the distance of each alternative through a positive and negative ideal solution matrix the distance for each alternative through the positive ideal solution matrix and the negative ideal solution matrix can be calculated by equations (5) and (6).     n j jiji yyd 1 2 )( .....................................................(5)     n j jiji yyd 1 2 )( .....................................................(6) where, i = 1, 2, 3, …, m the following results from calculating the distance of the positive ideal solution (d+). 3970.11   d ; 6537.02   d ; 2629.13   d ; 5997.14   d while the following are the results of calculating the negative ideal solution distance (d-). 9126.01   d ; 5390.12   d ; 0908.13   d ; 4867.04   d 5. calculating the preference value on each alternative the last step is to find the preference value for each alternative. the result of the largest vi is the best alternative. calculating the preference value can apply the following equation (7).     ii i i dd d v .....................................................................(7) where, i = 1, 2, 3, …, m following are the calculation steps to get the preference value. 3951.0 3970.19126.0 9126.0 1   v 7019.0 2629.13590.1 5390.1 2   v 4634.0 2629.10908.1 0908.1 3   v 2333.0 5997.14867.0 4867.0 4   v based on the results of vi or the reference value above, the one with the largest weight is the best alternative or solution. the results of the reference values for each alternative are presented in table 4 below. table 4. reference value of each alternative alternative preference value prolink smart 4g lte (a1) 0.3951 alcatel mifi 4g lte (a2) 0.7019 moviemax mobile wi-fi (a3) 0.4634 bolt modem mifi juno (a4) 0.2333 after the topsis analysis has been carried out, the topsis algorithm is applied in a decision support system to choose a wi-fi modem with the php programming language notepad++ and the database using mysql. the interface display on the decision support system for selecting a wi-fi modem is presented in figure 2. figure 2. system main menu interface the developed system has facilities such as alternative management, weights, inputting alternative values, performing topsis calculations, and displaying the best alternative recommendations. users can enter alternative data and delete alternative data. after the main menu is accessed, the user can perform alternative management using the wi-fi modem list feature. the interface of the wi-fi modem list feature can be seen in figure 3. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.388 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 352 figure 3. wi-fi modem list menu interface after inputting the alternative data, system users can select a wi-fi modem via the recommendations menu. in this menu, system users will give weighting to each criterion. then, the user can continue it by pressing the calculate menu. the system will show the calculation process using the topsis method in the calculate menu. in addition, the system also presents recommendations in the form of the best alternative results. figure 4 below is a display of the topsis calculation results. figure 4. the best alternative results interface furthermore, the system will be tested to ensure that the system developed is error-free. the test technique used is the black-box testing method. black-box testing will test the functionality of the developed software. the black-box testing carried out is presented in table 5 below. table 5. black-box testing results no. functionality expected results results 1 main menu interface the main menu can appear when the system is accessed. valid 2 managing alternatives users can perform valid no. functionality expected results results alternative management by adding or deleting data 3 managing weights users can provide a weighted value from predefined criteria valid 4 recommendation users can select the wi-fi modem by entering the desired weight valid 5 topsis calculation the system will display the calculation of the topsis method in the form of a detailed calculation process from start to finish. valid 6 best alternative results users can see the best alternative results valid table 5 above shows that the system has been functioning properly. it is because, based on black-box testing, all test elements are valid. in addition, the calculation results issued by the system with conventional calculation results show the same results. it means that the topsis method has worked well on the developed system. conclusions and suggestions conclusion this study implements the multiple attribute decision making (madm) approach using the technique for order preference by similarity to ideal solution (topsis) on a website-based wi-fi modem selection system. the topsis method can obtain the best alternative by calculating the shortest distance in the positive ideal solution and the farthest distance in the perfect negative solution. developed facilities such as alternative management, weights, and entering alternative values can perform topsis calculations and display the best alternative results. based on black-box testing tests, it shows that the system built can run well. in addition, the calculation of the topsis method generated by the system with manual calculations produces the same values and results. suggestion several suggestions can be considered to improve further research, including developing an android-based system so the system can be used on 353 smartphones without the need to open a browser. in addition, it can develop using various methods to produce a more optimal system. references arifin, n. y., borman, r. i., ahmad, i., tyas, s. s., sulistiani, h., hardiansyah, a., & suri, g. p. 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(2018). pemanfaatan internet untuk belajar pada mahasiswa. jurnal penelitian bimbingan dan konseling, 3(1), 37–49. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.388 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 354 jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 139 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional analisis kepuasan pengguna aplikasi ojek online menggunakan metode technology acceptance model (tam) 1novian putra utama, 2fintri indriani 1program studi sistem informasi stmik nusa mandiri jakarta www.nusamandiri.ac.id novianputraid@gmail.com 2program studi sistem informasi akuntansi universitas bina sarana informatika www.bsi.ac.id/ubsi fintri.fni@bsi.ac.id abstractcongestion in jakarta is a phenomenon that has become a scourge of the community, congestion occurs because of the increasing number of motorized vehicles in jakarta, the lack of public awareness of driving and traffic and the lack of public awareness of the use of public transportation which is one of the solutions to reduce congestion in jakarta. based on the description on the background of the problem, it can be identified several problems, including 1) does ease of perception (percived ease of use) have a positive effect on the acceptance of the online motorcycle taxi application system in indonesia? 2) does the perceived usefulness (percived usefulness) have a positive effect on the acceptance of online motorcycle taxi applications in indonesia ?. the purpose of this study is to analyze and test whether ease of perception (percived ease of use) has a positive effect on the acceptance of online motorcycle taxi application systems in indonesia, and to analyze and test whether the benefits of perceived use (percived usefulness) have a positive effect on the acceptance of online motorcycle taxi applications in indonesia. keywords: user satisfaction, online motorcycle taxi application, tam abstrakkemacetan di jakarta adalah suatu fenomena yang sudah menjadi momok masyarakat , kemacetan terjadi karena semakin banyaknya kendaraan bermotor yang ada di jakarta, masih kurangnya kesadaran masyarakat berkendaraan dan berlalu lintas dan kurang nya tingkat kesadaran masyarakat akan penggunaan transportasi umum yang menjadi salah satu solusi untuk mengurangi kemacetan di jakarta. berdasarkan uraian pada latar belakang masalah, maka dapat di idetifikasikan beberapa masalah, diantaranya 1) apakah kemudahan persepsian (percived ease of use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di indonesia? 2) apakah kemanfaatan persepsian (percived usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di indonesia?. tujuan penelitian ini untuk menganalisis dan menguji apakah kemudahan persepsian (percived ease of use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di indonesia, dan untuk menganalisis dan menguji apakah kemanfaatan persepsian (percived usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di indonesia. kata kunci: kepuasan pengguna, aplikasi ojek online, tam pendahuluan ojek online saat ini adalah suatu fenomena yang patut kita perhatikan, seperti yang kita ketahui sekarang terdapat tiga raksasa penyedia layanan ojek berbasis online yaitu, go-jek, grabbike, dan ubermotor. mereka bersaing dalam menarik minat masyarakat untuk menggunakan layanan yang mereka sediakan diantaranya dengan meningkatkan software aplikasi untuk semakin mudah di gunakan oleh masyarakat dari berbagai kalangan seperti anak-anak, orang dewasa hingga lansia. kemacetan di jakarta adalah suatu fenomena yang sudah menjadi momok masyarakat (fahmi, umyati, riyanto, & basuki, 2015), kemacetan terjadi karena semakin banyaknya http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 140 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional kendaraan bermotor (sengkey, jansen, & wallah, 2011) yang ada di jakarta, masih kurangnya kesadaran masyarakat berkendaraan dan berlalu lintas (soesilowati, 2008) dan kurang nya tingkat kesadaran masyarakat akan penggunaan transportasi umum yang menjadi salah satu solusi untuk mengurangi kemacetan di jakarta. tingkat kepadatan lalu lintas di dki jakarta terus meningkat. hal ini telah menyebabkan tingkat kemacetan semakin tinggi, yang menimbulkan kerugian di masyarakat (soebali & mahendra, 2017). menurut arie setiadi moerwanto, kepala badan penelitian dan pengembangan kementerian pekerjaan umum dan perumahan rakyat, kerugian yang diakibat kemacetan di jakarta mencapai rp. 65 triliun per tahun (rachman, 2015). dinas perhubungan dki jakarta mencatat kerugian masyarakat dari dampak kemacetan di sejumlah wilayah jakarta mencapai rp 150 triliun per tahun. (zuraya, 2016). berdasarkan uraian pada latar belakang masalah, maka dapat di idetifikasikan beberapa masalah, diantaranya 1) apakah kemudahan persepsian (percived ease of use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di indonesia? 2) apakah kemanfaatan persepsian (percived usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di indonesia? mengacu pada identifikasi masalah yang di uraikan di atas, maka di dapatkan beberapa hipotesis dalam penelitian ini h1 : terdapat persepsi terhadap kemudahan penggunaan (perceived ease of use). h2 : terdapat persepsi terhadap manfaat penggunaan (perceived usefulness). adapun penelitian ini dilakukan untuk mengetahui seberapa besar pengaruh persepsi pemanfaatan dan kemudahan terhadap aplikasi ojek online. penelitian ini mengadopsi model technology acceptance model (tam) dalam upaya pengembangan model penelitiannya. tujuan penelitian ini untuk menganalisis dan menguji apakah kemudahan persepsian (percived ease of use) berpengaruh positif terhadap penerimaan sistem aplikasi ojek online di indonesia, dan untuk menganalisis dan menguji apakah kemanfaatan persepsian (percived usefulness) berpengaruh positif terhadap penerimaan aplikasi ojek online di indonesia? metode penelitian dalam penyusunan penelitian dengan mengambil objek penelitian pengguna jasa ojek online yang saat ini telah mencapai 10 juta unduhan untuk go-jek, 10 juta unduhan untuk grabbike, dan 10 juta unduhan untuk uberbike (sumber : playstore, 2017). a. tahapan penelitian dalam penelitian ini ditujukan untuk menganalisa dan mendeskripsikan tentang datadata tingkat kepuasan pengguna aplikasi ojek online dengan metode technology acceptance model (tam). penelitian ini adalah penelitian survei yang dilakukan dengan kuesioner sebagai pengumpul data-data yang diperlukan. berikut ini adalah kerangka pemikiran yang telah di rancang untuk melakukan penelitian yang sedang dikaji. studi pendahuluan identikasi masalah perceived usefulness perceived ease of use metode yang digunakan technology acceptance model (tam) pengumpulan data kuesioner / angket analisis data uji kualitas data (uji validitas & uji reliabilitas) uji asumsi klasik (uji normalitas, uji multikolineritas, uji heterokedastisitas) uji regresi linier berganda uji hipotesis (uji f & uji t, uji koefisien determinasi simultan, uji koefisien determinasi parsial) metode perhitungan kesimpulan dan saran gambar 1. struktur penelitian b. intrumen penelitian alat yang digunakan berupa kuesioner yang memuat sejumlah pertanyaan tertulis dari responden. instrumen yang digunakan dalam penelitian ini menggunakan kuesioner yang sebagai alat pengumpul data-data yang diperlukan. dan untuk variabel penelitan menggunakan variabel : 1. kemudahan (ease of use) variabel ini dibuat untuk mengukur sejauh mana pengguna percaya bahwa aplikasi ojek online mudah untuk digunakan. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 141 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel 1. item-item konstruk kemudahan persepsian dimensi no. indikator kemudahan persepsian 1. mudah dipelajari 2. dapat dikontrol 3. jelas & dapat dipahami 4. fleksibel 5. mudah untuk menjadi terampil/mahir 6. mudah digunakan 2. kegunaan (usefulness) variabel ini dibuat untuk mengukur sejauh mana pengguna percaya bahwa menggunakan aplikasi ojek online akan meningkatkan kinerja. tabel 2. item-item konstruk kemanfaatan persepsian dimensi no. indikator kemanfaatan persepsian 1. mempercepat pekerjaan 2. meningkatkan kinerja 3. meningkatkan produktivitas 4. efektifitas 5. mempermudah pekerjaan 6. bermanfaat 3. penerimaan sistem variabel ini merupakan konstruk endogen untuk mengetahui pengaruh antara konstruk kemudahan penggunaan persepsian dan konstruk kemanfaatan persepsian terhadap penerimaan sistem aplikasi ojek online. tabel 3. item-item konstruk penerimaan sistem dimensi no. indikator penerimaan sistem 1. pengguna selalu menggunakan 2. selalu mengakses 3. kepuasan pengguna kuesioner suatu teknik pengumpulan data dengan mengajukan beberapa pernyataan secara tertulis dan diberikan langsung kepada responden (sugiyono, 2010). dalam penelitian ini kuesioner yang digunakan berbentuk skala likert. kuesioner dilakukan dengan membagikan 100 responden yang menggunakan jasa aplikasi ojek online. data yang diperoleh dari kuesioner ini akan diolah dengan perhitungan persentase, yaitu melihat berapa persentase responden dengan skor sebagai berikut: sangat setuju (ss) = 5 setuju (s) = 4 netral (n) = 3 tidak setuju (ts) = 2 sangat tidak setuju (sts) = 1 c. metode pengumpulan data, populasi dan sample penelitian metode pengumpulan data dalam penelitian ini, metode pengumpulan data menggunakan riset lapangan. riset lapangan digunakan untuk mengumpulkan data dari responden. pengumpulan data dilapangan dilakukan dengan menggunakan kuesioner yang diberikan kepada responden secara langsung. populasi dan sample penelitian 1. populasi populasi dalam penelitian ini adalah seseorang yang di ambil secara acak untuk di jadikan sampel penelitian atau seseorang yang sudah dikenal oleh penulis yang kemudian di jadikan sampel dalam penelitian ini. populasi dalam penelitian ini sebanyak 10.000.000 orang mengunduh aplikasi ojek online dan diperoleh dari play store 2017. 2. sample penelitian dalam penelitian ini teknik sampling yang digunakan adalahteknik simple random sampling karena sampel diambil secara acak tanpa memperhatikan strata yang ada dalam populasi tersebut.untuk menentukan jumlah besaran sampel penulis menggunakan rumus slovin dengan batas toleransi kesalahan sebesar 10%. berikut adalah rumus sampel yang akan dipakai : 𝑛 = 𝑁 1+𝑁 𝑒2 ............................................................................ (1) dimana: n = ukuran sampel; n = ukuran populasi; e = batas toleransi kesalahan 𝑛 = 10.000.000 1 + 10.000.000 (0,10)2 𝑛 = 10.000.000 100.001 𝑛 = 99,9 = 100 jumlah sampel yang dibutuhkan dari hasil perhitungan menggunakan rumus slovin adalah sebesar 99,9 yang kemudian dibulatkan menjadi 100. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 142 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional d. metode analisis data metode analisis data yang digunakan dalam penelitian ini menggunakan analisis persamaan regresi linier ganda, uji asumsi klasik (normalitas, multikolinearitas, dan heterokedastisitas), koefisien determinasi, uji f, dan uji t. hasil penelitian dan pembahasan a. gambaran umum responden penelitian ini akan menjelaskan tentang hubungan antara variabel kemanfaatan persepsian dan kemudahan persepsian terhadap penerimaan sistem pada aplikasi ojek online di indonesia. populasi pada penelitian ini adalah masyarakat dki jakarta yang menggunakan aplikasi ojek online. sampel yang diambil pada penelitian ini adalah 100 orang. di dalam penelitian ini, akan dilihat karakteristiknya melalui jenis kelamin, usia dan pekerjaan. hal ini agar dapat mengetahui gambaran secara umum pada pengguna aplikasi ojek online. 1. profil responden berdasarkan jenis kelamin tabel 4. jenis kelamin jenis kelamin frekuensi persentase (%) laki-laki 36 36% perempuan 64 64% total 100 100% tabel 4 menjelaskan bahwa dari 100 responden yang ada responden yang paling banyak menggunakan aplikasi ojek online pada kota dki jakarta adalah responden yang berjenis kelamin perempuan yaitu sebesar 64% atau berjumlah 64 responden, sedangkan responden laki-laki sebesar 36% yang berjumlah 36 responden. dari informasi tersebut, maka dapat disimpulkan bahwa rata-rata yang menjadi responden pengguna aplikasi ojek online adalah perempuan daripada responden laki-laki. 2. profil responden berdasarkan usia tabel 5. usia usia frekuensi persentase (%) 17 – 20 tahun 31 31% 21 – 30 tahun 48 48% ≥ 30 tahun 21 21% total 100 100% dari tabel 5, dapat dijelaskan bahwa responden yang paling banyak menggunakan aplikasi ojek online dalam penelitian ini adalah mereka yang berusia antara 21 – 30 tahun dengan persentase 48% atau sebanyak 48 responden, responden berusia 17 – 20 tahun dengan persentase 31% atau sebanyak 31 responden dan yang lainnya adalah responden dengan berusia lebih dari atau sama dengan 30 tahun dengan persentase 21% atau sebanyak 21 responden. dapat disimpulkan bahwa rata-rata yang menjadi responden penelitian adalah responden yang berusia antara 21 – 30 tahun yaitu sebanyak 48 responden. hal ini dikarenakan menurut fredereca dan chairy mengatakan usia 21 – 30 tahun di katakan mampu mengenali suatu kebutuhan barang atau jasa, serta mampu mengambil keputusan untuk memilih suatu produk/jasa dan dapat memahami mengenai informasi yang di sajikan . 3. profil responden berdasarkan pekerjaan tabel 6. pekerjaan pekerjaan frekuensi persentase (%) pelajar/mahasiswa 21 21% wiraswasta 20 20% karyawan 38 38% ibu rumah tangga 16 16% lainnya 5 5% total 100 100 berdasarkan tabel 6, dapat dilihat bahwa dari 100 responden yang ada, responden yang berstatus pelajar/mahasiswa memiliki persentase sebesar 21% atau sebanyak 21 responden. untuk responden dengan pekerjaan wiraswasta mempunyai persentase sebesar 20% atau sebanyak 20 responden. untuk responden dengan pekerjaan sebagai karyawan memiliki persentase yang cukup tinggi di bandingkan dengan jenis pekerjaan lainnya sebesar 38% atau sebanyak 38 responden. responden dengan pekerjaan sebagai ibu rumah tangga memiliki persentase sebesar 16% atau sebanyak 16 responden. sedangkan responden dengan pekerjaan lainnya mempunyai persentase 5% atau sebanyak 5 responden. dari data diatas dapat disimpulkan bahwa persentase yang paling tinggi untuk tingkat pekerjaan terdapat pada pekerjaan karyawan. maka dari informasi yang telah dikumpulkan dapat dikatakan bahwa dalam penelitian ini responden dengan pekerjaan karyawan yang lebih banyak menggunakan aplikasi ojek online guna memudahkan aktifitas bekerja mereka. b. uji kualitas data untuk menguji kualitas data penelitian, maka akan dilakukan uji validitas untuk melihat kesesuaian butir pertanyaan kuesioner dan uji http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 143 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional reliabilitas untuk menguji tingkat kepercayaan dari butir pertanyaan kuesioner agar dapat dihandalkan. 1. uji validitas data sampel sebesar 100 responden maka df (degree of freedom) = n-2 adalah sebesar 98 dengan tingkat signifikan 5%, maka diperoleh r tabel yaitu 0,1966. pada tabel 7, menjelaskan bahwa dari pengujian validitas yang telah dilakukan untuk variabel kemudahan persepsian, kemanfaatan persepsian dan penerimaan sistem dengan total pernyataan/pertanyaan sebanyak 15 butir. dari 15 butir yang ada, 6 butir pernyataan untuk variabel kemudahan persepsian, 6 butir pernyataan untuk variabel kemanfaatan persepsian, 3 butir pernyataan untuk variabel penerimaan sistem. hasil pengujian adalah diperoleh data yang valid adalah sebanyak 15 pernyataan. dari 15 butir pernyataan yang ada, maka dapat dibuktikan dengan membandingkan r hitung dan r tabelnya, karena semuanya > 0,1966 maka semua data yang telah diuji dinyatakan valid, sehingga dapat dilanjutkan ke proses pengolahan data selanjutnya. tabel 7. uji validitas data 100 responden variabel item r hitung r tabel keterangan kemudahan persepsian kp1 0,384 0,1966 valid kp2 0,631 0,1966 valid kp3 0,750 0,1966 valid kp4 0,643 0,1966 valid kp5 0,719 0.1966 valid kp6 0,834 0,1966 valid kemanfaatan persepsian kep1 0,793 0,1966 valid kep2 0,719 0,1966 valid kep3 0,811 0,1966 valid kep4 0,841 0.1966 valid kep5 0,806 0.1966 valid kep6 0,672 0,1966 valid penerimaan sistem ps1 0,735 ,1966 valid ps2 0,767 0,1966 valid ps3 0,816 0,1966 valid 2. uji reliabilitas dalam penelitian ini menggunakan analisis cronbach’s alpha.. tabel 8. uji reliabilitas data 100 responden variabel cronbach’s alpha nilai alpha minimal n of items keterangan kemudahan persepsian 0,830 0,600 6 reliabel kemanfaatan persepsian 0,863 0,600 6 reliabel penerimaan sistem 0,660 0,600 3 reliabel suatu kuesioner dapat dinyatakan reliabel apabila nilai cronbach’s alpha memiliki hasil yang positif. n of items (number of items) artinya adalah jumlah pertanyaan kuesioner yang peneliti lakukan yaitu totalnya berjumlah 21 butir pertanyaan yang mencakup 3 variabel yaitu kemudahan persepsian, kemanfaatan persepsian dan penerimaan sistem. dari informasi yang tersedia pada tabel 8, maka dapat diketahui bahwa nilai cronbach’s alpha untuk variabel kemudahan persepsian adalah 0,830 dengan 6 butir pertanyaan, nilai cronbach’s alpha untuk variabel kemanfaatan persepsian adalah 0,863 dengan 6 butir pertanyaan, nilai cronbach’s alpha untuk variabel penerimaan sistem adalah 0,660 dengan 3 butir pertanyaan. dengan demikian, dapat simpulkan bahwa butir pertanyaan/pernyataan kuesioner ini realiabel karena semua butirnya lebih besar dari 0,1654 atau cronbach’s alpha memiliki hasil yang positif dan semua variabel memiliki alpha > 0,600. ini berarti data hasil kuesioner dapat dipercaya. uji asumsi klasik penelitian ini akan menggunakan uji normalitas, uji multikolinieritas dan uji heteroskedastisitas dalam melakukan pengujian asumsi klasik. a. uji normalitas penelitian ini menggunakan uji kolmogorovsmirnov z untuk menguji normalitas data variabel dengan bantuan software spss 23. berikut hasil uji normalitas yang telah peneliti lakukan : tabel 9. uji normalitas data asymp. sig. (2-tailed) kolmogorov-smirnov test 0,054 0,050 berdasarkan tabel 9, hasil pengujian normalitas data variabel terikat dan variabel bebas keduanya dinyatakan memiliki data berdistribusi normal karena angka signifikasinya yaitu 0,054 lebih besar dari nilai signifikasi (ɑ) yaitu 0,050. b. uji multikolinieritas uji multikolinieritas bertujuan untuk menguji apakah model regresi ditemukan adanya korelasi antar variabel bebas (independent). pengujian multikolinieritas dilakukan dengan melihat nilai dari variance inflation factor (vif) lebih kecil dari 10 dan nilai tolerancenya lebih besar dari 0,10. hal ini berarti tidak terjadi multikolinieritas dalam model regresi. tabel 10. uji multikolinieritas http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 144 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional variabel collinearity statistic tolerance vip kemudahan persepsian 0,254 3,824 kemanfaatan persepsian 0,454 1,328 berdasarkan tabel 10, menunjukkan bahwa variabel kemudahan persepsian memiliki nilai vif sebesar 3,824 dan lebih kecil dari 10 dan nilai tolerancenya 0,254 yang lebih besar dari 0,10 artinya variabel kemudahan persepsian tidak terjadi multikolinieritas. pada variabel kemanfaatan persepsian nilai vif sebesar 1,328 lebih kecil dari 10 dan nilai tolerancenya sebesar 0,454 lebih besar dari 0,10 yang berarti variabel kemanfaatan persepsian tidak terjadi multikolinieritas. uji heteroskedastisitas berikut hasil pengujian heteroskedastisitas yang telah peneliti lakukan : tabel 11. uji heteroskedastisitas variabel sig. kemudahan persepsian 0,291 kemanfaatan persepsian 0,676 berdasarkan tabel 11, diketahui bahwa nilai signifikasi kemudahan persepsian sebesar 0,291 lebih besar dari 0,05 artinya tidak terjadi heteroskedastisitas pada variabel kemudahan persepsian. variabel kemanfaatan persepsian diketahui nilai signifikasinya adalah 0,676 lebih besar dari 0,05 yang artinya tidak terjadi heteroskedastisitas. analisis regresi linier berganda tabel 12. uji regresi linier berganda variabel b t sig. constant 9,786 8,314 0,000 kemudahan persepsi 0,263 3,024 0,003 kemanfaatan persepsi 0,108 1,495 0,033 model persamaan regresi yang dapat dituliskan dari hasil tersebut dalam bentuk : y = ɑ + b1x1 + b2x2 + e y = 9,786 + 0,263 x1 + 0,108 x2 + e persamaan regresi tersebut dapat dijelaskan sebagai berikut : a) konstanta positif sebesar 9,786 memiliki arti bahwa penerimaan sistem akan bertambah sebesar 9,786 jika variabel kemudahan persepsian dan kemanfaatan persepsian adalah konstan. b) koefisien regresi variabel kemudahan persepsian yang positif sebesar 0,263 menyatakan bahwa setiap adanya penambahan konstanta sebesar 1 maka akan menambah kepuasan pelanggan sebesar 0,263. c) koefisien regresi variabel kemanfaatan persepsian yang positif sebesar 0,108 menyatakan bahwa setiap adanya penambahan konstanta sebesar 1 maka akan menambah kepuasan pelanggan sebesar 0,108. uji hipotesis 1. uji statistika t hasil pengujian nilai t digunakan untuk mengetahui apakah secara parsial kemudahan persepsian dan kemanfaatan persepsian berpengaruh secara signifikan atau tidak terhadap variabel penerimaan sistem. a. pada tabel 12 untuk variabel kemudahan persepsian menunjukkan nilai t = 3,024 dengan nilai signifikansi 0,003 < 0,05. dengan demikian diambil kesimpulan bahwa variabel kemudahan persepsian memiliki pengaruh yang signifikan terhadap penerimaan aplikasi ojek online di indonesia dengan sistem tam (technology acceptance model) b. pada tabel 12 untuk variabel kemanfaatan persepsian menunjukkan nilai t = 1,495 dengan nilai signifikansi 0,033 < 0,05. dengan demikian diambil kesimpulan bahwa variabel kemanfaatan persepsian memiliki pengaruh yang signifikan terhadap penerimaan aplikasi ojek online di indonesia dengan sistem tam (technology acceptance model) 2. uji statistika f berdasarkan tabel 13, diperoleh hasil uji f yang berfungsi untuk mengetahui bagaimanakah pengaruh semua variabel independen terhadap variabel dependen secara bersamaan. berdasarkan hasil tabel diperoleh fℎ𝑖tu𝑛𝑔 sebesar 3,080 dengan tingkat signifikansi 0,005 dibawah dari nilai signifikansi 0,05. maka dapat dikatakan model regresi dapat dipakai untuk memprediksi variabel penerimaan sistem sebagai variabel dependen dipengaruhi oleh kemudahan persepsian dan kemanfaatan persepsian secara bersamaan. tabel 13. uji f model df f signifikansi 1 regression 2 3,080 0,005 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 145 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional residual 97 total 99 maka rumusan hipotesis penelitian ini adalah : ho : tidak terdapat pengaruh secara signifikan antara kemudahan persepsian dan kemanfaatan persepsian secara bersama-sama terhadap penerimaan sistem tam (technology acceptance model). h1 : terdapat pengaruh secara signifikan antara kemudahan persepsian dan kemanfaatan persepsian secara bersama-sama terhadap penerimaan sistem tam (technology acceptance model) koefisien determinasi berdasarkan tabel 14, diperoleh koefisien korelasi sebesar 0,409 yang artinya terdapat pengaruh yang kuat antara kemudahan persepsian dan kemanfaatan persepsian terdahap penerimaan sistem aplikasi ojek online. nilai adjusted r square yang diperoleh sebesar 0,142 (14,2%) menunjukkan bahwa variabel penerimaan sistem dipengaruhi oleh variabel kemudahan persepsian dan kemanfaatan persepsian sebesar 14,2%, sedangkan sisanya 85,8% dipengaruhi oleh variabel lain diluar penelitian ini. tabel 14. koefisien determinasi r adjusted r square 0,409 0,142 pembahasan 1. kemudahan persepsian terhadap penerimaan sistem pengujian yang telah dilakukan pada regresi berganda dengan menggunakan uji t menunjukkan kemudahan persepsian berpengaruh secara positif dan signifikan terhadap penerimaan sistem. hal ini ditunjukkan dengan nilai t = 3,024 dengan nilai signifikansi 0,003 yang lebih kecil dari 0,05, dengan demikian hipotesis pertama diterima. nilai positif dapat diartikan bahwa apabila pengaruh kemudahan meningkat maka penerimaan sistem aplikasi ojek online juga akan meningkat. hal ini juga mendukung keenam indikator kemudahan persepsian menurut fatmawati (2015) yaitu, indikator pertama dengan memiliki tingkat rℎ𝑖tu𝑛𝑔 sebesar 0,384 dimana lebih besar dari rtabel, indikator kedua dengan tingkat rℎ𝑖tu𝑛𝑔 sebesar 0,631, indikator ketiga dengan rℎ𝑖tu𝑛𝑔 sebesar 0,750, indikator keempat sebesar 0,643, indikator kelima dengan rℎ𝑖tu𝑛𝑔 sebesar 0,719 dan indikator keenam sebesar 0,834 dinyatakan valid. pada pengujian reliabilitas dengan menggunakan analisis cronbach’s alpha menunjukkan bahwa variabel kemudahan persepsian bernilai positif sebesar 0,830 dengan 6 butir pernyataan yang dapat dipercaya. pada pengujian asumsi klasik dengan menggunakan uji normalitas, uji multikolinieritas dan uji heteroskedastisitas. nilai variabel kemudahan persepsian dengan pengujian multikolinieritas menunjukkan bahwa nilai tolerance 0,254 dimana lebih besar dari 0,10 serta nilai variance inflation factor (vif) 3,824 dimana lebih kecil dari 10 dengan ini variabel kemudahan persepsian tidak menunjukkan adanya multikolinieritas. variabel kemudahan persepsian juga menunjukkan bahwa tidak terjadinya heteroskedastisitas dimana nilai signifikasi sebesar 0,291 lebih besar dari 0,05. 2. kemanfaatan persepsian terhadap penerimaan sistem pengujian yang telah dilakukan pada variabel kemanfaatan persepsian menunjukkan bahwa keenam indikator dinyatakan valid dan dapat dilanjutkan pada pengujian selanjutnya (fatmawati, 2015). terlihat pada uji validitas data dimana indikator pertama sebesar 0,793, indikator kedua sebesar 0,719, indikator ketiga sebesar 0,811, indikator keempat sebesar 0,841, indikator kelima sebesar 0,806 dan indikator keenam sebesar 0,672 masing-masing indikator menunjukkan bahwa nilai rℎ𝑖tu𝑛𝑔 lebih besar dari 0,1966 yang merupakan nilai rtabel. pada uji reliabilitas data nilai pada variabel kemanfaatan persepsian sebesar 0,863 dimana nilai alpha minimal 0,600. keenam indikator kemanfaatan persepsian dinyatakan reliabel atau dapat dipercaya karena nilai cronbach’s alpha yang bernilai positif dan lebih besar dari rtabel. pada pengujian asumsi klasik didapat nilai tolerance untuk uji multikolinieritas sebesar 0,454 serta nilai variance inflation factor (vif) sebesar 1,328 dengan ini menunjukkan bahwa tidak terjadinya multikolinieritas pada variabel kemanfaatan persepsian. pada pengujian heteroskedastisitas menunjukkan bahwa nilai pada variabel kemanfaatan persepsian sebesar 0,676 lebih besar dari nilai 0.05. dari hasil uji t menyatakan bahwa variabel kemanfaatan persepsian memiliki pengaruh secara signifikan terhadap penerimaan sistem berdasarkan hasil output spss yang menunjukan bahwa thitung 1,495 lebih besar dari ttabel 1,661 dan signifikasi dibawah dari 0,05 yaitu sebesar 0,000. sementara pada hasil perhitungan koefisien determinasi dapat disimpulkan bahwa variabel independen dalam penelitian ini mampu menerangkan nilai adjusted r square yang diperoleh sebesar 0,142 (14,2%) menunjukkan bahwa variabel penerimaan sistem dipengaruhi http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 146 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional oleh variabel kemudahan persepsian dan kemanfaatan persepsian sebesar 14,2%, sedangkan sisanya 85,8% dipengaruhi oleh variabel lain diluar penelitian ini. berdasarkan uji f hasil tabel diperoleh fℎ𝑖tu𝑛𝑔 sebesar 3,080 dengan tingkat signifikansi 0,005 dibawah dari nilai signifikansi 0,05. maka dapat dikatakan model regresi dapat dipakai untuk memprediksi variabel penerimaan sistem sebagai variabel dependen dipengaruhi oleh kemudahan persepsian dan kemanfaatan persepsian secara bersamaan. referensi fahmi, m., umyati, u., riyanto, b., & basuki, k. h. (2015). pemodelan pemilihan moda dengan metode stated preference, studi kasus perpindahan dari sepeda motor ke brt rute semarang – kendal. jurnal karya teknik sipil, 4(4), 343–352. retrieved from https://ejournal3.undip.ac.id/index.php/jkts /article/view/10318 fatmawati, e. (2015). technology acceptance model (tam) untuk menganalisis penerimaan terhadap sistem informasi di perpustakaanm informasi perpustakaan. iqra’: jurnal perpustakaan dan informasi, 9(1), 1–13. https://doi.org/10.30829/iqra.v9i1.66 rachman, t. (2015). kerugian akibat macet di jakarta capai rp 65 triliun per tahun | republika online. retrieved august 13, 2017, from https://www.republika.co.id/berita/nasiona l/jabodetabeknasional/15/05/22/noqqrokerugian-akibat-macet-di-jakarta-capai-rp65-triliun-pertahun sengkey, s. l., jansen, f., & wallah, s. e. (2011). tingkat pencemaran udara co akibat lalu lintas dengan model prediksi polusi udara skala mikro. jurnal ilmiah media engineering, 1(2). retrieved from https://ejournal.unsrat.ac.id/index.php/jime /article/view/4218 soebali, l. f., & mahendra, i. (2017). analisa faktor-faktor yang mempengaruhi penerimaan dan penggunaan aplikasi go-jek menggunakan unified theory of acceptance and use of technology (utaut). jurnal pilar nusa mandiri, 13(1), 136–144. https://doi.org/10.33480/pilar.v13i1.348 soesilowati, e. (2008). dampak pertumbuhan ekonomi kota semarang terhadap kemacetan lalulintas di wilayah pinggiran dan kebijakan yang ditempuhnya. jejak: jurnal ekonomi dan kebijakan, 1(1). https://doi.org/10.15294/jejak.v1i1.1447 sugiyono. (2010). metode penelitian bisnis. bandung: alfabeta. zuraya, n. (2016). dishub: kemacetan di dki jakarta sebabkan kerugian rp 150 triliun. retrieved march 1, 2017, from https://www.republika.co.id/berita/nasiona l/jabodetabeknasional/16/04/25/o66jqo383-dishubkemacetan-di-dki-jakarta-sebabkankerugian-rp-150-triliun http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 325 determination of priorities of elementary school rehabilitation at asahan using simple addictive weight dian aprillia1*), william ramdhan2, wan mariatul kifti3 jurusan informasi stmik royal kisaran, kabupaten asahan, indonesia https://stmikroyal.ac.id/ daprillia827@gmail.com1*), william.ramdhan052@gmail.com2, kifti.inti@gmail.com3 (*)corresponding author abstract in the budgeting process for school building rehabilitation activities in asahan regency, there are still inaccuracies in selecting prioritized primary schools for rehabilitation. this study aimed to apply the simple additive weighting (saw) method to determine five primary schools that were prioritized for repair. this research method uses quantitative methods. the data source comes from the east kisaran and west kisaran elementary schools. the data were analyzed using the saw method based on the criteria weight depending on the matrix value and normalization. the results showed the 5 largest criteria weights, namely uptd sdn 010097 selawan (0.940), uptd sdn 014689 lestari (0.884), uptd sdn 010039 sentang (0.880), sd taman kasih karunia (0.847), and uptd sdn 018453 siumbut-umbut (0.820). ). this study concluded that the double exponential smoothing method could make it easier to determine which primary school decisions are prioritized for rehabilitation. keywords: decision support; elementary school; priority; rehabilitation; simple additive weighting abstrak proses penganggaran kegiatan rehabilitasi gedung sekolah di kabupaten asahan masih terdapat ketidak tepatan dalam pemilihan sekolah dasar yang diprioritaskan untuk direhabilitasi. tujuan penelitian ini adalah menerapkan metode simple additive weighting (saw) untuk menentukan 5 sekolah dasar yang diprioritaskan untuk direhabilitasi. metode penelitian ini menggunakan metode kuantitatif. sumber data berasal dari data sekolah dasar daerah kisaran timur dan kisaran barat. data dianalisis menggunakan metode saw berdasarkan bobot kriteria yang tergantung dari nilai matriks dan normalisasi. hasil penelitian menunjukkan 5 bobot kriteria terbesar, yaitu uptd sdn 010097 selawan (0,940), uptd sdn 014689 lestari (0,884), uptd sdn 010039 sentang (0,880), sd taman kasih karunia (0,847), dan uptd sdn 018453 siumbut-umbut (0,820). penelitian ini menyimpulkan bahwa metode double exponential smoothing dapat mempermudah menentukan keputusan sekolah dasar yang diprioritaskan untuk direhabilitasi. kata kunci: dukungan keputusan; sekolah dasar; prioritas; rehabilitasi; pembobotan aditif sederhana introduction school facilities and infrastructure are components of education, which is also the main problem faced by schools (wardani, 2021). it is due to the limitations of school facilities and the lack of good management from the manager, such as damaged school buildings, inadequate learning media, and lack of classrooms so that there is one study group placed in a multimedia room that is not by the standard of classroom size. (sahid & rachlan, 2019). lack of planning in the procurement of facilities so that procurement activities often occur that do not match the specifications needed by users, uneven distribution of facilities, and lack of care and maintenance of existing infrastructure facilities(sahid & rachlan, 2019). damaged school buildings can affect the quality of education for students because children are psychologically not comfortable studying in buildings that are almost collapsed (bustari, 2016). in budgeting for school building rehabilitation activities in asahan regency, there are often inaccuracies in selecting schools that need to be rehabilitated, considering that currently, the rehabilitation of primary schools is only based on mailto:william mailto:kifti.inti@gmail.com3 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 jurnal riset informatika vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 326 the level of the worst damage. the factors that cause inaccuracy in budgeting are the absence of an accurate database of school conditions and a comprehensive system for determining the priority scale for handling school building maintenance. so far, the determination of the priority scale for handling school building maintenance only focuses on the criteria for the level of damage. schools that should be more deserving of maintenance but do not receive care. in other cases, the status of the land is not clear, but it is receiving rehabilitation. as a result, there is often an inaccuracy in determining the priority of handling the maintenance of school buildings that really must be rehabilitated, considering that currently, the rehabilitation of primary schools is only based on the level of the worst damage(mulyadi,2019). the decision support system can be used as a tool to make a decision on which primary school is the priority for rehabilitation (prasetia,2019), so it is hoped that it can help the asahan district education office in making policy decisions, to obtain valid, objective and reliable information about elementary schools that are priority rehabilitation. the simple additive weighting (saw) method is a decision support system that can select the best alternative from several other options because of the ranking process after determining the weight for each attribute. the simple additive weight (saw) method is often also known as the weighted addition method. the basic concept of the simple additive weighting (saw) method is to find the weighted sum of the performance ratings for each alternative on all attributes. the simple additive weighting (saw) method is recommended to solve the selection problem in a multi-process decision-making system. the simple additive weighting (saw) method is a method that is widely used in decision-making that has many attributes(frieyadie, 2016)(lubis & fadil, 2020). this study aims to apply the saw method to objectively determine priority primary schools for rehabilitation in asahan. research methods type of research this type of research is quantitative research. time and place of research this research was conducted from february 2022 to june 2022. the study was conducted at the department of education in the head of profile. jl ahmad yani, kisaran naga. procedures 1. problem identification problem identification is the first step in applying simple additive weighting. problem identification aims to determine the appropriate data to be analyzed using the simple additive weighting method. 2. method, source, and data collecting this research method is qualitative. the data used in this study is the data of the east kisaran and west kisaran regional elementary schools. the techniques used for data collection include the following: a) field research in field research, researchers directly visit the research site and take the data needed for research. the field research was conducted using direct interviews with the principals of kisaran timur and kisaran barat elementary schools. b) literature research literature research is carried out by collecting references from journals or academic books related to the problems discussed and used as support for comparisons in thesis completion. 3. data collecting at this stage, the data obtained is processed into new information that is easier to understand. 4. data analysis after the data is processed, the system is analyzed using the saw method based on the matrix value, normalization, and the number of weights as parameters in making decisions. result and discussion the decision support system is interactive, helping decision-making through data and decision models to solve semi-structured and unstructured problems. the basic concept of the simple additive weighting method is to find the weighted sum of the performance ratings for each alternative on all attributes(resti, 2017). the problems identified were the problems faced by the asahan district education office. namely, the assessment team's selection of primary schools prioritized for rehabilitation was still carried out manually, so it was inefficient to use the budget because every performance assessment always carried out procurement and doubling instruments. in addition, there is much interest in providing an evaluation of the selection of primary schools as a priority for rehabilitation so that the assessment is not carried out transparently. a decision support system, namely saw, is needed to jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 327 overcome these obstacles. the data analyzed in this study are referred to as criteria data which can be seen in table 1. table 1. criteria no. alternative criteria 1. c1 building age 2. c2 number of students 3. c3 operational permit 4. c4 rate of damage 5. c5 facilities after the criteria data was determined, the criteria conversion was carried out. conversion of standards is the value of the existing criteria for the calculation process. values in the conversion criteria consist of 1 to 5. conversion criteria can be seen in tables 2, 3, and 4. table 2. conversion of building age criteria building age (years) value >6 5 5-6 4 4-5 3 3-4 2 1-2 1 table 3. conversion of student criteria number of students value >200 5 151-200 4 101-150 3 51-100 2 10-50 1 table 4. conversion of criteria for operational permits operational permits (month) value 49-60 5 37-48 4 25-36 3 13-24 2 0-12 1 table 5. conversion of damage level criteria rate of damage value worst (>50%) 5 poor (41%-50%) 4 pretty good ( 31% 40%) 3 good (21% 30%) 2 very good (10 % 20%) 1 table 6. facilities criteria facilities (number of building) value 1-5 5 5-10 4 10-15 3 15-20 2 >20 1 after the conversion of criteria is carried out, the standard weights are determined. see tables 5 and 6, which are useful for describing the criteria' importance. the importance of the requirements can be seen in table 7. table 7. criteria weight alternative criteria weight attribute c1 building age 5 benefit c2 number of students 2 benefit c3 operational permit 4 benefit c4 rate of damage 3 benefit c5 facilities 1 benefit furthermore, the name of the education unit is determined as the data to be decided by the saw method. the decision by the saw method is based on the value of the decision matrix. the value of the decision matrix is the value of each alternative against each criterion. the value is based on the value of the previously converted criteria. decision makers provide alternative values based on the level of importance of each criterion needed (setiawan, 2017). the saw method requires normalizing the decision matrix to a scale that can be compared with all available alternative ratings. the decision matrix can be seen in table 8. table 8. decision matrix value code alternative c1 c2 c3 c4 c5 a1 sds it ar-roja 3 4 4 3 3 a2 sd taman kasih karunia 5 4 4 4 4 a3 uptd sdn 010039 sentang 4 5 4 5 2 a4 uptd sdn 010086 selawan 5 3 3 5 3 a5 uptd sdn 010087 selawan 3 3 4 4 2 a6 uptd sdn 010088 selawan 4 4 4 5 4 a7 uptd sdn 010093 selawan 5 3 4 3 3 a8 uptd sdn 010096 karang anyer 2 3 4 4 4 a9 uptd sdn 010097 selawan 5 4 5 5 4 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 jurnal riset informatika vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 328 code alternative c1 c2 c3 c4 c5 a10 uptd sdn 013849 siumbut-umbut baru 3 3 3 5 4 a11 uptd sdn 013853 selawan 4 4 4 5 4 a12 uptd sdn 013854 selawan 3 3 3 4 4 a13 uptd sdn 013855 selawan 2 4 4 4 4 a14 uptd sdn 013856 selawan 5 5 3 3 4 a15 uptd sdn 014671 sentang 3 5 4 3 4 a16 uptd sdn 014685 siumbut baru 4 5 3 4 3 a17 uptd sdn 014689 lestari 5 5 4 4 3 a18 uptd sdn 015921 kedai ledang 3 4 4 3 3 a19 uptd sdn 017108 sentang 4 3 5 4 3 a20 uptd sdn 018065 teladan 3 4 2 5 3 a21 uptd sdn 018453 siumbut-umbut 5 3 4 4 4 a22 sd harapan bunut 2 4 2 3 3 a23 sd islam manbaul hidayah 3 3 3 5 3 a24 sd swasta al washliyah 74 sidomukti 1 4 4 5 4 a25 sd taman siswa sidodadi 4 2 5 3 3 a26 sd tpi kisaran 3 3 3 3 4 the saw method requires the process of normalizing the decision matrix (x) to a scale that can be compared with all available alternative ratings(susilowati et al., 2019)(pratama et al.,, 2017)(buraerah, 2020). the calculation of the normalization matrix starts from the values that have been collected from each alternative and its criteria. normalization of this matrix is used to find the value of the performance rating on each criterion (wiyono, 2017). previous studies used the decision and normalization matrix to determine the ranking(mulyati, 2016). normalization matrix values can be seen in table 9. table 9. normalization matrix value elementary school (c1) (c2) (c3) (c4) (c5) sds it ar-roja 0,6 0,8 0,8 0,6 0,667 sd taman kasih karunia 1 0,8 0,8 0,8 0,5 uptd sdn 010039 sentang 0,8 1 0,8 1 1 uptd sdn 010086 selawan 1 0,6 0,6 1 0,667 uptd sdn 010087 selawan 0,6 0,6 0,8 0,8 1 uptd sdn 010088 selawan 0,8 0,8 0,8 1 0,5 uptd sdn 010093 selawan 1 0,6 0,8 0,6 0,667 uptd sdn 010096 karang anyer 0,4 0,6 0,8 0,8 0,5 uptd sdn 010097 selawan 1 0,8 1 1 0,5 uptd sdn 013849 siumbut-umbut baru 0,6 0,6 0,6 1 0,5 elementary school (c1) (c2) (c3) (c4) (c5) uptd sdn 013853 selawan 0,8 0,8 0,8 1 0,5 uptd sdn 013854 selawan 0,6 0,6 0,6 0,8 0,5 uptd sdn 013855 selawan 0,4 0,8 0,8 0,8 0,5 uptd sdn 013856 selawan 1 1 0,6 0,6 0,667 uptd sdn 014671 sentang 0,6 1 0,8 0,6 0,5 uptd sdn 014685 siumbut baru 0,8 1 0,6 0,8 0,667 uptd sdn 014689 lestari 1 1 0,8 0,8 0,667 uptd sdn 015921 kedai ledang 0,6 0,8 0,8 0,6 0,667 uptd sdn 017108 sentang 0,8 0,6 1 0,8 0,667 uptd sdn 018065 teladan 0,6 0,8 0,4 1 0,667 uptd sdn 018453 siumbut-umbut 1 0,6 0,8 0,8 1 sd harapan bunut 0,4 0,8 0,4 0,6 0,667 sd islam manbaul hidayah 0,6 0,6 0,6 1 0,667 sd swasta al washliyah 74 sidomukti 0,2 0,8 0,8 1 0,5 sd taman siswa sidodadi 0,8 0,4 1 0,6 0,667 sd tpi kisaran 0,6 0,6 0,6 0,6 0,5 after obtaining the normalized matrix value, the number of weights is calculated by adding the product of the normalized matrix with the weight value. the normalized matrix values can be seen in table 10. table 10. total weight code name weig ht ranki ng a01 sds it ar-roja 0,684 17 a02 sd taman kasih karunia 0,84 7 4 a03 uptd sdn 010039 sentang 0,88 0 3 a04 uptd sdn 010086 selawan 0,818 8 a05 uptd sdn 010087 selawan 0,720 14 a06 uptd sdn 010088 selawan 0,820 6 a07 uptd sdn 010093 selawan 0,791 11 a08 uptd sdn 010096 karang anyer 0,620 24 a09 uptd sdn 010097 selawan 0,94 0 1 a10 uptd sdn 013849 siumbut-umbut baru 0,673 19 jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 329 code name weig ht ranki ng a11 uptd sdn 013853 selawan 0,820 7 a12 uptd sdn 013854 selawan 0,633 22 a13 uptd sdn 013855 selawan 0,647 21 a14 uptd sdn 013856 selawan 0,780 10 a15 uptd sdn 014671 sentang 0,700 15 a16 uptd sdn 014685 siumbut baru 0,764 12 a17 uptd sdn 014689 lestari 0,88 4 2 a18 uptd sdn 015921 kedai ledang 0,684 16 a19 uptd sdn 017108 sentang 0,818 9 a20 uptd sdn 018065 teladan 0,658 20 a21 uptd sdn 018453 siumbut-umbut 0,82 0 5 a22 sd harapan bunut 0,511 26 a23 sd islam manbaul hidayah 0,684 18 a24 sd swasta al washliyah 74 sidomukti 0,620 23 a25 sd taman siswa sidodadi 0,751 13 a26 sd tpi kisaran 0,593 25 based on table 10, 5 elementary schools that deserve rehabilitation are 5 elementary schools with the 5 largest weight values, namely uptd sdn 010097 selawan (0.940), uptd sdn 014689 lestari (0.884), uptd sdn 010039 sentang (0.880), sd taman kasih karunia (0.847), and uptd sdn 018453 siumbut-umbut (0.820). the greater the number of weights, the greater the opportunity (setiadi et al., 2018; topadang et al., 2020). analysis with the saw method uses predetermined criteria to reference the ranking (syam & rabidin, 2019)(helilintar, winarno, & fatta, 2016). the ranking process is the sum of the normalized matrix multiplication r with the preference weight vector so that the largest value is chosen as the best alternative(subagio et al., 2017). the research stage in the application of the saw method consists of determining the criteria that will be used as a reference in decision making, determining the suitability of each alternative for each criterion, making a decision matrix based on the criteria (cj) then normalizing the matrix based on the equation adjusted to the type of attribute so that it can obtain a normalized matrix. (r), and ranking as the final result, by adding the normalized matrix multiplication (r) with the weight vector, the largest value was selected as the best alternative (ermin, sunardi, & fadil, 2020). conclusions and suggestions conclusion the saw method as a decision support system can determine the priority of primary school rehabilitation at the asahan district education office based on the number of weights. the saw method states that 5 elementary schools are entitled to rehabilitation based on the largest number of weights, namely uptd sdn 010097 selawan (0.940), uptd sdn 014689 lestari (0.884), uptd sdn 010039 sentang (0.880), sd taman kasih karunia (0.847), and uptd sdn 018453 siumbutumbut (0.820). suggestion the saw method should also be compared with other methods to strengthen the decision support system's results. references buraerah, m. f. (2020). penerapan metode simple additive weighting (saw) dalam menentukan karakteristik lahan terbaik untuk tanaman ubi jalar (ipomoea batatas l.). smart comp :jurnalnya orang pintar komputer, 9(2), 80–84. https://doi.org/10.30591/smartcomp.v9i2.1 916 ermin, e., sunardi, s., & fadil, a. (2020). metode simple additive weighting pada penentuan penerimaan karyawan. format : jurnal ilmiah teknik informatika, 8(2), 125. https://doi.org/10.22441/format.2019.v8.i2. 005 frieyadie, f. (2016). penerapan metode simple additive weight (saw) dalam sistem pendukung keputusan promosi kenaikan jabatan. jurnal pilar nusa mandiri, 12(1), 37– 45. https://doi.org/10.33480/pilar.v12i1.257 helilintar, r., winarno, w. w., & fatta, h. al. (2016). penerapan metode saw dan fuzzy dalam sistem pendukung keputusan penerimaan beasiswa. creative information technology journal, 3(2), 89. https://doi.org/10.24076/citec.2016v3i2.68 lubis, d. j., & fadil, n. m. (2020). penerapan metode p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.424 jurnal riset informatika vol. 4, no. 4, september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 330 saw (simple additive weighting) untuk menentukan siswa bermasalah di smk taruna terpadu 2 bogor. teknois : jurnal ilmiah teknologi informasi dan sains, 10(1), 35–44. https://doi.org/10.36350/jbs.v10i1.76 mulyadi, 2019. prosedur penentuan prioritas pemeliharaan gedung sekolah menengah atas negeri di kabupaten balangan. jurnal teknologi berkelanjutan, 8(1), 1-5. mulyati, s. (2016). penerapan metode simple additive weighting untuk penentuan prioritas pemasaran kemasan produk bakso sapi. 1(1), 33–37. pratama, m. n. p., sevtiana, a., & martha, d. (2017). penerapan metode simple additive weighting (saw) pada sistem seleksi penerimaan calon siswa baru (studi kasus: smk negeri 1 cirebon). jurnal digit, 5(2), 159–170. prasetia, m. 2019. sistem pendukung keputusan prioritas rehabilitasi bangunan sdn/min di kabupaten pulang pisau. jurnal teknologi berkelanjutan, 8(2), 41-49. resti, n. c. (2017). penerapan metode simple additive weighting (saw) pada sistem pendukung keputusan pemilihan lokasi untuk cabang baru toko pakan ud. indo multi fish. intensif, 1(2), 102. https://doi.org/10.29407/intensif.v1i2.839 sahid, d.r. & rachlan, e.r. 2019. pengelolaan fasilitas pembelajaran guru dalam meningkatkan mutu pembelajaran pendidikan jasmani di sekolah menengah kejuruan (smk). indonesian journal of education management and administration review, 3(1), 25-39. setiadi, a., yunita, y., & ningsih, a. r. (2018). penerapan metode simple additive weighting(saw) untuk pemilihan siswa terbaik. jurnal sisfokom (sistem informasi dan komputer), 7(2), 104–109. https://doi.org/10.32736/sisfokom.v7i2.572 setiawan, a. (2017). implementasi metode saw dalam penerimaan siswa baru pada sma negeri 16 medan. jurasik (jurnal riset sistem informasi dan teknik informatika), 2(1), 96. https://doi.org/10.30645/jurasik.v2i1.23 sigit eko wiyono, l. (2017). penerapan metode simple additive weighting ( saw ) pada sakinah supermarket untuk. 26(1), 24–28. subagio, r. t., abdullah, m. t., & jaenudin. (2017). penerapan metode saw (simple additive weighting) dalam sistem pendukung keputusan untuk menentukan penerima beasiswa. prosiding saintiks ftik unikom, 2, 61–68. susilowati, t., sucipto, s., nungsiyati, n., kartika, t. a., & zaman, n. (2019). penerapan metode simple additive weighting (saw) pada amri supermarket banjar jaya untuk pemilihan karyawan terbaik. explore: jurnal sistem informasi dan telematika, 10(2). https://doi.org/10.36448/jsit.v10i2.1317 syam, s., & rabidin, m. (2019). metode simple additive weighting dalam sistem pendukung keputusan pemilihan karyawan berprestasi (studi kasus : pt. indomarco prismatama cabang tangerang 1). unistek, 6(1), 14–18. https://doi.org/10.33592/unistek.v6i1.168 topadang, a., irwansyah, i., & safruddin, s. (2020). analisis pemilihan beasiswa kurang mampu pada sekolah dasar katolik hati kudus samarinda. just ti (jurnal sains terapan teknologi informasi), 12(2), 66–72. wardani, s.d.k. 2021. pengelolaan sarana dan prasarana dalam menunjang mutu pembelajaran peserta didik di masa pandemi covid-19. jurnal inspirasi manajemen pendidikan, 9(3), 516-531. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 105 texture feature extraction of pathogen microscopic image using discrete wavelet transform hasan basri information system institut teknologi dan bisnis bank rakyat indonesia jakarta, indonesia http://bri-institute.ac.id/ basrihasan@bri-institute.ac.id abstract this study used a case study of jabon leaves, and the pathogen is one of the causes of disease that attack the leaves of jabon, one of the leaf spots and leaf blight. discovery of leaf spot disease in different pathogens and leaf blight. the pathogen was obtained from the leaf spot of curvularia sp. 1 and pestalotia sp., while the pathogen came from curvularia sp. 2 and botrytis sp. identify the pathogen as soon as possible to minimize its effects. improper handling can lead to increased virulence and resistance to the pathogen. improper handling also can cause a disease outbreak (disease epidemic) in a region. this study is the first step in identifying the pathogens responsible for jabon leaf disease. in this study, the application of koch's postulates method to achieve the purification of pathogens and retrieve the microscopic pathogen image as the data acquisition stage. furthermore, use of the segmentation stage to separate the object pathogen from the background, and one of the methods used is otsu thresholding. the extraction process of pathogen microscopic image using discrete wavelet transform (dwt), dwt extraction results can be obtained using energy and entropy information. keywords: dwt; pathogen; extraction feature abstrak penelitian ini menggunakan studi kasus daun jabon, patogen merupakan salah satu penyebab penyakit yang menyerang daun jabon, salah satunya bercak daun dan hawar daun. penemuan penyakit bercak daun pada patogen yang berbeda begitu pula pada penyakit hawar daun. patogen diperoleh dari bercak daun culvularia sp. 1 dan pestalotia sp., sedangkan patogen yang berasal dari hawar daun culvularia sp. 2 dan botrytis sp. patogen harus diidentifikasi sesegera mungkin untuk meminimalkan efek yang ditimbulkannya. penanganan yang tidak tepat dapat menyebabkan peningkatan virulensi dan resistensi patogen. penanganan yang tidak tepat juga berpotensi menimbulkan wabah penyakit (wabah penyakit) di suatu wilayah. tujuan dari penelitian ini adalah tahap awal untuk mengidentifikasi patogen yang ada pada penyakit daun jabon. pada penelitian ini, metode postulat koch diterapkan untuk mendapatkan purifikasi patogen dan kemudian mengambil citra patogen mikroskopis sebagai tahap akuisisi data. selanjutnya adalah tahap segmentasi yang digunakan untuk memisahkan objek patogen dengan backgroundnya, sedangkan salah satu metode yang digunakan adalah otsu thresholding. proses ekstraksi citra mikroskopis patogen menggunakan discrete wavelet transform (dwt), hasil ekstraksi dwt dapat diperoleh dengan menggunakan informasi energi dan entropi. kata kunci: dwt; pathogen; fitur ekstraksi introduction jabon merah (neolamarckia macrophylla (wall.) as a timber plant has the potential to meet the national timber demand (larekeng, qalbi, rachmat, iswanto, & restu, 2022). several diseases attack the jabon, including root, stem, and leaf disease. most jabon disease is caused by fungi (warisno & dahana k, 2011). the fungus that attacks the leaves of jabon causes spots, blight, and dead buds. the fungus has several properties, one of which is that it causes plant diseases. these fungi are categorized as pathogens (agrios g, 2005). the spread of pathogens is very broad (hadi s, 2001). jabon seeds are among the most vulnerable to pathogen attacks (aisah, 2014). it is necessary to p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 106 handle this as early as possible, so the growth of jabon is not hampered. according to herliyana, the fungi have an abundance of type, only known about 98.998 species, and which are a pathogen number of about 8000 species (herliyana, 2013). herliyana et al. obtained several types of pathogens from red jabon leaves, a pathogen found in spotting diseases, namely pestalotia sp. and rhizoctonia sp., in blight diseases, namely lasiodiplodia theobromae, fusarium sp., colletotrichum sp., marsonia sp., gleosporium sp. 1 and gleosporium sp. 2 (herliyana, sakbani, herdiyeni, & munif, 2020). disease control should be adjusted to consider the type of pathogen. one way to know the pathogen is by conducting the pathogen identification process, which is done by using the aid of microscope magnification (streets, 1972). sudarsan et al. researched microscope image processing (mip) and developed a cwtbased computer vision algorithm to characterize soil particle size from digital images captured with a microscope. the wavelet technique's efficacy in detecting an image's particle size is promising, and the portability of the image acquisition device produces excellent proximate soil sensors (sudarsan, ji, adamchuk, & biswas, 2018). santosh et al. also research mip. the discrete wavelet transform (dwt) is comparable to a microscope because distinct signal components can be distinguished by simply adjusting the focus (santosh & barpanda, 2020). this study, using primary data, collected a 2043 microscopic pathogen image of jabon leaf disease that is spotting and blight, then extracted using the discrete wavelet transform (dwt) method. table 1. morphological and texture features of the pathogen pathogen characteristic culvularia sp 1. is a darkcolored conidia with clear tip cells. 3-to-5-cell conidia, characterized by curving and enlarged central cells. culvularia sp one is derived from jabon leaf spot disease. culvularia sp.2. the difference between culvularia sp. one and culvularia sp.2. in culvularia sp.1, each cell constraint has a light black color, while culvularia sp.2 has two cell boundaries in the center of wider black conidia. the origin pathogen characteristic of the disease is the pathogen of jabon leaf blight. pestalotia sp. has dark conidia with some clear-colored cells. the tip of the cell is tapered, with two or more tails. pestalotia sp. can cause leaf spot disease jabon. botrytis sp. has long-shaped mycelium features, cylindrical, clear color, and irregular branches. conidia surround conidiaphore. color conidia clear or grey, cell 1. botrytis sp. is one of the causes of leaf blight jabon the number of pathogens is very high; therefore, to determine the type of pathogen, a researcher must manually examine the manual to determine the pathogen's characteristics and identify similar characteristics. each image of a pathogen using computer vision can be identified because pathogens have different characteristics s, as shown in table 1. bangun et al. conducted another study to identify the microscopic pathogen image of the white jabon leaf. that is pathogen colletotrichum sp., curvularia sp., and fusarium sp. using morphological characteristic extraction methods and producing the best features of compactness and roundness for classification (bangun, herdiyeni, & herliyana, 2016). figure 1. cutting only one pathogen in the study above by bangun et al., the image's distinguishing features were obtained by cutting only the pathogenic image, as shown in figure 1. however, the entire image cannot be extracted. in this study, we attempted to extract colony image pathogens from a full image without cutting them individually. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 107 other challenges of the microscopic image are complex images, objects that can be stacked, blurred images in part because the objects are stacked, and many other objects that become noise. research methods the method proposed in this research includes three stages: data acquisition, preprocessing, and feature extraction. data acquisition this process was done in the forest pathology laboratory of the silviculture department of the ipb forestry faculty. the sample of jabon leaf studied is a jabon leaf from "persemain permanent ipb." the acquisition phase of this pathogen microscopic image data uses the postulat koch method to obtain pure pathogens (streets, 1972) and then photographed using the optilab camera with 400x. magnification and a maximum resolution of 5 megapixels for the image produced under 1 megabyte. figure 2. stage of image capture and sick leaf selection figure 3. purification of pathogen isolation and pathogen image capture figures 2 and 3 depict the various stages of postulate koch's method for identifying foliar disease-causing pathogens. preprocessing the preprocessing step is to select the image to be extracted, some images with good pathogen image quality are selected, and the next process is to crop the selected image of good quality to remove the overly dominant noise. figure 3. cropping culvularia sp. 1 to remove the dominant noise during postulate koch's, dominant noise occurs due to shooting and several other factors. so as in figure 4, noise that is too dominant must be removed because it will greatly affect the value of the feature extraction of pathogen image features. the image is segmented to get the object of a microscopic pathogen image separated from its background, and the separator of the pathogen microscopic image with its background is one of them using the otsu thresholding method, otsu thresholding is the optimum method of global thresholding (rafael c. gonzalez & woods, 2008). this method maximizes the differences between the two regions using discriminant analysis (otsu & n., 1996) (naga kiran d & kanchana v, 2019). in this part, otsu thresholding will generate a mask to get the microscopic object of the pathogen with black/white background, as in figure 5. figure 5. segmentation stages, create a mask using otsu thresholding figure 6. result of pathogen segmentation p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 108 the method of otsu thresholding is a rapid method of computation. it very well separates microscopic objects of pathogens against the background with the condition that the intensity of the pixel of a pathogen microscope object with its background has a pixel intensity value that is not too close. figure 6 depicts the results of otsu segmentation, which produces a pathogen object image with greater prominence for the subsequent extraction procedure. feature extraction dwt dwt is used to extract image texture characteristics using the scaling function and wavelet function (madhu & kumar, 2022). an image can be extracted to produce four coefficients. 𝑊𝜑 (𝑗0, 𝑚, 𝑛) = 1 √𝑀𝑁 ∑ ∑ 𝑓(𝑥, 𝑦)𝑁−1𝑦=0 𝑀−1 𝑥=0 ∗ 𝜑𝑗0,𝑚,𝑛 (𝑥, 𝑦) ... ......................................................................................... (1) 𝑊𝜓 𝑖 (𝑗, 𝑚, 𝑛) = 1 √𝑀𝑁 ∑ ∑ 𝑓(𝑥, 𝑦)𝑁−1𝑦=0 𝑀−1 𝑥=0 ∗ 𝜓𝑖 𝑗,𝑚,𝑛 (𝑥, 𝑦), 𝑖 = {𝐻, 𝑉, 𝐷} .................................. (2) four coefficients from dwt extraction can be analyzed: ll, lh, hl, and hh. dwt can analyze the information of an object in the image with the method of multiresolution analysis (mra), which was introduced by stephane mallat (rafael c. gonzalez & woods, 2008). mra is used to analyze the depth of information contained in an image. at this stage, the image is processed using dwt with levels 1 – 4 of decomposition (multiresolution). some extraction images using dwt can be seen from the different spectrums of each image, showing that each pathogen has different texture characteristics when extracted using dwt. the ll coefficient shows the approximation of the original image. as for the other results of the ll coefficient, it can reduce or eliminate small noises in the image so that the ll coefficient gives the value of a cleaner image that is close to the pure value of an image. the higher the dwt decomposition level of an image, the ll coefficient is reduced in size and approximates the image approximation. the coefficients of lh, hl, and hh can each show the texture value of an image and the edge of an image based on horizontal, vertical, and diagonal orientation. lh, hl, and hh coefficients at dwt levels increasingly show an edge and image characteristic. as for dwt extraction can be used some family wavelet that is daubechies, symlet, and coiflet. wavelet family is different only on the value of high filter and low filter just in processing an image but can show different results in any retrieval of information contained in the image. figures 7, 8, 9, and 10 display examples of the spectrum images of each pathogen studied. we can see that each studied pathogen has a unique spectrum. figure 7. dwt extraction results from the pathogen coefficients ll, lh, hl, and hh in the culvularia sp. 1 spectrum figure 8. dwt extraction results from the pathogen coefficients ll, lh, hl, and hh in the botrytis sp. spectrum figure 9. dwt extraction results from a pathogen, coefficients ll, lh, hl, and hh in the culvularia sp. 2 spectrum figure 10. dwt extraction results from the pathogen coefficients ll, lh, hl, and hh in the pestalotia sp. spectrum jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 109 image extraction results using dwt then calculated the information contained therein using entropy and energy. energy is the sum of the absolute value of data. the energy can present the texture image characteristic, and the energy can be used to obtain the image character extracted with dwt (tan et al., 2014) (tampinongkol, herdiyeni, & herliyana, 2020). 𝐸 = ∑ |𝑓(𝑛)| 𝑁 𝑛=1 where e represents energy, n represents the amount of data, and f (n) represents the position of data to n. entropy is used to measure pixel diversity in an image to generalize the uncertainty that appears in an image (wang et al., 2015) (widiyanto, sukra, madenda, wardani, & wibowo, 2018). 𝑆 = − ∫ ℎ𝑛 log2(ℎ𝑛 ) 𝐺 𝑛=1 where n is the gray level of some subband, he is the n-probability of the gray level, and g is the total of the gray level. results and discussion extract results using the dwt with symlet family wavelet, which measured information using energy and entropy. energy is used to calculate the coefficients of lh, hl, and hh since that coefficient can represent the texture contained in an image. whereas the ll coefficient is computed using entropy to measure the uniformity of the texture in the approximation image. figure 11. distribution of data of each pathogen (energy) figure 11, the distribution of extracts calculated using wavelet energy, shows an overlap in botrytis sp. and culvularia sp. 2. the largest standard deviation is found in botrytis sp. with values ranging from 925 to 647.2. extreme outliers influence the value with a value of 11121374. as for culvularia sp. 2., the data distribution is heavily piled into other classes of pathogens, which can cause many errors in the class culvularia sp.2. at the same time, the largest outlier is shown in culvularia sp.1. figure 12. distribution of data of each pathogen (entropy) figure 12 shows the distribution of entropy values for the four pathogens and highly stacked data, with the highest distribution seen in botrytis sp. and the largest outliers. there is also the main data distribution between quartile one to quartile third largest culvularia sp. 1 with a range of 1.78 605 greater than the others. conclusions and suggestions conclusion after dwt image feature extraction, the four pathogens (botrytis sp., culvularia sp.1, culvularia sp.2, and pestalotia sp.) exhibited distinctive characteristics. additional findings from this study, object culvularia sp. 1 and culvularia sp. 2. have a pixel intensity that is much different from the pixel background intensity of the image. segmentation using the otsu thresholding method is perfect for such image types. the tail and the tip of pestalotia sp. have a pixel intensity that is not much different from the pixel intensity of the background image, so the otsu thresholding method is not suitable for detecting the tail and the tip of pestalotia sp. suggestion the image used has an image resolution of under 1 megabyte. higher quality is expected to p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.488 jurnal riset informatika vol. 5, no. 1. december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 110 capture more of the texture of a pathogen so that it can capture more accurate images. references agrios g. 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(2011). peluang investasi: jabon tanaman kayu masa depan. jakarta: gramedia pustaka utama. widiyanto, s., sukra, y., madenda, s., wardani, d. t., & wibowo, e. p. (2018). texture feature extraction based on glcm and dwt for beef tenderness classification. 2018 third international conference on informatics and computing (icic), 1–4. ieee. https://doi.org/10.1109/iac.2018.8780569 jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.491 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 149 implementation of the association method in the analysis of sales data from manufacturing companies fachri amsury1, nanang ruhyana2, andry agung riyadi3, ihsan aulia rahman4 information system / faculty of technology and information university of nusa mandiri fachri.fcy@nusamandiri.ac.id data science / faculty of technology and information university of nusa mandiri fachri.fcy@nusamandiri.ac.id1, nanang.ngy@nusamandiri.ac.id2, andriagu1603@nusamandiri.ac.id3, ihsanaulia24@gmail.com4 (*) corresponding author abstract the company produces sales data every day. over time, the data increases, and the amount becomes very large, and the data is only stored without understanding the benefits that exist from these data due to limitations in proper knowledge in analyzing the data, especially transaction data. sale. in order to overcome these problems, a study focused on reprocessing sales transaction data in 2018 with a data mining technique approach using the knowledge discovery in database (kdd) concept using the association method and apriori algorithm and a supporting application, namely rapidminer. this study aims to help companies find customer buying habits or patterns based on 2018 sales transaction data. the results of this study produce 316 association rules where the best rules are generated on record 309 with pro 889 & pro 868 pro 869 rules. keywords: data mining; kdd; asosiasi; algoritma apriori abstrak perusahaan setiap harinya menghasilkan data penjualan, semakin berjalannya waktu data tersebut meningkat dan jumlahnya menjadi sangat besar, data tersebut hanya disimpan tanpa mengerti manfaat yang terdapat dari data-data tersebut, karena keterbatasan dalam pengetahuan yang tepat dalam melakukan analisa pada data tersebut khusunya pada data transaksi penjualan. demi mengatasi permasalahan tersebut dilakukan sebuah penelitian yang berfokus dalam mengolah kembali data transaksi penjualan tahun 2018 dengan pendekatan teknik data mining menggunakan konsep knowledge discovery in database (kdd) menggunakan metode asosiasi dan algoritma apriori, serta menggunakan aplikasi pendukung yaitu rapidminer. tujuan penelitian ini berusaha membantu perusahaan dalam mengetahui kebiasaaan atau pola pembelian pelanggan berdasarkan data transaksi penjualan 2018. hasil penelitian ini menghasilkan 316 aturan asosiasi dimana aturan terbaik dihasilkan pada record 309 dengan aturan pro 889 & pro 868 ⇒ pro 869. kata kunci: data mining; kdd; asosiasi; algoritma apriori introduction industries in the property sector, such as housing, shops, apartments, and so on, are currently experiencing significant progress due to increasing population growth (fadilah, purnama, & zamrudy, 2019). the development of the property industry has also increased the need for building materials. one example of building materials that have an essential role is instant cement. instant cement is a ready-to-use building material with the easy application just by mixing water, and it already contains sand, so there is no need to add other mixed ingredients (susilowati, 2012). the development and competition in the instant cement industry are getting stricter with many competitors and high demand for instant cement products. business people must be able to find solutions and create a marketing strategy so that the business develops and survives (listriani, setyaningrum, & eka, 2018). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 150 companies can use technology to find a solution in formulating an appropriate marketing strategy, such as utilizing their sales transaction data (wijayanti, 2017). companies usually only use transaction data to view stock, calculate the number of sales and calculate monthly profit (rahmawati & merlina, 2018). sales transaction data is mostly only collected and archived without analysis on sales transaction data because the data that is growing increasingly from time to time makes it difficult for companies to utilize the data and not know what to do with it (samuel, sani, budiyantara, ivone, & frieyadie, 2022). use sales transaction data by analyzing the behavior and habits of customers who often buy instant cement products. an approach used is market basket analysis, which t utilization of sales transaction data by analyzing customer behavior and habits regarding what instant cement products are frequently purchased. market basket analysis approach to analyze consumer spending patterns (fitrina, kustanto, & vulandari, 2018). millions of data stored in databases must be applied to the data change process to become helpful information for companies by applying data mining techniques (silvanie, 2020). data mining is a technique that aims to extract information from large data sets to analyze and extract data to gain knowledge (adha, sianturi, & siagian, 201 c.e.). one approach to data mining in various fields is the a priori algorithm (putra, raharjo, sandi, ridwan, & prasetyo, 2019). the apriori algorithm can find all items of association rules in transaction data that meet the minimum and minimum requirements because it is easy to understand, and some research literature has been proposed (lestari & hafiz, 2020). one of the applications of the a priori algorithm in the research conducted by (gumilang, 2021) is the problem faced with providing product data that many customers buy as data and implementing the a priori algorithm on web-based applications. the research results are applications built based on the application of the a priori algorithm consisting of item set selection and association rules. the following research was conducted by (junaidi, 2019). the problem in the company is that it has several data on sales of goods, but using this data is not to develop future sales plans. the solution is to use the frequent pattern growth method, and the company can make decisions in determining products that require more inventory than other products, with a reference threshold of 60% and 90% confidence and paying attention to the relationship between support and confidence. based on the results of observations and interviews with the company's sales management, utilization of sales transaction data is not optimal for determining unique buying habits and patterns of customers when buying more than one type of goods in one transaction by looking at transaction records on the company's internal applications and excel data. the application of data mining and an a priori algorithm approach can be a solution for companies to provide an overview in seeing customer purchasing patterns in order to be able to recommend products that are suitable for customers and as a reference to determine the amount of product production that is most sought after by customers. the transaction data used in this study is sales data for installed cement in 2018 in its application using the rapidminer application in helping to find association rules. the expected results in this study are so that the company can recommend the right product to customers and make a more effective sales strategy in the future, which will come. research methods research using the concept of knowledge discovery in a database to create association rules for instant cement sales transactions (takdirillah, 2020). types of research this study applies a qualitative method. qualitative research has the advantage of gaining a deep and fundamental understanding of the object being observed in a systematic scientific manner. time and place of research the author researched instant cement manufacturing companies to obtain historical instant cement sales transaction data in 2018. research time for two months by conducting research. research target / subject the study aims to apply an a priori algorithm approach to find association rules and generate customer purchasing patterns at instant cement manufacturing companies based on sales transaction data that occurred in the 2018 period. procedure the framework applied is based on the steps and procedures of this research process using knowledge discovery in database. jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.491 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 151 source : (takdirillah, 2020) figure 1. knowledge discovery in database figure 1 describes the stages of the kdd process used to find and identify a pattern in the database. the data source of this research comes from instant cement sales transaction data in 2018. the data is selected and cleaned from noise, and the data is selected and transformed using the data mining process. data mining involves looking for patterns or information from the a priori algorithm approach. the final stage is evaluation, namely translating the patterns generated from data mining and presented in a form that interested parties easily understand. data, instruments, and data collection techniques this study uses primary data from interviews and instant cement sales transaction data from january 2018 – december 2018. secondary data comes from research books and journals related to the research conducted. this study also conducted direct observations of the company to find out information related to existing sales information systems to assist in analyzing customer purchasing patterns and conducted interviews with sales managers regarding the instant cement sales transaction process to find out the workflow of related parties and obtain the required data. data analysis technique data analysis in figure 2 describes the stages of the research carried out as follows: 1. problem identification the research begins by identifying the problems that occur in the company, namely having a lot of sales transaction data but only storing it in a database and not knowing how to use it, even though sales transaction data can be analyzed and to obtain important information contained therein. 2. preprocessing sales transaction data must be processed before data mining techniques can be applied by removing noise from the data, selecting data, and transforming data so it can be processed using data mining techniques. problem identification have a lot of data, only stored in data storage, no data analysis preprocessing collect sales transaction data, data selection, & data transformation methode association algorithm apriori research result customer buying pattern figure 2. research stages 3. method this study applies the association method to determine the relationship and interrelationships between products purchased in a sales transaction based on instant cement sales data for 2018. 4. algorithm the algorithm approach applied in this research is the a priori algorithm. the primary process is to combine each item until no more combinations are formed using the minimum support parameter. (anggraini, putri, & utami, 2020). at this point, the search for a combination of items that satisfies the minimum requirements of the support value is described in equation 1, and the confidence level is expressed in equation 2. 𝑠𝑢𝑝𝑝𝑜𝑟𝑡(𝐴 ∩ 𝐵 ) = ∑ 𝑡𝑟𝑎𝑛𝑠 𝑐𝑜𝑛𝑡𝑎𝑖𝑛 𝐴 𝑎𝑛𝑑 𝐵 𝑇𝑜𝑡𝑎𝑙 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 …………….(1) equation 1 is a formula for finding the support value of two items. 𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑃(𝐵|𝐴) = ∑ 𝑡𝑟𝑎𝑛𝑠 𝑐𝑜𝑛𝑡𝑎𝑖𝑛 𝐴 𝑎𝑛𝑑 𝐵 𝑇𝑜𝑡𝑎𝑙 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝐴 ……….(2) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 152 equation 2 is a formula for finding the confidence value by calculating the association rules a – b. 5. research results the results of this study are to obtain a pattern of instant cement purchase transactions so that companies know the best-selling products on the market, recommend the right products to customers, and increase sales of instant cement in the future. results and discussion the observation and interview process results carried out at manufacturing companies producing instant cement obtained transaction data on sales of instant cement products in january december 2018. based on these data, the implementation of data mining techniques uses the association method. association method to determine customer purchasing patterns through the a priori algorithm approach. the table below is a list of item codes and item names. table 1. list of item code and description item no item code description item 1 pro 686 perekat keramik 2 pro 688 perekat keramik area dalam 3 pro 689 perekat keramik area luar 4 pro 787r topping screed 5 pro 788r perata lantai 6 pro 858 plester finish 7 pro 866 acian profilan 8 pro 867 acian putih 9 pro 868 acian plesteran & beton 10 pro 869 acian plesteran 11 pro 878 plesteran premium 12 pro 879r pasangan bata merah 13 pro 888 perekat pasangan beton ringan 14 pro 889 perekat pasangan beton ringan 15 pro 889r perekat pasangan beton ringan table 1 explains the code for instant cement products and detailed descriptions of the existing products. the testing phase uses data from the transaction date and item code. figure 3. example of sales data 2018 the next stage is changing the data in tabulation form and saving it in an excel file. then the process of analysis using the association method. figure 4. example of data tabulation jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.491 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 153 figure 4 explains the results of changing data into tabulations in the form of binary numbers 0 and 1. the next step is importing the data that has become tabulated using the read excel operator in the rapidminer application. the following is an analysis model created using the rapidminer application. figure 5. rapidminer model figure 5 is a model created in the rapid miner application, starting through the data import process using the read excel operator. the data is transformed using numerical to binomial operators, followed by an algorithmic approach using the fpgrowth operator and creating association rules with the create association rules operator. the study carried out observations and several data tests to determine boundary parameters, with a minimum support value of 20% and a minimum confidence of 60%. the limit value was adjusted to obtain the most optimal association rule results. association rule result the data processing results using the rapidminer application obtain several association rules that provide many regulations for instant cement sales transaction data, and these results provide a reference to assist company decisionmaking. based on the rules of the association, that has the highest level of trust, support, and confidence. figure 6. association rules in rapidminer figure 6 shows the association rules formed using the rapid miner application. based on these results, rules number 309 – 316 get the highest confidence value of 100% and produce a lift value of 1,070, with a total of 316 rules formed. based on the results of these rules, it can be explained that.  customers buy pro 889 & pro 868 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customers buy pro 889 & pro 689 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customer buys pro 888 & pro 688 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customers buy pro 888 & pro 689 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customers buy pro 688 & pro 868 instant cement products, and the chances of customers also buying pro 869 products are 100%.  customers buy pro 868 & pro 689 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customers buy pro 868 & pro 689 instant cement products, and the chance of customers also buying pro 869 products is 100%.  customers buy pro 868 & pro 878 instant cement products, and the chance of customers also buying pro 869 products is 100%. rapidminer application with a 100% confidence value recommends that pro 869 is a solution for plastering plaster, while the highest demand is dominated by pro 888 & pro 889 types for adhesive solutions. lightweight concrete pairs, so the pro 869 type is the most suitable. it is recommended because after the process of installing lightweight brick materials for foundation walls, etc. the next stage is the wall plastering process, where the right product solution is pro 869. this may occur due to the increasing housing, shops, office, or hotel property industry. conclusions and suggestions conclusion the results of this study are recommended to provide consideration for company management to implement data mining methods in assisting decision-making by applying the association method to instant cement sales transaction data. the company can direct sales and marketing by providing customers with product recommendations and attractive promos based on the association rules. based on the results of making association rules using instant cement sales data, all p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 154 regulations formed from processing the results of this study are recommended to provide consideration for company management to implement data mining methods in assisting decision-making by applying the association method to instant cement sales transaction data. the company can direct sales and marketing by providing customers with product recommendations and attractive promos based on the association rules. suggestion the association method is a suitable recommendation to find association rules for the company's products to assist companies in formulating appropriate and accurate marketing strategies and production planning. this analysis can also be developed using transaction data in the following year, such as 2019-2021, to obtain more critical information from the resulting association rules. research development can also be done by combining or comparing several other algorithms, such as linear regression algorithms, neural network support vector machines, etc. references adha, n., sianturi, l. t., & siagian, e. r. 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(2020). pencarian frequent itemset dengan algoritma apriori dan python. studi kasus: data transaksi penjualan eceran online di uk. jurnal nasional informatikan, 1(2), 103– 113. retrieved from http://ejournalibik57.ac.id/index.php/junif/article/view/124 susilowati, a. (2012). pemanfaatan sampah daun sebagai bahan campuran papan panel semen dengan bahan ikat mu plesteran. jurnal politeknolog, 11(1), 31–38. retrieved from https://jurnal.pnj.ac.id/index.php/politeknolog i/article/view/20 takdirillah, r. (2020). penerapan data mining menggunakan algoritma apriori terhadap data transaksi sebagai pendukung informasi strategi penjualan. edumatic : jurnal pendidikan informatika, 4(1), 37–46. https://doi.org/10.29408/edumatic.v4i1.2081 wijayanti, a. w. (2017). analisis hasil implementasi data mining menggunakan algoritma apriori pada apotek. jurnal edukasi dan penelitian informatika (jepin), 3(1), 60. https://doi.org/10.26418/jp.v3i1.19534 jurnal riset informatika vol. 4, no. 4 september 2022 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 421 assessment effectiveness analysis system using g-form with tam method at sd galatia3 jakarta barat asrul sani1*), agus budiyantara2, rizaldy khair3, siti aisyah4 1.2 informatics engineering study program stmik widuri jakarta, indonesia www.kampuswiduri.ac.id asrul.5779@gmail.com; agusbudiyantara@kampuswiduri.ac.id 3 computer technology study program polytechnic lp3i medan medan, indonesia www.plm.ac.id rizaldy.khair@plm.ac.id 4 graphic engineering study program politeknik negeri media kreatif medan, indonesia www.polimedia.ac.id sitiaisyah@polimedia.ac.id (*) corresponding author abstract peserta didik merasakan dampak dari penyebaran covid-19 seperti perubahan penyediaan layanan dalam lembaga pendidikan, seperti pendidikan formal pada semua tingkatan, pendidikan non -formal, sampai akademi. berdasarkan pengumuman kementerian pendidikan dan kebudayaan nomor 4 tahun 2020 tentang pelaksanaan kebijakan pendidikan pada masa darurat penyebaran virus corona (covid-19). pemerintah mulai memberlakukan sistem pembelajaran daring (dalam jaringan). masalah yang muncul selama masa pembelajaran daring ini adalah, masih banyak orang yang belum siap dalam menghadapi teknologi, baik dari segi pengajar maupun pelajar, hal ini terjadi di awal-awal masa pandemi. sehingga proses pembelajaran yang selama ini dilaksanakan secara normal, baik dalam hal mengajar hingga memberikan penilaian juga mengalami hambatan. hal ini dikarenakan tenaga pengajar (guru) selalu memiliki data penilaian harian yang biasa dilakukan sehari-hari selama pembelajaran normal. namun selama masa pjj ini guru-guru jadi bingung dalam memberikan penilaian harian pada siswanya. tujuan penelitian untuk menganalisa dan mengevaluasi sistem pembelajaran daring menggunakan platform yang mudah dioperasikan oleh guru dan bisa diakses dimana saja yaitu google form. di aplikasi google form terdapat suatu sistem perhitungan otomatis berupa feedback siswa dimana guru tidak repot melakukan perhitungan secara manual terhadap hasil evaluasi belajar siswa. metode analisis yang digunakan adalah technology acceptance model (tam) yang mampu untuk mengetahui sikap para pengguna terhadap teknologi yang digunakan, sehingga para guru mudah mengetahui apakah tugas yang diberikan ke siswa dikerjakan sendiri oleh siswa bukan bantuan orang tua. keywords: tam; covid-19; platform; google form; application abstract students feel the impact of the spread of covid-19, such as changes in the provision of services in educational institutions, such as formal education at all levels, non-formal education, to academics. the government began to implement an online learning system (online). the problem during this online learning period is that many people are still not ready to face technology, both teachers and students. it happened in the early days of the pandemic. it results in challenges for the standard learning process, including challenges for teaching and giving assessments. it is because the teaching staff (teachers) always mailto:sitiaisyah@polimedia.ac.id p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 jurnal riset informatika vol. 4, no. 4 september 2022 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 422 have daily assessment data that is usually carried out daily during everyday learning. however, during this pjj period, teachers became confused about giving daily assessments to their students. the research aims to analyze and evaluate the online learning system using a platform that is easy to operate by teachers and can be accessed anywhere, namely google forms. in the google form application, there is an automatic calculation system in the form of student feedback where teachers do not bother to manually calculate the results of student learning evaluations. the analysis method used is the technology acceptance model (tam), which can determine users' attitudes towards the technology used, so that teachers can easily find out whether the tasks given to students are done by the students themselves, not with the help of parents. keywords: tam; covid-19; platform; google form; application introduction the covid-19 pandemic from the beginning of 2020 impacted every aspect of indonesia, including education. indonesian government policy in mid-march 2020 through the ministry of education and culture and the ministry of religious affairs of the republic of indonesia. implement a work-from-home policy. the government's demands during the pandemic related to the learning process have become a new polemic in the learning system in indonesia. it is undeniable that teachers must adapt their teaching methods to the needs of current students, especially during a pandemic. it is necessary to change the education system that aims to accommodate the needs of students. online learning (online) has become familiar today, and online learning meets students' learning needs during the pandemic (baety & munandar, 2021). however, it is denied that it also poses obstacles to education. the problem that occurs with the emergence of new barriers related to learning is a problem that has been studied in depth (handarini & wulandari, 2020). obstacles in the learning process can result in a decrease in student interest in learning. one of the obstacles is the teachers' limited ability in the information and communication technology field. according to the minister of national education number 16 of 2007, ict competence for teachers has at least two functions: ict as self-development and ict as a support for learning. this study aims to make it easier for teachers/teaching staff to carry out the online learning process during the pandemic, from teaching to daily assessments. by utilizing platform technology and google forms, teachers can minimize difficulties in terms of learning and evaluation so that teachers easily get learning feedback followed by their students, whom parents can help at home. this research was conducted at sd galatia 3 west jakarta. the urgency in this study is to assist teachers in assessing students' daily activities during learning at home. utilization of google form technology for feasibility research to evaluate daily student behavior (munawaroh et al., 2021). this research is very relevant to the rirn 2017-2045 in the field of information and communication technology, then prn 2020-2024 on the theme framework/platform supporting the creative industry and control in the field of education. the objectives of this study are as follows: 1. analyzing and evaluating the online learning system using google forms for daily assessment. 2. to find out the responses of students and teachers to the use of google forms as an evaluation tool for daily assessment. 3. to find out the shortcomings in the use of the google form for daily assessment research methods the idea of user attitudes and behavior served as the foundation for this study, which led to an emphasis on tra from viewpoints examined from a psychological standpoint (ayuni et al., 2020). on this tra principle, we can know how much we measure the relevant attitude components of a person's behavior, group between beliefs and attitudes, and determine external stimuli so that the reaction and perception of users caused by this tra model to the information system determined by the attitude and behavior of the user. then in 1986, davis conducted dissertation research that adapted the tra, and davis disseminated the results of his dissertation research to the journal mis quarterly in 1989. then from the effects of his dissertation research conducted by davis, a theory called tam (technology acceptance model) emerged, which emphasizes the perception of ease of use and usefulness, which has to do with predicting the attitude toward using information systems. the tam model's application is much wider than the tra model (darna & herlina, 2018; fatmawati, 2015). the tam model is the basis for evaluating user behavior attitudes in using technology based on jurnal riset informatika vol. 4, no. 4 september 2022 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 423 its use from the beginning to the end of use which can be seen in figure 1 below: figure 1. tam construction structure information: pu = perceived usefulness peu = perceived ease of use atu= attitude toward using biu = behavioral intention to use types of research this research uses a quantitative approach to collecting data through interviews, observations, literature studies, and questionnaires. the questionnaire will distribute to 85 students of galatian elementary school 3. in making the questionnaire, the author adopted the technology acceptance model (tam) technology as a process (mulatsih, 2020), which will later be translated into a questionnaire in the form of a google form by taking the variables contained in the tam, namely perceived ease of use, perceived usefulness, attitude toward using, and behavioral intention to use. from these variables will be created five questions each. the software used by the author to process quantitative data is statistical product and service solutions (spss). spss is a unique program for processing statistical data and is known to be reliable in assisting researchers in testing and analyzing statistical information. time and place of research the study is approximately four months, from march 2022 to july 2022. the study location was at the galatia 3 elementary school (sd), west jakarta. research targets/ subjects in this study, the author chose the target/subject of the study, namely teachers in grades 1-6 of sd galatia 3, based on the results obtained by the author concluding that sd galatia 3 uses google form to conduct daily assessments carried out since the covid-19 pandemic and the implementation of distance learning rules in 2020. in grades 1-5, teachers using google forms to conduct daily assessments are considered ineffective because of complaints from students about poor internet networks and sometimes google form links to have problems. meanwhile, the grade 6 teacher's opinion on using google forms to conduct a daily assessment considers very effective because there have been no complaints from students until now (erawati et al., 2017). google form is more practical, easy, and precise because it can see the grades directly, and children only click on the google form link shared by the teacher, after which the child can already do the daily assessment so that they do not need to download the google form application. therefore, teachers choose google forms to conduct daily assessments for their students. the advantage of google forms for teachers to do daily assessments is that it is more than time-saving, does not need corrections, can immediately see their student's grades, and is easier and more practical to use. other applications besides google forms to conduct daily assessments are google classroom and quizizz. the shortcomings of the google form for teachers to conduct daily assessments are that teachers cannot monitor children's work directly and cannot know whether the child has done it or not before the child submits. in addition, the daily assessment is also used to fill in student biodata, practice questions, and quizzes (rijali, 2018; sani et al., 2021). from the results of collecting questionnaire data on students of sd galatia 3, totaling 77 students, there were several demographic categories of respondents, namely: table 1. demographics of respondents no category limitation sum overall number percentage 1 age 6-7 years 24 students 77 students 31,2% 8-9 years 23 students 29,9% 10-12 years 30 students 39% 2 gender man 43 students 77 students 55,8% woman 34 students 44,2% 3 class 1 sd 18 students 77 students 23,4% 2 sd 9 students 11,7% p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 jurnal riset informatika vol. 4, no. 4 september 2022 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 424 no category limitation sum overall number percentage 3 sd 12 students 15,6% 4 sd 15 students 19,5% 5 sd 11 students 14,3% 6 sd 12 students 15,6% 4 are you guys able to use yes 74 students 77 students 96,1% no 3 students 3,9% 5 whether in using google forms to do daily assessments, you are still accompanied by parents? yes 40 students 77 students 52% no 37 students 48,1% 6 are there any network constraints when working on the daily assessment in google form? yes 36 students 77 students 46,8% no 41 students 53,2% 7 do you use a device in the form of a cellphone in filling out the daily assessment on google form? yes 64 students 77 students 83,1% no 13 students 16,9% 8 do you use a device in the form of a laptop to fill out the daily assessment on google form? yes 13 students 77 students 16,9% no 64 students 83,1% 9 whether the device you are using is your own? yes 71 students six students 77 students 92,2% 7,8% no based on the table above, the demographic categories of respondents' data are viewed based on age, gender, class, and demographic categories of respondents in terms of application can be seen based on six questions, namely, can you use google form? is it in using google forms to do the daily assessment? do your parents still accompany you?, are there any network problems when working on the daily assessment on google form?, do you use a device in the form of a cellphone in filling out the daily assessment on google form?, do you use a device in the form of laptop in filling out the daily assessment in google form?, and is the device you are using its own?. described according to the data obtained from respondents, namely the age of 6-7 years, totaling 24 students with a percentage of 31.2%, aged 8-9 years, totaling 23 students with a percentage of 29.9%, aged 10-12 years totaling 30 students with a percentage of 39%. then the male sex amounted to 43 students, the percentage was 55.8%, and the female sex was 34 students, 44.2%. furthermore, in grade 1 elementary school, there are 18 students with a percentage of 23.4%, grade 2 elementary schools are nine students with a percentage of 11.7%, grade 3 elementary schools are 12 students with a percentage of 15.6%, classes four elementary schools totaled 15 students with a percentage of 19.5%, grade 5 elementary schools totaled 11 students with a percentage of 14.3%, and finally, grade 6 elementary schools totaled 12 students with 15.6%. in terms of application, as many as 74 students can use google form with a percentage of 96.1%, so it concluded that most elementary school students could use google forms well. then the students whose parents accompanied them in using the google form to work on the daily assessment 40 students, with a percentage of 52%, and those whose parents did not attend as many as 37 students, with a percentage of 48.1%, so concluded that most elementary school students are still accompanied when using google form to do the daily assessment. furthermore, as many as 36 students experienced network problems when working on the daily assessment on google forms, 46.8%, and those who did not experience network problems amounted to 41 students, 53.2%. hence, it concluded that most elementary school students do not experience network problems when working on the daily assessment on google forms. then as many as 64 students used cell phone devices to fill out the no category limit total number percentage 9. is the device you are using your own? yes, 71 students, 77 students 92.2% not six students 7.8%47 daily assessment on google form with a percentage of 83.1% and in addition to mobile phones, the devices used to fill out the daily assessment on google form are laptops totaling 13 students with a percentage of 16.9%, so concluded that most elementary school students use mobile jurnal riset informatika vol. 4, no. 4 september 2022 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 425 phones in filling out the daily assessment on google form. the devices used are mostly owned by themselves, with 92.2% of 71 students. procedure in the initial procedure, this research went through several stages: the permit process, data collection, questionnaires, distributing questionnaires, and conducting interviews (sani et al., 2020, 2022). the author gets a letter of application for a research permit for practical work students from the campus to be given to the galatians 3 elementary schools so that the author is granted permission to conduct research at the galatians 3 elementary school. at the beginning of this permit process, the author requests and fills out a form already available at baak to participate in the practical work lecture activities. then the author asked for a reply letter from the galatian 3 elementary school that the school invited the author to conduct research at galatian 3 elementary school. this reply letter is given to the author to be handed back to baak. the school allowed the authors to research at galatian 3 elementary school within four months. some of these processes can see in the flowchart in figure 2 below. figure 2 permit process flowchart figure 3 data retrieval process flowchart the process of collecting data starts with the author asking permission from the wakasek to ask for the data needed by the author, and then the wakasek provides the data. after the author gets these data, figure 4 flowchart of questionnaire making in the process of making a questionnaire, starting from the author make a questionnaire with as many as 20 questions made in the form of a google form figure 5 flowchart of questionnaire division the questionnaire process starts with asking permission from the vice principal to distribute the questionnaire link to elementary school students in grades 1-6. the author gives the questionnaire link to the vice principal. then the vice principal shares the link with the teacher in grades 1-6, then the teacher in grades 1-6 shares the link with his students and the students fill out the questionnaire. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 jurnal riset informatika vol. 4, no. 4 september 2022 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 426 moreover, the author's next task is periodically checking the questionnaire results. figure 6 interview process flowchart the interview process starts with the author asking permission for the vice principal to interview teachers in grades 1-6. the author shares the interview form directly if the teacher is at school (kurniasi et al., 2020). meanwhile, if the teacher is not current at school, the author provides an interview link through a google form. after that, the teacher fills out the interview form data collection techniques, instruments, and data tam method indicators based on variables from the tam method used in making questionnaire questions about the use of google form to conduct a daily assessment distributed to students in grades 1-6 of galatian 3 elementary school, there are several indicators adjusted to the questionnaire questions regarding perceived ease of use, perceived usefulness, attitude toward using, attitude toward using, and behavioral intention to use. each of these variables has its indicators, which are described in table 2 below: table 2. tam method indicators variable indicator perceived ease of use (peu) peu1 easy to use peu2 very helpful peu3 easy to remember peu4 it can be done anywhere peu5 it does not require much effort perceived usefulness (pu) pu1 help facilitate pu2 very helpful pu3 increase productivity variable indicator pu4 feel faster pu5 improve the performance attitude toward using (atu) atu1 like to use atu2 enthusiasm in using atu3 can be done independently atu4 feel bored atu5 feeling happy behavioral intention to use (biu) biu1 will use continuously biu2 intend to continue to use biu3 wanting to access other systems the following is the test of the questionnaire results on students in grades 1-6 of galatia 3 elementary school, totaling 77 students, tested with four tam method variables to determine whether the use of google form as a medium for daily assessment has been accepted or not by users, which described on table 3 below. table 3. overall results of questionnaire calculations using the tam method no variable average criteria 1 perceived ease of use 86,49% excellent 2 perceived usefulness 84,99% excellent 3 attitude toward using 78,75% good 4 behavioral intention to use 75,41% good total 325,64% average 81,41% (good) based on the results above, it can be seen that the average respondent's answer score from 5 questions perceived user convenience. the result obtained was 86.49% which is classified as "excellent." it shows that the daily assessment using google forms is quite good. results and discussion hypothesis test between variables in order to ascertain the link between two variables, hypothesis testing is used. figure 7 below illustrates the size of the relationship between these variables as a percentage. jurnal riset informatika vol. 4, no. 4 september 2022 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 427 figure 7 relationships between variables based on the picture above, the author took a hypothesis in the study, namely: 1) h1: is there a relationship between the perceived ease of use variable and perceived usefulness? 2) h2: is there a relationship between the perceived usefulness variable and the attitude toward using? 3) h3: is there a relationship between the perceived ease of use variable and the attitude toward using? 4) h4: is there a relationship between the attitude toward using a variable and the behavioral intention to use it? table 4. hypothesis test results hypothesis significance r square h1 0,00 0,518 (51,8%) h2 0,00 0,502 (50,2%) h3 0,00 0,272 (27,2%) h4 0,00 0,441 (44,1%) it can be concluded that the first hypothesis has a relationship between the perceived ease of use variable and perceived usefulness with a significance value of 0.00 (<0.05), whose relationship percentage is 51.8%. the two variables have a relationship because the daily assessment using google forms is very helpful and easy to use to improve student's performance and productivity in learning. in addition, the steps to fill out the google form to do the daily assessment questions are easy to remember, do not require hard effort, and can be done anywhere. therefore, students feel that it is faster to do the daily assessment, and google form helps facilitate online learning for students. the second hypothesis is the relationship between the perceived usefulness variable and the attitude toward using with a significance value of 0.00 (<0.05), whose relationship percentage is 50.2%. the two variables have a relationship because google form helps help to facilitate online learning, so students feel happy and enthusiastic about using google forms for online learning. the third hypothesis is the relationship between the perceived ease of use variable and the attitude toward using with a significance value of 0.00 (<0.05), whose relationship percentage is 27.2%. these two variables have a relationship because students do not need much effort to do the daily assessment questions on the google form and the steps are easy to remember. therefore, students can do it independently and like to use google forms in online learning. however, the percentage of the relationship between these two variables is the lowest compared to the percentage of relationships of other variables, and this may be because some students feel bored using google forms in online learning. the fourth hypothesis is the relationship between the attitude toward using the variable and the behavioral intention to use it with a significance value of 0.00 (<0.05), whose relationship percentage is 44.1%. these two variables have a relationship because students like to use google forms in online learning, so students intend to continue using google forms to support the learning process conclusions and suggestions conclusion the conclusions from the results of the questionnaire obtained are as follows: based on testing using the spss application, in the validity test, two questions were eliminated, namely biu4 and biu5, because they had a significance value of > 0.05, namely 0.36 and 0.84 so that the two questions are not processed further from valid items, a reliability test was then carried out, and cronbach's alpha results were obtained above 0.7 which means that the questionnaire filled out by respondents had a good level of consistency. from the results of the questionnaire that the author got to find out the attitude of student admissions towards using google forms in conducting a daily assessment, the author got an average result of 81.41%, which is relatively good. the average result is taken from the four variables of the tam method. one variable has a connection with the results of hypothesis tests using the spss application (hasyim & listiawan, 2014). the relationship between the perceived ease of use variables with perceived usefulness was 51.8%, perceived usefulness with attitude toward using was 50.2%, perceived ease of use with attitude toward using63 was 27.2%, and attitude toward using with the behavioral intention to use was 44.1%. responses from teachers and students regarding google forms are very effective despite shortcomings. for example, teachers cannot directly monitor the results of children's work. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.439 jurnal riset informatika vol. 4, no. 4 september 2022 accredited rank 3 (sinta 3), an excerpt from the decree of the minister of ristek-brin no. 200/m/kpt/2020 428 suggestion the advice from the author regarding the research that the author conducted on daily assessments using google forms in online learning at sd galatia 3 is that in conducting daily assessments using google forms, teachers should use additional applications such as zoom or google meet to monitor children's work directly. teachers should brief parents, especially in grades 1-3, to get their children used to being independent in doing daily assessments through google forms so that they are purely the result of children's work. in the validity test, there are two invalid questions. in this study, there are still two invalid questions. the statement is retested. acknowledgments researchers would like to express their gratitude to the ministry of education and culture, drpm, and lldikti region 3 for financing the novice lecturer research grant for the fiscal year 2022 for the 2022 implementation year, with contract number 403 / ll3 / ak. 04/vi/2022, as well as stmik widuri, who has facilitated lecturers to carry out and participate in activities in the pdp scheme through bima. references ayuni, d., marini, t., fauziddin, m., & pahrul, y. 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(2021). the effect of student behavior on the use of campus journals by adopting the tam method. jitk (jurnal ilmu pengetahuan dan teknologi komputer), 6(2), 187–194. jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 205 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional perancangan dan penerapan metode weighted product dalam sistem pendukung keputusan pembelian laptop nur sumarsih program studi sistem informasi stmik nusa mandiri www.nusamandiri.ac.id nursumarsih8@gmail.com abstrak konsumen pada saat membeli laptop biasanya bertujuan sekedar mengerjakan pekerjaan sekolah atau kantor yang sebagian besar hanya untuk mengetik laporan atau mencari informasi lewat internet. pasar di indonesia sangat besar, sehingga berbagai merk dan jenis laptop yang tersedia saat ini dijual dengan harga yang bervariasi dan kompetitif, sehingga para calon pembeli menjadi tambah bingung untuk membeli. kebanyakan para pembeli, membeli laptop dengan spesifikasi yang tidak disesuaikan dengan kegunaannya. pendekatan yang digunakan untuk memecahkan masalah tersebut menggunakan metode weighted product. penelitian ini bertujuan untuk membantu konsumen dalam pemilihan laptop melalui kriteria-kriteria yang telah ditentukan. hasil perhitungan pemilihan laptop dengan metode weighted product yang didapat dengan hasil nilai tertinggi adalah laptop toshiba satellite c55 dan hasil terendah adalah laptop acer aspire e1-470. kata kunci: metode weighted product, pemilihan laptop, penunjang keputusan abstract consumers when buying a laptop usually aims just to do school or office work, mostly just to type reports or find information via the internet. the market in indonesia is very large, so that various brands and types of laptops available today are sold at varied and competitive prices, so potential buyers are even more confused about buying. most buyers, buy laptops with specifications that are not adapted for their use. the approach used to solve the problem uses the weighted product method. this study aims to assist consumers in choosing a laptop through predetermined criteria. the results of the calculation of the selection of laptops with the weighted product method obtained with the highest value is the toshiba satellite c55 laptop and the lowest result is the acer aspire e1-470 laptop. keywords: weighted product method, laptop selection, decision support pendahuluan pesatnya pasar laptop membuat para produsen saling berebut untuk menjadi pemimpin pasar laptop. dengan munculnya berbagai kompetitor membuat produsen berlomba-lomba memberikan produk terbaik kepada konsumen melalui produk yang mereka tawarkan, sehingga menimbulkan persaingan yang lebih kompetitif. biasanya konsumen membeli laptop hanya sekedar mengerjakan pekerjaan sekolah atau kantor yang sebagian besar hanya untuk mengetik laporan atau mencari informasi lewat internet, dengan kebutuan yang ada kita bisa membeli laptop yang berspesifikasi menengah. saat ini berbagai merk dan jenis laptop yang ada pasar indonesia, dijual dengan harga yang bervariasi dan kompetitif, sehingga para calon pembeli menjadi tambah bingung untuk memilihnya (khairina, ivando, & maharani, 2016), (saputra, sari, & mesran, 2017), dan kesulitan dalam menentukan pilihan (n. syafitri, syafitri, sutardi, & dewi, 2016) yang sesuai dengan kebutuhannya. banyak juga para pembeli, membeli laptop dengan spesifikasi yang tidak disesuaikan dengan kegunaannya (n. syafitri et al., 2016). terkadang konsumen, tentunya terkadang kita kesulitan dalam memilih laptop disesuaikan dengan anggaran yang ada. sesuai dengan permasalahan yang sudah dikemukakan diatas, pada penelitian yang penulis lakukan, menggunakan metode weighted product (wp), dikarenakan berdasarkan penelitian sebelumnya, yang dilakukan oleh syafitri (2016), memberikan solusi dari 5 (lima) laptop (n. a. syafitri, sutardi, & dewi, 2016) yang menjadi pilihan didapatkan akurasi yang baik dengan metode wp tersebut. begitu juga penelitian oleh rani (2014) dengan pemilihan sepeda motor http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 206 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dengan metode wp, menghasilkan hasil yang baik dalam pemilihan sepeda motor (rani, 2014). ruang lingkup pembahasannya dimulai dari menentukan kriteria yang dipakai dalam penelitian ini adalah harga, jenis processor, kapasias ram, hardisk, dan vga (video graphics accelerator), menentukan alternatif berupa laptop dengan beberapa merk, dan sistem ini akan menghasilkan penilaian pemilihan laptop yang nantinya akan menghasilkan ranking dari nilai terbesar ke terkecil. tujuan penelitian ini untuk membantu konsumen dalam pemilihan laptop melalui kriteriakriteria yang telah ditentukan. adanya sistem pendukung keputusan dengan metode weighted product ini konsumen dapat memilih laptop yang sesuai dengan kebutuhan. metode penelitian penelitian ini kriteria yang digunakan sebanyak 5 (lima) kriteria, yaitu : a. harga pada dasarnya adalah sebagai tolak ukur terpenting bagi sebagian besar calon pembeli. b. ram (random access memory) sebagai media penyimpanan temporer dalam sebuah computer, menjadi pertimbangan penting, dikarenakan ram sangat mempengaruhi kinerja dari sebuah computer. semakin besar kapasitas dan seamakin tinggi kecepatan dari ram sebuah laptop, semakin bagus dan cepat pula kinerja laptop tersebut. c. processor sebagai otak dari komputer merupakan salah satu pertimbangan penting dalam pemilihan laptop. processor dengan kecepatan yang inggi mempu memproses dan melakukan perhitungan dengan cepat pula. d. hardisk sebagai media penyimpanan semi-permanen menjadi pertimbangn penting, dimana semakin besar kapasitas harddisk sebuah laptop, semakin banyak pula data-data yang bisa disimpan oleh penggunanya. e. vga (video graphics accelerator) sebagai pengolah data grafis dalam sebuah laptop menjadi pertimbangan penting, khususnya bagi calon pembeli laptop yang bertujuan untuk menggunakan laptopnya sebagai media bermain game ataupun sebagai media bekerja yang menggunakan aplikasi-aplikasi multimedia yang berat. sedangkan untuk alternatif pilihan produk dari toko penjual laptop adalah sebagai berikut : a. acer aspire one z1402 b. lenovo s400 c. toshiba satellite c55 d. acer aspire e1-470 e. asus s46cb skala pengukuran yang digunakan dalam observasi kepada responden adalah skala likert, dimana akan didapat jawaban berupa sangat setuju, setuju, netral, tidak setuju, dan sangat tidak setuju. dalam penelitian ini analisa yang digunakan adalah analisis data kuantitatif, karena data yang didapat berupa simbol angka atau bilangan yang dapat menghasilkan suatu kesimpulan yang berlaku di dalam suatu parameter. metode weighted product (wp) metode weighted product menggunakan teknik perkalian untuk menghubungkan ratingattribute, dimana rating tiap atribut harus dipangkatkan terlebih dahulu dengan atribut bobot yang bersangkutan (kusumadewi, hartati, harjoko, & wardoyo, 2006). . dalam penelitian ini akan menggunakan metode weighted product dimana di dalam penentuan sebuah keputusan dengan cara perkalian untuk menghubungkan rating atribut, dimana rating setiap atribut dipangkatkan dulu dengan bobot atribut yang bersangkutan. langkah-langkah yang dilakukan dalam penyelesaian masalah menggunakan metode weighted product seperti dibawah ini. 1. normalisasi atau perbaikan bobot 𝑊𝑗 = 𝑊𝑗 ∑ 𝑤𝑗 ..................................................................... (1) melakukan normalisasi atau perbaikan bobot untuk menghasilkan nilai 𝑤𝑗 = 1 dimana 1, 2, …, n adalah banyak alternatif dan ∑ 𝑤𝑗 adalah jumlah keseluruhan nilai bobot. 2. menentukan nilai vektor (s) 𝑆𝑖 = ∏ 𝑋𝑖𝑗 𝑊𝑗 ∏ 𝑋𝑖𝑗 𝑊𝑗 𝑛 𝑗−1 𝑛 𝑗−1 ............................. (2) , dengan i = 1, 2, ..., n 3. menentukan nilai vector (s) dengan cara mengalikan seluruh kriteria dengan alternatif hasil normalisasi atau perbaikan bobot yang berpangkat positif untuk kriteria keuntungan (benefit) dan yang berpangkat negatif untuk kriteria biaya (cost). dimana (s) merupakan preferensi kriteria, (x) merupakan nilai kriteria dan (n) merupakan banyaknya kriteria. 4. menentukan nilai vektor (v) 𝑉𝑖 = ∏ 𝑥𝑖𝑗 𝑤𝑗 𝑛 𝑗=1 ∏ (𝑋𝑗 𝑤)𝑛𝑗=1 𝑤𝑗 .......................................................... (3) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 207 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional , dengan i = 1, 2, ..., n (3) menentukan nilai vector (v) dimana vector (v) merupakan preferensi alternatif yang akan digunakan untuk perangkingan dari masing-masing jumlah nilai vector (s) dengan jumlah seluruh nilai vector (s) . start input nilai aternatif, input kriteria (c), input kriteria, bobot (w) perbaikan bobot kriteria pemangkatan vektor s terhadap bobot kriteria vektor v proses alternatif keputusan output alternatif keputusan end gambar 1. algortima metode weighted product bobot kriteria tersebut yang dijadikan pengujian didapat dari hasil kuesioner dimana responden memilih tingkat kepentingan sesuai dengan kebutuhan yang sesuai dalam melakukan pemilihan laptop, kemudian dilakukan normalisasi bobot atau perbaikan bobot dengan menentukan vektor s yaitu nilai dari setiap alternatif, perhitungan ini dilakukan dimana data yang akan dikalikan yang sebelumnya dilakukan pemangkatan dengan bobot masing–masing kriteria. setelah masing–masing vektor s mendapatkan nilai langkah selanjutnya adalah menentukan nilai vektor v yang digunakan untuk perangkingan alternatif. setelah perhitungan menggunakan vektor v selesai, langkah selanjutnya adalah memasukan semua hasil perhitungan ke dalam tabel sesuai nilai tertinggi dari nilai vektor v, maka akan didapatkan hasil perhitungan yang menunjukkan perangkingan nilai vektor v yang terbesar hingga terkecil, sehingga didapat alternatif terbaik rekomendasi pemilihan laptop berdasarkan nilai tertinggi vektor v. hasil dan pembahasan a. data penelitian pada tahap ini penulis mengumpulkan data pemilihan laptop yang diperlukan dalam melakukan perhitungan menggunakan metode weighted product. berikut ini adalah kriteria yang dijadikan acuan dalam memilih laptop dengan menggunakan metode weighted product. tabel 1. kriteria kriteria simbol harga c1 ram c2 processor c3 hardisk c4 vga c5 dari tabel tersebut, maka ditentukan suatu tingkatan kepentingan kriteria berdasarkan nilai bobot pada setiap kriteria dengan nilai bobot 1 sampai dengan 5, pembobotan ini mengacu pada skala likert, yaitu: tabel 2. nilai bobot pernyataan bobot sangat tidak penting 1 tidak penting 2 cukup penting 3 penting 4 sangat penting 5 b. data pengujian pada tahap ini akan dilakukan pengujian dengan menggunakan metode weighted product untuk pengolahan data menentukan keputusan pemilihan laptop. 1. metode weighted product ada beberapa langkah untuk melakukan perhitungan menentukan keputusan pemilihan laptop dengan menggunakan metode weighted product adalah sebagai berikut. a. menentukan alternatif menentukan alternatif yang akan digunakan dalam perhitungan. pada pengujian ini akan digunakan 5 sampel data laptop. tabel 3. data laptop no laptop spesifikasi kode harga ram processor hdd vga 1 acer aspire one z1402 4.390.000 2gb intel core i3 500 gb intel hd 5.500 a 2 lenovo s400 3.900.000 4gb intel core i3 500 gb amd radeon hd 7450 b 3 toshiba satellite 6.390.000 4gb intel core i3 1tb intel hd c http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 208 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional no laptop spesifikasi kode harga ram processor hdd vga c55 5.500 4 acer aspire e1-470 5.490.000 2gb intel core i3 500 gb intel hd 4000 d 5 asus s46cb 7.102.000 4gb intel core i3 500 gb nvidia geforce gt 740 e b. menentukan perbaikan bobot kriteria menetukan bobot preferensi atau menentukan tingkat kepentingan berdasarkan tingkat kepentingan masing-masing kriteria. berikut adalah nilai bobot yang diberikan oleh responden, yaitu: tabel 4. masukkan responden kriteria nilai harga 5 kapasitas ram 3 jenis processor 4 kapasitas hardisk 3 vga 2 selanjutnya akan dilakukan perbaikan bobot terlebih dahulu dengan bobot awal w= (5, 3, 4, 3, 2), dengan w adalah bobot masing-masing kriteria yang responden berikan. berikut adalah hasil dari perhitungan perbaikan bobot kriteria. tabel 5. perbaikan bobot kriteria kriteria nilai bobot harga 5 0,294 ram 3 0,176 processor 4 0,235 hardisk 3 0,176 vga 2 0,118 menentukan bobot setiap alternatif langkah selanjutnya adalah memberikan bobot kriteria untuk masing-masing data laptop yang terdapat pada tabel 3 data laptop. berikut adalah bobot kriteria setiap laptop, yaitu tabel 6. bobot kriteria setiap laptop kriteria alternatif a b c d e harga 4 5 3 4 3 ram 1 2 2 1 2 processor 5 5 5 5 5 hardisk 3 3 5 3 3 vga 3 3 3 2 5 menghitung vector s setelah mendapatkan perhitungan nilai perbaikan bobot kriteria, maka langkah berikutnya adalah menghitung vector s dimana perhitungan ini akan dikalikan tetapi sebelumnya dilakukan pemangkatan dengan bobot masing-masing kriteria. dengan bobot sebagai pangkat positif untuk kriteria yang menguntungkan dan bobot negatif untuk kriteria biaya. berikut adalah hasil dari perhitungan vector s, yaitu: tabel 7. perhitungan vector s alternatif bobot a 1,339 b 1,417 c 1,798 d 1,276 e 1,746 menentukan vector v setelah mendapatkan nilai vector s, langkah selanjutnya adalah menentukan perangkingan alternatif laptop dengan cara membagi nilai vector v yang digunakan untuk perankingan bagi setiap alternatif dengan nilai total dari semua nilai alternatif vector s. setelah perhitungan menggunakan vector v selesai, langkah selanjutnya adalah memasukkan semua hasil perhitungan ke dalam table sesuai nilai tertinggi dari vector v, maka akan didapat nilai tertinggi sebagai nilai rekomendasi. tabel 8. nilai hasil alternatif nilai a 0,176 b 0,187 c 0,237 d 0,168 e 0,230 maka hasil dari perhitungan pemilihan laptop dengan menggunakan metode weighted product menyatakan bahwa nilai tertinggi adalah alternatif c laptop toshiba satellite c55, kedua alternatif e laptop asus s46cb, ketiga alternatif b laptop lenovo s400, ke empat alternatif a laptop acer aspire one z1402 dan terendah adalah alternatif d laptop acer aspire e1-470. setelah dilakukan perhitungan secara manual, maka selanjutnya akan dilakukan perhitungan dengan menggunakan microsoft excel. berikut adalah perhitungan yang telah dilakukan, yaitu tabel 9. perbaikan bobot kriteria kriteria harga ram prosesor hardisk vga cost/benefit cost benefit benefit benefit benefit bobot 5 3 4 3 2 jumlah bobot 17 perbaikan bobot 0,2941 0,1765 0,23529 0,1765 0,1176 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 209 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pada tabel 9 diatas dapat dilihat bahwa nilai yang dihasilkan dengan menggunakan microsoft excel tidak jauh berbeda dengan hasil yang dilakukan dengan menggunakan perhitungan manual. tabel 10. perhitungan vector s alternatif / kriteria harga ram prosesor hardisk vga vector s acer aspire one z1402 4 1 5 3 3 1,34189 lenovo s400 5 2 5 3 3 1,42016 toshiba satellite c55 3 2 5 5 3 1,80608 acer aspire e1 470 4 1 5 3 2 1,27938 asus s46cb 3 2 5 3 5 1,75261 selanjutnya hasil dari perhitungan vector s hasilnya pun tidak jauh berbeda dengan hasil yang dilakukan dengan perhitungan manual. tabel 11. perhitungan vector v alternatif vector v acer aspire one z1402 0,1766 lenovo s400 0,1869 toshiba satellite c55 0,2376 acer aspire e1 470 0,1683 asus s46cb 0,2306 berdasarkan perhitungan dengan microsoft excel, maka hasil yang didapat sama dengan perhitungan secara manual dimana nilai tertinggi adalah alternatif c laptop toshiba satellite c55, kedua alternatif e laptop asus s46cb, ketiga alternatif b laptop lenovo s400, ke empat alternatif a laptop acer aspire one z1402 dan terendah adalah alternatif d laptop acer aspire e1-470. gambar 1. grafik hasil nilai perhitungan weighted product kesimpulan untuk menggunakan metode weighted product dibutuhkan kriteria yang akan dijadikan pertimbangan, kriteria yang telah ditentukan adalah harga, kapasitas ram, jenis processor, kapasitas hardisk, dan vga. membangun sistem pendukung keputusan pemilihan laptop menggunakan metode weighted product, langkah pertama yang dilakukan adalah menentukan kriteria dan alternatif laptop yang akan dibandingkan, kemudian data terebut akan dihitung dengan menggunakan metode weighted product. hasil perhitungan pemilihan laptop dengan metode weighted product yang didapat dengan hasil nilai tertinggi adalah laptop toshiba satellite c55 dan hasil terendah adalah laptop acer aspire e1-470. daftar referensi khairina, d. m., ivando, d., & maharani, s. (2016). implementasi metode weighted product untuk aplikasi pemilihan smartphone android. jurnal infotel, 8(1), 16–23. retrieved from http://ejournal.st3telkom.ac.id/index.php/in fotel/article/view/47 kusumadewi, s., hartati, s., harjoko, a., & wardoyo, r. (2006). fuzzy multi-attribute decision making (fuzzy madm). yogyakarta: graha ilmu. rani, s. (2014). sistem pendukung keputusan pemilihan sepeda motor berbasis webdengan metode weighted product. pelita informatika budi darma, 7(3), 62–66. saputra, i., sari, s. i., & mesran, m. (2017). penerapan elimination and choice translation reality (electre) dalam penentuan kulkas terbaik. komik (konferensi nasional teknologi informasi dan komputer), 1(1), 295–305. https://doi.org/10.30865/komik.v1i1.512 syafitri, n. a., sutardi, s., & dewi, a. p. (2016). penerapan metode weighted product dalam sistem pendukung keputusan pemilihan laptop berbasis web. semantik, 2(1), 169–176. retrieved from http://ojs.uho.ac.id/index.php/semantik/arti cle/view/762 syafitri, n., syafitri, n. a., sutardi, s., & dewi, a. p. 0 0,05 0,1 0,15 0,2 0,25 acer aspire one z1402 lenovo s400 toshiba satellite c55 acer aspire e1 470 asus s46cb http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 210 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional (2016). penerapan metode weighted product dalam sistem pendukung keputusan pemilihan laptop berbasis web. semantik, 2(1), 169–176. retrieved from http://ojs.uho.ac.id/index.php/semantik/arti cle/view/762 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 1 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sistem pendukung keputusan pemilihan penerima piala 5 tingkat efl di kumon danau sunter dengan metode ahp frieyadie1; lenny purnawati2 program studi sistem informasi stmik nusa mandiri frieyadie@nusamandiri.ac.id; lennypurnawati22@gmail.com abstract kumon gave awards in the form of trophies as a form of appreciation for the achievements shown by kumon students. this is also expected to increase the motivation of kumon students to work on worksheets according to their level independently. at lake sunter kumon, there are 2 levels of trophies given to students, namely 3-level trophies and 5 levels. each kumon must select several students who meet the criteria set by the head office. these criteria include attitudes and behaviors while in class, levels achieved and abilities possessed by the student. the number of students who are increasing every year causes the process of determining students who are entitled to receive trophies 5 levels in the efl kumon lake sunter class is still not optimal which can hinder the performance of decision makers. this is because at this time the assessment process for each student's development is still in hardcopy. the problem solving method used is the ahp method. the purpose of the research was to optimize the process of determining 5 efl level trophy recipient students at lake sunter kumon. facilitate decision makers in determining 5-level efl trophy recipients in lake sunter kumon. speed up the search process for data on students who meet the criteria and provide valid results regarding students who are entitled to receive 5 efl trophies at kumon lake sunter. keywords: english as foreign language (efl), kumon, ahp abstrak kumon memberikan penghargaan berupa piala sebagai bentuk penghargaan atas prestasi yang ditunjukkan siswa kumon. hal ini juga diharapkan dapat meningkatkan motivasi siswa kumon dalam mengerjakan lembar kerja sesuai tingkatan levelnya secara mandiri. di kumon danau sunter, terdapat 2 tingkatan piala yang diberikan kepada siswa yaitu piala 3 tingkat dan 5 tingkat. setiap kumon harus memilih beberapa siswa yang memenuhi kriteria-kriteria yang sudah ditetapkan oleh kantor pusat. kriteria-kriteria tersebut diantaranya adalah sikap dan perilaku selama berada di kelas, level yang dicapai dan kemampuan yang dimiliki siswa tersebut. jumlah siswa yang semakin bertambah setiap tahunnya menyebabkan proses penentuan siswa yang berhak menerima piala 5 tingkat di kelas efl kumon danau sunter masih kurang optimal yang dapat menghambat kinerja dari para pengambil keputusan. hal ini dikarenakan pada saat ini proses penilaian perkembangan setiap siswa masih dalam bentuk hardcopy. metode pemecahan masalah yang digunakan metode ahp. tujuan penelitian yang dilakukan untuk mengoptimalkan proses penentuan siswa penerima piala 5 tingkat efl di kumon danau sunter. mempermudah para pengambil keputusan dalam menentukan siswa penerima piala 5 tingkat efl di kumon danau sunter. mempercepat proses pencarian data-data siswa yang memenuhi kriteria dan memberikan hasil yang valid mengenai siswa yang berhak menerima piala 5 tingkat efl di kumon danau sunter. kata kunci: english as foreign language (efl), kumon, ahp pendahuluan pendidikan di sekolah formal pada saat ini masih dirasa kurang cukup dalam meningkatkan prestasi anak bagi kebanyakan orangtua murid. karena hal itu pula, bimbingan belajar dijadikan salah satu solusi alternatif bagi orangtua yang ingin meningkatkan prestasi dan kualitas belajar anaknya di sekolah. kumon merupakan salah satu dari begitu banyak tempat bimbingan belajar non formal di indonesia. kumon menawarkan 2 (dua) mata pelajaran unggulan yang menjadi fokus utama, yaitu math (matematika) dan reading program. sedangkan di indonesia, reading program lebih dikenal dengan sebutan english as foreign language (efl). program kumon terdiri dari rangkaian lembar kerja (worksheet) yang memiliki beberapa tingkatan level. lembar http://creativecommons.org/licenses/by-nc/4.0/ mailto:lennypurnawati22@gmail.com issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 2 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. kerjanya telah dirancang sedemikian rupa sehingga siswa dapat memahami sendiri bagaimana cara menyelesaikan soal-soal yang ada. lembar kerja kumon dipersiapkan dalam bentuk yang sangat small-steps, hal ini memungkinkan setiap siswa untuk maju dengan lancar dari soal yang mudah ke soal yang lebih sulit dan akhirnya mencapai materi tingkat sma. versi internasional program matematika (math) terdiri dari 20 level (dari level 6a sampai level o) dan 5 level pilihan, sehingga total ada 4.420 lembar kerja bolak-balik. sedangkan program english as foreign language (efl) terdiri dari 21 level (dari level 7a sampai level o), sehingga total ada 4.200 lembar kerja bolak-balik. dengan lembar kerja dalam bentuk small-steps ini, siswa kumon juga diharapkan dapat menggali kemampuan pemahaman mereka secara mandiri dengan melihat contoh soal yang ada sebelumnya. jika siswa terus belajar dengan kemampuannya sendiri, maka ia akan mampu mengejar bahan pelajaran yang setara dengan tingkatan kelasnya dan bahkan melampauinya. pembelajaran kooperatif dapat meningkatkan kinerja siswa, interaksi, dan kemampuan berfikir kritis (bliss & lawrence, 2009) tujuan pembelajaran kooperatif adalah melatih siswa memanajemen waktu dan saling ketergantungan positif antarkelompok (kupczynski, mundy, goswami, & meling, 2012). pembelajaran dengan metode kumon mengaitkan antarkonsep, ketrampilan, kerja individual, dan menjaga suasana nyaman dan menyenangkan, sehingga siswa menjadi lebih mandiri untuk mengerjakan soal. pertama kali yang ditekankan pada pembelajaran kali ini suasana pembelajaran yang nyaman dan menyenangkan (tiyanto, binadja, & santoso, 2014) dalam jangka waktu satu tahun sekali, kumon memberikan penghargaan berupa piala sebagai bentuk penghargaan atas prestasi yang ditunjukkan siswa kumon. hal ini juga diharapkan dapat meningkatkan motivasi siswa kumon dalam mengerjakan lembar kerja sesuai tingkatan levelnya secara mandiri. di kumon danau sunter, terdapat 2 tingkatan piala yang diberikan kepada siswa yaitu piala 3 tingkat dan 5 tingkat. setiap kumon harus memilih beberapa siswa yang memenuhi kriteria-kriteria yang sudah ditetapkan oleh kantor pusat. kriteria-kriteria tersebut diantaranya adalah sikap dan perilaku selama berada di kelas, level yang dicapai dan kemampuan yang dimiliki siswa tersebut. jumlah siswa yang semakin bertambah setiap tahunnya menyebabkan proses penentuan siswa yang berhak menerima piala 5 tingkat di kelas efl kumon danau sunter masih kurang optimal yang dapat menghambat kinerja (darmanto, latifah, & susanti, 2014) dari para pengambil keputusan. hal ini dikarenakan pada saat ini proses penilaian perkembangan setiap siswa masih dalam bentuk hardcopy (rijayana & okirindho, 2012) berbentuk folder. dengan jumlah siswa yang banyak juga menyebabkan proses yang cukup lama (lemantara, setiawan, & aji, 2013) dalam mencari data-data siswa yang memenuhi kriteria-kriteria tersebut dan hasilnya kurang valid (suryati & purnama, 2010). oleh karena itu, perlu adanya sistem yang mendukung proses penentuan penerima piala 5 tingkat di kelas efl kumon danau sunter sehingga dapat memudahkan dan mempersingkat waktu yang dibutuhkan dalam penyeleksian. berdasarkan permasalahan peneltian dapat dirumuskan beberapa masalah diantaranya, kurang optimalnya proses pemilihan penerima (widhianto, 2015) piala 5 tingkat efl di kumon danau sunter, menghambat kinerja dari para pengambil keputusan(umar, fadlil, & yuminah, 2018), membutuhkan waktu yang cukup lama dalam mencari data-data siswa yang memenuhi kriteria, hasil dari proses penentuan siswa penerima piala 5 tingkat efl di kumon danau sunter saat ini kurang valid. tujuan penelitian yang dilakukan untuk mengoptimalkan proses penentuan siswa penerima piala 5 tingkat efl di kumon danau sunter. mempermudah para pengambil keputusan dalam menentukan siswa penerima piala 5 tingkat efl di kumon danau sunter. mempercepat proses pencarian data-data siswa yang memenuhi kriteria dan memberikan hasil yang valid mengenai siswa yang berhak menerima piala 5 tingkat efl di kumon danau sunter. metodologi penelitian a. metode pengumpulan data pengumpulan data bertujuan untuk mendapatkan data yang berkaitan dengan penelitian. metode pengumpulan data menjadi tolak ukur benar atau tidaknya suatu penelitian tersebut dilakukan. teknik pengumpulan data dilakukan untuk mengamati variabel yang akan diteliti melalui metode tertentu. adapun teknik pengumpulan data yang dilakukan dalam penelitian penentuan siswa yang menerima piala 5 tingkat efl di kumon danau sunter adalah sebagai berikut: 1. metode observasi adapun observasi yang dilakukan penulis termasuk dalam jenis observasi partisipasif. yaitu penulis terlibat langsung dengan kegiatan seharihari orang yang sedang diamati atau yang digunakan sebagai sumber data penelitian. sambil http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 3 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. melakukan pengamatan, penulis ikut melakukan apa yang dikerjakan oleh sumber data. 2. metode wawancara dalam menggunakan metode ini peneliti mengadakan tanya jawab secara langsung dengan membawa instrumen penelitian sebagai pedoman pertanyaan tentang hal-hal yang akan ditanyakan dengan cara menanyakan beberapa pertanyaan untuk mencari data tentang pengaruh metode kumon terhadap tingkat kemandirian anak. 3. studi pustaka studi pustaka dilakukan untuk memperoleh informasi dalam rangka menganalisa permasalahan melalui penelaahan terhadap berbagai sumber tertulis melalui pendapatpendapat para ahli yang ada dalam buku atau jurnal, juga untuk menunjang instrumen pengumpulan data dan memperdalam kajian terhadap permasalahan penelitian. hal ini dapat menunjang solusi terhadap permasalahan dan dapat dijadikan acuan dalam bentuk teori yang berisi tentang sistem pendukung keputusan menggunakan metode ahp (analytical hierarchy process) yang digunakan untuk mengolah data siswa-siswa yang berhak menerima piala 5 tingkat efl di kumon danau sunter. dari ketiga metode pengumpulan data di atas, maka akan didapatkan data yang berisi informasi mengenai objek yang akan diteliti. data terdiri dari 2 macam, yaitu: 1. data primer data primer merupakan data yang didapat secara langsung di lapangan. dalam penelitian ini, data primer dikategorikan sebagai data penunjang. adapun data primer yang didapat adalah informasi mengenai kumon danau sunter seperti sejarah dan struktur organisasinya. 2. data sekunder data sekunder adalah data yang didapat dari perusahaan, dimana data tersebut merupakan data yang sudah ada sebelumnya dan sebagai arsip perusahaan. dalam penelitian ini, data sekunder dijadikan sebagai data utama. data sekunder didapatkan dari hasil observasi dan wawancara yang dilakukan kepada pembimbing kumon, kepala asisten dan kepala asisten efl. data sekunder yang didapat dalam penelitian ini adalah data yang mengenai kriteria apa saja yang dijadikan sebagai tolak ukur pemilihan siswa yang berhak menerima piala 5 tingkat, daftar nama siswa yang masih aktif serta level yang sudah mereka capai saat ini. sedangkan dari metode studi pustaka, diperoleh informasi-informasi mengenai metode ahp (analytical hierarchy process) yang akan digunakan peneliti dalam mengolah data yang sudah diperoleh dalam penelitian ini. b. populasi dan sampel penelitian sugiyono (2010:80) menyatakan bahwa “populasi adalah wilayah generalisasi yang terdiri atas: obyek atau subyek yang mempunyai kualitas dan karakteristik tertentu yang ditetapkan oleh penulis untuk dipelajari dan kemudian ditarik kesimpulannya”. sedangkan sugiyono (2010:81) berpendapat bahwa “sampel adalah bagian dari jumlah dan karakteristik yang dimiliki oleh populasi tersebut”. jika populasi yang akan dipelajari penulis terlalu besar dan penulis tidak mampu untuk mempelajari dari keseluruhan populasi, maka penulis dapat menggunakan sampel dari populasi tersebut sebagai bahan penelitian. dalam penelitian penentuan penerima piala 5 tingkat ini, penulis menggunakan sampel yang diambil dari populasi asisten pengajar di kumon danau sunter yang berjumlah 12 orang. penulis mengambil 5 responden yang terdiri dari kepala asisten, kepala asisten efl, asisten pengajar efl dan pembimbing. c. metode analisis data ada beberapa dasar yang harus dipahami dalam menyelesaikan persoalan dengan menggunakan metode ahp, diantaranya: 1. decompotition mendefinisikan persoalan dengan cara memecah persoalan yang utuh menjadi unsurunsur dan digambarkan dalam bentuk hirarki. 2. comparative judgement langkah pertama menentukan prioritas elemen dengan membuat perbandingan berpasangan, yaitu membandingkan elemen secara berpasangan sesuai kriteria yang diberikan. matriks perbandingan berpasangan diisi menggunakan bilangan untuk membuat penilaian tentang kepentingan relatif dua elemen dan dituliskan dalam bentuk matriks perbandingan berpasangan (pairwise comparison). 3. syntesis of priority dari matriks pairwise comparison kemudian dicari eigen vektor untuk mendapatkan local priority. pertimbangan terhadap perbandingan berpasangan disintesis untuk memperoleh global priority. hal-hal yang dilakukan dalam langkah ini adalah: a. menjumlahkan nilai dari setiap kolom pada matriks. b. membagi setiap nilai dari kolom dengan total kolom yang bersangkutan untuk memperoleh normalisasi matriks. c. menjumlahkan nilai dari setiap baris dan membagi dengan jumlah elemen untuk mendapatkan nilai rata-rata. 4. consistency http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 4 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. dalam pembuatan keputusan, mengetahui seberapa baik konsistensi merupakan hal yang penting karena penelitian tidak menginginkan keputusan berdasarkan pertimbangan dengan konsistensi yang rendah. untuk itu, beberapa hal yang dilakukan dalam langkah ini adalah sebagai berikut: a. lakukan perkalian setiap nilai pada kolom pertama dengan prioritas relatif elemen pertama, nilai pada kolom kedua dengan prioritas relatif elemen kedua dan begitu seterusnya. b. jumlahkan setiap baris yang ada. c. hasil dari penjumlahan baris dibagi dengan elemen prioritas relatif yang besangkutan. d. jumlahkan hasil bagi dengan banyaknya elemen yang ada, kemudian hasilnya disebut λ maks e. hitung consistency index (ci) dengan rumus keterangan: n = banyaknya elemen f. hitung consistency ratio (cr) dengan rumus cr = ci/ri keterangan: cr = consistency ratio ci = consistency index ri = random consistency index g. memeriksa konsistensi hierarki. jika nilainya lebih dari 10%, maka penilaian data judgement harus diperbaiki. namun jika ratio konsistensi (ci/ri) kurang atau sama dengan 0,1 maka hasil perhitungan dapat dinyatakan benar. hasil dan pembahasan metode analytical hierarchy process (ahp) digunakan untuk menentukan hasil penelitian dan pembahasan. prinsip-prinsip dasar dari metode analytical hierarchy process (ahp) adalah decompotition, comparative judgement, synthesis of priority, dan consistency. decompotition suatu tahap dimana persoalan yang utuh didefinisikan dan disederhanakan menjadi persoalan yang lebih kecil. persoalan digambarkan dalam bentuk hierarki, dan dikelompokkan menjadi tiga bagian, yaitu tujuan, kriteria dan alternatif. tiga kriteria yang digunakan dalam penelitian ini adalah level, kelas dan kemampuan. kriteria pertama adalah level, hal ini dapat dilihat dari nilai, ketangkasan, ketepatan dan swp (standar waktu penyelesaian) saat siswa tersebut mengerjakan soal. kriteria kedua adalah kelas, yang dinilai dari kriteria ini yaitu kehadiran, kerajinan, sikap belajar dan alur kelas yang dilakukan siswa tersebut saat proses belajar mengajar di kelas berlangsung. sedangkan kriteria ketiga adalah kemampuan, kriteria ini dinilai dari pemahaman, orc (oral reading comprehension) dan kemandirian siswa selama mengerjakan soal . kriteria dan alternatif penilaian penerima piala 5 tingkat efl dijelaskan pada gambar struktur hierarki berikut ini: pemilihan penerima piala level kelas kemampuan nilai ketangkasan ketepatan swp kehadiran kerajinan sikap belajar alur kelas pemahaman orc mandiri salista ivana sayesha tujuan kriteria sub kriteria alternatif sumber: (frieyadie & purnawati, 2017) gambar 1. hirarki penilaian prestasi hirarki diatas menjelaskan pemecahan masalah yang terdiri dari tujuan, kriteria, sub kriteria, dan alternatif. kriteria yang digunakan pada hirarki di atas dijelaskan pada tabel 1 berikut ini: tabel 1. penjelasan kriteria pemilihan penerima piala 5 tingkat efl kriteria penjelasan level menilai tingkatan level yang sudah dicapai siswa kelas menilai sikap siswa tersebut selama berada di kelas kemampuan menilai tingkatan kemampuan yang dimiliki siswa sumber: (frieyadie & purnawati, 2017) sub kriteria yang digunakan dalam hirarki sebelumnya dijelaskan pada tabel 2 berikut ini: tabel 2. penjelasan sub kriteria pemilihan penerima piala 5 tingkat efl sub kriteria penjelasan nilai dilihat dari nilai yang diperoleh siswa tersebut ketangkasan menilai tingkat konsentrasi siswa tersebut saat mengerjakan soal ketepatan menilai tingkat ketepatan siswa saat menjawab soal http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 5 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. swp menilai standar waktu penyelesaian siswa dalam menyelesaikan setiap soal kehadiran menilai jumlah kehadiran siswa dalam mengikuti kelas kerajinan menilai tingkat kerajinan siswa dalam membuat homework (pekerjaan rumah) sikap belajar menilai sikap belajar siswa selama mengerjakan soal di kelas alur kelas menilai urutan alur kelas yang dilakukan siswa, mulai dari siswa datang sampai siswa pulang pemahaman menilai tingkat pemahaman siswa terhadap materi yang diberikan orc menilai tingkat kelancaran siswa dalam berbicara bahasa inggris mandiri menilai tingkat kemandirian siswa saat mengerjakan soal sumber: (frieyadie & purnawati, 2017) consistency tahap consistency ini bertujuan untuk menentukan kebenaran nilai eigen vektor yang diperoleh dari proses synthesis of priority yang telah dibuat sebelumnya. tahap consistency ini dilakukan sebanyak 15 kali, diantaranya sebagai berikut: a. level 1 berdasarkan kriteria utama hasil nilai dari λ maksimum didapatkan: (3.004 + 3.003 + 3.004) / 3 = 3.0037 menghitung indeks konsistensi ci = ( 3.0037 – 3 ) / ( 3 – 1 ) = 0.0018 menghitung rasio konsistensi (consistency ratio) tabel 3. random consistency index size 1 2 3 4 5 6 7 8 9 ri 0 0 0,58 0,9 1,12 1,24 1,32 1,41 1,49 sumber: (frieyadie & purnawati, 2017) cr = ci / ri = 0.0018 / 0.58 = 0.0032 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. b. level 2 berdasarkan kriteria level hasil dari λ maksimum didapatkan (4.402 + 4.187 + 4.243 + 4.250) / 4 = 4.2706 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 4.2706 – 4 ) / ( 4 – 1 ) = 0.0902 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0902 / 0.9 = 0.1 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. c. level 2 berdasarkan kriteria kelas hasil nilai dari λ maksimum didapatkan. λ = (4.052 + 4.033 + 4.045 + 4.032) / 4 = 4.0408 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 4.0408 – 4 ) / ( 4 – 1 ) = 0.0136 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0136 / 0.9 = 0.0151 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. d. level 2 berdasarkan kriteria kemampuan hasil nilai dari λ maksimum didapatkan. λ = (3.003 + 3.001 + 3.003) / 3 = 3.0024 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0024 – 3 ) / ( 3 – 1 ) = 0.0012 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0012 / 0.58 = 0.0021 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 6 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. konsisten dan eigen vektor yang dihasilkan dapat diandalkan. e. level 3 berdasarkan sub kriteria nilai hasil nilai dari λ maksimum didapatkan. λ = (3.018 + 3.008 + 3.005) / 3 = 3.0106 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0106 – 3 ) / ( 3 – 1 ) = 0.0053 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0053 / 0.58 = 0.0091 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. f. level 3 berdasarkan sub kriteria ketangkasan hasilnya adalah nilai dari λ maksimum. (3.000 + 3.000 + 3.000) / 3 = 3.000 tahap kedua dari proses consistency adalah menguji konsistensi hirarki, dengan cara: menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.000 – 3 ) / ( 3 – 1 ) = 0.0000 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0000 / 0.58 = 0.0000 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. g. level 3 berdasarkan sub kriteria ketepatan hasil nilai dari λ maksimum didapatkan. λ = (3.191 + 3.082 + 3.129) / 3 = 3.1342 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.1342 – 3 ) / ( 3 – 1 ) = 0.0671 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0671 / 0.58 = 0.1157 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. h. level 3 berdasarkan sub kriteria swp (standar waktu penyelesaian) hasil nilai dari λ maksimum didapatkan. λ = (3.030 + 3.022 + 3.073) / 3 = 3.0415 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0415 – 3 ) / ( 3 – 1 ) = 0.0207 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0207 / 0.58 = 0.0358 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. i. level 3 berdasarkan sub kriteria kehadiran hasil nilai dari λ maksimum didapatkan. λ = (3.058 + 3.039 + 3.008) / 3 = 3.0349 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0349 – 3 ) / ( 3 – 1 ) = 0.0175 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0175 / 0.58 = 0.0301 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 7 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. konsisten dan eigen vektor yang dihasilkan dapat diandalkan. j. level 3 berdasarkan sub kriteria kerajinan hasil nilai dari λ maksimum didapatkan. λ = (3.006 + 3.004 + 3.007) / 3 = 3.0057 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0057 – 3 ) / ( 3 – 1 ) = 0.0029 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0029 / 0.58 = 0.0049 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. k. level 3 berdasarkan sub kriteria sikap belajar hasil nilai dari λ maksimum didapatkan. λ = (3.095 + 3.027 + 3.085) / 3 = 3.0690 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0690 – 3 ) / ( 3 – 1 ) = 0.0345 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0345 / 0.58 = 0.0595 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. l. level 3 berdasarkan sub kriteria alur kelas hasil nilai dari λ maksimum didapatkan. λ = (3.020 + 3.009 + 3.022) / 3 = 3.0169 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0169 – 3 ) / ( 3 – 1 ) = 0.0084 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0084 / 0.58 = 0.0145 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. m. level 3 berdasarkan sub kriteria pemahaman hasil nilai dari λ maksimum didapatkan. λ = (3.010 + 3.007 + 3.014) / 3 = 3.0101 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0101 – 3 ) / ( 3 – 1 ) = 0.0050 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0050 / 0.58 = 0.0087 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. n. level 3 berdasarkan sub kriteria orc (oral reading comprehension) hasil nilai dari λ maksimum didapatkan. λ = (3.000 + 3.000 + 3.000) / 3 = 3.0000 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0000 – 3 ) / ( 3 – 1 ) menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0000 / 0.58 = 0.0000 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 8 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. o. level 3 berdasarkan sub kriteria mandiri hasil nilai dari λ maksimum didapatkan. λ = (3.007 + 3.002 + 3.007) / 3 = 3.0054 menghitung indeks konsistensi (consistency index) ci = ( λ maksimum – n ) / ( n – 1 ) = ( 3.0054 – 3 ) / ( 3 – 1 ) = 0.0027 menghitung rasio konsistensi (consistency ratio) cr = ci / ri = 0.0027 / 0.58 = 0.0046 jika nilai cr < 0.1 (10%) maka dapat diterima, yang berarti matrik perbandingan berpasangan level 1 berdasarkan kriteria utama telah diisi dengan pertimbangan-pertimbangan yang konsisten dan eigen vektor yang dihasilkan dapat diandalkan. setelah proses consistency dilakukan, tahap selanjutnya adalah melakukan perhitungan untuk pengambilan keputusan. langkah-langkahnya sebagai berikut: a. gabungan eigen vektor pada level 3 (level alternatif) dikali dengan eigen vektor pada level 2 (level sub kriteria). tabel 4. eigen vektor keputusan kriteria level nilai ketangkasan ketepatan swp eigen vektor ev keputusan salista 0,562 0,553 0,443 0,225 0,301 0,454 ivana 0,277 0,174 0,204 0,191 x 0,218 = 0,216 sayesha 0,161 0,273 0,353 0,584 0,256 0,330 0,225 sumber: (frieyadie & purnawati, 2017) tabel 5. eigen vektor keputusan kriteria kelas kehadiran kerajinan sikap belajar alur kelas eigen vektor ev keputusan salista 0,576 0,362 0,487 0,408 0,398 0,469 ivana 0,343 0,228 0,137 0,177 x 0,320 = 0,254 sayesha 0,081 0,410 0,376 0,415 0,119 0,276 0,163 sumber: (frieyadie & purnawati, 2017) tabel 6. eigen vektor keputusan kriteria kemampuan pemahaman orc mandiri eigen vektor ev keputusan salista 0,318 0,176 0,450 0,380 0,354 ivana 0,227 0,176 0,132 x 0,169 = 0,175 sayesha 0,455 0,648 0,418 0,451 0,471 sumber: (frieyadie & purnawati, 2017) b. hasil operasi perkalian dari ketiga kriteria tersebut selanjutnya dikalikan dengan eigen vektor pada level 1 (level kriteria). tabel 7. eigen vektor keputusan level kelas kemampuan eigen vektor ev kepututsan salista 0,454 0,469 0,354 0,376 0,421 ivana 0,216 0,254 0,175 x 0,256 = 0,211 sayesha 0,330 0,276 0,471 0,368 0,368 sumber: (frieyadie & purnawati, 2017) c. hasil operasi perkalian tersebut disebut sebagai eigen vektor keputusan, keputusan ditentukan oleh nilai yang mempunyai jumlah paling besar. dari eigen vektor keputusan terlihat bahwa: a. salista memiliki bobot prioritas tertinggi yaitu 0.421 b. sayesha memiliki bobot prioritas kedua yaitu 0.368 c. ivana memiliki bobot prioritas terendah yaitu 0.211 jika digambarkan dalam bentuk grafik maka dapat dilihat jumlah prosentasenya sebagai berikut: http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 9 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sumber: (frieyadie & purnawati, 2017) gambar 2. presentase eigen vektor keputusan berdasarkan hasil presentase diketahui bahwa penerima piala 5 tingkat efl berdasarkan kriteria-kriteria yang telah ditentukan oleh kantor pusat kumon adalah salista. setelah menentukan penerima piala dilakukan perhitungan untuk pengujian rasio konsistensi hirarki (crh). rumus yang digunakan untuk pengujian crh adalah sebagai berikut: 𝐶𝑅𝐻 = 𝑀 �̅� 𝐶𝑅𝐻 = {𝐶𝐼 𝐿𝑒𝑣𝑒𝑙 1 + (𝐸𝑉 𝐿𝑒𝑣𝑒𝑙 1)(𝐶𝐼 𝐿𝑒𝑣𝑒𝑙 2)} {𝑅𝐼 𝐿𝑒𝑣𝑒𝑙 1 + (𝐸𝑉 𝑙𝑒𝑣𝑒𝑙 1)(𝑅𝐼 𝐿𝑒𝑣𝑒𝑙 2)} 𝐶𝑅𝐻 = 0.0018 + (0.376 0.256 0.368) ( 0.0902 0.0136 0.0021 ) 0.58 + (0.376 0.256 0.368) ( 0.90 0.90 0.58 ) 𝐶𝑅𝐻 = 0.0396 1.362 = 0.0291 hasil perhitungan crh diketahui bahwa nilai crh kurang dari 0,1 atau kurang dari 10%, berarti hirarki secara keseluruhan konsisten sehingga dapat disimpulkan keputusan yang ditetapkan dapat diandalkan. kesimpulan pemilihan penerima piala 5 tingkat efl (english as foreign language) di kumon danau sunter dengan menggunakan metode ahp (analytical hierarchy process) di software expert choice 11 dapat mengoptimalkan proses pengambilan keputusan dalam memilih siswa yang memenuhi kriteria. seiring dengan proses pengambilan keputusan yang lebih optimal, hal ini akan berdampak pada meningkatnya kinerja dari pengambil keputusan. selain itu, waktu yang diperlukan dalam mengambil keputusan juga menjadi lebih cepat dari sebelumnya. dari hasil penelitian dengan menggunakan software expert choice 11, dapat disimpulkan bahwa salista menjadi solusi terbaik dari kedua alternatif yang disajikan dengan presentase sebesar 42,09% referensi bliss, c. a., & lawrence, b. 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(2015). sistem penunjang keputusan kelayakan penerima jamkesmas (jaminan kesehatan masyarakat) dengan metode saw di kecamatan singosari berbasis web. j-intech, 3(01), 60–66. retrieved from http://jurnal.stiki.ac.id/jintech/article/view/99 http://creativecommons.org/licenses/by-nc/4.0/ 177 comparison of breast cancer classification using decision tree id3 and k-nearest neighbors algorithm to predict the best performance of algorithm zyhan faradilla daldiri 1*) , desti fitriati 2 informatics engineering 1,2 universitas pancasila 1,2 jakarta, indonesia zyhandilla1308@gmail.com 1*) , desti.fitriati@univpancasila.ac.id 2 (*) corresponding author abstract one of the leading causes of death is cancer. the most common cancer in women is breast cancer. breast cancer (carcinoma mammae) is a malignant neoplasm originating from the parenchyma. breast cancer ranks first in terms of the highest number of cancers in indonesia and is among the first contributors to cancer deaths. globocan data in 2020 shows that the number of new breast cancer cases reached 68,858 (16.6%) of the total 396,914 new cancer cases in indonesia. meanwhile, deaths reached more than 22 thousand cases (romkom, 2022). this death rate is increasing due to insufficient information about breast cancer’s early symptoms and dangers. of this lack of information, a system is needed that can provide information about breast cancer, such as early diagnosis. several parameters and classification data mining techniques can predict which patients will develop breast cancer and which do not. in this study, a comparison of the classification of breast cancer using the decision tree id3 algorithm and the k-nearest neighbors algorithm will be carried out. attribute data consists of menopause, tumor-size, node-caps, deg-malig, breast-squad, and irradiant. the main objective of this study is to improve classification performance in breast cancer diagnosis by applying feature selection to several classification algorithms. the decision tree id3 algorithm has an accuracy rate of 93.333%, and the k-nearest neighbors algorithm has an accuracy rate of 76.6667%. keywords: breast cancer; woman; classification; comparison; decision tree algorithm; k-nearest neighbors algorithm abstrak salah satu penyebab kematian yang utama adalah kanker. kanker yang paling umum pada wanita adalah kanker payudara. kanker payudara (carcinoma mammae) didefinisikan sebagai suatu penyakit neoplasma ganas yang berasal dari parenchyma. kanker payudara menempati urutan pertama terkait jumlah kanker terbanyak di indonesia serta menjadi salah satu penyumbang kematian pertama akibat kanker. data globocan tahun 2020, jumlah kasus baru kanker payudara mencapai 68.858 kasus (16,6%) dari total 396.914 kasus baru kanker di indonesia. sementara itu, untuk jumlah kematiannya mencapai lebih dari 22 ribu jiwa kasus(romkom,2022). angka kematian ini meningkat karena kurangnya informasi tentang gejala awal dan bahaya dari kanker payudara itu sendiri. dari kurangnya informasi tersebut, maka dibutuhkan sebuah sistem yang dapat memberikan informasi tentang penyakit kanker payudara seperti diagnosa secara dini. teknik data mining klasifikasi dapat digunakan untuk memprediksi pasien mana yang terkena kanker payudara dan tidak dengan beberapa parameter yang ada. dalam penelitian ini akan dilakukan perbandingan klasifikasi penyakit kanker payudara dengan menggunakan algoritma decision tree id3 dan algoritma k-nearest neighbors. atribut data yang digunakan terdiri dari menopause, tumor-size, node-caps, deg-malig, breast, breast-squad dan irradiant. tujuan utama penelitian ini adalah untuk meningkatkan peforma klasifikasi pada diagnosis kanker payudara dengan menerapkan seleksi fitur pada beberapa algoritma klasifikasi. algoritma decision tree id3 memiliki tingkat akurasi yaitu 93,333% dan algoritma k-nearest neighbors memilki tingkat akurasi yaitu 76,6667%. kata kunci : kanker payudara; wanita; klasifikasi; perbandingan; algoritma decision tree; algoritma knearest neighbors 178 introduction information technology continues to progress from time to time. information technology has provided various helpful information and data for people’s lives. technology also plays a role in advancing various institutions, one of which is health agencies. the data generated in the health sector can be in the form of data about diseases that are considered deadly such as cancer, which of course this data can be used to dig deeper into information related to cancer itself, both for the treatment or prevention of patients who have not or have experienced cancer (ramadhan & kurniawati, 2020). information from the data can be used to find new patterns of information by processing or extracting information. this new pattern can be used to classify cancer patients based on recurrence or non-recurrence of the disease. this knowledge can help the medical side handle patients to minimize the number of cancer patients who experience recurrence of the disease. currently, breast cancer is a type of cancer that is very frightening for women around the world, and this also applies in indonesia. cancer prevention can be done early by being aware of cancer at its initial appearance so that it can have a high cure rate (hasanah, 2018). therefore, it is necessary to carry out prevention efforts to increase public awareness in recognizing the symptoms and risks, especially breast cancer, in determining appropriate preventive measures and early detection. breast cancer is a malignant tumor formed from breast cells that grow and develop uncontrollably so that it can spread to organs near the breast or to other parts of the body (buana briliant, 2020). breast cancer is still a disease with a high mortality rate in women. based on data from the who (world health organization), in 2020, the number of breast cancer cases will reach 68,858 (16.6%) of 396,914 new cancer cases in indonesia. meanwhile, deaths reached more than 22 thousand cases (kementrian kesehatan indonesia, 2022). judging the significant mortality rate in indonesia makes us aware of the importance of knowing the symptoms of breast cancer to prevent increased deaths caused by breast cancer. the research data is used to test the accuracy of breast cancer diagnosis using public data from the uci machine learning repository breast cancer dataset. the dataset is tested by processing the data using data mining. data mining is a series of processes to find values in data sets that are not known manually (prakarsya & prambayun, 2020). so that the breast cancer dataset that has extensive data can be analyzed in making decisions using data mining. one of the data mining methods in the data exploration process is the data classification technique. with this data classification technique, it is possible to classify the breast cancer dataset using the id3 decision tree algorithm and the k-nearest neighbors algorithm with the results of the accuracy of the classification of each of these algorithms. this study will be compared for the best performance evaluation in breast cancer detection. thus, the results of this comparison can be used as a reference for diagnosing breast cancer at an early stage. research objectives and methods used 1. to find out which algorithm performance produces higher accuracy. 2. to improve classification performance in breast cancer diagnosis. benefits of research 1. assist in developing further research related to algorithm comparison problems. 2. it can be used as a reference in research on breast cancer. research contribution this study can provide knowledge about the results of research that has been carried out in obtaining information about comparing the kneirest neighbors algorithm and the id3 decision tree algorithm in classifying breast cancer. research methods types of research the data obtained in this study were sourced from the uci machine learning repository website, entitled breast cancer dataset. the data taken is data randomly seen from the classification results obtained. research time and place the study was conducted for two weeks, the first week at the end of may 2022 and the second week at the beginning of june 2022. the study was carried out at home by analyzing and calculating the data obtained. data mining data mining is looking for information models that can be useful in database storage. data mining is one of a series of knowledge search processes in a database known as knowledge discovery in database (kdd). kdd relates to integration techniques and scientific discoveries for interpretation and visualization so that they are easy to understand (zulfa et al., 2020). some techniques frequently mentioned in the data mining 179 literature include clustering, classification, association rule mining, and neural networks. classification classification is a data processing technique that determines data into predetermined groups or classes (bahri & lubis, 2020). this method helps produce functions or models that explain classes in data used to predict classes of objects that have not been labeled (arie wijaya et al., 2021). the classification method refers to forming data groups by applying algorithms in the classification process. decision tree decision tree id3 is a tree modeling technique that can implement a series of decisions. the id3 decision tree has tree nodes that represent the attributes that have been tested, and each branch is a division of the test results, and leaf nodes represent certain class groups (setio et al., 2020). the process in the id3 decision tree is to change the form of a data table into a tree model (rionaldi, 2022). in the calculation phase using the decision tree id3 algorithm, there are several stages, namely: 1. specifies attribute data with data values in the dataset. 2. looking for the same data. 3. searching for data is false. 4. searching for data is accurate. 5. calculate entropy with the following formula (1): entropy (s) = − 𝑁𝑚𝑗 𝑁 ∑𝑖 𝑃𝑚𝑗 (𝑖) 𝑙𝑜𝑔2 𝑃𝑚𝑗 (𝑖) ...................... (1) 6. add up the entropy results. 7. make a decision tree according to the results obtained. k-nearest neighbors the k-nearest neighbor (knn) algorithm is a case search approach that calculates the closeness between new and old cases based on matching the weights of several existing features (setiawan et al., 2020). k in k-nn is the number of neighbors that will be taken to determine decisions (mustafa & wayan simpen, 2019). the k-nn algorithm in this study has several steps, which are as follows: 1. converting qualitative data into quantitative data. 2. calculate using the euclidean formula on the data. the euclidean formula is as follows: 𝑑(𝑝, 𝑞) = √∑ (𝑞𝑖 − 𝑝𝑖)2𝑛𝑖=1 ........................................ (2) 3. determine the closest distance from the results of the euclidean formula calculation (2). 4. determine the k to be taken. 5. determine the existing prediction results by the taken k. procedure in this study, a comparison was made between the decision tree id3 algorithm and the knearest neighbors algorithm to get the best classification results. the stages used in this research are: 1. dataset processing. 2. calculations using the decision tree id3 algorithm. 3. calculations using the k-nn algorithm. 4. perform data comparisons using the two algorithms. 5. calculate the accuracy (3) of the two algorithms with the following formula: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 𝑥100% ............................... (3) data, instruments, and data collection techniques this study uses primary data and as many as 30 test data. the technique of collecting criteria and alternative data to be used in system testing was obtained by taking a dataset from the uci machine learning repository site, namely https://archive.ics.uci.edu/ml/datasets/breast+ca ncer. for information on breast cancer data, this study used six attributes, namely menopause, tumor-size, node-caps, deg-malig, breast, breastsquad, and irradiant. data analysis technique the data in this study consisted of quantitative and qualitative data. for qualitative data, researchers convert them into numbers (quantitative data) to make it easier to calculate both manual and system calculations. results and discussion based on the research stages, the first step is processing breast cancer datasets. the attributes of the dataset can be seen in table 1. table 1. attributes of the breast cancer dataset attribute menopause 180 attribute tumor size node caps deg malig breast breast squad from these attributes, 30 data samples were taken randomly to be tested. the sample data can be seen in table 2. table 2. breast cancer sample data menopause tumor size invnodes nodecaps degmalig breast breast squad irradiat ge40 15-19 0-2 no 2 right left_up no ge40 25-29 0-2 no 3 left right_up no ge40 30-34 0-2 no 1 right right_up no ge40 30-34 0-2 no 2 left left_low yes ge40 25-29 0-2 no 3 left right_low yes ge40 30-34 0-2 no 3 right left_up yes ge40 15-19 0-2 no 3 right left_up yes ge40 30-34 0-2 no 1 right central no ge40 15-19 0-2 no 1 left right_low no ge40 20-24 0-2 no 1 left left_low no ge40 15-19 0-2 no 3 right left_up yes ge40 15-19 0-2 no 2 left left_up yes ge40 30-34 0-2 yes 2 right right_up yes lt40 30-34 0-2 no 1 left left_low no lt40 15-19 0-2 no 2 left left_low no lt40 30-34 0-2 no 3 right left_up no lt40 15-19 0-2 no 3 right left_up no premeno 30-34 0-2 no 2 right right_up yes premeno 20-24 0-2 no 3 right left_low yes premeno 35-39 0-2 yes 3 right left_up yes premeno 35-39 0-2 yes 3 right left_low yes premeno 25-29 0-2 no 1 right left_low yes premeno 25-29 0-2 no 2 left left_up yes premeno 20-24 0-2 no 2 left right_low no premeno 30-34 0-2 no 3 right left_up yes premeno 20-24 0-2 no 1 left right_low no premeno 15-19 0-2 no 1 left left_low no premeno 20-24 0-2 no 2 left central no premeno 35-39 0-2 no 2 right right_up no premeno 20-24 0-2 no 3 left left_up yes after taking the test data, the next step is calculating the data classification using the decision tree id3 algorithm. the first stage in decision tree id3, which determines data attributes with values in the test data, can be seen in table 3. table 3. value data based on attributes attribute (m) value data (j) menopause ge40; lt40; premeno tumor size 15-19; 20-24; 25-29 30-34; 35-39 node caps yes; no deg malig 1; 2; 3 breast left; right breast squad left low; left up; central right low; right up after determining the value of the test data according to the attributes, calculations can be made to determine the same data, determine false data, determine accurate data, calculate entropy, and add up the results of the entropy values in each of the data attributes with n as the amount of data being tested. the results can be seen in table 4. table 4. entropy calculation results m j nmj/n false true entropy menopause ge40 13/30 6/13 7/13 0,4314818959 0,848017758 181 m j nmj/n false true entropy lt40 4/30 4/4 0/4 0 premeno 13/30 5/13 8/13 0,416535862 tumor size 15-19 8/30 5/8 3/8 0,2545157341 0,9354977189 20-24 6/30 4/6 2/6 0,1836591668 25-29 4/30 1/4 3/4 0,1081704166 30-34 9/30 4/9 5/9 0,297322818 35-39 3/30 1/3 2/3 0,09182958341 node-caps yes 3/30 0/3 3/3 0 0,8919684539 no 27/30 15/27 12/27 0,8919684539 deg-malig 1 8/30 7/8 1/8 0,1449505182 0,8027951013 2 10/30 5/10 5/10 0,3333333333 3 12/30 3/12 9/12 0,3245112498 breast left 14/30 9/14 5/14 0,438800114 0,9478315823 right 16/30 6/16 10/16 0,5090314682 breastsquad left low 8/30 4/8 4/8 0,2666666667 0,8466241924 left up 11/30 3/11 8/11 0,3099620101 central 2/30 2/2 0/2 0 right low 4/30 3/4 1/4 0,1081704166 right up 5/30 3/5 2/5 0,1618250991 from the results of the entropy calculation, tree data from deg-malig can be taken because the results obtained have the smallest value. then proceed with calculating the entropy again based on the data from the deg-malig with value = 1, which can be seen in table 5. table 5. data based on deg malig attribute with value=1 menopause tumor size inv-nodes node-caps deg-malig breast breast squad irradiat ge40 30-34 0-2 no 1 right right_up no ge40 30-34 0-2 no 1 right central no ge40 15-19 0-2 no 1 left right_low no ge40 20-24 0-2 no 1 left left_low no lt40 30-34 0-2 no 1 left left_low no premeno 25-29 0-2 no 1 right left_low yes premeno 20-24 0-2 no 1 left right_low no premeno 15-19 0-2 no 1 left left_low no from this data, entropy calculations can be made based on the values in the deg-malig attribute data. the results of the calculation of the deg-malig attribute data with value = 1 can be seen in table 6. table 6. entropy calculation results based on deg malig data with value=1 m j nmj/n false true entropy menopause ge40 4/8 4/4 0/4 0 0,3443609378 lt40 1/8 1/1 0/1 0 premeno 3/8 2/3 1/3 0,3443609378 tumor size 15-19 2/8 2/2 0/2 0 0 20-24 2/8 2/2 0/2 0 25-29 1/8 0/1 1/1 0 30-34 3/8 3/3 0/3 0 breast left 5/8 5/5 0/5 0 0,3443609378 right 3/8 2/3 1/3 0,3443609378 breast-squad left low 4/8 3/4 1/4 0,4056390622 0,4056390622 central 1/8 1/1 0/1 0 right low 2/8 2/2 0/2 0 right up 1/8 1/1 0/1 0 repeated patterns can be used to calculate the entropy of the deg-malig attribute data with value=2 and value=3. after these calculations, the tree results will be obtained as in figure 1. 182 figure 1. classification of id3 decision tree results furthermore, testing the existing data with the knn algorithm by performing the steps on the mentioned methodology by changing the qualitative data into quantitative ones. the data is as in table 7. table 7. changes in value data attribute value (qualitative) value (quantitative) menopause ge40 lt40 premeno 1 2 3 tumor size 15-19 20-24 25-29 30-34 35-39 1 2 3 4 5 node caps yes no 1 2 breast left right 1 2 breast squad left low left up central right low right up 1 2 3 4 5 after changing the value of the data, the test dataset will be like in table 8. table 8. data changes based on quantitative value data menopause tumor size inv-nodes node-caps deg-malig breast breast squad 1 1 0-2 2 2 2 2 1 3 0-2 2 3 1 5 1 4 0-2 2 1 2 5 1 4 0-2 2 2 1 1 1 3 0-2 2 3 1 4 1 4 0-2 2 3 2 2 1 1 0-2 2 3 2 2 1 4 0-2 2 1 2 3 1 1 0-2 2 1 1 4 1 2 0-2 2 1 1 1 1 1 0-2 2 3 2 2 1 1 0-2 2 2 1 2 1 4 0-2 1 2 2 5 2 4 0-2 2 1 1 1 2 1 0-2 2 2 1 1 2 4 0-2 2 3 2 2 2 1 0-2 2 3 2 2 3 4 0-2 2 2 2 5 3 2 0-2 2 3 2 1 3 5 0-2 1 3 2 2 3 5 0-2 1 3 2 1 3 3 0-2 2 1 2 1 3 3 0-2 2 2 1 2 3 2 0-2 2 2 1 4 3 4 0-2 2 3 2 2 3 2 0-2 2 1 1 4 3 1 0-2 2 1 1 1 3 2 0-2 2 2 1 3 3 5 0-2 2 2 2 5 3 2 0-2 2 3 1 2 the change in the data value can be calculated using the euclidean formula, determining the closest distance in ascending order and the 183 result of the specified k. the results of these calculations are as follows. table 9. knn calculation results with 1st data trial menopause tumor size invnodes nodecaps degmalig breast breast squad irradiat euclidean nearest distance (k) k=5 classify 1 1 0-2 2 2 2 2 n 0 1 y n 1 3 0-2 2 3 1 5 n 15 24 n 1 4 0-2 2 1 2 5 n 19 25 n 1 4 0-2 2 2 1 1 y 11 18 n 1 3 0-2 2 3 1 4 y 10 14 n 1 4 0-2 2 3 2 2 y 10 14 n 1 1 0-2 2 3 2 2 y 1 2 y y 1 4 0-2 2 1 2 3 n 11 18 n 1 1 0-2 2 1 1 4 n 6 8 n 1 2 0-2 2 1 1 1 n 4 7 n 1 1 0-2 2 3 2 2 y 1 2 y y 1 1 0-2 2 2 1 2 y 1 2 y y 1 4 0-2 1 2 2 5 y 19 25 n 2 4 0-2 2 1 1 1 n 13 22 n 2 1 0-2 2 2 1 1 n 3 6 n 2 4 0-2 2 3 2 2 n 11 18 n 2 1 0-2 2 3 2 2 n 2 5 y n 3 4 0-2 2 2 2 5 y 22 27 n 3 2 0-2 2 3 2 1 y 7 9 n 3 5 0-2 1 3 2 2 y 22 27 n 3 5 0-2 1 3 2 1 y 23 29 n 3 3 0-2 2 1 2 1 y 10 14 n 3 3 0-2 2 2 1 2 y 9 13 n 3 2 0-2 2 2 1 4 n 10 14 n 3 4 0-2 2 3 2 2 y 14 23 n 3 2 0-2 2 1 1 4 n 11 18 n 3 1 0-2 2 1 1 1 n 7 9 n 3 2 0-2 2 2 1 3 n 7 9 n 3 5 0-2 2 2 2 5 n 29 30 n 3 2 0-2 2 3 1 2 y 7 9 n judging from table 9, the knn calculation is based on the test data on the first data to find out the prediction results obtained in the knn classification. in this study, 30 existing data will be tested using the knn algorithm so that the calculation is repeated with test data on the 2nd to 30th data. after calculating the knn classification data on the 30 data, the knn classification can be generated in table 10. table 10. knn calculation classification results no menopause tumor size invnodes nodecaps degmalig breast breast squad irradiat result 1 ge40 15-19 0-2 n 2 right left_up n y 2 ge40 25-29 0-2 n 3 left right_up n y 3 ge40 30-34 0-2 n 1 right right_up n n 4 ge40 30-34 0-2 n 2 left left_low y n 5 ge40 25-29 0-2 n 3 left right_low y y 6 ge40 30-34 0-2 n 3 right left_up y y 7 ge40 15-19 0-2 n 3 right left_up y y 8 ge40 30-34 0-2 n 1 right central n n 9 ge40 15-19 0-2 n 1 left right_low n n 10 ge40 20-24 0-2 n 1 left left_low n n 11 ge40 15-19 0-2 n 3 right left_up y y 12 ge40 15-19 0-2 n 2 left left_up y y 13 ge40 30-34 0-2 y 2 right right_up y y 14 lt40 30-34 0-2 n 1 left left_low n y 184 no menopause tumor size invnodes nodecaps degmalig breast breast squad irradiat result 15 lt40 15-19 0-2 n 2 left left_low n n 16 lt40 30-34 0-2 n 3 right left_up n y 17 lt40 15-19 0-2 n 3 right left_up n y 18 premeno 30-34 0-2 n 2 right right_up y y 19 premeno 20-24 0-2 n 3 right left_low y y 20 premeno 35-39 0-2 y 3 right left_up y y 21 premeno 35-39 0-2 y 3 right left_low y y 22 premeno 25-29 0-2 no 1 right left_low y y 23 premeno 25-29 0-2 no 2 left left_up y y 24 premeno 20-24 0-2 no 2 left right_low n n 25 premeno 30-34 0-2 no 3 right left_up y y 26 premeno 20-24 0-2 no 1 left right_low n n 27 premeno 15-19 0-2 no 1 left left_low n y 28 premeno 20-24 0-2 no 2 left central n n 29 premeno 35-39 0-2 no 2 right right_up n n 30 premeno 20-24 0-2 no 3 left left_up y y this study compared test data using the decision tree id3 algorithm and the knn algorithm as the classification method. the results of the predictions using the decision tree id3 algorithm and the knn algorithm are equated with the results of the existing test data so that the data is obtained, like in table 11. table 11. data comparison no menopause tumor size invnodes nodecaps degmalig breast breast squad irradiat id3 knn 1 ge40 15-19 0-2 no 2 right left_up no no yes 2 ge40 25-29 0-2 no 3 left right_up no yes yes 3 ge40 30-34 0-2 no 1 right right_up no no no 4 ge40 30-34 0-2 no 2 left left_low yes yes no 5 ge40 25-29 0-2 no 3 left right_low yes yes yes 6 ge40 30-34 0-2 no 3 right left_up yes yes yes 7 ge40 15-19 0-2 no 3 right left_up yes yes yes 8 ge40 30-34 0-2 no 1 right central no no no 9 ge40 15-19 0-2 no 1 left right_low no no no 10 ge40 20-24 0-2 no 1 left left_low no no no 11 ge40 15-19 0-2 no 3 right left_up yes yes yes 12 ge40 15-19 0-2 no 2 left left_up yes no yes 13 ge40 30-34 0-2 yes 2 right right_up yes yes yes 14 lt40 30-34 0-2 no 1 left left_low no no yes 15 lt40 15-19 0-2 no 2 left left_low no no no 16 lt40 30-34 0-2 no 3 right left_up no no yes 17 lt40 15-19 0-2 no 3 right left_up no no yes 18 premeno 30-34 0-2 no 2 right right_up yes yes yes 19 premeno 20-24 0-2 no 3 right left_low yes yes yes 20 premeno 35-39 0-2 yes 3 right left_up yes yes yes 21 premeno 35-39 0-2 yes 3 right left_low yes yes yes 22 premeno 25-29 0-2 no 1 right left_low yes yes yes 23 premeno 25-29 0-2 no 2 left left_up yes yes yes 24 premeno 20-24 0-2 no 2 left right_low no no no 25 premeno 30-34 0-2 no 3 right left_up yes yes yes 26 premeno 20-24 0-2 no 1 left right_low no no no 27 premeno 15-19 0-2 no 1 left left_low no no yes 28 premeno 20-24 0-2 no 2 left central no no no 29 premeno 35-39 0-2 no 2 right right_up no no no 30 premeno 20-24 0-2 no 3 left left_up yes yes yes the accuracy between the algorithms is calculated from the comparison data to determine which algorithm is more accurate. the accuracy test is carried out to measure how much accuracy is obtained from the tests carried out. accuracy can be calculated using the formula (3) above. by calculating the accuracy, the accuracy of the 185 decision tree id3 algorithm is 93.333%, while the accuracy of the knn algorithm is 76.6667%. conclusions and suggestions conclusions from this research, it can be concluded that the classification calculation using the decision tree id3 algorithm has more accurate results than the classification calculation using the knn algorithm on the breast cancer dataset. these results can be seen through the accuracy value obtained by calculating the classification using the decision tree id3 algorithm, which results in 93.333% having a more excellent accuracy value than the knn algorithm, which results in 76.6667%. so it can be said that using decision tree id3 classification calculations on breast cancer estimates has accurate results on the incidence of breast cancer so that it can be used as a reference in predicting and preventing breast cancer early. suggestions in further research, the classification algorithm with decision tree id3 is expected to be used in the health sector and other fields by recomparing other classification algorithms to find the best accuracy results. reference arie wijaya, y., bahtiar, a., kaslani, k., & r, n. 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(2020). implementasi data mining untuk menentukan strategi penjualan buku bekas dengan pola pembelian konsumen menggunakan metode apriori. teknika: jurnal sains dan teknologi, 16(1), 69–82. https://doi.org/10.36055/tjst.v16i1.7601 jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 147 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem informasi evaluasi karyawan berbasis web pada pt praweda ciptakarsa informatika jakarta susi susanti program studi sistem informasi stmik nusa mandiri www.nusamandiri.ac.id susisr.praweda@gmail.com abstrak proses evaluasi pada pt. praweda ciptakrsa informatika masih bersifat konvensional. selama ini cara berkompetensi yang ada pada pt. praweda ciptakarsa informatika masih belum sesuai dengan pengharapan karyawan, karena masih adanya penilaian yang tidak transparan yang dilakukan saat penilaian kinerja. untuk menjawab permasalahan diatas, perusahaan membutuhkan suatu sistem yang dapat menyimpan data dan informasi mengenai parameter evaluasi kinerja, menyimpan hasil evaluasi, serta membantu tim leader dalam melaksanakan proses evaluasi kinerja karyawan. sistem informasi karyawan dapat menghasil laporan hasil evaluasi karyawan, yang dapat membantu atasan dalam pengambilan penilaian terhadap karyawan yang bersangkutan secara transparan. kata kunci: sistem informasi evaluasi, evaluasi karyawan, web base abstract the evaluation process at pt. praweda ciptakrsa informatika is still conventional. during this time the competency methods in pt. praweda ciptakarsa informatika is still not in line with employee expectations, because there are still non-transparent assessments made during performance appraisals. to answer the above problems, companies need a system that can store data and information about performance evaluation parameters, store evaluation results, and assist team leaders in carrying out employee performance evaluation processes. employee information systems can produce employee evaluation reports, which can help superiors in making evaluations of the employees concerned transparently. keywords: evaluation information system, employee evaluation, web base pendahuluan perusahaan sebagai salah satu organisasi berkaitan erat dengan lingkungan yang ada disekitarnya, baik internal maupun ekternal perusahaan. semakin pesatnya persaingan di dunia bisnis membuat perusahaan dituntut untuk dapat mempertahankan keberadaannya. maka dari itu perusahaan perlu mengembangkan kualitas dalam memanfaatkan segala kesempatan dan menghadapi tantangan-tantangan yang ada. dalam lingkungan internal, karyawan menjadi aset penting dalam menjalankan segala aktifitas yang terjadi di perusahaan. hal ini membuat perusahaan perlu melakukan pengembangan karyawan yang ada di dalamnya. karyawan yang berkualitas dan dapat berkembang akan membuat perusahaan semakin berkembang dan berkualitas pula. pt. praweda ciptakarsa informatika merupakan salah satu perusahaan yang bergerak di bidang teknologi informasi. dalam mengembangkan perusahaan, kinerja karyawan menjadi aspek yang perlu diperhatikan. jika kinerja karyawan baik maka akan berpengaruh baik pula bagi perkembangan perusahaan, namun jika kinerja karyawan dibawah standar perusahaan maka perusahaan perlu melakukan pembinaan dan pengembangan terhadap karyawan tersebut. untuk mengetahui sejauh mana kinerja dari karyawan, maka perusahaan memerlukan suatu sistem yang dapat mengevaluasi kinerja karyawan. proses evaluasi pengembangan karyawan pt. praweda ciptakarsa informatika, penilaian tersebut dirasa masih bersifat subjektif (saefudin & wahyuningsih, 2017), turunnya motivasi kerja karyawan, hingga tingginya intensi turnover karyawan (evita, muizu, & atmojo, 2017), sementara ini masih penilaian evaluasi pengembangan dilakukan oleh pimpinan ditulis pada kertas penilaian kinerja karyawan, kemudian hitungan hasil penilian ditulis pada kertas tersebut, hal ini bisa saja terjadi salah hitung, apa bila lembar penilaian tersebut bisa saja tercecer dan hilang (natanael & mulyono, 2017). http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 148 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional evaluasi karyawan belum memiliki skema data untuk menyimpan data pada periode penilaian kinerja sebelumnya. masih ditemukannya kelemahan (septiani, syafi’i, & rasyidi, 2015) dalam penyampaian informasi kegiatan pembelajaran dan evaluasi yang terdapat pada perusahaan tersebut. selama ini proses evaluasi pada pt. praweda ciptakrsa informatika masih bersifat konvensional (adhar, 2014) selama ini cara berkompetensi yang ada pada pt. praweda ciptakarsa informatika masih belum sesuai dengan pengharapan karyawan, karena masih adanya penilaian yang kurang objektif (mahdang, lamangida, & mohi, 2016) yang dilakukan saat penilaian kinerja. untuk menjawab permasalahan diatas, perusahaan membutuhkan suatu sistem yang dapat menyimpan data dan informasi mengenai parameter evaluasi kinerja, menyimpan hasil evaluasi, serta membantu tim leader dalam melaksanakan proses evaluasi kinerja karyawan. dan berdasarkan observasi dan wawancara dilapangan maka diperlukan aplikasi evaluasi kinerja karyawan. tujuan penelitian ini untuk mewujudkan suatu program apalikasi sistem informasi evaluasi berbasis web guna membantu dalam pemahaman materi yang diberikan oleh perusahaan itu sendiri. sistem informasi evaluasi karyawan berbasis web ini dirancang untuk mengelola karyawan agar dapat berprestasi dan berkompentensi dengan baik dan transparan. sistem informasi evaluasi karyawan yang akan membantu tim leader serta karyawan dalam melakukan proses evaluasi dan melihat kinerja karyawan dari berbagai periode. selain itu sistem informasi dapat memberikan sumber data yang dapat digunakan untuk evaluasi kinerja metode penelitian jenis penelitian penelitian ini menggunakan pendekatan penelitian rancangan eksperimental (experimental design) sistem informasi waktu dan tempat penelitian tempat penelitian pada pt. praweda ciptakarsa informatika yang beralamat di rukan permata senayan blok a 07 lt. 04 grogol utara, kebayoran lama, jakarta selatan. target/subjek penelitian target/subjek penelitian pada aplikasi yang berfokus pada metode kegiatan evaluasi, serta materi yang harus dipahami karyawan. aplikasi hanya membuat proses kegiatan menjadi lebih mudah dari segi efisiensi waktu. data, intrumen, dan teknik pengumpulan data tahap pengumpulan data adalah pengumpulan data dari tempat penelitian sesuai yang dibutuhkan dalam membangun aplikasi pengkajian. a. observasi proses observasi dilakukan dengan mengamati secara langsung proses evaluasi kerja karayawan di cabang pt. praweda ciptakarsa informatika yang beralamat di rukan permata senayan blok a 07 lt. 04 grogol utara, kebayoran lama, jakarta selatan, telepon 021-5328575. dengan mengamati semua proses yang berjalan. b. wawancara wawancara adalah pengumpulan data dengan cara tanya jawab kepada bapak suhud arifin, selaku tim leader dari divisi terkait yang dilakukan langsung di pt. praweda ciptakarsa informatika. c. studi pustaka pengumpulan data yang bersumber dari bebagai buku yang menjadi referensi dan pencarian dengan media internet untuk memperoleh data-data tambahan dalam rangka melengkapi penulisan penelitian. studi literatur adalah pengumpulan data melalui buku-buku, jurnal dan bacaan bacaan yang ada kaitannya dengan judul penelitian. teknik analisis sistem informasi tahap yang dilakukan untuk pembuatan aplikasi ini adalah menggunakan metode waterfall. dimana tahap demi tahap proses yang dilalui harus menunggu selesainya tahap sebelumnya dan berjalan berurutan. adapun penjelasannya adalah sebagai berikut: a. analisa kebutuhan sistem tahap analisa kebutuhan sistem mempelajari proses yang berlangsung antar muka, yaitu admin dapat melihat seluruh data yang ada pada aplikasi tersebut dan dapat melakukan edit serta input seperti edit materi, edit soal evaluasi, edit id user dan cetak laporan nilai. sedangkan untuk user atau karyawan yang menjadi peserta evaluasi dapat melihat data user itu sendiri, materi, data tugas evaluasi, data nilai dan data pribadi. b. design pada tahap ini, penulis mendesain sistem dengan hubungan antar entitas yang ada dalam sistem dijabarkan dalam software arsitektur menggunanakan uml (use case diagram, activity http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 149 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional diagram, squence diagram, class diagram, component diagram dan deployment diagram) sedangkan desain database menggunakan erd (entity relationship diagram) dan lrs (logical record structure). adapun untuk merancang user interface meggunakan codeigniter dengan tampilan sederhana agar mudah digunakan oleh pengguna. c. code generation pada penelitian ini, bahasa pemograman yang akan digunakan adalah php dan codeignter dengan teknik pemrograman oop (object oriented programming) serta mysql sebagai software untuk membangun database-nya. d. testing dalam penelitian ini penggunakan metode pengujian yang digunakan yaitu black box testing, pengujian ini akan dilakukan meliputi halaman login, registrasi, materi yang diberikan perusahaan dan form evaluasi. jika hasil pengujian sesuai dengan yang diharapkan maka dapat digunakan. e. support sistem yang telah diuji kemudian diserahkan ke pengguna untuk dioperasikan sesuai kebutuhanya. tahapan perawatan dibutuhkan dalam masa itu dengan dilakukan pengecekan kesalahan operasionalnya. hasil penelitian dan pembahasan a. analisis kebutuhan softrware berikut ini spesifikasi kebutuhan (system requirement) dari sistem informasi evaluasi pegawai. halaman tim leader a1. tim leader dapat melakukan login kedalam sistem a2. tim leader dapat menegelola data pegawai a3. tim leader dapat mengelola data pengajar a4. tim leader dapat mengelola data tugas atau evaluasi a5. tim leader dapat mengelola data materi a6. tim leader dapat mengelola mata pelajaran departemen a7. tim leader dapat mengelola laporan evaluasi halaman pegawai b1. pegawai harus melakukan register b2. pegawai harus melakukan login b3. pegawai dapat melihat dan membuat pesan b4. pegawai dapat mengelola profil b6. pegawai dapat melihat jadwal kegiatan b7. pegawai dapat mengerjakan tugas atau evaluasi b. desain 1. desain sistem a. desain sistem menggunakan use case diagram 1) use case diagram tim leader tim leader mengelola data pegawai mengelola data pengajar mengelola data tugas evaluasi mengelola materi mengelola data nilai mengelola laporan kinerja login kedalam sistem gambar 1. use case diagram tim leader 2) use case diagram pegawai pegawai registrasi login melihat pesan mengelola profile pegawai melihat jadwal kegiatan mengerjakan evaluasi melihat hasil evaluasi gambar 2. use case diagram pegawai a) deskripsi use case mengerjakan evaluasi tabel 1. deskripsi use case mengerjakan evaluasi use case mengerjakan evaluasi requirements pegawai sudah terdaftar sebagai peserta. goal pegawai dapat mengerjakan soalsoal evaluasi yang diberikan dengan benar. pre –conditions pegawai sudah login post –conditions jika valid makan masuk kedalam ruang evaluasi failed end condition gagal masuk kedalam ruang evaluasi primary actors pegawai main flow / basic path 1. pegawai membuka ruang evaluasi 2. membukan menu tugas dan melihat list tugas yang tersedia http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 150 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 3. membuka link tugas yang tersedia 4. mulai mengerjakan soal dan menjawab soal evaluasi invariant b. desain sistem menggunakan activity diagram memilih soal pada list tugas pesan untuk melanjutkan pengerjaan soal mengerjakan soal evaluasi simpan evaluasi kerjakan? merge kerjakan? gambar 3. use case diagram pegawai c. desain sistem menggunakan class diagram +tambah() +ubah() +hapus() +simpan() +update() -id_pegawai : string -nip : string -nama_peg : string -tmp_lhr_peg : string -tgl_lhr_peg : date -jns_kel_peg : string -agama_peg : string -alamat_peg : string -status_peg : string -foto_peg : string pegawai +tambah() +ubah() +hapus() +simpan() +update() -id_pegawai : string -id_materi : string -id_evaluasi : string -id_pengajar : string -nilai_evaluasi : decimal -tgl_evaluasi : date -tgl_bual : date -judul_evaluasi : string -informasi : string -durasi : integer evaluasi 1 1..* +tambah() +ubah() +hapus() +simpan() +update() -id_materi : string -judul_materi : string -konten_materi : string -file_materi : string -status_publish : char -id_pengajar : string materi 1..* 1 +tambah() +ubah() +hapus() +simpan() +update() -id_pengajar : string -nip : string -nama_peg : string -tmp_lhr_peg : string -tgl_lhr_peg : date -jns_kel_peg : string -agama_peg : string -alamat_peg : string -status_peg : string -foto_peg : string pengajar 1..* 1 1..* 1 -id_pengumuman : string -tgl_pengumuam : date -judul_pengumuam : string -kontent_pengumuam : string -tgl_buka : date -tgl_tutup : date -id_pengajar : string pengumuman 1..* 1 gambar 4. class diagram sistem informasi evaluasi d. desain sistem menggunakan sequence diagram pegawai form login valdasi login message1 login(uname, pass) pegawai querylogin() infoquerylogin()infologin() gambar 5. sequence diagram sistem informasi evaluasi 2. desain database rancangan diagram data model sistem informasi evaluasi akan digunakan sebagai database sistem informasi evaluasi dan table-tabel yang akan digunakan pada sistem informasi evaluasi ini. pegawai pk id_pegawai nip nama_peg temp_lahir_peg tgl_lahir_peg agama_peg jenis_kel_peg alamat_peg status_peg foto_peg evaluasi pk id_evaluasi tgl_buat judul_evaluasi durasi informasi pengajar pk id_pengajar nip nama_peg temp_lahir_peg tgl_lahir_peg agama_peg jenis_kel_peg alamat_peg status_peg foto_peg evaluasi_pegawai fk1 id_pegawai fk2 id_evaluasi fk4 id_materi tgl_evaluasi nilai_evaluasi materi pk id_materi judul_materi konten_materi file_materi status_publish fk1 id_pengajar pengumuman pk id_pengumuman tgl_pengumuman judul_pengumuman konten_pengumuman tgl_buka tgl_tutup fk1 id_pengajar gambar 6. diagram data model sistem informasi evaluasi 3. desain user interface a. rancangan halaman login rancangan halaman login ini digunakan untuk para pegawai yang menjadi peserta evaluasi dapat masuk kedalam sistem informasi evaluasi. berikut adalah tampilan halaman karyawan saat akan login, masukan username dan password klik login. gambar 7. halaman login evaluasi pegawai b. rancangan halaman tugas pegawai rancangan halaman tugas pegawai digunakan untuk supaya pegawai yang sudah masuk kedalam sistem informasi evaluasi dapat memilih tugas apa yang harus dikerjakan saat ini. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 3 juni 2019 p-issn: 2656-1743 e-issn: 2656-1735 151 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional gambar 8. tampilan halaman tugas pegawai c. rancangan halaman evaluasi pegawai rancangan halaman evaluasi pegawai digunakan pegawai untuk melakukan evaluasi yang dilaksanakannya gambar 9. halaman pada saat melakukan evaluasi c. code pada pembahasan ini penulis menggunakan web framework codeigniter, dengan teknik pemrograman objek oriented programming (oop). bahasa script programming yang digunakan yaitu php. d. pengujian (testing) penelitian ini penggunakan metode pengujian yang digunakan yaitu black box testing. a. pengujian form login tabel 2. hasil pengujian black box testing form login user n o skenario pengujian test case hasil yang diharap kan hasil peng ujian kesim pulan 1 mengoson gkan semua isian data login, lalu langsung mengklik username : (kosong) password : (kosong) sistem akan menola k akses login dan menam sesua i hara pan valid tombol ‘login’. pilkan pesan “userna me (email) dibuthk an” 2 hanya mengisi data nama admin dan mengoson gkan data kata sandi, lalu langsung mengklik tombol ‘login’. username : (susi.susanti@ praweda.co.id) password : (kosong) sistem akan menola k akses login dan menam pilkan pesan “userna me (email) dibutuh kan” sesua i hara pan valid 3 hanya mengisi data kata sandi dan mengoson gkan data nama admin, lalu langsung mengklik tombol ‘login’. username : (kosong) password: (1) sistem akan menola k akses login dan menam pilkan pesan “userna me (email) dibutuh kan” sesua i hara pan valid 4 menginpu tkan dengan kondisi salah satu data benar dan satu lagi salah, lalu langsung mengklik tombol ‘login’. username : (susi.susanti@ praweda.co.id) password : (1) sistem meneri ma akses login dan kemudi an langsun g menam pilkan berand a pegawa i. sesua i hara pan valid e. support tahapan support diperlukan untuk mendukung sistem penerimaan pegawai berbasis web pada pt. ugasan berkat jaya. agar sistem dapat dibangun dan terus berjalan dengan baik melalui dukungan hardware dan software yang sesuai dengan kebutuhan sistem. 1. publikasi web untuk website sistem informasi evaluasi karyawan pada pt. praweda ciptakrsa informatika ini tidak dipublikasikan, karena sistem informasi ini masih terbatas, hanya intranet saja. 2. spesifikasi hardware dan software http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 3 juni 2019 152 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pada pembuatan aplikasi ini dibutuhkan dua perangkat, yaitu perangkat keras (hardware) dan perangkat lunak (software) dengan spesifikasi sebagai berikut: tabel 1. spesifikasi hardware dan software no. kebutuhan keterangan 1 sistem operasi windows xp atau di atasnya 2 processor lga 3.0 ghz 3 ram 512 mb atau di atasnya 4 harddisk 80 gb atau di atasnya 5 cd-rom 52x 6 monitor svga 14” 7 key board 107 key 8 printer deskjet atau di atasnya 9 mouse standard mouse 10 browser mozilla firefox, google chrome, internet explorer 11 software php, codeigniter, mysql simpulan dan saran simpulan sistem informasi evaluasi karyawan yang dibuat dapat membantu dalam kegiatan evaluasi kinerja karyawan dan dapat digunakan sebagai indikator untuk meningkatkan kualitas karyawan dimasa yang akan datang dapat dijadikan solusi alternatif untuk membantu dalam proses evaluasi karyawan. sistem informasi karyawan dapat menghasil laporan hasil evaluasi karyawan, yang dapat membantu atasan dalam pengambilan penilaian terhadap karyawan yang bersangkutan secara transparan. saran sistem yang dibangun masih memiliki beberapa kekurangan dan keterbatasan, oleh sebab itu ada beberapa hal yang perlu dikembangkan selanjutnya agar menjadi lebih baik, antara lain: harus adanya perluasan lingkup materi yang diberikan, serta ruang lingkup pengujian tidak hanya pada satu divisi saja, tetapi semua divisi dalam perusahaan tersebut. sistem yang dibangun dikembangkan dapat berupa web berbasis internet, bukan intranet saja, agar cakupan untuk mengkasenya lebih luas dan mudah, tidak harus dilingkungan kantor saja tetapi dapat diakses dimana saja dan kapan saja. update merupakan hal penting dalam suatu website, sehingga informasi yang disampaikan dapat terus disesuaikan dengan perkembangan perusahaan. daftar referensi adhar, d. (2014). sistem pendukung keputusan pengangkatan jabatan karyawan pada pt.ayn dengan metode profile matching. jatisi (jurnal teknik informatika dan sistem informasi), 1(1), 16–29. https://doi.org/10.35957/jatisi.v1i1.18 evita, s. n., muizu, w. o. z., & atmojo, r. t. w. (2017). penilaian kinerja karyawan dengan menggunakanmetodebehaviorally anchor rating scale dan management by objectives (studi kasus pada pt qwords company international. pekbis jurna, 9(1), 18–32. retrieved from https://ejournal.unri.ac.id/index.php/jpeb/ article/view/4051 mahdang, a., lamangida, t., & mohi, w. k. (2016). analisis pelaksanaan penilaian kinerja pegawai negeri sipil di lingkungan sekretariat dewan provinsi gorontalo. publik (jurnal ilmu administrasi), 5(1), 1–9. https://doi.org/10.31314/pjia.5.1.1-9.2016 natanael, b., & mulyono, h. (2017). analisis dan perancangan sistem informasi penilaian kinerja karyawan pada pt. bpr universal sentosa. informasi, jurnal manajemen sistem, 2(1), 295–302. retrieved from http://ejournal.stikomdb.ac.id/index.php/manajemensisteminform asi/article/view/440 saefudin, s., & wahyuningsih, s. (2017). sistem pendukung keputusan untuk penilaian kinerja pegawai menggunakan metode analytical hierarchy process (ahp) pada rsud serang. jsii (jurnal sistem informasi), 1(0). https://doi.org/10.30656/jsii.v1i0.78 septiani, a. h., syafi’i, a., & rasyidi, a. (2015). penerapan audit operasional atas manajemen fungsi sdm untuk menilai kinerja karyawan pada pt.pioneer flour mill industries sidoarjo. equity, 1(2), 32–41. retrieved from http://fe.ubhara.ac.id/ojs/index.php/equity/ article/view/57 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 23 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem pendukung keputusan untuk menentukan jurusan pada sman 1 bawang jawa tengah dengan topsis galih pramoda dibya ardana1, frisma handayanna2 program studi teknik informatika sekolah tinggi manajemen informatika dan komputer nusa mandiri http://www.nusamandiri.ac.id *1galihpramoda97@gmail.com; 2frisma.fha@nusamandiri.ac.id abstrak sma negeri 1 bawang adalah sekolah menengah atas negeri yang berada di jawa tengah, setiap tahunnya sma negeri 1 bawang membuka penerimaan siswa baru, setiap siswa sma negeri 1 bawang akan ditempatkan pada kelas jurusan sesusai bidang atau kemampuan masing-masing siswa, salah satu contoh masalah yang penulis ambil adalah disekolah tersebut terdapat 2 jurusan yaitu ipa dan ips yang dimana pihak sekolah mengalami kesulitan untuk menentukan jurusan setiap siswanya, karena seiring berjalannya waktu siswa yang mendaftar di sma negeri 1 bawang bertambah banyak. untuk meringankan pihak sekolah dalam menentukan jurusan siswa, maka penulis melakukan penelitian spk menggunakan metode topsis untuk membantu pihak sekolah menentukan penjurusan setiap siswanya. dalam penelitian ini data dikumpulkan dari data sekunder dalam bentuk rekap nilai ujian nasional siswa, peneliti akan melakukan perhitungan data nilai rekap nilai ujian siswa sesuai bobot kriteria mata pelajaran, dikarenakan jumlah populasi data yang dikumpukan peneliti terlalu banyak yaitu 156 data digunakanlah rumus slovin untuk mendapatkan sampel penelitian menjadi 61 data dan software microsoft excel untuk menghindari kesalahan pehitungan serta mempercepat perhitungan, hasil yang didapat dari penelitian ini adalah dari banyaknya sampel yaitu 61 data ditentukanlah bahwa 37 siswa masuk pada jurusan ipa dan 24 siswa masuk pada jurusan ips. kata kunci: metode topsis, menentukan jurusan, spk abstract bawang 1 public high school is a state high school located in central java. every year sma 1 bawang opens new admissions, every student of bawang 1 high school will be placed in a class according to their respective fields or abilities. one example of a problem the authors take is in the school there are 2 majors namely science and social sciences where the school has difficulty in determining the direction of each student, because as time goes by students who enroll in sma 1 bawang increase. to relieve the school in determining the student's majors, the authors conducted spk research using the topsis method to help the school determine the direction of each student. in this study data were collected from secondary data in the form of national exam scores recap, students will calculate the value of student exam value recap according to the criteria weight of the subjects, because the population of data collected by researchers is too much, 156 data used slovin formula to get samples the study became 61 data and microsoft excel software to avoid accounting errors and speed up calculations. the results obtained from this study were that from the number of samples, 61 data determined that 37 students entered the science department and 24 students entered the social studies department. keywords: topsis method, determining department, spk pendahuluan sma negeri 1 bawang adalah sekolah menengah atas negeri yang berada di jawa tengah, setiap tahunya sma negeri 1 bawang membuka penerimaan siswa baru, setiap siswa sma negeri 1 bawang akan ditempatkan pada kelas jurusan sesusai bidang atau kemampuan masing-masing siswa, seiring berjalanya waktu siswa yang mendaftar di sma negeri 1 bawang bertambah banyak, disekolah tersebut terdapat 2 jurusan yaitu ipa dan ips yang dimana pihak sekolah mengalami kesulitan untuk menentukan jurusan setiap siswanya, oleh karena itu penulis melakukan penelitian spk menggunakan metode topsis (mardiana & tanjung, 2019) untuk http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 24 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional membantu pihak sekolah menentukan penjurusan setiap siswanya. adapun kriteria-kriteria yang dibutuhkan dalam proses penentuan jurusan ini antara lain nilai ujian nasional smp siswa yang telah diajukan kepada pihak sma sebagai syarat pendafaran sekolah. teknik topsis (trchnique for order preference by similiarity to ideal solution) telah berjalan dengan baik dan dapat menghasilkan bobot kriteria penilaian dan informasi yang jelas dan cepat dibandingkan dengan perhitungan manual sehingga sd negeri kebalen 07 dapat menggunakannya sebagai alat untuk membuat keputusan yang tepat (susliansyah et al., 2019) algoritma topsis merupakan sebuah metode yang digunakan untuk membuat urutan ranking berdasarkan hasil perhitungan, dengan penilaian bobot kriteria yang di tentukan (dashti et al., 2010). jurusan siswa man ii yogyakarta yaitu ipa, ips, bahasa, dan agama. sistem ini dapat menentukan jurusan siswa yang tepat berdasarkan ranking dari hasil perhitungan algoritma topsis dan kuota kelas (prayoga & pradnya, 2017) metode topsis diharapkan mampu menyeleksi keputusan terbaik dari beberapa keputusan yang diharapkan dalam pemberian bonus tahunan karyawan. topsis menggunakan prinsip bahwa alternatif yang terpilih harus mempunyai jarak terdekat dari solusi ideal positif dan jarak terpanjang (terjauh) dari solusi ideal negatif dari sudut pandang geometris dengan menggunakan jarak euclidean (jarak antara dua titik) untuk menentukan kedekatan relatif dari suatu alternatif (agusli et al., 2017). metode penelitian mengindentifikasi masalah dalam penentuan jurusan di sman 1 bawang. adapun metode penelitian yang digunakan adalah: teknik pengumpula data a. observasi pada tahap observasi mendapatkan data-data dan fakta dari pengamatan langsung di sman 1 bawang. b. wawancara pada tahap wawancara mengadakan atau melakukan wawancara langsung dengan guru bk dan bagian penerimaan siswa baru, untuk mendapatkan keterangan-keterangan yang diperlukan sebagai bahan penulisan laporan seperti kriteria-kriteria apa saja untuk penentuan jurusan siswa pada sekolah sman 1 bawang. hipotesa h0 : tidak adanya validitas data kriteria pada nilai siswa yang digunakan untuk menentukan jurusan yang diminati siswa dengan metode topsis. h1 : adanya validitas data kriteria pada nilai siswa yang digunakan untuk menentukan jurusan yang diminati siswa dengan metode topsis. tahap penelitian tahapan yang dilakukan dalam penelitian ini digambarkan dalam bagan sebagai berikut: gambar 1. tahapan dalam penelitian hasil penelitian dan pembahasan populasi populasi merupakan wilayah generalisasi yang terdiri dari obyek/subyek yang memiliki kuantitas dan karakteristik tertentu yang ditetapkan oleh peneliti untuk dipelajari dan kemudian ditarik kesimpulannya (siyoto & sodik, 2015). populasi juga bukan hanya sekedar jumlah yang ada pada obyek maupun subyek yang dipelajari, populasi dalam penelitian ini adalah seluruh siswa yang ada di sma negeri 1 bawang yang berjumlah 156. karena jumlah anggota populasi terlalu banyak, maka dapat dilakukan penentuan sampel. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 25 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sampel sampel adalah sebagian dari jumlah dan karakteristik yang dimiliki oleh populasi tersebut, ataupun bagian kecil dari anggota populasi yang diambil menurut prosedur tertentu sehingga dapat mewakili populasinya (siyoto & sodik, 2015). untuk menentukan sampel pada penelitian ini penulis menggunakan rumus slovin untuk menghitung jumlah sampel minimal 𝑛 = n 1+𝑁𝑒2 .......................................................... (1) keterangan: n= sampel minimal n= populasi e= error margin penyelesainnya: n = 156/(1+(156 x 0,12) n = 156/(1+(156 x 0,01) n = 156/ (1 + 1,56) n = 156/(2,56) n = 60,93. jadi sampel minimalnya adalah 61 penentuan kriteria dan bobot penilaian a. kriteria penilaian tabel 1. kriteria penilaian aspek kriteria kriteria bahasa indonesia c1 bahasa inggris c2 matematika c3 ipa c4 un c5 menjelaskan tentang kriteria penilaian yang ada di sma negeri 1 bawang, sebagai acuan penentuan penjurusan siswa. b. bobot penilaian pemberian bobot pada setiap kriteria sebagai berikut: 1. nilai bobot bahasa indonesia tabel 2. bobot penilaian bahasa indonesia nilai bobot keterangan 9.30-11.25 5 sangat penting 7.30-9.25 4 penting 5.30-7.25 3 cukup penting 3.30-5.25 2 kurang penting 1.25-3.25 1 sangat kurang penting merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur kriteria bahasa indonesia yang sudah ditentukan 2. nilai bobot bahasa inggris tabel 3. bobot penilaian bahasa inggris nilai bobot keterangan 9.30-11.25 5 sangat penting 7.30-9.25 4 penting 5.30-7.25 3 cukup penting 3.30-5.25 2 kurang penting 1.25-3.25 1 sangat kurang penting merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur kriteria bahasa inggris yang sudah ditentukan 3. nilai bobot matematika tabel 4. bobot penilaian matematika nilai bobot keterangan 9.30-11.25 5 sangat penting 7.30-9.25 4 penting 5.30-7.25 3 cukup penting 3.30-5.25 2 kurang penting 1.25-3.25 1 sangat kurang penting merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur kriteria matematika yang sudah ditentukan 4. nilai bobot ipa tabel 5. bobot penilaian ipa nilai bobot keterangan 9.30-11.25 5 sangat penting 7.30-9.25 4 penting 5.30-7.25 3 cukup penting 3.30-5.25 2 kurang penting 1.25-3.25 1 sangat kurang penting merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur kriteria ipa yang sudah ditentukan 5. nilai bobot ujian nasional tabel 6. bobot penilaian ujian nasional nilai bobot keterangan 31.60-35.50 5 sangat penting 27.60-31.50 4 penting 23.60-27.50 3 cukup penting 19.60-23.50 2 kurang penting 15.50-19.50 1 sangat kurang penting merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur kriteria ujian nasional yang sudah ditentukan 6. nilai bobot setiap kriteria tabel 7. bobot penilaian setiap kriteria kriteria jurusan ipa ips c1= bahasa indonesia 4 4 c2= bahasa inggris 4 4 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 26 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional c3= matematika 5 5 c4= ipa 5 4 c5= un 5 5 merupakan kriteria nilai bobot yang berfungsi untuk dapat mengukur setiap kriteria yang sudah ditentukan pada setiap jurusan. data sekunder data sekunder adalah data yang di dapat dan digunakan oleh peneliti berasal dari lembaga tertentu. lembaga ini bisa berupa apaan juga, seperti biro pusat statitistik, bank indonesia atau lembaga penyedia data lainnya. data nilai ujian nasional smp ini di dapatkan dari sma negeri 1 bawang pada bagian tata usaha, setiap siswa yang ingin mendaftar di sma negeri 1 bawang diwajibkan menyerahkan data nilai ujian nasional smp sebagai syarat pendaftaran dan data tersebut di olah dan dikumpulkan oleh bagian tata usaha dengan tujuan untuk menentukan jurusan masing-masing siswa. adapun sampel yang ditampilkan dari data 1 sampai 10 dan 61 sebagai berikut: tabel 8. data nilai sma negeri 1 bawang no nama ind ing mtk ipa nun 1 rulita lailan fajrin 8.60 5.60 9.25 6.50 29.95 2 ilma yang fauni 8.80 7.20 6.50 6.25 28.75 3 nata syafaatul udhma 7.80 5.20 8.50 7.25 28.75 4 ilma fitriyana 9.20 6.20 7.25 5.25 27.90 5 maya wulandari 7.60 4.80 8.00 5.00 25.40 6 heni ekawati 8.40 7.20 5.00 6.25 26.85 7 melsha yunita 7.40 6.60 7.00 5.00 26.00 8 yeni nur asih 9.20 5.00 7.00 5.50 26.70 9 sitoresmi idayani 8.40 7.00 4.75 6.50 26.65 10 olivia hapsari 8.20 4.80 5.75 6.00 24.75 61 antonius felik 7.25 5.25 5.00 3.25 20.75 topsis menggunakan prinsip bahwa alternatif yang terpilih harus mempunyai jarak terdekat dari solusi ideal positif dan jarak terpanjang (terjauh) dari solusi ideal negatif dari sudut pandang geometris dengan menggunakan jarak euclidean (jarak antara dua titik) untuk menentukan kedekatan relatif dar suatu alternatif (nofriansyah, 2014) langkah penyelesaian metode topsis a. membuat matriks keputusan yang ternormalisasi. 𝑟𝑖𝑗 = xij √ ∑m 𝑖=1 𝑥2𝑖𝑗 . (2) keterangan: rij = matrik ternormalisasi [i][j] xij = matrik keputusan [i][j] r11 = x11 √x11 + x21 + x31+x41 + x51 + ⋯ n |𝑋1| = √42 + 32 + 42 + 32 + 42 = 8,124 𝑟11 = 𝑋11 𝑋1 = 4 8,124 = 0,492 𝑟21 = 𝑋21 𝑋1 = 3 8,124 = 0,369 𝑟31 = 𝑋31 𝑋1 = 4 8,124 = 0,492 𝑟41 = 𝑋41 𝑋1 = 3 8,124 = 0,369 𝑟51 = 𝑋51 𝑋1 = 4 8,124 = 0,492 tabel.9 hasil perhitungan matriksternormalisasi no c1 c2 c3 c4 c5 1 0,492 0,369 0,492 0,369 0,492 2 0,544 0,272 0,408 0,408 0,544 3 0,512 0,256 0,512 0,384 0,512 4 0,544 0,408 0,408 0,272 0,544 5 0,571 0,286 0,571 0,286 0,429 6 0,583 0,438 0,292 0,438 0,438 7 0,583 0,438 0,438 0,292 0,438 8 0,583 0,292 0,438 0,438 0,438 9 0,583 0,438 0,292 0,438 0,438 10 0,583 0,292 0,438 0,438 0,438 61 0,640 0,426 0,426 0,213 0,426 merupakan hasil perhitungan dari matriks keputusan yang ternormalisasi dari data siswa berjumlah 61 orang dan yang ditampilkan hanya dari data ke 1 sampai ke 10 dan 61. b. membuat matriks keputusan yang ternormalisasi terbobot v selanjutnya menghitung proses ternormalisasi v, dimana setiap alternatif diambil berdasarkan nilai dari kriteria nilai bobot dikali dengan kriteria hasil normaliasasi. rumus nilai matriks keputusan ternormalisasi terbobot v diambil berdasarkan: 𝑉𝑖𝑗 = 𝑤𝑖 𝑟𝑖𝑗 (3) dengan i= 1,2....,m;dan j =1,2,.....n. keterangan: http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 27 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional vij = elemen dari matriks keputusan yang ternormalisasi terbobot v wi = bobot dari kriteria ke-j rij = elemen matriks keputusan yang ternormalisasi r. 1. jurusan ipa 𝑉1.1 = 𝑊1. 𝑅11 = 4 𝑥 0,492 = 1,969 𝑉2.1 = 𝑊1. 𝑅21 = 4 𝑥 0,369 = 1,477 𝑉3.1 = 𝑊1. 𝑅31 = 5 𝑥 0,492 = 2,462 𝑉4.1 = 𝑊1. 𝑅41 = 5 𝑥 0,369 = 1,846 𝑉5.1 = 𝑊1. 𝑅51 = 5 𝑥 0,492 = 2,462 tabel.10 hasil perhitungan ternormalisasi dengan bobot v jurusan ipa no c1 c2 c3 c4 c5 1 1,969 1,477 2,462 1,846 2,462 2 2,177 1,089 2,041 2,041 2,722 3 2,049 1,024 2,561 1,921 2,561 4 2,177 1,633 2,041 1,361 2,722 5 2,286 1,143 2,857 1,429 2,143 6 2,334 1,750 1,459 2,188 2,188 7 2,334 1,750 2,188 1,459 2,188 8 2,334 1,167 2,188 2,188 2,188 9 2,334 1,750 1,459 2,188 2,188 10 2,334 1,167 2,188 2,188 2,188 61 2,558 1,706 2,132 1,066 2,132 merupakan hasil perhitungan ternormalisasi dengan bobot v jurusan ipa dari data siswa berjumlah 61 orang dan yang ditampilkan hanya dari data ke 1 sampai ke 10 dan 61. 2. jurusan ips 𝑉1.1 = 𝑊1. 𝑅11 = 4 𝑥 0,492 = 1,969 𝑉2.1 = 𝑊1. 𝑅21 = 4 𝑥 0,369 = 1,477 𝑉3.1 = 𝑊1. 𝑅31 = 5 𝑥 0,492 = 2,462 𝑉4.1 = 𝑊1. 𝑅41 = 4 𝑥 0,369 = 1,477 𝑉5.1 = 𝑊1. 𝑅51 = 5 𝑥 0,492 = 2,462 tabel.11 hasil perhitungan ternormalisasi dengan bobot v jurusan ips no c1 c2 c3 c4 c5 1 1,969 1,477 2,462 1,477 2,462 2 2,177 1,089 2,041 1,633 2,722 3 2,049 1,024 2,561 1,536 2,561 4 2,177 1,633 2,041 1,089 2,722 5 2,286 1,143 2,857 1,143 2,143 6 2,334 1,750 1,459 1,750 2,188 7 2,334 1,750 2,188 1,167 2,188 8 2,334 1,167 2,188 1,750 2,188 9 2,334 1,750 1,459 1,750 2,188 10 2,334 1,167 2,188 1,750 2,188 61 2,558 1,706 2,132 0,853 2,132 merupakan hasil perhitungan ternormalisasi dengan bobot v jurusan ips dari data siswa berjumlah 61 orang dan yang ditampilkan hanya dari data ke 1 sampai ke 10 dan 61. c. solusi ideal positif a+ dan solusi ideal negatif a 𝐴+ = (𝑌1 +, 𝑌2 +, … . 𝑌𝑛 + (4) 𝐴− = (𝑌1 −, 𝑌2 −, … . 𝑌𝑛 − (5) keterangan: vj = maxyij, jika j adalah atribut keuntungan min yij, jika j adalah atribut biaya 1. jurusan ipa tabel 12. solusi ideal positif a+ dan solusi ideal negatif a ipa c1 c2 c3 c4 c5 a+ 3,336 2,449 2,857 2,739 2,722 a1,696 0,834 0,700 0,737 0,981 merupakan hasil solusi ideal positif a+ dan solusi ideal negatif a pada jurusan ipa dilihat dari semua data ke 1 sampai ke 61. pada a+ dilihat nilai yang paling besar pada setiap kriteria sedangkan pada a dilihat nilai paling kecil pada setiap kriteria 2. jurusan ips tabel 13. solusi ideal positif a+ dan solusi ideal negatif a ips c1 c2 c3 c4 c5 a+ 3,336 2,449 2,857 2,191 2,722 a1,696 0,834 0,700 0,590 0,981 merupakan hasil solusi ideal positif a+ dan solusi ideal negatif a pada jurusan ips dilihat dari semua data ke 1 sampai ke 61. pada a+ dilihat nilai yang paling besar pada setiap kriteria sedangkan pada a dilihat nilai paling kecil pada setiap kriteria d. perhitungan jarak ai dengan solusi ideal positif d+ dan solusi ideal negatif d 𝐷𝑖 + = √∑ (𝑌𝑖𝑗 − 𝑌𝑗 +)2𝑛𝑗=𝑛 (6) keterangan: di+ = jarak alternatif ai dengan solusi ideal positif yj+ = solusi ideal positif [i] yij = matriks normalisasi [i][j] 1. perhitungan solusi ideal positif d+ jurusan ipa http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 28 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 𝐷1 + = √ (1,969 − 3,336)2 + (1,477 − 2,449)2 +(2,462 − 2,857)2 + (1,846 − 2,739)2 + (2,462 − 2,722)2 = 3, 833 𝐷2 + = √ (2,177 − 3,336)2 + (1,089 − 2,449)2 +(2,041 − 2,857)2 + (2,041 − 2,739)2 + (2,722 − 2,722)2 = 4,345 𝐷3 + = √ (2,049 − 3,336)2 + (1,024 − 2,449)2 +(2,561 − 2,857)2 + (1,921 − 2,739)2 + (2,561 − 2,722)2 = 4,471 𝐷4 + = √ (2,177 − 3,336)2 + (1,633 − 2,449)2 +(2,041 − 2,857)2 + (1,361 − 2,739)2 + (2,722 − 2,722)2 = 4,573 𝐷5 + = √ (2,286 − 3,336)2 + (1,143 − 2,449)2 +(2,857 − 2,857)2 + (1,429 − 2,739)2 + (2,143 − 2,722)2 = 4,862 2. perhitungan solusi ideal positif djurusan ipa 𝐷1 − = √ (1,969 − 1,969)2 + (1,477 − 0,834)2 +(2,462 − 0,700)2 + (1,846 − 0,737)2 + (2,462 − 0,981)2 = 7, 016 𝐷2 − = √ (2,177 − 1, 696)2 + (1,089 − 0, 834)2 +(2,041 − 0, 700)2 + (2,041 − 0, 737)2 + (2,722 − 0, 981)2 = 6, 826 𝐷3 − = √ (2,049 − 1, 696)2 + (1,024 − 0, 834)2 +(2,561 − 0, 700)2 + (1,921 − 0, 737)2 + (2,561 − 0, 981)2 = 7, 519 𝐷4 − = √ (2,177 − 1, 696)2 + (1,633 − 2,449)2 +(2,041 − 0, 700)2 + (1,361 − 0, 737)2 + (2,722 − 0, 981)2 = 6, 088 𝐷5 − = √ (2,286 − 1, 696)2 + (1,143 − 2,449)2 +(2,857 − 0, 700)2 + (1,429 − 0, 737)2 + (2,143 − 0, 981)2 = 6, 925 tabel 15. hasil perhitungan solusi ideal positif d+ dan positif djurusan ipa no nama di+ di 1 rulita lailan fajrin 3,833 7,016 2 ilma yang fauni 4,345 6,826 3 nata syafaatul udhma 4,471 7,519 4 ilma fitriyana 4,573 6,088 5 maya wulandari 4,862 6,925 6 heni ekawati 4,037 5,384 7 melsha yunita 3,864 5,438 8 yeni nur asih 3,684 6,294 9 sitoresmi idayani 4,037 5,384 10 olivia hapsari 3,684 6,294 61 antonius felik 4,830 4,987 merupakan hasil perhitungan solusi ideal positif d+ dan solusi ideal negatif d pada jurusan ipa dilihat dari semua data ke 1 sampai ke 61. 3. perhitungan solusi ideal positif d+ jurusan ips 𝐷1 + = √ (1,969 − 3,336)2 + (1,477 − 2,449)2 +(2,462 − 2,857)2 + (1,447 − 2,191)2 + (2,462 − 2,722)2 = 3, 833 𝐷2 + = √ (2,177 − 3,336)2 + (1,089 − 2,449)2 +(2,041 − 2,857)2 + (1,633 − 2,191)2 + (2,722 − 2,722)2 = 4,345 𝐷3 + = √ (2,049 − 3,336)2 + (1,024 − 2,449)2 +(2,561 − 2,857)2 + (1,536 − 2,191)2 + (2,561 − 2,722)2 = 4,471 𝐷4 + = √ (2,177 − 3,336)2 + (1,633 − 2,449)2 +(2,041 − 2,857)2 + (1,089 − 2,191)2 + (2,722 − 2,722)2 = 4,573 𝐷5 + = √ (2,286 − 3,336)2 + (1,143 − 2,449)2 +(2,857 − 2,857)2 + (1,143 − 2,191)2 + (2,143 − 2,722)2 = 4,862 4. perhitungan solusi ideal positif d+ jurusan ips 𝐷1 − = √ (1,969 − 1,969)2 + (1,477 − 0,834)2 +(2,462 − 0,700)2 + (1,477 − 0,590)2 + (2,462 − 0,981)2 = 6, 572 𝐷2 − = √ (2,177 − 1, 696)2 + (1,089 − 0, 834)2 +(2,041 − 0, 700)2 + (1,633 − 0,590)2 + (2,722 − 0, 981)2 = 6, 213 𝐷3 − = √ (2,049 − 1, 696)2 + (1,024 − 0, 834)2 +(2,561 − 0, 700)2 + (1,536 − 0, 590)2 + (2,561 − 0, 981)2 = 7, 014 𝐷4 − = √ (2,177 − 1, 696)2 + (1,633 − 2,449)2 +(2,041 − 0, 700)2 + (1,089 − 0,590)2 + (2,722 − 0, 981)2 = 5, 551 𝐷5 − = √ (2,286 − 1, 696)2 + (1,143 − 2,449)2 +(2,857 − 0, 700)2 + (1,143 − 0,590)2 + (2,143 − 0, 981)2 = 6, 526 tabel 16. hasil perhitungan solusi ideal positif d+ dan positif djurusan ips no nama di+ di 1 rulita lailan fajrin 3,546 6,572 2 ilma yang fauni 4,170 6,213 3 nata syafaatul udhma 4,229 7,014 4 ilma fitriyana 3,889 5,551 5 maya wulandari 4,243 6,526 6 heni ekawati 3,927 4,625 7 melsha yunita 3,274 5,002 8 yeni nur asih 3,575 5,535 9 sitoresmi idayani 3,927 4,625 10 olivia hapsari 3,575 5,535 61 antonius felik 3,822 4,662 merupakan hasil perhitungan solusi ideal positif d+ dan solusi ideal negatif d pada jurusan ips dilihat dari semua data ke 1 sampai ke 61. e. nilai preferensi untuk setiap alternatif (vi ) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 29 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 𝑉𝑖 = 𝐷𝑖− 𝐷𝑖− + 𝐷𝑖+ (7) i = 1,2,....m vi = kedekatan tiap alternatif terhadap solusi ideal di+ = jarak alternatif ai dengan solusi ideal positif di = jarak alternatif ai dengan solusi ideal negatif nilai vi yang lebih besar menunjukan bahwa alternatif ai lebih dipilih. 1. perhitungan nilai preferensi untuk setiap alternatif (vi) jurusan ipa 𝑉1 = 7,016 7,016 + 3,833 = 7,016 10,849 = 0,647 𝑉2 = 6,826 6,826 + 4,345 = 6,826 11,172 = 0,611 𝑉3 = 7,519 7,519 + 4,471 = 7,519 11,990 = 0,627 𝑉4 = 6,088 6,088 + 4,573 = 6,088 10,661 = 0,571 𝑉5 = 6,925 6,925 + 4,862 = 6,925 11,786 = 0,588 tabel 17. hasil perhitungan nilai preferensi untuk setiap alternatif (vi) jurusan ipa no nama vi 1 rulita lailan fajrin 0,647 2 ilma yang fauni 0,611 3 nata syafaatul udhma 0,627 4 ilma fitriyana 0,571 5 maya wulandari 0,588 6 heni ekawati 0,572 7 melsha yunita 0,585 8 yeni nur asih 0,631 9 sitoresmi idayani 0,572 10 olivia hapsari 0,631 61 antonius felik 0,508 merupakan hasil perhitungan nilai preferensi untuk setiap alternatif (vi) pada jurusan ipa dilihat dari semua data ke 1 sampai ke 61. 2. perhitungan nilai preferensi untuk setiap alternatif (vi) jurusan ips 𝑉1 = 6,572 6,572 + 3,546 = 6,572 10,118 = 0,650 𝑉2 = 6,213 6,213 + 4,170 = 6,213 10,383 = 0,598 𝑉3 = 7,014 7,014 + 4,229 = 7,014 11,244 = 0,624 𝑉4 = 5,551 5,551 + 3,889 = 5,551 9,440 = 0,588 𝑉5 = 6,526 6,526 + 4,243 = 6,526 10,769 = 0,606 tabel 18. hasil perhitungan nilai preferensi untuk setiap alternatif (vi) jurusan ips no nama vi 1 rulita lailan fajrin 0,650 2 ilma yang fauni 0,598 3 nata syafaatul udhma 0,624 4 ilma fitriyana 0,588 5 maya wulandari 0,606 6 heni ekawati 0,541 7 melsha yunita 0,604 8 yeni nur asih 0,608 9 sitoresmi idayani 0,541 10 olivia hapsari 0,608 61 antonius felik 0,549 merupakan hasil perhitungan nilai preferensi untuk setiap alternatif (vi) pada jurusan ips dilihat dari semua data ke 1 sampai ke 61. hasil penelitian tabel 19. perbandingan hasil perhitungan nilai preferensi setiap alternatif (vi) vi ipa vi ips hasil 0,647 0,650 ips 0,611 0,598 ipa 0,627 0,624 ipa 0,571 0,588 ips 0,588 0,606 ips 0,572 0,541 ipa 0,585 0,604 ipa 0,631 0,608 ipa 0,572 0,541 ipa 0,631 0,608 ipa 0,584 0,607 ips 0,518 0,537 ips 0,572 0,541 ipa 0,518 0,537 ips 0,572 0,533 ipa 0,640 0,626 ipa 0,640 0,626 ipa 0,663 0,636 ipa 0,406 0,441 ips 0,572 0,541 ipa 0,585 0,604 ipa 0,446 0,419 ipa 0,585 0,604 ipa 0,572 0,541 ipa 0,585 0,604 ipa 0,631 0,608 ipa 0,585 0,616 ips 0,631 0,608 ipa 0,518 0,537 ips 0,606 0,570 ipa 0,584 0,607 ips 0,317 0,337 ipa 0,317 0,337 ipa 0,563 0,561 ipa 0,535 0,508 ipa http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 30 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional vi ipa vi ips hasil 0,508 0,549 ips 0,535 0,508 ipa 0,398 0,372 ipa 0,430 0,384 ipa 0,345 0,320 ipa 0,430 0,384 ipa 0,336 0,306 ipa 0,445 0,483 ips 0,430 0,384 ipa 0,345 0,320 ipa 0,426 0,369 ipa 0,627 0,624 ipa 0,480 0,502 ips 0,631 0,608 ipa 0,590 0,540 ipa 0,487 0,494 ips 0,504 0,502 ips 0,504 0,502 ips 0,504 0,502 ips 0,504 0,502 ips 0,504 0,502 ips 0,504 0,502 ips 0,615 0,555 ipa 0,563 0,561 ips 0,508 0,549 ips 0,508 0,549 ips hasil penelitian dari perbandingan hasil perhitungan nilai preferensi setiap alternatif (vi) dari 61 siswa tersebut yaitu telah ditentukan bahwa 37 siswa masuk pada jurusan ipa dan 24 siswa masuk pada jurusan ips. simpulan dan saran simpulan hasil perhitungan dari preferensi alternatif v dari jurusan ipa dan ips diperbandingkan, apabila hasil perhitungan nilai ipa lebih besar dari pada ips maka siswa tersebut akan masuk jurusan ipa dan sebaliknya jika hasil perhitungan nilai ips lebih besar dari pada ipa maka siswa tersebut akan masuk jurusan ips hasil perhitungan dari seluruh data sampel dengan kriteria masingmasing jurusan telah ditentukan bahwa dari 61 data sampel ada sebanyak 37 siswa yang masuk pada jurusan ipa dan 24 siswa masuk pada jurusan ips. saran terkait dengan penelitian ini perlu dilakukan studi lebih lanjut mengenai data yang digunakan untuk penelitian, misalnya menggunakan data primer atau kueisioner yang dibagikan kepada siswa untuk menanyakan minat siswa dalam mengambil jurusan sehingga penentuan jurusan bukan hanya ditentukan dari pihak sekolah atau nilai ujian nasional. untuk penelitian selanjutnya peneliti diharapakan membuat aplikasi penghitung cepat sederhana untuk menyempurnakan penelitian ini. sistem pendukung keputusan dibangun pada sma tersebut dapat dikembangkan dengan metode fdam lain seperti ahp, saw, wp dan profile matching. daftar referensi agusli, r., dzulhaq, m. i., & khasanah, u. (2017). sistem pendukung keputusan pemberian bonus tahunan karyawan menggunakan metode topsis. jurnal sisfotek global. dashti, z., pedram, m. m., & shanbehzadeh, j. (2010). a multi-criteria decision making based method for ranking sequential patterns. proceedings of the international multiconference of engineers and computer scientists 2010, imecs 2010, i, 611–614. mardiana, t., & tanjung, s. s. (2019). sistem pendukung keputusan pemilihan perguruan tinggi swasta menggunakan topsis. jurnal riset informatika, 1(2), 25–34. https://doi.org/10.34288/jri.v1i2.30 nofriansyah, d. (2014). konsep data mining vs sistem pendukung keputusan. deepublish. prayoga, b. s., & pradnya, w. m. (2017). sistem pendukung keputusan jurusan di man ii yogyakarta menggunakan algoritma topsis. semnasteknomedia online, 5(1), 55–60. siyoto, s., & sodik, m. a. (2015). dasar metodologi penelitian. literasi media publishing. susliansyah, s., rahadjeng, i. r., sumarno, h., & deleaniara. m, c. m. (2019). penerapan metode topsis dalam penilaian kinerja guru tetap sd negeri kebalen 07. jurnal pilar nusa mandiri, 15(1), 7–14. https://doi.org/10.33480/pilar.v15i1.2 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 2. maret 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 237 clustering the impacts of the russia-ukraine war on personnel and equipment wargijono utomo sistem informasi universitas krisnadwipayana jakarta, indonesia https://unkris.ac.id/ wargiono@unkris.ac.id abstract in post-pandemic recovery efforts, uncertainty arose due to the unresolved conflict between the russiaukraine war. this conflict impacts world security stability and affects the economic, energy, and food sectors. this conflict also impacts humanity by causing death to civilians and military personnel, including children in ukraine. the clustering analysis results of the impact of the russian-ukrainian war show losses and losses in personnel and war equipment, with three cluster optimization methods used through kmeans. of the two methods that can be recommended, namely elbow and silhouette, both produce k=3. the profiling results show that losses or losses in ukrainian personnel and war equipment are categorized into three clusters, with cluster one being the lowest category, cluster two being the very high category, and cluster three being the moderate category. this research is helpful for state agencies, international organizations (ngos), and other stakeholders. keywords: clustering; k-means; elbow; silhouette; gap statistics abstrak dalam upaya pemulihan pascapandemi, ketidakpastian muncul akibat konflik yang belum terselesaikan antara perang rusia-ukraina. konflik ini berdampak pada stabilitas keamanan dunia, dan juga mempengaruhi sektor ekonomi, energi dan pangan. konflik ini juga berdampak pada kemanusiaan dengan menyebabkan kematian warga sipil dan personel militer, termasuk anak-anak di ukraina. hasil analisis clustering dampak perang rusia-ukraina menunjukkan kerugian dan kerugian personel dan peralatan perang, dengan tiga metode optimasi cluster yang digunakan melalui k-means. dari dua metode yang dapat direkomendasikan yaitu elbow dan silhouette, keduanya menghasilkan k=3. hasil profiling menunjukkan bahwa kerugian atau kehilangan personel dan peralatan perang ukraina dikategorikan menjadi tiga klaster, dengan klaster satu kategori paling rendah, klaster dua kategori sangat tinggi, dan klaster tiga kategori sedang. penelitian ini bermanfaat bagi lembaga negara, organisasi internasional (lsm), dan pemangku kepentingan lainnya. kata kunci: clustering; k-means; elbow; silhouette; gap statistics introduction in post-pandemic joint recovery efforts, the world is uncertain due to the implications of the conflict between russia and ukraine, which has not been resolved to date. the conflict that is still happening has an impact on the world, security stability, and its impact on the economy, energy, and food which one day will have an indirect impact on defense and security(darmayadi & megits, 2023; nerlinger & utz, 2022; paul, 2015). in addition, the outbreak of the russian-ukrainian military conflict had implications for humanity, resulting in the deaths of civilians, military personnel, and even children in ukraine(haque et al., 2022; osokina et al., 2022). statista.com, quoted from the office of the un high commissioner for human rights (ohchr), verified 6,952 civilian deaths during the russian invasion of ukraine as of january 9, 2023. of these, 431 were children. subsequently, 11,144 people were reported injured. various studies that have been conducted on the impact of the russian and ukrainian wars include the eu in the south caucasus and the impact of the russia-ukrainewar with a qualitative approach(paul, 2015), the impact of the russiaukraine war on the cryptocurrency market with the iv-gmm method, the impact of the russia-ukraine p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 jurnal riset informatika vol. 5, no. 2. maret 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 238 conflict on energy firms: a capital market perspective using the average abnormal returns (aar) method(nerlinger & utz, 2022), the human toll and humanitarian crisis of the russia-ukraine war: the first 162 days using the descriptive statistics method(haque et al., 2022). based on various previous studies, this paper aims to cluster the impact of the russian war on ukraine on the human side, such as the number of deaths and losses in armament using the k-means algorithm and cluster optimization using three methods, including elbow, silhouette, and gap statistics while clustering data processing using r programming(sinaga & yang, 2020). clustering is an unsupervised learning method that allows the grouping of objects based on different characteristics(yuan & yang, 2019). the purpose of cluster analysis is to discover the structure in forming groups of similar cluster objects. clustering is needed to identify the structure of the data. this research can contribute to state agencies, international organizations (ngos), and stakeholders. this paper is organized into four parts to be systematic, including the first part of the research background, the second part of the research methodology, the third part of the results and discussion, and the fourth part of the conclusions. research methods studies on the impact of the russianukrainian war on personnel and war equipment are carried out using structured, planned, and systematic quantitative methods to make it better. the research process involves several stages, as shown in figure 1. figure 1. research methodology clustering clustering is a method in data mining that aims to group (or classify) items in data into several groups (or clusters) based on similarities in their features. the goal of clustering is to discover hidden structures in data and understand how items are related to one another. clustering is an unsupervised machine-learning technique that involves grouping similar data points based on similarity or distance metrics. the goal of clustering is to identify natural groupings within a dataset that can be used for further analysis or to gain insight into the underlying structure of the data. clustering algorithms typically require no prior knowledge of the data or its structure and instead attempt to partition the data into distinct clusters based on their similarity or dissimilarity. there are many different clustering algorithms, including k-means, hierarchical, and density-based clustering, each with strengths and weaknesses. clustering is widely used in many applications, such as image and text processing, marketing segmentation, customer profiling, bioinformatics, and anomaly detection, to name a few the k-means algorithm k-means is one of the most popular clustering algorithms. this algorithm divides data into k adjacent groups based on the distance between data points. this process is done by determining k central points or centroids representing each group and then placing each data point into the closest group based on the shortest distance to the nearest centroid. given a set of objects, the primary aim of the k-means clustering is to optimize the following objective function(cohn & holm, 2021; govender & sivakumar, 2020): 𝐽 = ∑ ∑ ‖𝑥𝑖 −𝑖 𝜖 𝑐 𝑗 𝑐𝑗 ‖ 2𝑘 𝑗=1 .............................................. (1) the formula involves a criterion function (represented by "j") and various variables, including the i-th observation (represented by "xi"), the j-th cluster center (represented by "cj"), the set of objects in the j-th cluster (represented by "cj"), and the number of clusters (represented by "k"). the distance between the data object and the cluster's center is represented by a norm denoted by "‖∗‖." the goal of the criterion function is to minimize the distance between each data point and the cluster center it is located in. the k-means iterative clustering method is commonly executed in the following manner: 1. please select a value for k and use it to establish the initial set of k centroids. 2. group each object with the centroid nearest to it. 3. calculate the mean of the cluster members to determine the new centroids for each k cluster. 4. iterate steps 2 and 3 until there is no modification in the criterion function after an iteration. the elbow, silhouette, gap statistic elbow method: the elbow method is a clustering evaluation method that plots the number jurnal riset informatika vol. 5, no. 2. maret 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 239 of clusters against the within-cluster-sum-ofsquared-errors (wcss) values (sinaga & yang, 2020). the basic idea of this method is to choose the number of clusters that give the best results in terms of wcss. in the wcss plot versus the number of clusters, the "elbow" point on the graph indicates the optimal number of clusters to choose from. silhouette score: silhouette score is a clustering evaluation metric that considers the distance between data points and centroids of other clusters. silhouette scores range between -1 and 1, with a value of 1 indicating that the data point is strongly related to the current cluster and not related to other clusters, while a value of -1 indicates that the data point is better moved to another cluster. gap statistics: gap statistics is a clustering evaluation method comparing actual data distribution with the same random distribution. this method measures how well the clustering algorithm separates data into clusters and evaluates the optimal number of clusters. a higher gap statistic value indicates that the clustering algorithm better separates data into clusters. r-programming language r is a programming language for data analysis, visualization, and statistical modeling [16]. developed in 1993, r has a very active community and has thousands of packages that can assist in performing data analysis tasks, from data cleaning to statistical modeling (peng, 2015). the advantages of the r programming language are open-source, compatibility with various systems, and thousands of packages available(mailund, 2017). powerful visualization capabilities: r has many packages that allow easy and efficient data visualization. while it has many advantages, r also has some disadvantages, such as r has many features and packages that make it very powerful, but it also makes the learning curve quite steep. slower performance compared to other programming languages: because it was written in an interpretive language, r's performance is sometimes slower than other compilation-oriented programming languages. in general, r is a compelling and flexible programming language used by data analysts and statisticians to tackle data analysis and statistical modeling tasks results and discussion this study used the dataset from kaggle.com, which was accessed on january 8, 2023, and consisted of two excel format files, namely losses equipment, and personnel, each of which was 319 data consisting of 18 for equipment variables and five variables for personnel, which can be seen from table 1. from the dataset tables 1 and 2, cleaning and unifying the data consists of one personnel attribute and eight equipment attributes, then transformed into one dataset consisting of 9 attributes and 319 data, as shown in figure 2. table 1. personnel dataset table 2. dataset of equipment losses date day aircraft helicopter … 2/25/2022 2 10 7 … 2/26/2022 3 27 26 … ⋮ ⋮ ⋮ ⋮ ⋮ 1/7/2023 318 285 272 … 1/8/2023 319 285 272 … before processing k-means clustering data using r-studio, install the required library packages such as tidyverse, cluster, factoextra, and dplyr to be used (dmitry & yerkebulan, 2022; zhu, idemudia, & feng, 2019). in the first stage, create scripts or code to import excel datasets from the r studio application, which can be used. as seen in code 1, then the results of the import dataset can be seen in figure 2. code_1_import dataset excel library(readxl) data_ukraina <read_excel("d:/dokumen /data ukraina.xlsx") view(data_ukraina) figure 2. results of import dataset excel after importing the dataset is complete, then coding or scripting for standardizing or normalizing data processing, determining the number of clusters, implementing k-means clustering, determining classes, data visualization, and group profiling. after that, in the second stage, coding is done to standardize data or create scale, as seen in code 2. date day personnel pow 2/25/2022 2 2800 0 2/26/2022 3 4300 0 ⋮ ⋮ ⋮ ⋮ 1/7/2023 318 110740 0 1/8/2023 319 111170 0 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 jurnal riset informatika vol. 5, no. 2. maret 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 240 code_2_the scala distribution and cluster determination # normalization of data with scala >data_ukraina_scale <scale(data_ukraina) #data standard >print(data_ukraina_scale) # determining the number of clusters >fviz_nbclust(data_ukraina_scale, kmeans, method = "wss") # metode elbow >fviz_nbclust(data_ukraina_scale, kmeans, method = "silhouette") # metode silhouette >fviz_nbclust(data_ukraina_scale, kmeans, method = "gap_stat") # metode gap_stat data normalization in r studio converts data from different scales to a uniform or the same scale(kaparang, moningkey, & sumual, 2021; shelly et al., 2020). normalization is done to correct differences in scale between variables that can affect the statistical analysis and predictive models that are performed. scale: this function is used to standardize data by converting data values into zscores, which are an average value of zero and a standard deviation of one, and the result is that not all of the data can be displayed because there are many, as shown in figure 3. figure 3. data normalization with scale next, in the third stage, determine the number of clusters using the elbow, silhouette, and statistical gaps to determine the most optimal number of clusters. then coding can be seen in code 2. significantly towards four and then bends or forms an elbow, so it can be concluded that the optimal number of clusters k = 3 can be seen in figure 4a. furthermore, the silhouette method with an average value approach is used to estimate the quality of the clusters formed, and the higher the average value, the better the quality. from the results of this analysis, several clusters are considered optimal, namely k = 3 and k = 5, which can be seen in figure 4b, because they have the highest average silhouette value compared to the number of other clusters a. elbow b. silhouette c. gap statistic figure 4. determining the number of clusters: a. elbow, b. silhouette, and c. gap statistics. the statistical gap method is a cluster quality evaluation method used to determine the optimal number of clusters in cluster analysis. this method compares the statistical gap value between the actual data and the data generated randomly, which is at k=1. it can be seen in graph 4c. based on the test of the three methods, two methods can be used to determine the optimal cluster, including elbow and silhouette. code_3_the application of k-means, visualization, and profiling # application of k-means clustering >final <kmeans(data_ukraina_scale, centers= 3, nstart = 25) print(final) # cluster visualization >fviz_cluster(final, data_ukraina_scale) # group profiling >data_ukraina %>% mutate(cluster = final$cluster) %>% group_by(cluster) %>% summarise_all("mean") jurnal riset informatika vol. 5, no. 2. maret 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 241 figure 5. application of k-means clustering in the fourth stage of implementing kmeans[18]–[20], based on figure 5, there are 3 clusters with details 67, 106, and 145. where for the average value of the variable clusters personnel, aircraft, helicopters, tanks, apcs, field artillery, mrl, drones, and the naval ship can be seen in clusters 1, 2, and 3. figure 6. data visualization in addition, in clusters, the number of squares indicates the distance between objects in the cluster. it can be seen that the distance for cluster 1 is 136.39153, for cluster 2 is 97.69972, and for cluster 3 is 135.63607. therefore, the distance value for each cluster is 87.0%. in the fifth stage, from the results of the cluster analysis using the k-means algorithm, the results are in the form of three clusters as shown in the visualization of figure 6, namely the results of the k-means clustering visualization plot which consists of three clusters distinguished between three colors, namely red, blue and green. the red color describes the results of cluster 1, the blue color describes the results of cluster 2, and the green color explains the results of cluster 3. it can be seen that each plot color has a different number of members. the following is a display of k-means clustering results. figure 7. group profiling p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 jurnal riset informatika vol. 5, no. 2. maret 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 242 based on the results above, the last stage can be profiling (figure 7) for each group formed (chantaramanee et al., 2022; lee & chung, 2016). cluster 1 has the lowest category of loss or loss of personnel and war equipment compared to other groups. cluster 2 has a very high category of loss and loss of personnel and war equipment. meanwhile, cluster 3 experienced moderate category losses and losses of personnel and war equipment in ukraine. conclusions and suggestions conclusion based on the results of a clustering analysis of the impact of russia's war on ukraine, there were losses in personnel and war equipment. three cluster optimization methods are used using kmeans, where two methods can be recommended for analysis: elbow, which produces k=3, and silhouette, which also produces k=3. the profiling results show that losses or losses in ukrainian personnel and war equipment are categorized into three clusters. cluster one is in the lowest category, cluster two is in the very high category, and cluster three is in the medium category. suggestion in order to broaden the scope of further research and make it more objective, the dataset must also use datasets originating from russia. in addition, other techniques can be combined with this research to find a more optimal k value or compare it with other clustering methods. references chantaramanee, a., nakagawa, k., yoshimi, k., nakane, a., yamaguchi, k., & tohara, h. 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(2019). improved logistic regression model for diabetes prediction by integrating pca and kmeans techniques. informatics in medicine unlocked, 17(january), 100179. https://doi.org/10.1016/j.imu.2019.100179 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.512 jurnal riset informatika vol. 5, no. 2. maret 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 244 jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 195 comparative analysis of using word embedding in deep learning for text classification mukhamad rizal ilham1, arif dwi laksito2*) fakultas ilmu komputer universitas amikom yogyakarta yogyakarta, indonesia mukhamad.ilham@students.amikom.ac.id1, arif.laksito@amikom.ac.id2*) (*) corresponding author abstract a group of theory-driven computing techniques known as natural language processing (nlp) are used to interpret and represent human discourse automatically. from part-of-speech (pos) parsing and tagging to machine translation and dialogue systems, nlp enables computers to carry out various natural languagerelated activities at all levels. in this research, we compared word embedding techniques fasttext and glove, which are used for text representation. this study aims to evaluate and compare the effectiveness of word embedding in text classification using lstm (long short-term memory). the research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. according to the experiments' results, it seems that fasttext is superior compared to the glove technique. the accuracy obtained reaches 90%. the number of epochs did not significantly improve the accuracy of the lstm model with glove and fasttext. it can be concluded that the fasttext word embedding technique is superior to the glove technique. keywords: word embedding; sentiment analysis; deep learning; lstm abstrak natural language processing (nlp) adalah seperangkat teknik komputasi yang didorong oleh teori untuk secara otomatis menganalisis dan mewakili bahasa manusia. nlp memungkinkan komputer untuk melakukan berbagai tugas terkait bahasa alami di semua tingkatan, mulai dari penguraian dan penandaan part -ofspeech (pos) hingga machine translation dan sistem dialog. dengan banyaknya data dan peningkatan jumlah dokumen yang signifikan per hari, klasifikasi teks menjadi semakin penting karena digunakan dalam berbagai aplikasi seperti penyaringan informasi, penyaringan spam, hingga mengkategorikan text. tujuan dari penelitian ini untuk menganalisis perbandingan performa kinerja word embedding glove dan fasttext pada klasifikasi text. dalam penelitian ini juga menggunakan model deep learning algoritma lstm (long short term memory). berdasarkan hasil eksperiman metodologi fasttext lebih unggul dibanding dengan teknik glove akurasi yang didapatkan mencapai 90% dengan menggunakan pelatihan di semua epoch dan perbandingan akurasi masing masing epoch tidak kelihatan signifikan. dapat disimpulkan bahwa teknik word embedding fasttext lebih unggul dibanding dengan teknik glove. kata kunci: word embedding; sentiment analisis; deep learning; lstm introduction a group of theory-driven computing techniques known as natural language processing (nlp) are used to interpret and represent human discourse automatically. nlp research has evolved from punch cards and batch processing, where decoding a single sentence took up to seven minutes, to an era like google, where millions of web pages can be processed in less than a second (young, hazarika, poria, & cambria, 2018). from part-of-speech (pos) parsing and tagging to machine translation and dialogue systems, nlp enables computers to carry out various natural language-related activities at all levels. text categorisation, which is used in various applications, including information filtering, spam filtering, and text categorisation, is becoming more and more crucial due to the vast quantity of data and considerable growth in the number of documents produced daily. the main research topics include efficient document text mailto:mukhamad.ilham@students.amikom.ac.id mailto:arif.laksito@amikom.ac.id p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 196 representation and the selection of better deep learning algorithms. the technique of automatically comprehending, gathering, and analysing textual data to extract sentiment information from views expressed in text is known as sentiment analysis or opinion mining (wang, nulty, & lillis, 2020). one technique to convert words into continuous vectors of a certain length is word embedding. word embedding converts words into vectors that summarise their syntactic and semantic information. therefore, word embedding is considered suitable as a feature representation in neural network models for natural language processing (nlp) tasks (deho, agangiba, aryeh, & ansah, 2018). word weighting is a pre-processing data strategy that assigns an appropriate weight to each term to represent the term's relevance to the text. this model plays a vital role in improving text classification with high efficiency. word embedding is a crucial technique in deep learning since it can analyse the text as an input for the deep learning model. deep learning is a technique for feature extraction, pattern recognition, and classification that involves employing several layers of processing to build different models and execute classification tasks from the gathered data (imaduddin, widyawan, & fauziati, 2019). the deep learning algorithm used in this research is lstm (long short-term memory), one of the variations of rnn (recurrent neural network). lstm can be used to overcome the weakness of rnn, which is its inability to store data during learning if too much data has to be stored. the bag of words technique, the first technique created for encoding words into vector form, marked the beginning of the development of word embedding. in 1972 karen spärck jones introduced the tf-idf (term frequency inverse document frequency) technique, a combination of tf (term frequency) and idf (inverse document frequency) is a statistical measure that describes the words in several documents (jones, 1972). to build practical neural network-based word insertion training in 2013, tomas mikolov and his colleagues at google created the new word2vec approach (mikolov, chen, corrado, & dean, 2013). after a year, jeffrey penington and his colleagues created the glove (global vectors) technique, an extension of the effective word2vec learning technology (brennan, loan, watson, bhatt, & bodkin, 2017). the last technique, fasttext, was developed by facebook in 2017, which is very fast and effective in learning word representation and text classification (bojanowski, grave, joulin, & mikolov, 2017). there have been numerous studies in the area of sentiment analysis that used word embedding techniques like bag of word (imaduddin et al., 2019; marukatat, 2020), word2vec (alsurayyi, alghamdi, & abraham, 2019; imaduddin et al., 2019; kilimci & akyokus, 2019; marukatat, 2020; rahman, sari, & yudistira, 2021), doc2vec (imaduddin et al., 2019), glove (alsurayyi et al., 2019; imaduddin et al., 2019; kilimci & akyokus, 2019), and fasttext (kilimci & akyokus, 2019; marukatat, 2020). those word embedding approaches were then evaluated using rnn (kilimci & akyokus, 2019; rahman et al., 2021), cnn(kilimci & akyokus, 2019), lstm (kilimci & akyokus, 2019; rahman et al., 2021), naïve bayes (rahman et al., 2021). a study by (deho et al., 2018) offered word embedding to identify the polarity of sentiment (positive, negative, or neutral) from existing text. this improved the accuracy of sentiment categorisation. additionally, a new technique, known as improved word vector (iwv), was presented by (bojanowski et al., 2017) to increase the precision of pre-trained word embedding in sentiment analysis. the study's findings indicate that the word embedding technique can increase the precision of text classification (deho et al., 2018). the iwv approach significantly improves the researcher's proposed sentiment analysis technique (rezaeinia, ghodsi, & rahmani, 2017). the performance of word embedding word2vec continuous bag of words (cbow), word2vec, doc2vec, glove, and fastext was compared by other researchers in addition to new approaches being suggested and word embedding techniques being identified. accuracy of 95.52% on the domain of hotel reviews from the traveloka site with a total of 5,000 reviews. the glove method has the highest accuracy rate compared to other methods (imaduddin et al., 2019). this research is similar to previous research (kamiş & goularas, 2019) that glove can improve almost all configuration performance. the effectiveness of deep learning has been compared in another research. previous research by (alsurayyi et al., 2019) compared rnn combined with lstm, rnn combined with bi-lstm (bidirectional lstm), and cnn (convolutional neural networks) for word representation using word2vec and glove techniques. the results showed that rnn combined with bi-lstm using the glove technique got better accuracy than other methods. this study used the domain of restaurant reviews from yelp. researchers (rahman et al., 2021) compared lstm, naïve bayes, rnn, and word representation using the word2vec technique. the jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 197 outcomes demonstrated that lstm was better than other approaches. this study uses glove and fasttext word embedding in the lstm model for text representation. this research aims to analyse and compare the effectiveness of word embedding on text represented by lstm deep learning architecture. research methods this research is expected to provide highaccuracy text classification and good performance between word embedding gloves and fasttext techniques evaluated using lstm. to achieve the expected research objectives, we used the methodology shown in figure 1. figure 1. flowchart data collection and labelling the spotify application review dataset from the kaggle.com website is used in this study. the datasets of our study can be downloaded from the url: https://www.kaggle.com/datasets/ mfaaris/spotify-app-reviews-2022. spotify app reviews on google play store were collected from january 1, 2022, to july 9, 2022. the dataset consists of 5 columns, namely time_submitted, review, rating, total_thumbsup, replay, and 61,594 rows. however, the columns used for this study are the review column and the rating column. pre-processing before the data is used for sentiment analysis, several preparatory processes must be done to get the best classification results. in the first stage, symbols, punctuation marks, and emojis are removed from the data set. the second stage, tokenisation and case folding is breaking down the sentences in the dataset into words, also known as tokens, and converting all capital letters into lowercase letters. the third step is filtering or removing stop words, taking important words and discarding words that are unimportant or have no meaning. figure 2. percentage of labelling data the library used for filtering is nltk (natural language toolkit), developed by steven bird and edward loper at the university of pennsylvania in 2001 (botrè, lucarini, memoli, & d’ascenzo, 1981). the fourth step is stemming, converting unstandardised words into common words or removing affixes. the last step is labelling data. the data is grouped into positive and negative sentiments based on application ratings, as in the research (alsurayyi et al., 2019; imaduddin et al., 2019), which only uses positive sentiments and negative sentiments in data labelling. this study's total percentage of labelling data is shown in figure 2. word embedding a. glove word embedding converts words into a continuous vector form with a predefined text length. many methods have been developed to convert words into vectors, including a bag of words, tf-idf (jones, 1972), word2vec (mikolov et al., 2013), glove (brennan et al., 2017), and fasttext (bojanowski et al., 2017). glove is a method that combines local context-based learning in word2vec p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 198 with global statistical matrix factorisation techniques like lsa (brennan et al., 2017). matrix factorisation and the skip-gram method are combined in the glove methodology. the cooccurrence matrix created by glove (word context x) is used for prediction and calculation outside the existing corpus (imaduddin et al., 2019). 𝐽0 ∑ 𝑓(𝑋𝑖𝑗 )(𝑤𝑖 𝑇 𝑤�̃� + 𝑏𝑖 + 𝑏�̃� − 𝑙𝑜𝑔𝑋𝑖𝑗 ) 2𝑣 𝑖,𝑗=1 ........... (1) where each element 𝑋𝑖𝑗 indicates the number of times the word appears in word j, for 𝑤𝑗 is a vector for the context word 𝑤𝑖 vector of the main word and 𝑏𝑖 , 𝑏𝑗 scalar bias for the main word and context (kilimci & akyokus, 2019). b. fasttext fasttext, an open-source project from facebook research is a fast and efficient technique for learning word representations and performing text classification often used for nlp. the primary function of fasttext insertion is to analyse the internal structure of words. this works particularly well in morphologically complex languages as it allows learning of self-representations for various word morphologies (bojanowski et al., 2017). 𝑠(𝑤, 𝑐) = ∑ 𝑍𝑔 𝑇 𝑣𝑐𝑔𝜖𝑔𝜔 .................................................... (2) the word2vec-proposed negative sampling skip-gram model is implemented by fasttext using a modified skip-gram function. the word score is calculated by adding the vector representation of the n-grams in the set 𝐺𝑤 ⊂ {1, … , 𝐺}, which is the set of n-grams found in the word w. split dataset the data set is divided into training and testing data to train the machine learning model. in this experiment, the data is divided into a ratio of 80:20, with 80% of the data used to train the model and 20% used to test it. lstm long short-term memory (lstm) networks are a complex deep learning approach. lstm works very well on various problems and is widely used by many researchers (alsurayyi et al., 2019). due to its complex dynamics, lstm may quickly "memorise" data over a lengthy period. in a vector of memory cells called 𝑐𝑡 𝑙 ∈ 𝑅𝑛 , the "longterm" memory is stored. although different lstm designs vary in terms of connection layout and activation function. all lstm architectures have explicit memory cells that can store data for long periods of time. the lstm has the option of replacing, retrieving, or storing the memory cell for later (zaremba, sutskever, & vinyals, 2014). there are three gates for storing information for an extended period. the forget gate removes information from the cell that is not needed. the input gate adds beneficial information. the output gate pulls valuable information from the cell state for output values. through gates that let information flow through or are blocked by the lstm unit, the lstm unit decides what to store and when to permit reads, writes, and deletions(kilimci & akyokus, 2019) figure 3. lstm a collection of lstm architectures or memory cells are shown in figure 3. 𝑖𝑡 = 𝜎(𝑊𝑥𝑖 𝑥𝑡 + 𝑊ℎ𝑖 ℎ𝑡−1 + 𝑊𝑐𝑖 𝑐𝑡−1𝑏𝑖 ) ..................... (3) 𝑓𝑡 = 𝜎(𝑊𝑥𝑓 𝑥𝑡 + 𝑊ℎ𝑓 ℎ𝑡−1 + 𝑊𝑐𝑓 𝑐𝑡−1 + 𝑏𝑓 ) ........... (4) 𝑐𝑡 = 𝑓𝑡 𝑐𝑡−1 + 𝑖𝑡 𝑡𝑎𝑛ℎ (𝑊𝑥𝑐 𝑥𝑡 + 𝑊ℎ𝑐 ℎ𝑡−1 + 𝑏𝑐 ......... (5) ot = σ(wxoxt + whoht−1 + wcoct + bo) ................. (6) ℎ𝑡 = 𝑜𝑡 𝑡𝑎𝑛ℎ(𝑐𝑡 ) ................................................................ (7) where 𝑖𝑡 = input gate. 𝑓𝑡 = forget gate. 𝑜𝑡 = output gate. 𝑐𝑡 = sell activation vector. 𝑋𝑡 = the input at time t. ℎ𝑡−1= the previous state. 𝐶𝑡−1= the previous state memory. 𝐶𝑡 = current memory state. ℎ𝑡 = the current state. jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 199 evaluation the evaluation stage is a step to check the accuracy of the experimental results and measure the performance of the model that has been produced. the performance of the algorithm is measured in this study using a confusion matrix, and the metrics utilised for assessment are true positive (tp), true negative (tn), false positive (fp), and false negative (fn). 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃 .................................................... (8) 𝑃𝑟𝑒𝑐𝑖𝑠𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃+𝐹𝑃 ......................................................... (9) 𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃+𝐹𝑁 ................................................................ (10) 𝐹 − 𝑀𝑒𝑎𝑠𝑢𝑟𝑒 = 2 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙 ∗ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑅𝑒𝑐𝑎𝑙𝑙+𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ...... (11) results and discussion in this section, we analyse and compare how well word embedding represented by lstm architecture performs. this research was conducted using phyton version 3.10.7 and jupyter notebook version 6.4.11 and a device with an intel core i38100 processor, 8 gb ram and windows ten pro operating system. the dataset used in this study consists of 61,594 spotify application reviews from the google play store that have gone through preprocessing and labelling. as shown in figure 2, the dataset is divided into positive and negative sentiments, with negative sentiments based on reviews with ratings 1-2 and positive sentiments based on reviews with ratings 4-5. after pre-processing phase, word embedding techniques were employed to convert words into vector form with a predetermined length. in this step, we compared glove and fasttext in the english language with 300 dimensions. it took about 2 minutes 30 seconds to fetch 4.7 gb of glove data, while it took 5 minutes 50 seconds to load 4.2 gb of fasttext data. table 1. classification accuracy of the word embedding glove deep learning model glove accuracy time epoch 50 89% 33 minute 11 second epoch 100 89% 1 hour 7 minute 45 second epoch 200 89% 2 hour 16 minute 6 second table 2. classification accuracy of the word embedding fasttext deep learning model fasttext accuracy time epoch 50 90% 34 minute 15 second epoch 100 90% 1 hour 7 minute 22 second epoch 200 90% 2 hour 14 minute 56 second further, the dataset is split into training and testing data in a ratio of 80:20, with 80% of the data used to train the model and 20% to test it. to make the data more balanced, random oversampling (ros) was used to double the minority class and add it to the training dataset before starting the data split. the positive and negative opinions were 29,937 and 24,771 before the ros process. consequently, the number of labels in the minority (negative) was set at 29,937, which was also the number in the majority (positive). in this study, we used a single lstm architecture or one layer, 64 units, adam optimiser, and the learning rate is 0.001. the training was performed in three iterations of 50, 100, and 200 epochs. figures 4 to 6 illustrate the lstm in three epochs using the glove word embedding approach. training and validation have a wide gap in accuracy and loss. (a) (b) figure 4. training (a) accuracy and (b) loss using lstm and glove at 50 epochs p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 200 (a) (b) figure 5. training (a) accuracy and (b) loss using lstm and glove at 100 epochs (a) (b) figure 6. training (a) accuracy and (b) loss using lstm and glove at 200 epochs figures 7 to 9 depict the accuracy and loss during lstm training using fasttext word embedding with epochs of 50, 100, and 200. the fasttext word embedding technique is superior to all epochs compared to the glove technique, as seen in table 2. however, the training and validation have a wide gap in accuracy and loss, similar to glove word embedding. (a) (b) figure 7. training (a) accuracy and (b) loss using lstm and fasttext at 50 epochs (a) (b) figure 8. training (a) accuracy and (b) loss using lstm and fasttext at 100 epochs jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.507 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 201 (a) (b) figure 9. training (a) accuracy and (b) loss using lstm and glove at 200 epochs conclusions and suggestions bag of word, tf-idf, word2vec, glove and fasttext are some word embedding methods to display words in vector form. in this study, we compare glove and fasttext word embedding, two state-of-the-art word representation algorithms that use deep learning lstm architecture. according to the experiments' results, it seems that fasttext is superior compared to the glove technique. the accuracy obtained reaches 90%. the number of epochs did not significantly improve the accuracy of the lstm model with glove and fasttext. for all the scenarios tested, the training and validation have a wide gap in the model's accuracy and loss. it seems that model improvement is needed for future research. moreover, the early stop method for model training is crucial for overfitting and underfitting. the early stops technique can also achieve model convergence in the precise number of epochs. references alsurayyi, w. i., alghamdi, n. s., & abraham, a. 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(2014). recurrent neural network regularization. arxiv, (2013), 1–8. retrieved from http://arxiv.org/abs/1409.2329 jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 211 the implementation of c4.5 algorithm for determining the department of vocational high school mirza sutrisno1, jefri kusuma rambe2, asruddin3, ade davy wiranata4 teknik informatika, universitas muhammadiyah jakarta jakarta indonesia mirza.sutrisno@umj.ac.id*) ilmu komputer, universitas budi luhur jakarta indonesia jefriekussuma@gmail.com sistem komputer, universitas bung karno jakarta indonesia asruddin69@gmail.com teknik informatika, universitas muhammadiyah prof. dr. hamka jakarta indonesia adedavy@uhamka.ac.id (*)corresponding author abstract the selection of departments in vocational high schools (smk) is a must for students to determine the concentration of student learning interest for three years in a school. the lack of student knowledge and outreach about this department caused many students to choose their majors by the most choices and following other students. this problem can cause some difficulties for the students to participate in learning, and most fail. students must select their major based on their interests, abilities, and talents because every student has different abilities and talents. the c4.5 algorithm can provide convenience in grouping students based on majors. using the decision tree method with attributes such as grades in mathematics, english, interests, and talents, the system can recommend majors based on students' interest levels. the results of this study are the determination of the departments with the accuracy of the calculation using the confusion matrix method with a 98,55% accuracy rate and 100% recall rate value. keywords : vocational school; recommendation system; department selection; c4.5 algorithm abstrak pemilihan jurusan di sekolah menengah atas (smk) adalah sebuah keharusan bagi peserta didik dalam menentukan konsentrasi peminatan belajar siswa selama tiga tahun di sekolah. kurangnya pengetahuan siswa dan sosialisasi tentang jurusan ini menyebabkan tidak sedikit dari siswa menetukan pilihan berdasarkan pilihan terbanyak dari rekan sesama pelajar yang mengakibatkan kesulitan dalam mengikuti peminatan pembelajaran dan tidak sedikit yang gagal. setiap siswa perlu menemukan jurusan yang sesuai dengan minat, kemampuan, dan bakat mereka. dikarenakan setiap siswa memiliki kemampuan untuk berpikir serta bakat yang berbeda. algoritma c4.5 dapat memberikan kemudahan dalam pengelompokan mahasiswa berdasarkan jurusan. menggunakan metode decision tree dengan atribut-atribut yang digunakan seperti nilai matematika, bahasa inggris, minat dan bakat, sistem dapat merekomendasikan pilihan jur usan berdasarkan tingkat peminatan siswa. hasil dari penelitian ini adalah menentukan jurusan dengan akurasi perhitungan menggunakan metode confusion matrix yang dengan tingkat akurasi 98,55% dan nilai recall rate 100. kata kunci : smk; sistem rekomendasi; pemilihan jurusan; algoritma c4.5 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 212 introduction the students who continue to vocational high school (smk) level often struggle to determine the department and concentration of study choice. the various departments are not offered to the students who want to continue to the vocational level. the appropriate selection in the vocational will give the students some motivation and interest in learning. the students' mistakes in determining a department will impact problems such as failure and lost time, energy, and mind. the students must choose a department that suits their interests, abilities, and talent. students have different thinking skills and talents to do something (khairina et al., 2015). for selecting study programs in senior high school (shs), they have also developed a system for helping students select study programs. the cases used in the study include results of the intelligence test, students’ interests, and grades in several subjects (mulyana et al., 2015). an intelligent knowledge-based system was also developed to provide appropriate and accurate recommendations for determining student learning levels based on the assessment criteria in english language course (sutrisno & budiyanto, 2019). this research implements a systematic method for providing departmental recommendations for vocational high school (smk) students based on the specified criteria. data mining uses statistics, mathematics, artificial intelligence, and machine learning to extract and identify useful information and related knowledge from various large databases (turban et al., 2007). data mining has some functions for processing in several applications, such as description, estimation, prediction, classification, clustering, and association (larose, 2005). data mining is a series of processes to extract added value in the form of information that has not been known manually from a database. the resulting information is obtained by extracting and recognizing important or interesting patterns from the data contained in the database (soufitri et al., 2021). the algorithm of c4.5 determines the students who take the department according to their educational background, interests, and abilities of students. the major's selection parameter is a grade passing academy (gpa) in semesters 1 and 2. this research produces the experiments and evaluations showing that the accurate decision tree c4.5 algorithm is applied for determining the suitability of student majors with 93.31% accuracy and departmental recommendations of 82.64% (swastina, 2018). the other fields of education also use the c4.5 algorithm to classify the students’ successful predicates. the analysis used data mining using the c4.5 method, and the process used rapidminer software to make decision trees (luvia et al., 2017). the decision tree model uses the c4.5 algorithm to develop an effective selection system for vocational schools. the input variables were: interest, academic talent, national exam score, and gender. the input variables are interest, academic talent, national exam score, and gender. the c4.5 algorithm was used to build decision trees to describe the relationship between the input variables and the target variable in patterns. the patterns were used to classify the input variables into the target variable. the system results provide appropriate recommendations for up to 83.33% of the 48 tested data (prabowo & subiyanto, 2017). the algorithm of c4.5 with the decision tree method can provide predictive rule information to describe the association process with the predictions of the students who repeat their studies. the characteristics of classified data can be obtained through decisions and the rule of tree structures, so the testing phase with weka software can help predict that students will repeat the course (azwanti, 2018). the c4.5 algorithm can change a considerable fact into a decision tree representing the rule to determine prospective students' prediction retirement. the result of the research is that the application can classify the new students in a tree structure to produce a rule and predict the possibility of the retirement of new students (darmawan, 2018). the algorithm of c4.5 is used to classify students in determining majors by looking for patterns of rules based on supporting variables in the form of junior high school (smp) average report cards, academic test scores such as natural sciences (ipa) grades, social sciences (ips) grades, and language scores. the results of this study are in the form of a data mining application with the c4.5 algorithm to predict majors in science, social studies, or language. the level of accuracy obtained is 97.42% (kurniasari & fatmawati, 2019). classification with the c4.5 algorithm and the forward selection method to determine factors of late coming to school. the sample used was questionnaire data for class viii (eight) state junior high school students 271, totalling 270 students. using training data, specific attributes are determined to form a classifier model. the results of this study are the results of the accuracy of the c4.5 method of 60.74%, with the results of the tree showing congestion is a factor of school delay and jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 213 the results accuracy of 65.93% for forward selection and getting the three best attributes (puspitasari, 2020). implementing the decision tree method can be used in determining student majors using the c4.5 algorithm. data mining is a gain ratio of student report cards, interests, and talents. testing the c4.5 decision tree algorithm results can make more accurate predictions in research on department management and department recommendations for students (baktiar, 2022). research methods c 4.5 algorithm the algorithm of c4.5 is one of the algorithms applied in the data mining process. the c4.5 algorithm is an extension of quinlan's own id3 algorithm to generate a decision tree. like cart, the c4.5 algorithm recursively visits each decision node, and chooses optimal separation, until there is no further separation (larose, 2005). decision tree a decision tree is a very well-known method of classification and prediction. the decision tree method converts facts into decision trees that represent rules. the rules can be easily understood with natural language and expressed in database languages such as sql ( structured query language ) to find records in specific categories (luvia et al., 2017). the provisions of the c4.5 algorithm for building a decision tree are as follows: a. determining the highest gain value as the root b. creating the branches for each attribute c. sharing the cases in the branches d. repeat the process for each branch until all the cases in the branch have the same class. the stages calculation of the c4.5 decision tree algorithm has several stages : 1. preparing the data training. 2. the tree's root was determined from the highest gain value. 3. calculating the entropy value (larose, 2005) 𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑆) = ∑ −𝑛𝑖=1 𝑝𝑖 ∗ 𝑙𝑜𝑔2 𝑝𝑖 .......................... (1) 4. calculating the gain value. 𝐺𝑎𝑖𝑛(𝑆, 𝐴) = 𝑆 − ∑ |𝑆𝑖| |𝑆| 𝑛 𝑖=1 ∗ 𝑆𝑖 .................................. (2) 5. after the gain value is found, it will continue in the decision tree process. the research method consists of some steps from figure 1. figure 1. research’s step data collection the data collection technique uses secondary data from a study of the journal of determining majors using the naïve bayes method (khairina et al., 2015). the authors develop the data into 2000 data sets. data sets are presented in table 1. table 1. data set no mtk eng minat bakat jurusan 1 >75 >75 rpl multimedia rpl 2 >75 >75 rpl programing rpl 3 >75 >75 rpl teknik komputer rpl 4 >75 >75 mm multimedia mm 5 >75 >75 mm programing mm 6 >75 >75 mm teknik komputer mm 7 >75 >75 tkj multimedia tkj 8 >75 >75 tkj programing tkj 9 >75 >75 tkj teknik komputer tkj 10 >75 >75 rpl no rpl 11 >75 >75 mm no mm 12 >75 >75 tkj no tkj 13 >75 70-75 rpl multimedia rpl 14 >75 70-75 rpl programing rpl 15 >75 70-75 rpl teknik komputer rpl 16 >75 70-75 mm multimedia mm 17 >75 70-75 mm programing rpl p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 214 no mtk eng minat bakat jurusan 18 >75 70-75 mm teknik komputer mm 19 >75 70-75 tkj multimedia tkj 20 >75 70-75 tkj programing tkj 21 >75 70-75 tkj teknik komputer tkj 22 >75 70-75 rpl no rpl 23 >75 70-75 mm no mm 24 >75 70-75 tkj no tkj 25 70-75 >75 rpl multimedia rpl 26 70-75 >75 rpl programing rpl 27 70-75 >75 rpl teknik komputer tkj 28 70-75 >75 mm multimedia mm 29 70-75 >75 mm programing mm 30 70-75 >75 mm teknik mm 31 70-75 >75 tkj multimedia tkj 32 70-75 >75 tkj programing tkj 33 70-75 >75 tkj teknik tkj 34 70-75 >75 rpl no rpl 35 70-75 >75 mm no mm 36 70-75 >75 tkj no tkj 37 70-75 70-75 rpl multimedia mm 38 70-75 70-75 rpl programing rpl 39 70-75 70-75 rpl teknik tkj 40 70-75 70-75 mm multimedia mm 41 70-75 70-75 mm programing mm 42 70-75 70-75 mm teknik komputer mm 43 70-75 70-75 tkj multimedia tkj 44 70-75 70-75 tkj programing tkj 45 70-75 70-75 tkj teknik komputer tkj 46 70-75 70-75 rpl no rpl 47 70-75 70-75 mm no mm 48 70-75 70-75 tkj no tkj data processing data is processed and classified based on four criteria to calculate the entropy value and gain—data criteria in table 2. table 2. criteria criteria description mtk value of mathematics eng value of english minat interest of students bakat talent of students results and discussion the modelling of the c4.5 algorithm uses several stages, first calculating the entropy value and then the gain values from the training data. after obtaining the highest gain value, it will be converted into the decision tree. the calculation of the value is represented in node 1 table 3. table 3. counting from node 1 criteria sub criteria total case rpl mm tkj entropy gain total 2000 586 668 746 1,57801 mtk value > 75 1008 377 295 336 1,57778 0,0235157 value 70-75 992 209 373 410 1,53083 eng value > 75 1008 292 338 378 1,57703 1,1156631 value 70-75 992 294 330 368 1,57893 minat rpl 670 534 48 88 0,91799 1,2704865 mm 666 47 615 4 0 tkj 664 5 5 654 0 bakat multimedia 502 125 211 166 1,55295 0,1189128 programing 500 210 124 166 1,55265 teknik komputer 374 84 124 166 1,5321 no 498 167 167 164 1,58491 teknik 126 0 42 84 0 all criteria can represent the calculations with the calculations: jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 215 entropy total = (-total rpl / total case) * imlog2 (total rpl/ total case) + (-mm / total case) *imlog2 (total mm / total case) + (-tkj/total case) * imlog2 (total tkj / total case) (-586/2000)*imlog2(586/2000) + (-668/2000) *imlog2 (668/2000) + (-668/2000) * imlog2 (746/2000) = 1,57801 gain criteria value of mtk: (entropy total)-(total case criteria value > 75 / total case)* (entropy value > 75) ((total case value 70-75/total case)* entropy value 70-75) = (1,57801)-(1008/2000)*1,57778) ((992/2000)* 1,53083)= 0,0235157 gain criteria value of eng: (entropy total)-(total case criteria value > 75 / total case)* (entropy value > 75) ((total case value 70-75/total case)* entropy value 70-75) = (1,57801)-(292/2000)*1,57703) ((294/2000)* 1,57893)= 1,1156631 gain criteria value of minat: (entropy total)-(total case criteria value rpl/ total case)* (entropy rpl) ((total case value mm/total case)* entropy value mm) )-(total case criteria value tkj/ total case)* (entropy tkj) = (1,57801)-(670/2000)*0,91799) ((666/2000)*0) -(664/2000)* 0) = 1,2704865 gain criteria value of bakat: (entropy total)-(total case criteria value multimedia/ total case)* (entropy multimedia) ((total case value programming/total case)* entropy value programming) )-(total case criteria value teknik komputer/ total case)* (entropy teknik komputer) -(total case criteria value no/ total case)* (entropy no) -(total case criteria value teknik / total case)* (entropy teknik) = (1,57801)-(502/2000)*1,55295) ((500/2000)* 1,55265) -(374/2000)*1,5321) (498/2000)* 1,58491) -(126/2000)* 0)= 0,1189128 it can be seen that the gain value of the 4 attributes is: 1. mtk : 0,0235157 2. eng : 1,1156631 3. minat : 1,2704865 4. bakat : 0,1189128 moreover, the highest gain value is the minat criteria of 1,2704865, representing a decision tree. decision tree the decision tree formed from node one is represented in figure 2 by using rapid miner software. figure 2. the decision tree in addition to graphs, the decision tree can also be described as follows: minat = mm | eng = 70-75 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 216 | | bakat = multimedia: mm {rpl=0, mm=84, tkj=0} | | bakat = no: mm {rpl=3, mm=77, tkj=2} | | bakat = programing | | | mtk = 70-75: mm {rpl=0, mm=40, tkj=0} | | | mtk = >75: rpl {rpl=42, mm=0, tkj=0} | | bakat = teknik komputer: mm {rpl=0, mm=82, tkj=0} | eng = >75: mm {rpl=2, mm=332, tkj=2} minat = rpl | mtk = 70-75 | | bakat = multimedia | | | eng = 70-75: mm {rpl=0, mm=42, tkj=0} | | | eng = >75: rpl {rpl=41, mm=1, tkj=0} | | bakat = no: rpl {rpl=78, mm=2, tkj=2} | | bakat = programing: rpl {rpl=84, mm=0, tkj=0} | | bakat = teknik: tkj {rpl=0, mm=0, tkj=42} | | bakat = teknik komputer: tkj {rpl=0, mm=0, tkj=42} | mtk = >75: rpl {rpl=331, mm=3, tkj=2} minat = tkj: tkj {rpl=5, mm=5, tkj=654} the “minat” criteria produce the highest gain value with a result of 1,2704865, calculated in table 4 node 1.2. table 4. counting from node 1.2 crite ria jurus an total case r pl m m t kj entro py gain min at rpl 670 53 4 48 88 0,917 99 1,2704 865 mm 666 47 61 5 4 0 tkj 664 5 5 65 4 0 the decision tree formed from node 1.1 is represented in figure 3 using rapidminer software. figure 3. decision tree node 1.1 in addition to graphs, the decision tree can also be described as follows: tree minat = mm: mm {rpl=47, mm=615, tkj=4} minat = rpl: rpl {rpl=534, mm=48, tkj=88} minat = tkj: tkj {rpl=5, mm=5, tkj=654} confusion matrix testing a confusion matrix is a table that states the classification of the amount of data correct test and the number of incorrect test data (normawati & prayogi, 2021). the tests of data sets used 2000 data with the confusion matrix method tested using weka software to calculate the accuracy and recall value. the calculation of the confusion matrix is in table 5. table 5 confusion matrix classification actual value prediction true false a c tu a l v a lu e true 1971 (tp) 29 (fp) false 0 (fp) 0 (tn) 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 = 𝑡𝑝 + 𝑡𝑛 𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛 𝑋 100% = 1971 + 0 1971 + 0 + 29 + 0 𝑋 100% = 𝟗𝟖, 𝟓𝟓% 𝑹𝒆𝒄𝒂𝒍𝒍 = 𝑡𝑝 + 𝑡𝑛 𝑓𝑛 + 𝑡𝑝 𝑋 100% = 2000 0 + 2000 𝑋 100% = 𝟏𝟎𝟎% the results of confusion matrix testing using weka software with 10-fold validation can be seen in figure 4. figure 4. weka testing result the test results are also represented as area under curve (auc) in figure 5. the curve shows that of the 2000 data tested, 1971 data (98,55%) are correctly classified. jurnal riset informatika vol. 5, no. 2 march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 217 figure 5. area under curve (auc) conclusions and suggestions conclusion based on the description, explanation, and testing, the conclusions are that the c4.5 algorithm method can provide convenience for grouping students based on the departments. using the decision tree method with the attributes used, such as the value of mathematics, english, interests, and talents, produce a tkj department with the highest level of specialization. determining the departments in vocational high school can use the rapidminer application using the decision tree method and the c4.5 algorithm with the calculation accuracy using the confusion matrix method that has been done by using weka software with a 98,55% accuracy rate—and 100% recall rate value. suggestion the following research can try to add other criteria in determining student majors. the next researcher can use more collections of data sets and then test the result with different testing methods on more varied users. references azwanti, n. 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(2017). penerapan algoritma c4.5 untuk klasifikasi predikat keberhasilan mahasiswa di amik tunas bangsa. jurasik (jurnal riset sistem informasi dan teknik informatika), 1(1), 75–79. https://doi.org/10.30645/jurasik.v1i1.12 mulyana, s., hartati, s., wardoyo, r., & winarko, e. (2015). case-based reasoning for selecting study program in senior high school. international journal of advanced computer science and applications, 6(4), 136–140. https://doi.org/10.14569/ijacsa.2015.06041 8 normawati, d., & prayogi, s. a. (2021). implementasi naïve bayes classifier dan confusion matrix pada analisis sentimen berbasis teks pada twitter. jurnal sains komputer & informatika (j-sakti, 5(2), 697– 711. http://ejurnal.tunasbangsa.ac.id/index.php/j sakti/article/view/369 prabowo, i. m., & subiyanto, s. (2017). sistem rekomendasi penjurusan sekolah menengah kejuruan dengan algoritma c4.5. jurnal kependidikan, 1(1), 139–149. https://doi.org/10.21831/jk.v1i1.8964 puspitasari, c. (2020). implementation of c4.5 method to determine the factor of being late for coming to school. jurnal riset informatika, 2(3), 115–120. https://doi.org/10.34288/jri.v2i3.132 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i2.516 jurnal riset informatika vol. 5, no. 2 march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 218 soufitri, f., purwawijaya, e., hasibuan, e. h., & singarimbun, r. n. (2021). testing c4.5 algorithm using rapidminer applications in determining customer satisfaction levels. jurnal infokum, 9(2), 510–517. https://infor.seaninstitute.org/index.php/inf okum/article/view/198 sutrisno, m., & budiyanto, u. (2019). intelligent system for recommending study level in english language course using cbr method. international conference on electrical engineering, computer science and informatics (eecsi), 153–158. https://doi.org/10.23919/eecsi48112.2019. 8977047 swastina, l. (2018). penerapan algoritma c4 . 5 untuk penentuan jurusan mahasiswa. gema aktualita, 2(1), 93–98. https://doi.org/10.24252/insypro.v6i2.7912 turban, e., e. aronson, j., & liang, t.-p. (2007). decision support systems and business intelligence. decision support and business intelligence systems, 7/e, 1–35. https://doi.org/10.1017/cbo978110741532 4.004 jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 245 decision support system for selection of the best fuel for households using the weighted product (wp) method yasmin khairunnisa-1*), dewi primasari-2, nurul kamilah-3 fakultas teknik & sains, program studi teknik informatika, information systems concentration universitas ibn khaldun bogor, indonesia yasminkhairunnisa1@gmail.com-1*), dewiprimasari9@gmail.com-2, nurul.kamilah@uika-bogor.ac.id-3 (*) corresponding author abstract fuel is one of the most critical needs for the community in carrying out various household activities dominated by fuel oil. meanwhile, the availability of fuel is increasingly running low. most people in the botumoito area in gorontalo province work in the agricultural sector with low local income. availability of fuels such as kerosene and gas there is quite challenging because there is no source or supply of gas either directly from the gas field or terminal. the government needs to make the right policy on fuel selection according to the problems in the region. this research aims to design and build a decision support system for selecting the best fuel and implementing the weighted product (wp) method into the system. the method used in designing and manufacturing this system is the waterfall method. this research results in a decision support system that can help fuel ranking. the ranking results are based on the magnitude of the vector (v) value obtained from testing ten alternative fuels. bioethanol occupies the top priority with a vector value of 0.152. this research can help consultants, fuel experts, and local governments speed up their work in determining the best household fuel according to their respective regions. keywords: fuels; decision support systems; weighted products. abstrak bahan bakar merupakan salah satu kebutuhan yang paling penting bagi masyarakat dalam melakukan berbagai aktivitas rumah tangga yang didominasi oleh bahan bakar minyak (bbm). sementara itu, ketersediaan bbm semakin lama semakin menipis. sebagian besar penduduk di daerah botumoito di provinsi gorontalo bekerja di sektor pertanian dengan pendapatan warga setempat tidak terlalu tinggi. ketersediaan bahan bakar seperti minyak tanah dan gas disana cukup sulit diperoleh karena belum terdapat sumber atau pasokan gas baik secara langsung dari lapangan gas maupun terminal. pemerintah perlu membuat kebijakan yang tepat pada pemilihan bahan bakar sesuai dengan masalah yang ada di daerah. tujuan dari penelitian ini yaitu merancang dan membangun sistem pendukung keputusan dalam pemilihan bahan bakar terbaik serta mengimplementasikan metode weighted product (wp) kedalam sistem. metode yang digunakan dalam perancangan dan pengembangan sistem ini yaitu menggunakan metode waterfall. hasil dari penelitian ini yaitu sebuah sistem pendukung keputusan yang dapat membantu perankingan bahan bakar. hasil perankingan berdasarkan besarnya nilai vektor (v) yang didapatkan dari pengujian 10 alternatif bahan bakar, bioetanol menempati prioritas utama dengan nilai vektor 0,152. penelitian ini membantu konsultan, pakar bahan bakar dan pemerintah daerah dalam menentukan bahan bakar rumah tangga terbaik sesuai dengan wilayahnya masing-masing. kata kunci: bahan bakar; sistem pendukung keputusan; weighted products. introduction fuel is a material that can be converted into energy and is one of the most essential needs for society. most of the fuel is used in various household activities such as lighting, cooking, heating, cooling, and other household activities (rohim & triani, 2021). generally, the fuel used by indonesians for household activities is still dominated by fuel oil (ridlo al hakim, 2020). meanwhile, the availability of fuel is getting less and less along with the increasing population, accompanied by increasing demand and energy consumption. this is a concern in the context of mailto:yasminkhairunnisa1@gmail.com mailto:dewiprimasari9@gmail.com p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 246 energy security, given that indonesia's oil production continues to decline (umam et al., 2018). therefore, the government places restrictions on fuel subsidies as an essential step to increase efficiency in fuel use (afriyanti et al., 2018). the government needs to make the right policies on fuel selection for their respective regions. this decision is hoped to benefit the local community regarding price, environmental friendliness, availability, infrastructure, safety, and ease of use. many household fuel choices exist, such as lpg, kerosene, biogas, city gas, bioethanol, and others. each of these fuels has its advantages and disadvantages according to local conditions. therefore, the local government must be selective in choosing fuel according to the conditions of each area. the botumoito area in the province of gorontalo is the area that will be used in this study. this sub-district has an area of 486.23 km2, and the distance between the village of botumoito and the capital is about 2.5 km. the total population of botumoito itself is around 2,685 people, and from the employment perspective, most of the population of botumoito works in the agricultural sector. residents' income in this area is low, around 500,000 to 1 million monthly rupiahs per capita. the availability of fuels such as kerosene and gas there is quite challenging because there is no source or supply of gas either directly from the gas field or terminal. another cause is that national production has been reduced and sold in certain places (bps, 2018). in order to decide to choose the best fuel for an area, it is necessary to consult with experts. the expert needs to have a system that can help provide calculation output for fuel selection. decision support systems (dss) are interactive computer-based systems that help decision-makers utilize data and models to solve unstructured and semi-structured problems (limbong et al., 2020). dss requires a method to find alternative solutions to problems that occur. the dss method used in this study is weighted product (wp). the wp method is calculated based on the level of importance. this method is more efficient because the time needed in the calculation is shorter (susliansyah et al., 2019). the wp method is a settlement method that uses multiplication to connect attribute ratings where the rating must be raised to the first power along with the weight of the attribute in question (aldo, 2019). so the wp method does not allow for the same vector values for different criteria (anggraeni, 2017). the use of the wp method is expected to answer the needs of policymakers in determining the most appropriate type of fuel to use in an area. making the right decision can be one of the efforts to ensure energy security to support economic growth. this study applies the wp method because of the simple calculation concept for determining the weights of almost the same value criteria. research methods types of research this research uses a quantitative method that is systematic and uses mathematical models. time and place of research the research starts from june 2021 to may 2022. the research location is at the center for research and development of oil and gas technology "lemigas," at jl. ciledug raya, kav. 109, cipulir, kebayoran lama, south jakarta. research target this study aims to design and build a decision support system for choosing the best fuel for households using the weighted product (wp) method. therefore, it is hoped that this will help consultants, fuel experts, and local governments speed up work in determining the best household fuel according to their respective regions. then, the interview stages are carried out to collect alternative data, criteria, and weights. procedure this research uses the waterfall method, one of the sdlc models often used in developing information systems or software. this method uses a systematic and sequential approach (wahid, 2020). the early stage of this research method is analysis consisting of data collection and weighted product method. data collection begins with the stages of observation, interviews, and the application of literary studies to produce output in the form of criteria data, data on alternative fuels, and their weights which are then calculated using the weighted product method to produce recommendation values. the next step is system design, which is to build a program applying object-oriented programming through the unified modeling language (uml) design process to produce use cases, activities, sequences, and class diagrams. the next stage is coding in implementing web-based programs using html programming languages, php, mysql databases, and web browsers using chrome. furthermore, black box testing is carried jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 247 out at the testing stage so that the system can run properly. figure 1. research method data, instruments, and data collection techniques based on the results of interviews conducted with a fuel expert at lembaga minyak dan gas (lemigas), data that will be used in calculating the weighted product method can be compiled. this study will use ten alternative fuels as samples, as shown in table 1. table 1. alternative data alternative code alternative name a1 gas city a2 lpg alternative code alternative name a3 biogas a4 bioethanol a5 kerosene a6 dimethyl ether (dme) a7 coal briquettes a8 coconut shell briquettes a9 rice husk briquettes a10 electricity criteria values and weights were obtained from fuel experts. in the weighted product method, there are two types of criteria, namely price and benefits criteria. the benefit criterion is that the greater the value, the more selected, and the price criterion, namely, the smaller the value, the more selected (widaningsih & manggala, 2020). the criteria, weight, and type can be seen in table 2. table 2. criteria data and weight code criteria weigh t type k1 price 30 cost k2 availability 25 benefit k3 infrastructure 10 benefit k4 safety 15 benefit k5 ease of use 15 benefit k6 pollution level 5 benefit total 100 the quantitative values of the criteria for each of the alternatives used are as follows ; the price criterion (k1) is obtained based on the price consumers must pay for the fuel if the fuel is available in the area. if the fuel is unavailable, then the price obtained is based on market prices. the availability criteria (k2) are obtained based on the ease with which the community obtains fuel in the area. the ease is measured based on the amount of fuel compared to the needs and mileage of taking the fuel from sources available in the area. the rating is in table 3. table 3. availability criteria (k2) description value very difficult 1 difficult 2 easy 3 quite easy 4 very easy 5 the infrastructure criteria (k3) are measured based on fuel facilities or infrastructure to reach the consumer. the rating is in table 4. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 248 table 4. infrastructure criteria (k3) description value very difficult 1 difficult 2 easy 3 quite easy 4 very easy 5 the safety criteria (k4) are measured based on the flammability of the fuel and the ease of detecting the presence of the fuel. the rating is in table 5. table 5. safety criteria (k4) description value not safe 1 less safe 2 safe 3 safe enough 4 very safe 5 the criteria for convenience (k5) are measured by consumers or the community, starting from turning on the stove to turning it off. the rating is in table 6. table 6. convenience criteria (k5) description value very difficult 1 difficult 2 easy 3 quite easy 4 very easy 5 the criteria level of pollution (k6) is measured by the results of burning these fuels, which damage human health. the rating is in table 7. table 7. criteria the level of pollution (k6) description value very high 1 high 2 medium 3 low 4 very low 5 if the value of each criterion has been determined, a matching rating table, as in table 8, is created. table 8. matching rating for each alternative alternative criteria k1 k2 k3 k4 k5 k6 a1 50000 1 1 1 5 5 a2 90000 3 3 3 5 4 a3 110000 2 2 2 4 5 a4 56000 5 4 4 2 4 a5 90000 3 3 4 3 3 a6 60000 1 3 2 5 5 a7 100000 2 4 5 1 1 a8 300000 2 4 5 1 1 a9 360000 2 4 5 1 1 a10 110000 3 3 4 3 5 data analysis technique weighted product (wp) calculates based on the level of importance and can evaluate a collection of attributes by multiplying all criteria with alternative results and ranking between weights and alternative multiplication results. the data from the conformity assessment results in table 8. that have been obtained will be processed using the weighted product method to produce the best alternative fuel recommendations. first, make improvements to the value of the weight of the criteria by adding up the weight of each criterion, then each initial weight of the criteria is divided by the sum of the weights of the criteria using the following formula 1. 𝑊𝑗 = 𝑊𝑗 ∑𝑊𝑗 .............................................................................. (1) description: 𝑊 : criteria weight 𝑗 : criteria next, calculate the s vector for each fuel alternative by multiplying all the attributes for an alternative with the weight as a positive exponent for the benefit attribute and a negative exponent for the cost attribute using formula 2. 𝑆𝑖 = ∏ 𝑋𝑖𝑗 𝑊𝑗𝑛 𝑗=1 ................................................................. (2) description: 𝑖 : alternative 1,2,…,m. jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 249 𝑆 : vector s 𝑋 : criteria value 𝑊 : criteria weight 𝑗 : criteria 𝑛 : the number of criteria after obtaining the value of vector s, calculate vector (v). that is, divide the preferences of each alternative by the total number of vectors (s) with the following formula 3. v𝑖 = 𝑆𝑖 ∑ 𝑆𝑖 .............................................................................. (3) description: 𝑖 : alternative 1,2,…,m. 𝑉 : vector v 𝑆 : vector s 𝑋 : criteria value 𝑊 : criteria weight 𝑗 : criteria 𝑛 : the number of criteria * : the number of criteria that have been assessed on the s vector after the value of the vector v is obtained, sort it based on the most significant value of v. a sequence of the best alternative fuels is found, which will be the decision. results and discussion the weighted product method calculates the best alternative fuel recommendation value for households. the process begins with alternative data, criteria, and weights as input. then, it calculates the improvement of the criterion weights. at this stage, the criterion weights from k1 to k6 will be corrected for the weight values. the result table can be seen in table 9. table 9. the improvement weights code old weight new weight k1 30 0,3 k2 25 0,25 k3 10 0,1 k4 15 0,15 k5 15 0,15 k6 5 0,05 furthermore, the next step is to run the program using a web-based programming language, php, with a mysql database. the code can be seen in pseudocode_ improvement weights. pseudocode_ improvement weights $numb = 1; foreach ($kriteria as $k) : $numb++ $k['normalisasi_bobot'] $bobot = 0; $no = 1; foreach ($kriteria as $kb) : $bobot = $bobot + $kb['normalisasi_bobot'] if ($no++ == sizeof($kriteria)) : $kb['normalisasi_bobot'] else $kb['normalisasi_bobot'] endif endforeach $k['normalisasi_bobot'] $bobot $k['normalisasi_bobot'] if ($k['jenis_kriteria'] == 'benefit') : $hasil = $k['normalisasi_bobot'] * 1 $k['normalisasi_bobot'] * (1) else : $hasil = $k['normalisasi_bobot'] * -1 $k['normalisasi_bobot'] ?> * (-1) endif $hasil $this->db->where('id', $k['id']); $this->db->update('kriteria', ['hasil' => $hasil]); endforeach next calculates vector s at this stage, and it calculates the value of vector s from alternative fuels a1 to a10. the result table can be seen in table 10. table 10. vector s si w1 w2 w3 w4 w5 w6 result s1 0,039 1 1 1 1,27 1,08 0,054 s2 0,033 1,32 1,12 1,18 1,27 1,07 0,077 s3 0,030 1,19 1,07 1,11 1,23 1,08 0,057 s4 0,038 1,50 1,15 1,23 1,11 1,07 0,095 s5 0,033 1,32 1,12 1,23 1,18 1,06 0,074 s6 0,037 1 1,12 1,23 1,18 1,06 0,063 s7 0,032 1,19 1,15 1,27 1 1 0,055 s8 0,023 1,19 1,15 1,27 1 1 0,040 s9 0,022 1,19 1,15 1,27 1 1 0,037 s10 0,031 1,32 1,12 1,23 1,18 1,08 0,071 total 0,622 the code that calculates vector s can be seen in pseudocode_ vectors. pseudocode_ vectors foreach ($kriteria as $krt) : ?> 100 / (sizeof($kriteria)) $krt['nama_kriteria'] $krt['normalisasi_bobot'] ?>) endforeach ?> foreach ($nilai_kriteria_alternatif as $alt => $value): $no =1 ; $hasil = 1; foreach ($value['nilai'] as $nilai_kriteria): 100 / (sizeof($value['nilai'])) % print_r($this->uri->segment(4) . ' here');die; // $getnilai = $this->db>get_where('nilai_kriteria_alternatif', ['wilayah_id' => $this->uri->segment(2), 'kriteria_id' => $krit['id'], 'alternatif_id' => $alt['id']])->row_array(); p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 jurnal riset informatika vol. 5, no. 2. march 2023 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 250 $hasil = $hasil * (pow($nilai_kriteria['nilai'], $nilai_kriteria['kriteria_hasil'])); $nilai_kriteria['nilai'] $nilai_kriteria['kriteria_hasil'] if ($no++ < sizeof($value['nilai'])): else: endif ?> if (is_infinite($hasil) || is_nan($hasil)) $hasil = 0; $where = ['wilayah_id' => $this->uri->segment(4), 'alternatif_id' => $nilai_kriteria['alternatif_id']]; $cekhasil = $this->db-> get_where('hasil_normalisasi', $where)-> row_array(); if ($cekhasil) $this->db-> update('hasil_normalisasi', ['hasil' => $hasil], $where); else { $where['hasil'] = $hasil; $this->db->insert('hasil_normalisasi', $where); } endforeach $hasil = number_format($hasil, 3) endforeach after obtaining the value of vector s1 to s10, calculate vector (v). from the results of the v vector, the recommendations are ranked from the largest to the smallest. the ranking table can be seen in table 11. table 11. the ranking table vi fuel score rank v4 bioethanol 0,152 1 v2 lpg 0,124 2 v5 kerosene 0,118 3 v10 electricity 0,114 4 v6 dimethyl ether (dme) 0,101 5 v3 biogas 0,092 6 v7 coal briquettes 0,088 7 v1 gas city 0,086 8 v8 coconut shell briquettes 0,064 9 v9 rice husk briquettes 0,060 10 then the recommended result is bioethanol fuel with the most significant vector v value of 0.152. the code calculates vector v can be seen in the pseudocode_ vector. pseudocode_ vectorv $alternatif = $this->db->select(" alternatif.nama_alternatif, hasil_normalisasi.*")>join("alternatif", "alternatif.id = hasil_normalisasi.alternatif_id")>get_where('hasil_normalisasi', ['wilayah_id' => $this>uri->segment(4)]); $num = 1; foreach ($alternatif->result() as $rank) : rank->nama_alternatif number_format($rank->hasil, 3) $jumlah = 0; $no = 1; foreach ($alternatif->result() as $ar) : $jumlah = $jumlah + $ar->hasil ?> if ($no++ == $alternatif->num_rows()) number_format($ar->hasil, 3) ?> else : number_format($ar->hasil, 3) ?>+ endif endforeach $hasil = $rank->hasil <= 0 ? 0 : $rank->hasil / $jumlah number_format($rank->hasil, 3 number_format($jumlah, 3) number_format($hasil, 3) $this->db->where('id_hasil', $rank->id_hasil); if (is_infinite($hasil) || is_nan($hasil)) $hasil = '0'; $this->db->update('hasil_normalisasi', ['hasil_wp' => $hasil]); endforeach ?> system displays this displays the calculation results and ranking of the best fuel priorities. the chart results of the method can be shown in figure 11. figure 11. chart results page the ranking of the best fuel priorities page can be shown in figure 12. figure 12. the ranking page 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16 n il a i v e k to r v bahan bakar gas kota lpg biogas biotanol minyak tanah dimetil eter (dme) briket batubara briket batok kelapa briket sekam padi listrik jurnal riset informatika vol. 5, no. 2. march 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.506 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 251 conclusions and suggestions conclusion this study concludes that the wp system has been successfully developed to assist consultants, experts, and local governments in determining fuel in their regions. the weighted product method using five criteria of price, pollution level, availability, infrastructure, safety, and ease of use produces a ranking according to the magnitude of the vector (v) value obtained from testing ten alternative fuels, namely: (1) bioethanol (2) lpg (3) kerosene, (4) electricity, (5) dimethyl ether, (6) biogas, (7) coal briquettes, (8) city gas (9) coconut shell briquettes, and (10) rice husk briquettes. the system is designed using uml diagrams and built using a web programming language, php. system testing uses the black box method so that the system can run properly. suggestion this decision support system can be developed by comparing it with other methods, such as topsis or fuzzy logic, to support the accuracy of the data. the wp method can also support policymakers in making a policy for the best product among many products in some criteria. references afriyanti, y., sasana, h., & jalunggono, g. 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(2018). tantangan pengembangan energi panas bumi dalam perannya terhadap ketahanan energi di indonesia. swara patra : majalah ilmiah ppsdm migas, 8(3), 48–65. http://ejurnal.ppsdmmigas.esdm.go.id/sp/in dex.php/swarapatra/article/view/6 wahid, a. a. (2020). analisis metode waterfall untuk pengembangan sistem informasi. jurnal ilmu-ilmu informatika dan manajemen stmik, november, 1–5. widaningsih, s., & manggala, s. b. r. (2020). sistem pendukung keputusan pemilihan proyek perangkat lunak dengan menggunakan metode weighted product. media jurnal informatika, 12(2), 69–76. https://doi.org/10.35194/mji.v12i2.1198 widhyaestoeti, d., iqram, s., mutiyah, s. n., & khairunnisa, y. (2021). black box testing equivalence partitions untuk pengujian front-end pada sistem akademik sitoda. jurnal ilmiah teknologi infomasi terapan, 7(3), 211–216. https://doi.org/10.33197/jitter.vol7.iss3.20 21.626 https://doi.org/10.35194/mji.v12i2.1198 jurnal riset informatika vol. 2 no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 1 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. rancang bangun sistem informasi manajemen skripsi berbasis web menggunakan model waterfall ibnu rusdi, indra febria widy, noviyanti peratiwi 1teknik informatika sekolah tinggi manajemen informatika dan komputer nusa mandiri www.nusamandiri.ac.i ibnu.ibr@nusamandiri.ac.id 2,3teknik informatika sekolah tinggi teknik cendekia jl. islamic raya kompleks pendidikan islamic village kelapa dua tangerang www.cendekia.ac.id 2 indra.febria@cendekia.ac.id, 3 novi.per4tiwi@gmail.com abstrak dalam tulisan ini akan dibahas mengenai proses pembuatan manajemen skripsi berbasis web. penulis mengangkat tema ini karena saat ini mahasiswa sering sekali menjumpai permasalahan terhadap penulisan skripsi yang dimana membutuhkan banyak waktu dan juga membutuhkan banyak kertas untuk revisi dari dosen pembimbing. konfirmasi penulisan sangatlah penting bagi para mahsiswa dalam proses pembuatan penulisan mahasiswa, yang akan dilakukan penilaian oleh para dosen pembimbing yang dimana kreatifitas mahasiswa terlihat dalam tahap-tahap proses pembuatan penulisan skripsi nanti. model yang penulis gunakan menunjang sistem life development sistem (sdlc) yaitu model waterfall (air terjun) diharapkan dengan kemudahan dalam mengembangkan perangkat lunak (software) pada setiap tahap yang dikerjakan saling mendukung satu sama lain. tahapan pengerjaan dalam tulisan ini dimulai dari pengumpulan sumber permasalahan proses pembuatan penulisan mahasiswa dan menemukan tujuan untuk mempermudah mahasiswa dalam berkomunikasi dengan dosen pembimbing. dengan adanya manajemen skripsi ini diharapkan bisa digunakan sebagai data dalam membantu penulisan mahasiswa, dan membuat mahasiswa tidak harus menghamburkan kertas dalam revisi-revisi dari para dosen pembimbing. kata kunci: sistem informasi, manajemen skripsi, model waterfall abstract in this paper we will discuss the process of making a web-based thesis management. the author raises this theme because currently students often encounter problems with thesis writing which requires a lot of time and also requires a lot of paper for revision from the supervisor. writing confirmation is very important for students in the process of making student writing, which will be assessed by supervisors where student creativity is seen in the later stages of the thesis writing process. the model that the author uses supports the sistem life development system (sdlc), namely the waterfall model (waterfalls), which is expected to be easy in developing software (software) at each stage that is done mutually supporting one another. stages of work in this paper starts from gathering the source of the problem of the process of making student writing and finding a goal to facilitate students in communicating with the supervisor. with the management of this thesis it is hoped that it can be used as data in assisting students' writing, and making students not have to waste paper in the revisions of the supervisors. keywords: information sytems, thesis management, waterfall model pendahuluan dalam dunia pendidikan perlu adanya pengendalian informasi yang terkomputerisasi untuk mendukung penyampaian informasi yang lebih cepat, tepat, dan terpusat sehingga mengharuskan kebutuhan ini untuk direalisasikan dalam segala bidang. teknologi informasi dan komunikasi sebagai bagian dari ilmu pengetahuan dan teknologi secara umum adalah semua http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2 no. 1 desember 2019 2 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional teknologi yang berhubungan dengan pengambilan,pengumpulan,pengolahan,penyimpa nan,penyebaran, dan penyajian informasi (abyzar, 2019). pada penelitian sebelumnya dengan adanya sistem monitoring ini pihak akademik stikom bali akan dengan mudah mengetahui perkembangan skripsi mahasiswa, sehingga jika terjadi kendala yang dihadapi oleh mahasiswa dalam pengerjaan skripsi maka pihak akademik dengan mudah dapat mencarikan solusinya(ramayasa & arwana, 2015). penulis mencoba merancang sebuah sistem agar mempermudah dosen dalam melakukan pemeriksaan skripsi mahasiswa dan memudahkan dosen untuk selalu berinteraksi ke mahasisawa tanpa harus mertemu langsung dengan siswa. diharapkan sistem yang akan dikembangkan ini menjadi salah satu bentuk komputerisasi sistem konvensional untuk memberikan kemudahan dalam melakukan studi, dengan menggunakan teknologi secara optimal (constantianus & suteja, 2005). sistem yang dapat memudahkan mahasiswa untuk memanajement skripsi dan memberikan approval kepada mahasiswa bimbingan, tanpa harus bertemu langsung dengan dosen. penulis dalam merancang sistem menggunakan berbasis web agar dapat diakses secara mudah dan dibuat dengan user interface dinamis. model pengembangan sistem yang digunakan dalam penelitian ini yaitu model waterfall (aediyansyah, 2018). tujuan dikembangkan aplikasi manajemen skripsi agar dosen dan mahasiswa dapat berinteraksi secara interaktif sehingga kendala dosen yang sulit dihubungi dan sebagainya dapat berkurang dan memudahkan dalam proses pengontrolan dari bidang akademik. metode penelitian jenis penelitian penelitian ini menggunakan pendekatan kuantitatif dan terapan. suatu proses menemukan pengetahuan yang menggunakan data berupa angka sebagai alat menganalisis keterangan mengenai apa yang ingin diketahui (hidayat, 2012). target/subjek penelitian (untuk penelitian kualitatif) atau populasi-sampel (untuk penelitian kuantitatif) perlu diurai dengan jelas dalam bagian ini(friyadie & fatayat, 2019) . model pengembangan sistem model waterfall atau air terjun menyediakan pendekatan alur hidup perangkat lunak secara sekuensial atau terurut dimulai dari analisis, desain, pengkodean, pengujian, dan tahap pendukung (support) (sukamto & salahudin, 2018). berikut tahapan-tahapan yang akan penulis kerjakan dengan metode pengembangan software waterfall: 1. analisis kebutuhan software pada tahap ini dilakukan eksplorasi mengenai kebutuhan dari pengguna (user). yaitu dengan cara melakukan observasi pada stt cedndikia mengenai masalah-masalah yang perlu diselesaikan seputar manajemen skripsi dan kebutuhan yang diperlukan bidang akademik. diharapkan dengan pengembangan sistem ini diharapkan memudahkan proses monitoring, serta bagi dosen dengan mahasiswa proses bimbingan skripsi lebih interaktif. 2. desain setelah kebutuhan dari pengembangan sistem informasi manajemn skripsi ini telah diketahui, maka akan dilakukan desain sistem. desain sistem menggunakan uml (activity diagram, usecase diagram, deployment diagram) untuk desain database menggunakan entity relationship diagram (erd) dan logical record structure (lrs). 3. code generation (implementasi) pada tahap ini penulis menggunakan bahasa pemrograman php, html dan database phpmyadmin sedangkan dalam proses pemrograman menggunakan framework codeigniter dengan fitur tampilan web dinamis dan user friendly. 4. testing pada tahap ini penulis mendeskripsikan proses pengujian yang akan dilakukan dengan menggunakan blackbox testing untuk meminimalisir kesalahan (error) dan memastikan keluaran yang dihasilkan sesuai dengan yang diharapkan selain itu penulis menggunakan software uantuk menguji kemanan dari aplikasi web menggunakan acunetix threat. 5. support dalam mendukung sistem informasi yang akan dikerjakan diperlukan perangkat keras (hardware) yaitu peralatan dalam bentuk fisik yang menjalankan perangkat lunak (software) dan peralatan ini berfungsi untuk menjalankan instruksi yang diberikan dan mengeluarkannya dalam bentuk informasi. gambar 1. ilustrasi model waterfall http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2 no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 3 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. data, intrumen, dan teknik pengumpulan data untuk menunjang penelitian ini, penulis penulis menggunakan teknik pengumpulan data sebagai berikut: 1. observasi metode ini dilakukan dengan pengamatan langsung terhadap sistem manajemen skripsi yang digunakan oleh sekolah tinggi teknik (stt) cendekia. 2. wawancara metode ini merupakan suatu bentuk pengumpulan data dengan mengadakan tanya jawab langsung kepada bidang akademik sekolah tinggi teknik (stt) cendekia seputar objek yang akan diteliti. wawancara ini digunakan untuk mengumpulkan informasi yang tidak mungkin diperoleh lewat observasi. 3. studi pustaka pada teknik ini, penulis mencari atau menggali informasi atau pengetahuan dari berbagai referensi yang bersumber dari buku, jurnal dan lainnya yang berkaitan dengan objek penelitian yang dibahas dalam penulisan. hasil penelitian dan pembahasan analisa kebutuhan perangkat lunak langkah pertama yang dilakukan pada rancangan manajemen skripsi yang diterapkan, penulis mengidentifikasi kebutuhan yang diperoleh berdasarkan kebutuhan pengguna dan kebutuhan sistem. halaman ini terbagi menjadi tiga tampilan. tampilan pertama untuk administrator, yang kedua halaman untuk dosen, dan yang ketiga halaman untuk mahasiswa. 1. halaman untuk bagian admin a. mengelola data mahasiswa. b. mengelola data dosen . 2. halaman untuk dosen a. dosen dapat melakukan login. b. pada halaman ini dosen dapat mengelola data status penulisan yang disetujui . c. dosen dapat mengelola data status pengajuan yang belum disetujui. 3. halaman untuk mahasiswa a. pada halaman ini pengunjung dapat mahasiswa melihat status penulisan. b. mahasiswa melakukan login. c. mahasiswa melakukan pengajuan perancangan penulisan. perancangan perangkat lunak 1. perancangan sistem perangkat lunak perancangan sistem informasi manajemen skripsi ini dengan menggunakan use case diagram. use case diagram menggabarkan hubungan antara aktor dan kegiatan yang dapat dilakukannya terhadap aplikasi (sukamto & salahudin, 2018). gambar 2. use case diagram rancang bangun skripsi tabel 1. sekenario use case login use case name : login untuk mengakses sistem use case description : pengguna login ke dalam sistem untuk mengakses fungsifungsi sistem actors : admin, dosen, mahasiswa pre-condition : pastikan jaringan internet terkoneksi agar tidak ada kendala dalam mengakses sistem postcondition : sistem akan menampilkan popup setelah berhasil login, sesuai dengan hak akses main scenarios serial step (tahap/langkah) actor 1 input username, input password 2 validasi username dan password 3 jika benar diberikan akses ke sistem sesuai hak akses extension 2a invalid username, menampilkan popup pesan username salah http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2 no. 1 desember 2019 4 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 2b invalid password, menampilkan popup pesan password salah 2. perancangan database perancangan basis data utama dengan menggunakan entity ralationship diagram (erd), dengan mengidentifikasi jenis entitas dan attribut. jurusan jurusan_id nama dosen user_accountmahasiswa mahasiswa_list skripsi punya jurusan_id dosen_id nama telpon email nip siswa_id status create_dateperiode posisi foto siswa_id dosen_id list_id remark status skripsi_id siswa_id judul file deskripsi nama id nip email user_password type nama nim telepon foto create_date kelolapunya kelola lakukan 1 m 11 1 m m n m 1 gambar 3. entity relationship diagram (erd) aplikasi manajemen skripsi perancangan logical record structure (lrs), menjelaskan tentang alur dari suatu record. mahasiswa_list pk list_id dosen_id siswa_id periode status created_date skripsi pk skripsi_id siswa_id judul file deskripsi status created_date remark dosen pk dosen_id nama nip telpon email posisi created_date jurusan_id foto skripsi_detail pk skripsi_det_id skripsi_id judul file deskripsi status nilai jurusan pk jurusan_id nama user_account pk id nama nip email user_password type mahasiswa pk siswa_id nama nip jurusan_id telpon created_date foto gambar 4. logical record structure aplikasi manajemen skripsi 3. perancangan navigasi a. navigasi halaman pengguna manajemen skripsi. gambar 5. struktur navigasi aplikasi manajemen skripsi 4. rancangan user interface a. halaman login pengguna pada halam ini pengguna diinstruksikan memasukan atau input data dengan benar agar dapat mengakses sistem gambar 6. tampilan awal login pengguna b. tampilan data mahasiswa yang akan skripsi data mahasiswa yang sudah memenuhi criteria untuk mengikuti skripsi pada semester berjalan akan ditampilkan gambar 7. tampilan data mahasiswa yang skripsi http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2 no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 5 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. c. tampilan data dosen pembimbing skripsi data dosen pembimbing yang telah memenuhi syarat membimbing mahasiswa untuk skripsi pada stt cendekia. gambr 8. tampilan data dosen pada gambar 8 diatas, menampilkan daftar dosen stt cendekia yang telah memenuhi syarat menjadi dosen pembimbing skripsi mahasiswa. d. tampilan pengajuan skripsi ke dosen oleh mahasiswa gambar 9. tampilan pengajuan judul mahasiswa pada gambar 9 diatas, menampilkan bentuk konsultasi mahasiswa kepada dosen pembimbing skripsi melalui aplikasi manajemen skripsi. e. tampilan detail skripsi mahasiswa gambar 10. tampilan detail skripsi mahasiswa pada gambar 10 diatas, mahasiswa stt cendekia mengajukan per bab yang terdapat dalam skripsi. sehingga dosen pembimbing dapat melihat dan mengecek file yang telah diajukan serta dosen diharapkan memberikan catatan terhadap pengajuan tersebut agar mahasiswa mengetahui yang harus diperbaiki atau revisi. f. tampilan persetujuan (approve it) skripsi mahasiswa pada tampilan berikut akan menampilkan data pesetujuan dosen kepada mahasiswa yang telah selesai mengerjakan skripsi dan berinteraksi aktif dalam aplikasi manajemen skripsi. gambar 11. tampilan approve it (persetujuan) dosen pada gambar 11 diatas, apabila mahasiswa telah diperiksa semua pengajuan bab dan telah memperbaiki serta telah sesuai kaidah penulisan. dosen pembimbing dapat memberikan persetujuan melalui aplikasi manajemen skripsi dan mahasiswa diwajibkan menemui serta meminta tanda tangan dosen pembimbing secara langsung. code perangkat lunak <div class="page-container"> <!-begin content --> <div class="page-content-wrapper"> <!-begin content body --> <!-begin page content body --> <div class="page-content"> <div class="container"> <!-begin page breadcrumbs --> <ul class="page-breadcrumb breadcrumb"> <li> <a href="index.html">home</a> <i class="fa fa-circle"></i> </li> http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2 no. 1 desember 2019 6 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional </ul> <!-end page breadcrumbs --> <!-begin page content inner --> <div class="page-content-inner"> <div class="row"> <div class="col-md-12"> <!-begin profile sidebar --> <div class="profile-sidebar"> <!-portlet main --> <div class="portlet light profile-sidebar-portlet "> <!-sidebar userpic --> <div class="profile-userpic"> <img src="<?=base_url()?>/uploads/mahasiswa/<?= $profile->foto?>" class="img-responsive" alt=""> </div> <!-end sidebar userpic --> <!-sidebar user title --> <div class="profile-usertitle"> <div class="profile-usertitle-name"> <?=ucwords($profile->nama)?> </div> <div class="profile-usertitle-job">nip : <?=ucwords($profile->nip)?> </div> </div> <!-end sidebar user title --> <!-sidebar buttons --> <div class="profile-userbuttons"> <button type="button" class="btn btn-circle green btn-sm">follow</button> <button type="button" class="btn btn-circle red btn-sm">message</button> </div> <!-end sidebar buttons --> <!-sidebar menu --> <div class="profile-usermenu"> <ul class="nav"> <li> <a href="app_ticket.html"> <i class="icon-home"></i> ticket list </a> </li> <li class="active"> <a href="app_ticket_staff.html"> <i class="icon-settings"></i> support staff </a> </li> <li> <a href="app_ticket_config.html"> <i class="icon-info"></i> configurations </a> </li> </ul> </div> <!-end menu --> </div> <!-end portlet main --> </div> <!-end begin profile sidebar --> <!-begin ticket list content --> <div class="app-ticket app-ticket-list"> <div class="row"> <div class="col-md-12"> <div class="portlet light "> <div class="portlet-title tabbable-line"> <div class="caption caption-md"> <i class="icon-globe theme-font hide"></i> <span class="caption-subject font-blue-madison bold uppercase">list judul skripsi</span> </div> <div class="pull-right"> <div class="btn-group pull-right"> <button class="btn green btn-outline dropdowntoggle" data-toggle="dropdown">actions <i class="fa fa-angle-down"></i> </button> <ul class="dropdown-menu pull-right"> <li> <a dataaction="add" title="add" dataid="" onclick="get_modal(this)"> </script> pengujian perangkat lunak tabel 2. pengujian login dosen no . skenario pengujian test case hasil yang diharapkan hasil pengujia n kesimpula n 1. nama user dan password tidak diisi kemudian klik tombol login nama user: (kosong) password: (kosong) sistem akan menolak akses dan menampilka n pesan “login gagal” sesuai harapan valid 2. mengetikka n nama user dan password tidak diisi atau kosong kemudian klik tombol login nama user: 16091500 1 password : (kosong) sistem akan menolak akses dan menampilka n pesan “login gagal” sesuai harapan valid 3. nama user tidak di isi (kosong) dan password diisi kemudian klik tombol login nama user: (kosong) password: admin sistem akan menolak akses dan menampilka n pesan “login gagal” sesuai harapan valid 4. mengetikka n salah satu kondisi salah pada nama user atau password kemudian klik tombol login nama user: 16091500 1 (benar) password: 12345 (salah) sistem akan menolak akses akan kembali diarahkan ke halaman login dan menampilka n pesan “ login gagal” sesuai harapan valid http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2 no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 7 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. no . skenario pengujian test case hasil yang diharapkan hasil pengujia n kesimpula n 5. mengetikka n nama user dan password dengan data yang benar kemudian klik tombol login nama user : 16091500 1 (benar) password: ***** (benar) sistem akan menerima akses login dan akan menampilka n pesan “login berhasil!”. sesuai harapan valid hasil pengujian keamanan menggunakan software acunetix web vulnerability scanner terhadap aplikasi manajemen skripsi yang sudah di hosting dengan laman http://skripsinovi.cendekia.vclass.my.id/ ditunjukan pada gambar berikut: gambar 12. tampilan threat level hasil pengujian keamana pada gambar diatas level 0: safe menunjukan aplikasi manajemen skripsi aman digunakan. gambar 13. tampilan hasil scan dengan aplikasi acunetix web vulnerability scanner simpulan dan saran simpulan proses pengembangan aplikasi ini berhasil untuk membuat komunikasi mahasiswa bersama dosen menjadi lebih mudah, ramah linkungan,dapat memperlihatkan tahap proses pembuatan skripsi mahasiswa, dan memperlihatkan perkembangan maupun kreatifitas mahasiswa, juga dapat mempermudah mahasiswa untuk mengerjakan penulisan tanpa harus bertatapan langsung dengan dosen pembimbing. memudahkan bidang akademik memonitoring kegiatan bimbingan serta pemetaan pembimbing skripsi menjadi lebih baik kedepan. saran dalam pembuatan aplikasi ini masih terdapat banyak yang perlu dibenahi agar aplikasi ini menjadi semakin baik dan efektif. saran untuk penelitian di masa datang perlunya keterangan waktu pembuatan penulisan untuk memperjelas waktu penulisan yang di buat mahasiswa, tampilan menu type teks dibuat agar lebih menarik dan lebih bervariasi. dalam pengiriman file untuk diajukan ke dosen pembimbing terlebih dahulu diidentifikasi berupa file .doc atau .docx bukan berupa .pdf agar dosen pembimbing dapat dengan mudah memberikan komentar dan memberikan saran perbaikan. daftar referensi abyzar, a. (2019). perkembangan teknologi informasi dan komunikasi bidang pendidikan di indonesia. retrieved from www.kompasina.com website: https://www.kompasiana.com/abyzarchelse a0074/5d9c9d0c097f366b992563d2/perke mbangan-teknologi-informasi-dankomunikasi-pada-bidang-pendidikan-diindonesia aediyansyah, a. (2018). perancangan sistem informasi penjualan daur ulang botol bekas (pet) berbasis web. jurnal riset informatika, 1(1), 11–16. retrieved from http://ejournal.kresnamediapublisher.com/i ndex.php/jri/article/view/5 constantianus, f., & suteja, b. r. (2005). analisa dan desain sistem bimbingan tugas akhir berbasis web dengan studi kasus fakultas teknologi informasi. jurnal informatika ukm, 1(2), 93–105. retrieved from https://media.neliti.com/media/publications /219183-analisa-dan-desain-sistembimbingan-tuga.pdf friyadie, f., & fatayat, u. (2019). penggunaan model waterfall dalam http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2 no. 1 desember 2019 8 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional perancangan aplikasi penjualan kosmetik berbasis web. jurnal riset informatika, 1(4), 159–166. retrieved from http://ejournal.kresnamediapublisher.com/i ndex.php/jri/article/view/84 hidayat, a. (2012). pengertian dan penjelasan penelitian kuantitatif lengkap uji statistik. retrieved november 21, 2019, from statistikian.com website: https://www.statistikian.com/2012/10/pen elitian-kuantitatif.html ramayasa, i. p., & arwana, i. b. k. s. (2015). perancangan sistem monitoring pengerjaan skripsi pada stmik stikom bali berbasis web. konferensi nasional sistem dan informatika, 760–765. retrieved from https://pdfs.semanticscholar.org/4752/7e6 1c8ceb57f209080665654efb53ac5486e.pdf sukamto, r. a., & salahudin, m. (2018). rekayasa perangkat lunak terstruktur dan berorientasi objek (edisi revisi). retrieved from https://scholar.google.co.id/scholar?hl=id&a s_sdt=0,5&cluster=4129280622857038994 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 295 the best employee decision support system using the analytical hierarchy process method at pt asdp indonesia ferry (persero) deny novianti-1, anggi oktaviani-2, dahlia sarkawi-3, aldyanto-4, ahmad faren syahidan-5 sistem informasi / administrasi perkantoran universitas bina sarana informatika jakarta, indonesia denynov.dov@bsi.ac.id, dahlia.dls@bsi.ac.id informatika / teknologi informasi universitas nusamandiri jakarta, indonesia aldyanto1709@gmail.com, farensyahidan@gmail.com, anggi.aov@nusamandiri.ac.id (*) corresponding author abstract selecting the best employees aims to spur employee morale by improving performance and dedication. the selection of the best employees is selected based on company criteria. pt asdp indonesia ferry (persero) the best employee criteria applied by the company are work quantity, work quality, attendance, teamwork, and initiative. employee assessment is carried out every month by the assessment team (vic e president (vp) and manager). the problem faced is determining the best employees with criteria and alternatives that are calculated manually. this system is a decision support system (dss) built using the analytical hierarchy process (ahp) method. previously, the evaluation process for selecting the best employees had never been done. some of the problems encountered were the absence of an employee performance appraisal process, there was no appropriate selection method, and a decision support system (dss) was not available that could make it easier to assess the selection of the best employees. keywords: best employees, decision support system, analytical hierarchy process (ahp) abstrak pemilihan karyawan terbaik bertujuan untuk memacu semangat kerja karyawan dalam meningkatkan kinerja karyawan dan dedikasi di perusahaan tersebut. pemilihan karyawan terbaik dipilih berdasarkan kriteria perusahaan. pt asdp indonesia ferry (persero) kriteria-kriteria karyawan terbaik yang diterapkan perusahaan adalah kuantitas kerja, kualitas kerja, kehadiran, kerjasama tim, inisiatif. penilaian karyawan dilakukan pada setiap bulan oleh tim penilai yaitu vice president (vp) dan manager. masalah yang dihadapi adalah bagaimana menentukan karyawan terbaik dengan kriteria dan alternatif yang dihitung secara manual. sistem ini merupakan sistem pendukung keputusan (spk) yang dibangun dengan menggunakan metode analitycal hierarchi process (ahp). dimana sebelumnya proses evaluasi pemilihan karyawan terbaik belum pernah dilakukan. beberapa permasalahan yang ditemui adalah tidak adanya proses penilaian kinerja karyawan, belum ada metode pemilihan yang tepat, dan belum tersedia sistem pendukung keputusan (spk) yang dapat mempermudah dalam menilai pemilihan karyawan terbaik. kata kunci: karyawan terbaik, sistem pendukung keputusan (spk), analytical hierarchi process (ahp) introduction the presence of valuable human resources will likely be a significant supporting component in an organization's progress. with quality human resources, a company can do its job, proliferate and become famous. for this reason, it is essential to supervise human resources in a company by determining the best employees to increase employee determination in further developing their implementation, commitments, and tasks so that they become better and develop. to trigger employee performance, a company can choose the best workers every year by mailto:denynov.dov@bsi.ac.id mailto:dahlia.dls@bsi.ac.id mailto:aldyanto1709@gmail.com, mailto:aldyanto1709@gmail.com, mailto:anggi.aov@nusamandiri.ac.id p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 296 giving awards, prizes, and job promotions to selected workers. according to mondy and noe, employee performance appraisal includes behavior or attitudes, skills, discipline, attendance, knowledge, and goal achievement. for this situation, the author provides a new normalization or criteria for selecting the best employees: quantity of work, quality of work, teamwork, attendance, and initiative. inappropriate dynamics will cause the selected workers not to match the actual reality, while the better-weighted workers are sometimes not selected. an agency needs to conduct an employee performance assessment to determine the success or non-success in carrying out its duties (saefudin & wahyuningsih, 2017). the determination of who becomes the best telesales has gone through various stages of the process from within the telesales team respectively and passed the assessment stage from the management based on specific criteria set by (septiani et al., 2019). the application created can be used by decision-makers while still based on a more effective decision support system in selecting the best customer (taufik kurnialensya & rohmad abidin, 2020). decision support system using analytical hierarchy process (ahp) is a decision-making method for prioritizing various alternatives (nur ajny, 2020). based on the system, not based on someone's choice with the system, will help the company determine the best manager so that it does not take sides with one of the parties and make the wrong choice (aisyah & putra, 2022). to help related parties more effectively select the communities in need and those affected so that they are right on target as expected by all communities in sundawenang village by accurate and relevant data (sembiring et al., 2020). the concept of a decision support system can be applied as a tool to determine the ideal internet service provider as a wireless network in the home environment. one method that is relevant and has a calculation of consistency values in determining the priority level of criteria is the analytical hierarchy process (ahp) method (m. i. h. saputra & nugraha, 2020). it is hoped that by using one of the methods in the decision support system, the calculation process by determining the predetermined criteria can provide output in the form of recommendations from the head of the ppa kanit using the sumarno1 weight product method (sumarno & harahap, 2020). decision support system (spk) is a system aimed at supporting decision management and a computer-based system capable of solving unstructured problems. ahp is the most widely used method in solving multicriteria problems (r. h. saputra et al., 2019). a decision support system (spk) is an electoral system commonly used to determine choices according to predetermined criteria and alternatives (fatullah et al., 2022). the decision-making of a problem, be it a simple or complex problem, requires thorough and accurate information, the ability to analyze and process information and the correct solution method (djamain, 2015). the decision support system (spk) is a system that can provide alternative solutions to these problems. the decision support system is currently developing with various methods, including the ahp (analytical hierarchy process) method helping to make decisions (hartanto & prasetiyowati, 2012). the ahp method is a method used to assess actions that are associated with a comparison of the weight of importance between factors as well as a comparison of several alternative options. this method will provide weighting results of each choice according to many established criteria, namely price, location, and type. the choice with the most significant weight is a choice that is a recommendation to be chosen by consumers (ardiyanto et al., 2013). employee performance appraisals carried out by companies are generally only for assessing work performance, namely how work can be done well, achieve the targets set and achieve the desired end goal (hard skills). assessments related to employees' soft skills have not been carried out much. companies apply several criteria in conducting soft skill competency assessments, but the criteria still vary (umar et al., 2018). employee performance appraisal is essential to support the smooth running of the business. selection of the best employees will increase the motivation of employee performance. decision support system (spk) can make assessing the selection of the best employees easier, so an spk application is needed to assess the best employee selection. the methods that can be used are the ahp method, the ahp method produces alternative priorities and the weight of criteria in determining the best employees objectively based on the criteria given by the store owner as a decision maker who will provide bonus rewards for the best employees, the goal is to motivate employees in improving their performance (fu’adi & diana, 2022). pt asdp indonesia ferry (persero) it is one of the state-owned enterprises in indonesia that participates in the operation of ferry transportation services and administration of the ferry port for passengers, vehicles, and merchandise. the primary capability of this jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 297 company is to provide public transportation permits between adjoining regions and unite the big islands while simultaneously providing public transportation access to areas that do not yet have crossings to accelerate development (pioneer crossings). to develop employee performance, you have to choose the best workers so that they can compete with each other to be on top. some companies give grants to the best workers with the best capacities among different workers. therefore, the researcher wants to create a framework that recognizes the best pt asdp indonesia ferry (persero) workers who will be awarded later. this is expected to increase the spirit of representatives in their work, especially in providing the best assistance to consumers. the framework that will be built is a decision support network, or dss, using the analytical hierarchy process (ahp) method. one of the techniques in decision support is the analytical hierarchy process (ahp) technique, which is a strategy to determine the demand for work needs. several studies on decision support systems (dss) developed using the analytical hierarchy process (ahp) method include applying the ahp and saw methods for determining employee promotions. the criteria for selecting decision-making tools are skill, teamwork, discipline, and loyalty. judging from each focus above, the importance of choosing a network that supports decisions in choosing the best talent for workers at pt. asdp indonesia ferry (persero) and its impact can further develop employee performance and morale and influence organizations and associations. this affects the efficiency and improvement of workers. however, it is difficult for the higher-ups to decide just like that. several considerations need further consideration before selecting the best worker. whether it's a non-complex problem or a complex problem, concluding a problem requires accurate and precise data so that it can have much insight that is suitable to be the primary concern. with the advancement of information technology innovation, including equipment and programming, different effects have been traced on various aspects of human life. one of them is the emergence of a dynamic model called decision-making. the decision support system (dss) can dash effectively and successfully. the data that is monitored by the system must provide accurate, fast, coordinated and precise data through computerized data processing. on this basis, the researcher sets the title. research methods this research will be implemented in the analytical hierarchy process (ahp) method as the best employee decision support system at pt asdp indonesia ferry (persero). figure 1. flowchart ahp procedure or steps in the ahp method (tonni et al., 2020): 1. defining problems, determining solutions, and compiling a hierarchy of problems encountered. figure 2 problem definition example 2. determine the priority by creating a pairwise comparison matrix and representing the relative importance of the elements. table 1 criteria comparison format kriteria 1 kriteria 2 kriteria 3 kriteria 4 kriteria 1 k11 k12 k13 k1n kriteria 2 k21 k22 k23 k2n kriteria 3 k31 k32 k33 k3n kriteria 4 kn1 kn2 kn3 knn p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 298 3. synthesis, which considers comparing pairs to obtaining priority, is done: divide each value from the column by the corresponding column total to obtain a normalized matrix. add up the values of each row and divide by the number of elements to get the average value. 4. determine consistency. in making decisions, the level of consistency is essential to note because we do not want decisions based on considerations with low consistency, with a maximum value of consistency ratio (cr) of = 0.1 or 10%. the things that are done in this step are: multiply each value in the first column by the relative priority of the first element, the value in the second element by the relative priority of the second element, and so on. total each row. the result of the row sum is divided by the corresponding relative priority element. add up the quotient above with the number of elements whose result is called i max. 5. calculating the consistency ratio (cr) with the formula cr = ci/ir, where cr = consistency ratio, ci = consistency index, ir = random consistency index. 6. the consistency index (ci) is calculated using the formula. 𝐶𝐼 = 𝜆𝑚𝑎𝑥−𝑛 𝑛−1 ................................................................. (1) where n is the number of elements. 7. examining table 3's random consistency index. table 3 the random consistency index n 1 2 3 4 5 6 7 8 9 10 ir 0 0 0,6 0,9 1,1 1,2 1,3 1,4 1,4 1,5 n 11 12 13 14 15 16 17 18 19 20 ir 1,5 1,5 1,6 1,6 1,6 1,6 1,6 1,6 1,6 1,6 time and place of research this research was conducted at the head office of pt asdp indonesia ferry (persero). gedung kelapa gading trade center, jl. boulevard bar. raya, rt.2/rw.9, klp. gading bar, kec. klp. gading, kota jkt utara, daerah khusus ibukota jakarta 14240. the time of data collection and research used by researchers starts in may 2022. sum the values of each column in the matrix. research target or subject the objectives of this research are as follows: it is developing criteria for the analytical hierarchy process (ahp) method of selecting the best employees for awards. provide optimal and precise results in determining the best employees at pt. asdp indonesia ferry (persero) with the analytical hierarchy process (ahp) method in a decision support system. make it easier for pt. asdp indonesia ferry (persero) to find the best employees by using the analytical hierarchy process (ahp) method in a decision support system. results and discussion recommended system analysis the datasets needed in this study are primary and secondary data. the primary data used is data or information about employees at pt asdp indonesia ferry (persero), or flow data regarding selecting the best employees and data relating to the selection process for selecting the best employees. while secondary data is data obtained from books, journals, and the internet that supports research. at this stage, the analysis of the proposed system aims to produce a computerized employee selection system. the proposed system is to change the manual system into a computerized system in processing data to display new employees with the highest and lowest scores. the proposed system analysis includes uml: the use case diagram, activity diagram, and class diagram. selecting the best employee an overview before the decision-making system for selecting the best employees was carried out by dbd was that every activity of selecting the best employees was still conducted manually, and there was still an element of subjectivity from one party only so that all data on the best prospective employees did not have a fixed weight. mistakes often occurred, so prospective employees who did not meet the standards passed the selection process for selecting the best employees. likewise, the data storage place for the best prospective employees is not automatically stored in the system; it is still manual, so that it can pose a significant risk regarding personal data or information related to prospective employees. overview of the proposed system the general picture is that the user, as dhf, will choose and enter a weight value for each criterion according to the user's level of interest. the criteria are discipline, teamwork, skills, loyalty, jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 299 and tenure. after the user enters the weight value and presses the process button, the system will calculate it using the analytical hierarchy process (ahp) method. the system will display the alternative with the highest value that dominates the other values. analytical hierarchy process (ahp) i compared data between criteria and alternatives in a paired matrix using the ahp importance intensity scale. this process is carried out to determine the value of the consistency ratio comparison (also known as the consistency ratio or cr). the consistency requirement must be less than 10% or cr<0.1. before comparing paired matrices between criteria and alternatives, determine the intensity of interest for each criterion and alternative. determining the intensity of importance of each criterion and alternative is to avoid cr > 0.1 or inconsistency. the weakness of a manager when inputting comparison values between criteria into a paired matrix is that the input comparison values are often inconsistent. therefore, the intensity of the importance of each criterion and alternative is determined— the value of the intensity of interest given by the manager of pt. asdp indonesia ferry (persero) for each criterion and alternative is in the range of values from 1 to 9. the range of values from 1 to 9 is related to the comparison value developed by saaty. hierarchical structure representation after input (criteria data and alternatives), the representation is carried out in a hierarchical structure. the problems that must be formulated in building a hierarchical structure are identifying goals (goals), criteria, and alternatives (employees) that are assessed. the hierarchical structure of the best employee problem formulation can be seen in the image below. figure 3 a hierarchical structure for selecting the best employees identification of goals becomes the most critical decision in a case. the goal to be achieved in this thesis is the selection of the best employees. identifying the best employee selection criteria can be initialized with the symbol k. the best employee criteria are summarized in table 4 below. table 4 criteria for selecting the best employees no. criteria initials criteria name 1 k1 working quantity 2 k2 work quality 3 k3 teamwork 4 k4 presence 5 k5 initiative the alternative identification stage is to identify the employee as the object of assessment who is chosen to be the best employee. this thesis research takes an alternative sample of three employees, as shown in table 5 below. table 5 best employee candidate no. alternative alternatif name 1. a1 diah 2. a2 ayu 3. a3 viona comparison of criteria in the analytical hierarchy process (ahp) method this method has weights and several criteria needed to determine the best employees. the specified criteria are working quantity, quality of work, teamwork, presence, and initiative. the above criteria are initialized with the symbol k, as seen in table 4. from each of these criteria, the weight of each criterion will be determined to determine the intensity of interest in each criterion. the function of determining the intensity of importance of each criterion is to avoid cr > 0.1 or inconsistency. the value of the intensity of interest given by the manager or lead team project member for each criterion ranges from 1 to 9. the range of values from 1 to 9 relates to the comparison value developed by saaty. based on the assessment obtained from the questionnaire that we submitted to the project manager or lead team member regarding the weighting of the intensity of interest of each criterion or comparison of criteria, it can be seen in table 6 below. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 300 table 6 assessment obtained from the questionnaire no. comparison of criteria value 1. working quantity quality of work √ 5 2. working quantity teamwork √ 9 3. work quantity presence √ 3 4. work quantity initiative √ 9 5. quality of work √ teamwork 6 6. quality of work √ presence 5 7. work quality initiative √ 4 8. teamwork presence √ 3 9. teamwork √ initiative 5 10. presence initiative √ 3 the existence of the value of the intensity of the importance of the criteria (table 6) can be directly concluded from the ahp pairwise comparison matrix between each criterion. so, managers no longer need to compare one by one the value of the intensity of interest between criteria. the comparison of the paired matrix of the ahp criteria can be seen in table 7 below. table 7 ahp criteria pairwise comparison matrix criteria k1 k2 k3 k4 k5 k1 1 1/5 1/9 1/3 1/9 k2 5 1 6 5 ¼ k3 9 1/6 1 1/3 5 k4 3 1/5 3 1 1/3 k5 9 4 0,2 3 1 information: : lower triangular matrix comparison value : the comparison value of the upper triangular matrix (a mirror or reciprocal of the lower triangle value) the results in table 7 that the comparison value for himself (k1 to k1, k2 to k2, k3 to k3, and k4 to k4, k5 to k5) is 1, which means the intensity of interest is the same. the comparison of k1 with k2 is worth it. it can be explained that k2 is more important than k1. the comparison of k1 with k3 with a value of 9 can be explained by the fact that k3 is an absolute element rather than a k1 element. the comparison of k1 with k4 with a value of 3 can be explained by the fact that k4 is slightly more critical than k1. the comparison of k1 with k5 is worthwhile. it can be explained that k5 is an absolute element more than k1. comparison of alternatives on quantity of work the value of the intensity of interest given by the manager or lead team project member to each alternative is in the range of values from 1 to 9 relates to the comparison value developed by saaty. based on the assessment obtained from the questionnaire that we submitted to the project manager or lead team member regarding the weighting of the intensity of interest of each alternative or the comparison of alternatives, it can be seen in table 10. table 10 alternative comparison value on criteria no. alternative comparison => working quantity value 1 mrs. a √ mrs.b 2 2 mrs. a √ mrs. c 3 3 mrs. b √ mrs. c 2 the existence of alternative interest intensity values (table 10) can be directly concluded with ahp pairwise comparison alternatives between each alternative so that managers no longer need to compare the interest intensity values between alternatives individually. in table 11, the ahp alternative paired matrix comparison can be seen. table 11 alternative work quantity comparison matrix working quantity a1 a2 a3 a1 1 2 3 a2 0,5 1 2 a3 0,3333333333 0,5 1 the results in table 11 that the comparison value for itself (a1 to a1, a2 to a2, a3 to a3) is 1, which means the intensity of importance is the same. a1 is worth nearly the same as a2 but cannot be considered better than a2. the comparison of a1 with a3 with a value of 3 can be explained by a3 being better than a1. a comparison of a2 with a3 is worth 2 points, almost the same as a3, but cannot be considered better than a2. before calculating the priority weight value, the comparison value in each column cell is shown in table 12 below. table 12 the sum of each column of comparison values working quantity a1 a2 a3 a1 1 2 3 a2 0,5 1 2 a3 0,3333333333 0,5 1 jumlah 1,833333333 3,5 6 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 301 calculating the priority weight value is dividing each cell by the number in its column. column k1 =0.545 column k2 = 0.571, and so on after the results of the division of each column are obtained, the eigenvector value or priority weight can be calculated (seen in table 13). the priority weight value is the average value by adding up the values from each row and dividing them by the many criteria elements, and if they are added up, they will be worth one. table 13 priority weight value alternative eigen value sum average 0,5454545 0,5714286 0,5 1,6168831 0,538961 0,2727273 0,2857143 0,3333333 0,8917749 0,2972583 0,1818182 0,1428571 0,1666667 0,491342 0,1637807 sum 1 after obtaining the alternative priority weights, the maximum land value (λmax), or eigenvalue, is calculated, the sum of multiplying the priority weights with the number of columns. cr = 0.0015/0.58 = 0.0025 (consistent because it meets the requirements of cr 0.1) and so on, until the initiative's alternatives are compared. system implementation based on the analysis and system design results, the best employee selection system is implemented by applying the analytical hierarchy process method. results analysis this web-based system is designed specifically for users to provide recommendations for the best employment decisions based on the criteria applied at pt. asdp indonesia ferry (persero). the system has a main menu equipped with the ahp method to assist the calculation process and produce recommendations for the best employee decisions. a. attendance pairwise comparison matrix figure 7 attendance pairwise comparison matrix figure 7 displays the pairwise comparison matrix: mrs. a is 4.33333, mrs. b is 1.53333, and mrs. c is 9. produces a matrix criteria value including 1) principle eigen vector (max) of 3.05536, consistency index of 0.02768, and consistency ratio of 4.77% b. calculation results and ranking the system will display the best employee calculation menu, which displays calculations according to the desired month and year. if the month and year have been selected, the system will display the ahp ranking menu, as shown in figure 10 below. figure 10 menu display of ahp calculation results and ranking the ahp ranking menu display has several menu tabs that detail the ahp process on criteria, alternatives, and decision results in the form of a ranking. on the ranking menu tab, a list of the best employee recommendations is displayed that can be used as a consideration for managers in determining the best employee decisions. conclusions and suggestions conclusion by making a decision support system for the best employees, it can assist leaders in making decisions to determine the best employees according to the existing criteria and using the analytical hierarchy process (ahp) method, namely: the management has determined the criteria used in the decision support system for selecting the best employees: work quantity, work quality, teamwork, attendance, and initiative. the decision weight of the best employee using the ahp method is close to the weight of the manual calculation decision used at pt. asdp indonesia ferry (persero). there is an intensity value for each criterion and alternative from the company. the manager does not have to input the value of the paired matrix comparison because the system will operate automatically so that the comparison value is consistent (cr 0.1). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.454 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 302 suggestion from the research conducted, valuable suggestions can be generated for system development, such as adding other methods to complement the shortcomings of ahp. references aisyah, n., & putra, a. s. 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(2019). perancangan sistem informasi penyewaan alat berat. jusim (jurnal sistem informasi musirawas), 4(02), 127–135. https://doi.org/10.32767/jusim.v4i02.639 sumarno, s. m., & harahap, j. m. (2020). sistem pendukung keputusan dalam menentukan pemilihan posisi kepala unit (kanit) ppa dengan metode weight product. just it : jurnal sistem informasi, teknologi informasi dan komputer, 11(1), 37. https://doi.org/10.24853/justit.11.1.37-44 taufik kurnialensya, & rohmad abidin. (2020). 135-article text-311-1-10-20200609. sistem pendukung keputusan pelangganterbaik dan pemberian diskon menggunakanmetode saw & topsis, 13(1), 18–33. umar, r., fadlil, a., & yuminah, y. (2018). sistem pendukung keputusan dengan metode ahp untuk penilaian kompetensi soft skill karyawan. khazanah informatika : jurnal ilmu komputer dan informatika, 4(1), 27–34. https://doi.org/10.23917/khif.v4i1.5978 jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 187 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem informasi akuntansi penerimaan dan pengeluaran kas berbasis web (studi kasus: pt. sinar kapuas cemerlang) 1 lisnawanty, 2 bambang kurniawan program studi sistem informasi akuntansi kampus kota pontianak, fakultas teknologi informasi universitas bina sarana informatika 1 lisnawanty.lsy@bsi.ac.id ; 2 bambangkurniawan2565@gmail.com abstrak pt. sinar kapuas cemerlang merupakan suatu perusahaan swasta yang bergerak dibidang manufaktur dan perdagangan spring bed. pengolahan data pada transaksi kas, piutang, penjualan, dan pembayaran biaya menggunakan nota transaksi yang kemudian disalin dengan memanfaatkan microsoft excel. hal ini tentu berdampak pada lamanya waktu yang dibutuhkan untuk merekapitulasi data tersebut menjadi laporan penerimaan dan pengeluaran kas. maka dari itu, penelitian ini berfokus pada rancangan sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang. metode yang digunakan dalam pengembangan software adalah model waterfall. level akses pengguna terdiri dari bagian keuangan dan direktur. bagian keuangan mengolah data barang, data pelanggan, data akun, data sales, data produksi, data penjualan, data penerimaan piutang dan data biaya. dan direktur mengolah data pengguna, mengakses laporan persediaan barang, laporan penjualan, laporan buku besar, laporan penerimaan kas, laporan pengeluaran kas, laporan neraca saldo, laporan laba rugi dan laporan perubahan modal. sistem informasi akuntansi penerimaan dan pengeluaran kas ini diharapkan dapat meningkatkan performa pengolahan data penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang. kata kunci: penerimaan kas, pengeluaran kas, sistem informasi akuntansi abstract pt. sinar kapuas cemerlang is a private company engaged in the manufacturing and trading of spring beds. data processing on cash transactions, accounts receivable, sales, and payment of fees using transaction notes which are then copied using microsoft excel. this certainly has an impact on the length of time needed to recapitulate the data into cash receipts and disbursements reports. therefore, this research focuses on the design of accounting information systems for cash receipts and payments at pt. sinar kapuas cemerlang. the method used in software development is the waterfall model. the user access level consists of the finance department and director. the finance department processes goods data, customer data, account data, sales data, production data, sales data, receivables data and cost data. and the director processes user data, accesses inventory reports, sales reports, general ledger reports, cash receipt reports, cash disbursement reports, balance sheet reports, profit and loss reports and capital changes reports. the accounting information system for cash receipts and disbursements is expected to improve the performance of cash receipts and disbursements data processing at pt. sinar kapuas cemerlang. keywords: cash receipts, cash disbursements, accounting information systems pendahuluan kemajuan teknologi informasi dan komputer merupakan salah satu yang paling pesat dibandingkan bidang lainnya. pertumbuhannya semakin meningkat dari tahun ke tahun, hal ini ditandai dengan penggunaan komputer pada segala aspek, baik itu perusahaan jasa, perdagangan, maupun industri, mulai dari perusahaan skala kecil, besar, hingga multi nasional (sholikhah, sairan, & syamsiah, 2017). penggunaan aplikasi sebagai alat bantu dalam pengambilan keputusan dan aplikasi tersebut digunakan sebagai bahan untuk memperoleh hasil pencarian dari suatu pokok permasalahan. diharapkan hasilnya akan memberikan solusi serta mengambil keputusan dengan tepat. penerapan sistem informasi memberikan fasilitas seperti perolehan informasi yang cepat dan tepat pada saat dibutuhkan. informasi dirancang untuk keperluan pengolahan data dengan penerapan teknologi komputer sehingga seluruh proses kegiatan dapat dikelola menjadi informasi yang bermanfaat (firmansyah & udi, 2017). http://creativecommons.org/licenses/by-nc/4.0/ mailto:lisnawanty.lsy@bsi.ac.id mailto:bambangkurniawan2565@gmail.com p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 188 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem informasi yang baik dapat menghasilkan laporan keuangan yang akurat. laporan keuangan ini bersumber dari penerimaan dan pengeluaran kas yang terjadi selama periode transaksi. laporan keuangan menyediakan informasi tentang keuangan suatu perusahaan pada suatu periode akuntansi yang dapat digunakan untuk menggambarkan kinerja perusahaan tersebut (riswan & kesuma, 2014). pt. sinar kapuas cemerlang merupakan suatu perusahaan swasta yang bergerak di bidang manufaktur dan perdagangan spring bed. setiap transaksi seperti pengolahan data kas, piutang, penjualan, dan pembayaran biaya dicatat ke dalam nota-nota transaksi yang kemudian disalin menggunakan microsoft excel (hak, slamet, & kurniawati, 2014). berdasarkan hasil observasi, untuk menghasilkan laporan penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang memerlukan waktu yang lama (frieyadie, 2015) (arizona, 2017), dikarenakan bagian keuangan harus merekapitulasi satu per satu transaksi (primandani & widodo, 2012) untuk dijadikan laporan laba & rugi, laporan perubahan modal, neraca dan neraca lajur. ketika terjadi keterlambatan dalam penyajian laporan (amin, 2017) pada pt. sinar kapuas cemerlang, tentu akan berpengaruh pada proses pengambilan keputusan khususnya yang berkaitan dengan masalah keuangan. selain itu, sering terjadi selisih antara rekapitulasi yang dihasilkan antara sub bagian penjualan dengan laporan penerimaan dan pengeluaran kas yang dikelola oleh bagian keuangan pada pt. sinar kapuas cemerlang. alat yang digunakan untuk menganalisa laporan penerimaan dan pengeluaran kas juga belum mendukung secara maksimal dalam menilai kinerja keuangan walaupun sudah dibantu menggunakan microsoft excel dalam perhitungan dan penentuan keputusan. berdasarkan permasalahan tersebut, maka perlu adanya implementasi sistem informasi akuntansi yang merupakan sistem yang bertujuan untuk mengumpulkan dan memproses data serta melaporkan informasi yang berkaitan dengan transaksi keuangan (damayanti & hernandez, 2018). maka dari itu, penelitian ini dilakukan dengan tujuan memberikan solusi terhadap pengelolaan transaksi penerimaan dan pengeluaran kas di pt. sinar kapuas cemerlang, berupa sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang agar dapat mengelola data-data transaksi yang ada menjadi laporan keuangan dengan cepat dan akurat, baik pada level bagian keuangan maupun direktur. metode penelitian jenis penelitian penelitian ini menggunakan teknik deskriptif, yakni metode yang digunakan untuk meneliti suatu objek, suatu set kondisi, suatu sistem pemikiran, ataupun suatu kelas peristiwa pada masa sekarang (saifudin & ardani, 2017). metode yang digunakan dalam pengembangan software dalam penelitian ini adalah model waterfall, dengan alasan bahwa metode ini melakukan pendekatan secara sistematis dalam proses pengerjaan sistem. dengan kata lain metode ini tidak berfokus pada satu tahapan tertentu. tahapan dalam model waterfall ini mencakup tahap analisa kebutuhan perangkat lunak, desain, pembuatan kode program, dan pengujian. waktu dan tempat penelitian penelitian dilakukan pada tahun 2018 di pt. sinar kapuas cemerlang. target/subjek penelitian target penelitian (untuk penelitian kualitatif) dilakukan pada pt. sinar kapuas cemerlang dengan subjek penelitian pengolahan transaksi kas masuk dan kas keluar pada bagian keuangan. prosedur prosedur penelitian yang digunakan sebagaimana yang telah diuraikan pada bagian jenis penelitian mengikuti tahapan model waterfall sebagai berikut. 1. analisis kebutuhan perangkat lunak dalam tahapan ini, analisis kebutuhan berfokus pada kebutuhan sistem informasi akuntansi pengolahan laporan keuangan dimulai dari pengumpulan kas, utang, piutang, pembelian, penjualan, penyusutan dan pembayaran biaya. 2. desain tahapan ini mencakup rancangan basis data, pemodelan rancangan menggunakan unified modeling language (uml), dan rancangan tampilan antar muka. 3. pembuatan kode program pengkodean (coding) program menggunakan sublime text sebagai web editor bahasa pemrograman hypertext preprocessor (php) berdasarkan logika yang dirancang pada tahapan desain. 4. pengujian tahapan pengujian menentukan apakah sistem atau perangkat lunak yang telah dibuat sudah sesuai dengan kebutuhan pengguna http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 189 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional atau belum dengan metode black box testing sebagai media pengujian. teknik pengumpulan data teknik pengumpulan data berfungsi sebagai cara untuk mengumpulkan data dari suatu penelitian (saifudin & ardani, 2017). teknik pengumpulan data yang digunakan, terdiri dari observasi, wawancara dan studi pustaka. adapun penjelasan dari teknik pengumpulan data yang digunakan, diuraikan sebagai berikut: 1. observasi penulis melakukan observasi langsung ke pt. sinar kapuas cemerlang kabupaten mempawah yang beralamat wajok hulu kabupaten mempawah, kalimantan barat 78351 untuk mengamati secara langsung proses pengolahan laporan keuangan dimulai dari pengolahan data kas, utang, piutang, pembelian, penjualan, penyusutan dan pembayaran biaya dan pembuatan laporan keuangan yang diterapkan. 2. wawancara wawancara merupakan proses tanya jawab langsung dan sistematis dengan bapak wildem selaku kepala keuangan pada pt. sinar kapuas cemerlang untuk mendapatkan data-data terkait tentang penyusunan laporan keuangan. hasil penelitian dan pembahasan analisis kebutuhan perangkat lunak analisa kebutuhan kebutuhan merupakan tahap interaksi intensif antara analis sistem dengan komunitas pemakai sistem (end-user), dimana dalam tahapan ini menjelaskan tentang fungsi-fungsi atau kebutuhan pengguna dan kebutuhan sistem. a. kebutuhan pengguna kebutuhan pengguna dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang kabupaten mempawah terdiri dari dua (2) level pengguna, yaitu bagian keuangan dan direktur. skenario kebutuhan dari kedua level pengguna ini akan dijabarkan pada halaman berikut. a1 skenario kebutuhan bagian keuangan login; mengolah data barang; mengolah data pelanggan; mengolah data akun; mengolah data sales; mengolah data produksi; mengolah data penjualan; mengolah data penerimaan piutang; mengolah data biaya; logout a2 skenario kebutuhan bagian direktur login; mengolah data pengguna; mengakses laporan persediaan barang; mengakses laporan penjualan; mengakses laporan buku besar; mengakses laporan penerimaan kas; mengakses laporan pengeluaran kas; mengakses laporan neraca saldo; mengakses laporan laba rugi; mengakses laporan perubahan modal; logout b. kebutuhan sistem 1) pengguna harus melakukan login dengan cara mengisi username dan password, apabila berhasil login, maka setiap pengguna dapat mengakses sistem informasi penerimaan dan pengeluaran kas ini sesuai dengan level akses. 2) pengguna harus melakukan logout setelah selesai menggunakan aplikasi. 3) data buku besar dapat diakses atau dicari berdasarkan nama akun, nama bulan dan tahun. 4) laporan dapat diakses atau dicari berdasarkan bulan dan tahun pencarian, laporan ini juga dapat diakses secara keseluruhan untuk setiap tahunnya. desain desain pada sistem informasi akuntansi penerimaan dan pengeluaran kas digambarkan dalam bentuk use case diagram, struktur navigasi, dan entity relationship diagram (erd). use case diagram use case diagram pada gambar 1 berikut ini merupakan pemodelan yang menggambarkan interaksi antara actor dan sistem yang dirancang. sistem informasi akuntansi penerimaan dan pengeluaran kas dirancang berbasis web dengan 2 level pengguna, yaitu bagian keuangan dan direktur. ruang lingkup sistem digambarkan dalam use case diagram berikut ini. gambar 1. use case diagram sistem informasi akuntansi penerimaan dan pengeluaran kas http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 190 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional berdasarkan gambar 1 diatas, tabel 1 berikut ini merupakan deskripsi use case diagram. tabel 1. deskripsi use case diagram sistem informasi akuntansi penerimaan dan pengeluaran kas use case narative sistem penerimaan dan pengeluaran kas tujuan bagian keuangan dan direktur dapat mengelola dan mengakses data yang ada di dalam sistem informasi akuntansi penerimaan dan pengeluaran kas. deskripsi aplikasi ini memberikan fasilitas kepada bagian keuangan untuk mengolah data barang, data pelanggan, data akun, data sales, data produksi, data penjualan, data penerimaan piutang, data biaya. aplikasi ini juga memberikan fasilitas kepada direktur untuk mengolah data pengguna, mengakses laporan persediaan barang, laporan penjualan, laporan buku besar, laporan penerimaan kas, laporan pengeluaran kas, laporan neraca saldo, laporan laba rugi dan laporan perubahan modal. skenario utama aktor bagian keuangan dan direktur kondisi awal bagian keuangan & direktur harus melakukan login dengan cara mengisi username dan password. aksi aktor reaksi sistem 1. bagian keuangan memilih menu data barang 2. bagian keuangan memilih menu data pelanggan 3. bagian keuangan memilih menu data akun 4. bagian keuangan memilih menu sales 5. bagian keuangan memilih menu produksi 6. bagian keuangan memilih menu penjualan 7. bagian keuangan memilih menu penerimaan piutang 8. bagian keuangan memilih menu biaya 9. direktur memilih menu data pengguna 10. direktur memilih menu laporan persediaan barang 11. direktur memilih menu laporan penjualan 12. direktur memilih menu laporan buku besar 13. direktur memilih aplikasi menampilkan halaman data barang. aplikasi menampilkan halaman data pelanggan. aplikasi menampilkan halaman data akun. aplikasi menampilkan halaman data sales. aplikasi menampilkan halaman data produksi. aplikasi menampilkan halaman data penjualan. aplikasi menampilkan halaman data penerimaan piutang. aplikasi menampilkan halaman data biaya. aplikasi menampilkan halaman data pengguna. aplikasi menampilkan halaman laporan persediaan barang. aplikasi menampilkan halaman laporan penjualan. aplikasi menampilkan halaman laporan buku besar. aplikasi menampilkan halaman menu laporan penerimaan kas 14. direktur memilih menu laporan pengeluaran kas 15. direktur memilih menu laporan neraca saldo 16. direktur memilih menu laporan laba rugi 17. direktur memilih menu laporan perubahan modal laporan penerimaan kas. aplikasi menampilkan halaman laporan pengeluaran kas. aplikasi menampilkan halaman laporan neraca saldo. aplikasi menampilkan halaman laporan laba rugi. aplikasi menampilkan halaman laporan perubahan modal. kondisi akhir jika sesuai perintah maka aplikasi akan menampilkan menu yang dipilih oleh pengguna rancangan basis data rancangan basis data pada sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang digambarkan dalam bentuk entity relationship diagram (erd) berikut ini. gambar 2. entity relationship diagram sistem informasi akuntansi penerimaan dan pengeluaran kas struktur navigasi struktur navigasi merupakan halaman web atau dokumen web yang mempunyai struktur yang jelas serta tersusun dengan teratur. rancangan struktur navigasi pada web digunakan untuk mengkaitkan halaman web dengan halaman web yang saling keterhubungan satu sama lain dengan hypertext. struktur navigasi website bukan menggambarkan keterhubungan antar menumenu, melainkan dokumen website tersebut (frieyadie, 2019) model struktur navigasi yang digunakan adalah struktur hierarchy, yang menjelaskan hubungan antara dua atau lebih peringkat (frieyadie & fatayat, 2019). bagian keuangan dan direktur memiliki level akses yang berbeda. arah penggunaan sistem http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 191 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dari masing-masing level digambarkan pada struktur navigasi sebagai berikut. 1. struktur navigasi bagian keuangan gambar 3. struktur navigasi bagian keuangan 2. struktur navigasi direktur gambar 4. struktur navigasi direktur tampilan antar muka atau user interface dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang akan disajikan sesuai dengan level akses penggunanya, yaitu bagian keuangan dan direktur. untuk masuk ke halaman utama, masingmasing level harus berhasil melakukan login. a. bagian bagian keuangan fasilitas yang didapat oleh bagian keuangan dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang ini terdiri dari mengolah data barang, data pelanggan, data akun, data sales, data produksi, data penjualan, data penerimaan piutang dan data biaya. berikut ini adalah hasil tampilan antar muka atau user interface yang disediakan untuk level pengguna bagian keuangan. 1. tampilan menu data barang pada halaman ini, bagian keuangan dapat mengelola data barang mulai dari penambahan, pengubahan dan penghapusan data barang. gambar 5. tampilan menu data barang 2. tampilan menu data pelanggan pada halaman ini, bagian keuangan dapat mengelola data pelanggan mulai dari penambahan, pengubahan dan penghapusan data pelanggan. gambar 6. tampilan menu data pelanggan 3. tampilan menu data akun pada halaman ini, bagian keuangan dapat mengelola data akun mulai dari penambahan, pengubahan dan penghapusan data akun. gambar 7. tampilan menu data akun http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 192 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 4. tampilan menu data sales pada halaman ini, bagian keuangan dapat mengelola data sales mulai dari penambahan, pengubahan dan penghapusan data sales. gambar 8. tampilan menu data sales 5. tampilan menu data produksi pada halaman ini, bagian keuangan dapat mengelola data produksi mulai dari penambahan, pengubahan dan penghapusan data produksi. gambar 9. tampilan menu data produksi 6. tampilan menu data penjualan pada halaman ini, bagian keuangan dapat mengelola data penjualan mulai dari penambahan, pengubahan dan penghapusan data penjualan. gambar 10. tampilan menu data penjualan 7. tampilan menu data penerimaan piutang pada halaman ini, bagian keuangan dapat mengelola data penerimaan piutang mulai dari penambahan, pengubahan dan penghapusan data penerimaan piutang. gambar 11. tampilan menu data penerimaan piutang 8. tampilan menu data biaya pada halaman ini, bagian keuangan dapat mengelola data biaya mulai dari penambahan, pengubahan dan penghapusan data biaya. gambar 12. user interface menu data biaya b. bagian direktur fasilitas yang didapat oleh direktur dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang ini terdiri dari mengolah data pengguna, mengakses laporan persediaan barang, laporan penjualan, laporan buku besar, laporan penerimaan kas, laporan pengeluaran kas, laporan neraca saldo, laporan laba rugi dan laporan perubahan modal. berikut ini adalah hasil tampilan antar muka atau user interface yang disediakan untuk level pengguna direktur. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 193 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 1. tampilan menu data pengguna pada halaman ini, direktur dapat mengelola data pengguna mulai dari penambahan, pengubahan dan penghapusan data pengguna. gambar 13. tampilan menu data pengguna 2. tampilan menu laporan persediaan barang pada halaman laporan persediaan barang, direktur dapat melihat atau mengakses laporan persediaan barang sesuai dengan periode pencarian. gambar 14. tampilan menu laporan persediaan barang 3. tampilan menu laporan penjualan pada halaman laporan penjualan, direktur dapat melihat atau mengakses laporan penjualan sesuai dengan periode pencarian. gambar 15. tampilan menu laporan penjualan 4. tampilan menu laporan buku besar pada halaman laporan buku besar, direktur dapat melihat atau mengakses laporan buku besar sesuai dengan periode pencarian. gambar 16. tampilan menu laporan buku besar 5. tampilan menu laporan penerimaan kas pada halaman penerimaan kas, direktur dapat melihat atau mengakses laporan penerimaan kas sesuai dengan periode pencarian. gambar 17. tampilan menu laporan penerimaan kas 6. tampilan menu laporan pengeluaran kas pada halaman laporan pengeluaran kas, direktur dapat melihat atau mengakses laporan pengeluaran kas sesuai dengan periode pencarian. gambar 18. tampilan menu laporan pengeluaran kas http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 194 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 7. tampilan menu laporan neraca saldo pada halaman laporan neraca saldo direktur dapat melihat atau mengakses laporan neraca saldo sesuai dengan periode pencarian. gambar 19. tampilan menu laporan neraca saldo 8. tampilan menu laporan neraca laba rugi pada halaman laporan laba rugi, direktur dapat melihat atau mengakses laporan laba rugi sesuai dengan periode pencarian. gambar 20. tampilan menu laporan laba rugi 9. tampilan menu laporan perubahan modal pada halaman laporan perubahan modal, direktur dapat melihat atau mengakses laporan perubahan modal sesuai dengan periode pencarian. gambar 21. tampilan menu laporan perubahan modal pembuatan kode program perangkat lunak (software) yang diperlukan pembuatan sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang, yaitu: 1. beberapa komponen diantaranya: a. apache server versi 2.4.3. b. mysql versi 5.1.36 c. php versi 5.3.0 2. bahasa pemograman yang digunakan yaitu: php, html, css, javascript, jquery dan bootstraps yang diolah dengan sublime text 3 sebagai web editor. 3. web browser seperti google chrome dan mozilla firefox. pengujian pengujian dilakukan untuk menguji kelayakan fungsional dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang. teknik yang digunakan untuk pengujian ini adalah menggunakan tekniks black box testing. pengujian yang dilakukan hanya mengamati hasil eksekusi dan memeriksa kelayakan fungsional dari sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang yang telah dibuat. 1. pengujian browser tabel 2. black box testing browser 1. skenario pengujian buka dengan google chrome test case google chrome (terbuka) hasil yang diharapkan aplikasi dapat diakses dan tampilan sesuai hasil pengujian sesuai harapan kesimpulan valid 2. skenario pengujian buka dengan mozila firefox test case mozila firefox (terbuka) hasil yang diharapkan aplikasi dapat diakses dan tampilan sesuai hasil pengujian sesuai harapan kesimpulan valid 2. pengujian login tabel 3. black box testing halaman login 1. skenario pengujian jika salah satu kolom dikosongkan test case username: (kosong) hasil yang diharapkan aplikasi menolak akses dan menampilkan pesan “username atau password anda salah, silahkan coba lagi” hasil pengujian sesuai harapan kesimpulan valid 2. skenario pengujian kolom terisi namun tidak sesuai (username, password dan level akses tidak sesuai) test case username: (111111) password: (111111) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 195 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional hasil yang diharapkan aplikasi menolak akses dan menampilkan pesan “username atau password anda salah, silahkan coba lagi” hasil pengujian sesuai harapan kesimpulan valid 3. skenario pengujian kolom terisi dan sesuai (username dan password sesuai) test case username: (admin) password: (admin) hasil yang diharapkan aplikasi menerima akses dan mengantarkan pengguna ke halaman dashboard. hasil pengujian sesuai harapan kesimpulan valid simpulan dan saran simpulan berdasarkan hasil pembahasan yang telah diuraikan, maka dapat disimpulkan bahwa sistem informasi akuntansi penerimaan dan pengeluaran kas yang dibuat ini menyediakan pengolahan penerimaan dan pengeluaran kas yang dilengkapi dengan laporan keuangan. level akses pada aplikasi ini terdiri dari bagian keuangan dan direktur. bagian keuangan pengolahan data barang, data pelanggan, data akun, data sales, data produksi, data penjualan, data penerimaan piutang dan data biaya. direktur mengolah data pengguna, mengakses laporan persediaan barang, laporan penjualan, laporan buku besar, laporan penerimaan kas, laporan pengeluaran kas, laporan neraca saldo, laporan laba rugi dan laporan perubahan modal. dengan kata lain, sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang kabupaten mempawah menyediakan pengolahan data dan transaksi dengan sesuai dengan aturan akuntansi. saran saran yang dapat dijadikan sebagai bahan masukan dan pertimbangan untuk sistem informasi akuntansi penerimaan dan pengeluaran kas pada pt. sinar kapuas cemerlang kabupaten mempawah berikutnya adalah penambahan fungsi seperti pengadaan bahan baku untuk pembuatan spring bed. daftar referensi amin, r. (2017). rancang bangun sistem informasi penerimaan siswa baru pada smk budhi warman 1 jakarta. jitk (jurnal ilmu pengetahuan dan teknologi komputer), 2(2), 113–121. retrieved from http://ejournal.nusamandiri.ac.id/index.php /jitk/article/view/391 arizona, n. d. (2017). perancangan sistem informasi penjualan dan pembelian obat pada apotek zaira. in konferensi nasional ilmu sosial & teknologi (knist) 2017 (pp. 674–678). jakarta: lppm bsi. damayanti, & hernandez, m. y. (2018). sistem informasi akuntansi penerimaan dan pengeluaran kas pada kpri andan jejama kabupaten pesawaran, 12(2), 57–61. firmansyah, y., & udi, u. (2017). penerapan metode sdlc waterfall dalam pembuatan sistem informasi akademik berbasis web studi kasus pondok pesantren al-habib sholeh kabupaten kubu raya, kalimantan barat. jurnal teknologi dan manajemen informatika, 4(1). https://doi.org/10.26905/jtmi.v4i1.1605 frieyadie, f. (2015). pembangunan sistem informasi inventory menggunakan linear sequential model untuk peningkatan layanan inventory barang. jurnal techno nusa mandiri, 12(2), 209–114. https://doi.org/10.33480/techno.v12i2.45 1 frieyadie, f. (2019). struktur navigasi pada website. retrieved from http://frieyadie.web.id/struktur-navigasipada-website/ frieyadie, & fatayat, u. (2019). penggunaan model waterfall dalam perancangan aplikasi penjualan kosmetik berbasis web, 1(4). hak, a. n., slamet, c., & kurniawati, r. (2014). implementasi sistem informasi keuangan siswa berbasis sms gateway di smk muhammadiyah 2 kadungora. jurnal algoritma, 11(1). retrieved from http://www.sttgarut.ac.id/jurnal/index.php/ algoritma/article/view/119 primandani, v., & widodo, t. w. (2012). purwarupa sistem pembayaran retribusi jalan tol berbasis teknologi rfid. ijeis (indonesian journal of electronics and instrumentation systems), 2(1), 11–20. https://doi.org/10.22146/ijeis.2336 riswan, & kesuma, y. f. (2014). analisis laporan keuangan sebagai dasar dalam penilaian http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 196 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional kinerja keuangan pt. budi satria wahana motor, 5(1). retrieved from http://jurnal.ubl.ac.id/index.php/jak/article /view/449 saifudin, & ardani, f. p. (2017). sistem informasi akuntansi penerimaan dan pengeluaran kas dalam meningkatkan pengendalian internal atas pendapatan pada rsup dr . kariadi semarang. jurnal riset akuntansi keuangan, 2(2). sholikhah, i., sairan, m., & syamsiah, n. o. (2017). aplikasi pembelian dan penjualan barang dagang pada cv gemilang muliatama cikarang. jurnal teknik komputer, iii(1), 16– 23. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 197 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem pendukung keputusan penentuan rumah tangga miskin pada desa cibangkong dengan metode waspas sugeng riyadi1, tuti haryanti2, laela kurniawati3 program studi sistem informasi stmik nusa mandiri www.nusamandiri.ac.id 1sugengriyadi484@gmail.com, 2tuti@nusamandiri.ac.id, 3laela@nusamandiri.ac.id abstrak pada era sekarang ini, berbagai kegiatan proses bisnis membutuhkan penerapan teknolog informasi. pengambilan keputusan merupakan salah satu kegiatan proses bisnis yang dapat dibantu dengan adanya teknologi. penentuan keluarga miskin adalah suatu masalah yang rumit karena banyak kriteria yang harus terpenuhi untuk menentukan sebuah keluarga miskin dan layak menerima bantuan penanganan kemiskinan. oleh karena itu dibuatlah sistem pendukung keputusan dengan metode weighted aggregated sum product assesment (waspas) dengan kriteria yang telah ditetapkan kemudian diimplementasikan dengan menggunakan visual basic .net. sampel yang digunakan adalah warga desa cibangkong sebanyak 89 rumah tangga yang terdaftar mengajukan bantuan pemerintah. hasil penelitian diperoleh 12 rumah tangga sangat miskin, 28 rumah tangga miskin, 38 rumah tangga rentan miskin dan 11 rumah tangga tidak miskin. dengan sistem pendukung keputusan metode waspas ini pengolahan data akan menjadi lebih efektif dan efisien karena proses penilaian dan pembuatan laporan menjadi cepat. pegawai hanya memasukan data penduduk, data kriteria dan data penilaian penduduk, sedangkan proses perhitungan dan pembuatan laporan dilakukan oleh sistem. kata kunci: metode waspas, rumah tangga miskin, sistem penunjang keputusan abstract in the current era, various business process activities require the application of information technology. decision making is one of the business process activities that can be helped by the existence of technology. determining poor families is a complex problem because many criteria must be met to determine poor families and deserve assistance in dealing with poverty. therefore, a decision support system is made using the weighted aggregated sum product assessment (waspas) method with predetermined criteria and then implemented using visual basic .net. the sample used was 89 residents of cibangkong village who were registered to apply for government assistance. the results of the study obtained 12 very poor households, 28 poor households, 38 poor vulnerable households and 11 non-poor households. with a decision support system, the waspas method in processing data will be more effective and efficient because of the process of evaluating and making quick reports. employees only enter population data, criteria data and population assessment data, while the process of calculating and reporting is done by the system. keywords: waspas method, poor households, decision support system pendahuluan pada era sekarang ini, berbagai kegiatan proses bisnis membutuhkan penerapan teknologi informasi. dengan penerapan teknologi informasi diharapkan proses bisnis akan terbantu dengan sebuah sistem yang sesuai dengan pemakainya, sehingga dalam kegiatan proses bisnis sehari-hari dapat dilakukan dengan lebih cepat dan lebih baik dari sebelumnya. dengan begitu teknologi informasi merupakan salah satu kebutuhan yang sangat penting untuk menunjang aktivitas organisasi. menurut (pardiansyah, 2015), mengatakan “teknologi informasi (it) saat ini sudah menjadi kebutuhan yang sangat penting bagi hampir semua organisasi perusahaan karena dipercaya dapat membantu meningkatkan efektivitas dan efisiensi proses bisnis perusahaan”. pengambilan keputusan merupakan salah satu kegiatan proses bisnis yang dapat dibantu dengan adanya teknologi. dalam menentukan satu pilihan dari beberapa alternatif, sebuah organisasi perlu mempertimbangkan beberapa kriteria yang ada. pengambil keputusan harus mengambil keputusan dengan cepat dan akurat (irfansyah & sunoto, 2015). pengambilan keputusan harus dilakukan secara cepat, teliti, tepat sasaran dan dapat dipertanggungjawabkan menjadi kunci http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 198 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional keberhasilan dalam pengambilan keputusan di kemudian hari (sugiarti, nahulae, panggabean, & sianturi, 2018). diharapkan dengan adanya sistem penunjang keputusan pengambil keputusan dapat terbantu dalam menentukan pilihan yang akan diambil. kemiskinan merupakan kondisi sosial masyarakat yang tidak mempunyai kemampuan dalam memenuhi kebutuhan pokok yang layak bagi kemanusiaan (muzakkir, 2017). kebutuhan, kekurangan dan kesulitan merupakan masalah yang berhubungan dengan kemiskinan. kondisi kemiskinan tercipta karena tidak adilnya atau tidak meratanya dalam pelaksanaan pembangunan. kemiskinan saat ini semakin meningkat dan terjadi ketidakstabilan kondisi ekonomi yang menyebabkan menurunnya tingkat pendapatan masyarakat sehingga masyarakat tidak lagi memenuhi kebutuhannya sendiri (setiani, 2017). dalam rangka pembangunan pengentasan kemiskinan dilakukan program penanggulangan kemiskinan seperti program kartu indonesia sehat (kis), kartu indonesia pintar (kip), program keluarga harapan (pkh), bantuan pangan non tunai (bpnt) dan pembangunan rumah tidak layak huni (rtlh). agar program tersebut terlaksana dengan adil dan merata makan perlu dilakukan pendataan terhadap rumah tangga miskin, data kemiskinan merupakan suatu hal yang penting karena menjadi pertimbangan dalam proses perencanaan program penanggulangan kemiskinan dan pendataan tersebut untuk memudahkan dalam pelaksanaan program pembangunan. dalam pelaksanaan pendataan keluarga miskin pemerintah desa cibangkong mengalami kesulitan karena dalam penentuan rumah tangga miskin memiliki kriteria yang banyak sehingga butuh waktu lama untuk penentuan rumah tangga miskin. penentuan keluarga miskin adalah suatu masalah yang rumit (indrawati, hyoscyamina, qonitatin, & abidin, 2014), karena banyaknya kriteria yang harus terpenuhi untuk menentukan sebuah keluarga miskin dan layak menerima bantuan penanganan kemiskinan (saputra, kartini, & soesanto, 2016). pada akhir bulan maret 2019 pemerintah desa cibangkong menerima pengajuan 748 data rumah tangga yang harus di validasi agar nantinya penentuan keluarga miskin sesuai dengan kriteria-kriteria yang sudah ditentukan pemerintah. dengan banyaknya kriteria dan banyaknya pula keluarga untuk diseleksi membuat pemerintah desa karang rejo mengalami kesulitan dalam penentuan rumah tangga miskin (saputra, kartini, & soesanto, 2015). kriteria-kriteria yang disurvei tidak sesuai dengan ketentuan pemerintah (lubis, nadeak, & hondro, 2017). berdasarkan masalah di atas, penulis merancang sistem pendukung keputusan penentuan rumah tangga miskin pada desa cibangkong dengan metode (waspas). metode weighted aggregated sum product assesment waspas merupakan metode yang mengambil keputusan dengan efektif atas persoalan yang kompleks dengan menyederhanakan dan mempercepat proses pengambilan keputusan untuk memecahkan masalah tersebut (sianturi, siburian, hutagaol, & sahir, 2018). tujuan penelitian ini sebagai sistem pendukung keputusan,penilaian penentuan rumah tangga miskin sesuai dengan kriteria yang telah ditentukan pemerintah dan bantuan dari pemerintah akan tepat sasaran. metode penelitian a. metode weighted aggregated sum product assesment (waspas) menurut (handayani & marpaung, 2018), “metode waspas merupakan metode yang mengurangi kesalahan-kesalahan atau mengoptimalkan dalam penaksiran atau pemilihan nilai tertinggi dan terendah”. metode ini merupakan kombinasi unik dari pendekatan mcdm yaitu model jumlah tertimbang (weight sum model /wsm) dan model produk tertimbang (weight product model/wpm). pada awalnya membutuhkan normalisasi linier dari elemen matriks keputusan dengan menggunakan dua persamaan. langkah-langkah metode weighted aggregated sum product assesment (waspas) sebagai berikut: 1) menentukan normalisasi matriks dalam pengambilan keputusan x = [ 𝑥11 𝑥12 … 𝑥1𝑛 𝑥12 𝑥22 … 𝑥2𝑛 … … … … 𝑥𝑚1 𝑥𝑚2 … 𝑥3𝑛 ] ................................. (1) jika nilai maksimal dan minimal ditentukan maka persamaan menjadi sebagai berikut: a. jika kriteria benefit maka: 𝑋𝑖𝑗 = 𝑥𝑖𝑗 𝑀𝑎𝑥𝑖𝑋𝑖𝑗 ....................................................... (2) b. jika kriteria cost maka: 𝑋𝑖𝑗 = 𝑥𝑖𝑗 𝑀𝑖𝑛𝑖𝑋𝑖𝑗 ........................................................ (3) 2) menghitung nilai normalisasi matriks dan bobot waspas dalam pengambilan keputusan. q=0.5∑ 𝑋𝑖𝑗𝑊𝑗 + 0.5 ∏ (𝑋𝑖𝑗)𝑤𝑗𝑗=1 𝑛 𝑗=1 .......... (4) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 199 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional keterangan 0.5 adalah ketetapan 𝑄𝑖 = nilai dari 𝑄 ke 𝑖 𝑋𝑖𝑗𝑊 = perkalian nilai 𝑋𝑖𝑗 dengan bobot 𝑤 benefit = jika nilai terbesar adalah terbaik cost = jika nilai terkecil adalah terbaik a. instrumen penelitian instrumen yang digunakan dalam penelitian ini antara lain: 1). pedoman wawancara pedoman wawancara ini berisi uraian penelitian yang dituangkan dalam bentuk daftar pertanyaan agar proses wawancara dapat berjalan dengan baik dan memperoleh informasi lebih mendalam mengenai penilaian rumah tangga miskin yang ada di desa cibangkong. 2). pedoman observasi pedoman observasi digunakan untuk membuat deskripsi singkat yang berkenaan dengan perilaku yang diamati dan kegiatan yang mungkin diperlihatkan oleh individu-individu yang diamati. pedoman observasi pada penelitian ini berisi daftar rumah tangga di desa cibangkong yang mengajukan sebagai rumah tangga miskin dan daftar penilaian rumah tangga miskin. b. metode pengumpulan data metode pengumpulan data yang digunakan dalam penelitian ini sebagai berikut: 1). observasi penulis mengumpulkan data dengan mencari informasi melalui pengamatan secara langsung pada kantor pemerintah desa cibangkong dan pencatatan secara sistematis yang berkaitan dengan proses penentuan rumah tangga miskin yang ada di desa cibangkong. 2). wawancara penulis mengumpulkan data informasi melalui tanya jawab dan diskusi secara langsung dan secara terstruktur dengan pihak-pihak yang dapat memberi keterangan terkait proses penentuan rumah tangga miskin yang ada di desa cibangkong. 3). studi pustaka penumpulan data sekunder pada penelitian ini dilakukan dengan mengumpulkan data dan informasi melalui studi pustaka yaitu data-data yang diperoleh melalui buku-buku referensi, jurnal-jurnal penelitian terkait maupun ebook yang berhubungan dengan permasalahan yang diteliti guna mendukung penulisan skripsi ini. c. populasi populasi dalam penelitian ini adalah seluruh rumah tangga pemohon bantuan pemerintah terdaftar di desa cibangkong. di mana jumlah pemohon rumah tangga miskin yang terdaftar berjumlah 748 keluarga. d. sampel penelitian dalam menentukan ukuran sampel dari populasi rumah tangga miskin yang ada di desa cibangkong penulis menggunakan rumus slovin n=n/(1+(ne^2)) ................................................................ (5) dimana: n = jumlah sampel n = jumlah populasi e = batas toleransi kesalahan (error tolerance). n=748/(1+(748*〖0.1〗^2)) n=748/(1+(748*0.01)) n=748/(1+7.48) n=748/8.48 n=88.20 dengan menggunakan rumus slovin yang memiliki nilai kritis atau prosentase kelonggaran ketidakterikatan sebesar 10%, maka jumlah yang dibutuhkan adalah 89 rumah tangga miskin yang ada di desa cibangkong. e. metode analisis data untuk mencapai tujuan penelitian ini, maka metode analis data yang digunakan untuk menentukan rumah tangga miskin menggunakan metode weighted aggregated sum product assesment (waspas), berdasarkan perhitungan dengan metode ini menghasilkan peringkatan atau ranking yang terdiri dari empat kriteria yaitu sangat miskin, miskin, rentan miskin dan tidak miskin. nilai kriteria dapat dilihat pada tabel 1 tabel 1. nilai status kemiskinan nilai status 0.910 >= nilai ranking <= 1 sangat miskin 0.810 >= nilai rangking < 9.10 miskin 0.710 >= nilai ranking < 0.810 rentan miskin 0.510 >= nilai ranking < 0.710 tidak miskin sumber: (lestari & targiono, 2017) hasil dan pembahasan a. analisa kebutuhan perangkat lunak analisa kebutuhan digunakan untuk menentukan kebutuhan secara lengkap, maka kebutuhan dibagi menjadi dua bagian yaitu kebutuhan fungsional dan kebutuhan non fungsional 1). kebutuhan fungsional http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 200 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional a) admin dapat melihat, menambah, mengubah dan menghapus data pengguna atau petugas. b) admin atau operator dapat menambah periode seleksi. c) admin dapat melihat, menambah, mengubah dan menghapus data penduduk. d) admin dapat melihat, menambah, mengubah, menghapus data kriteria dan bobot kriteria beserta rincian nilai kriteria. e) admin atau operator dapat memasukan nilai data penduduk. f) sistem dapat menghitung nilai data penduduk dengan menggunakan metode waspas. g) sistem dapat menghasilkan laporan akhir penilaian penduduk beserta keputusannya. 2). kebutuhan non fungsional a). kinerja peningkatan terhadap sistem yang baru dalam mengolah data dan menampilkan informasi secara keseluruhan dalam menyelesaikan proses yang ditangani. adapun kinerja yang diharapkan dari sistem ini adalah waktu yang dibutuhkan untuk penilaian penduduk lebih cepat dan mempermudah pembuatan laporan hasil penilaian penduduk beserta keputusannya. b). keamanan (security) adapun kebutuhan yang mencakup dengan keamanan antara lain pengguna diberikan hak akses yang berbeda-beda dan sistem aplikasi dilengkapi dengan nama (username) dan kata sandi (password). c). informasi informasi yang dapat dilakukan oleh pengguna pada sistem ini berisi hal-hal yang dilakukan, antara lain: digunakan untuk memberi peringatan untuk pengguna jika data yang dimasukkan tidak lengkap. digunakan untuk menginformasikan pengguna apabila nama (username) dan kata sandi (password) yang dimasukkan salah, maka sistem akan menampilkan informasi bahwa data petugas tidak terdaftar. b. pengolahan data dan penghitungan dengan menggunakan metode waspas proses penentuan rumah tangga miskin pada desa cibangkong dengan menggunakan metode weighted aggregated sum product assesment (waspas) diperlukan perhitungan nilai kriteriakriteria dan bobot masing-masing kriteria sehingga didapatkan alternatif terbaik. tabel 1. bobot kriteria kode keterangan bob ot keterangan k1 status bangunan tempat tinggal yang ditempati 5 % benefit k2 status lahan tempat tinggal yang ditempati 5 % benefit k3 luas lantai 5 % benefit k4 jenis lantai terluas 10 % benefit k5 jenis dinding terluas 10 % benefit k6 jenis atap terluas 10 % benefit k7 jumlah kamar tidur 5 % benefit k8 sumber air minum 5 % benefit k9 cara memperoleh air minum 5 % benefit k10 sumber penerangan utama 5 % benefit k11 bahan bakar atau energi utama untuk memasak 5 % benefit k12 penggunaan fasilitas tempat buang air besar 5 % benefit k13 jenis kloset 5 % benefit k14 tempat pembuangan akhir tinja 5 % benefit k15 ijazah tertinggi yang dimiliki kepala rumah tangga 5 % benefit k16 pekerjaan utama kepala rumah tangga 5 % benefit k17 total harga aset yang dimiliki 5 % benefit menentukan matriks keputusan 𝑋 = [ 25 25 15 20 10 10 15 15 30 30 20 30 15 25 30 30 40 25 25 30 20 20 10 20 15 30 30 20 30 30 25 30 30 40 25 25 25 15 20 10 15 15 30 30 10 15 15 20 30 30 20 25 25 20 10 10 10 20 15 30 30 10 30 30 25 30 20 20 25 25 20 20 15 10 25 10 30 30 20 30 30 25 30 30 40 25 25 25 20 10 10 15 15 30 30 20 15 15 10 35 30 20 25 25 25 5 10 10 15 15 30 30 20 25 15 25 30 30 40 25 25 30 10 10 10 20 15 30 30 20 30 30 25 30 30 40 25 25 20 5 10 10 20 10 30 30 20 15 15 10 35 30 40 25 25 20 5 10 10 15 15 30 30 10 30 30 25 30 25 10 25 25 20 15 10 10 20 15 30 30 20 15 15 25 35 20 20 25 25 25 5 10 10 15 10 30 30 20 25 15 20 30 30 10 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 25 40 20 20 20 10 20 10 30 30 20 30 30 25 35 30 40] menghitung matrik ternormalisasi penelitian ini semua kriteria yang digunakan merupakan kriteria keuntungan (benefit) sehingga penghitungan matriks ternormalisasi dengan rumus: 𝑋𝑖𝑗 = 𝑥𝑖𝑗 𝑀𝑎𝑥𝑖𝑋𝑖𝑗 ......................................................................... (6) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 201 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional hasil perhitungan dapat dilihat pada matriks di bawah ini. x= [ 1 0.625 0.5 1 0.5 1 0.6 1 1 1 1 1 0.5 1 0.86 1 1 1 0.625 1 1 1 1 0.8 1 1 1 1 1 1 1 0.86 1 1 1 0.625 0.83 0.75 1 1 0.6 1 1 1 0.5 0.5 0.5 0.8 0.86 1 0.5 1 0.625 0.67 0.5 0.5 1 0.8 1 1 1 0.5 1 1 1 0.86 0.7 0.5 1 0.625 0.67 1 0.75 1 1 0.67 1 1 1 1 1 1 0.86 1 1 1 0.625 0.83 1 0.5 1 0.6 1 1 1 1 0.5 0.5 0.4 1 1 0.5 1 0.625 0.83 0.25 0.5 1 0.6 1 1 1 1 0.83 0.5 1 0.86 1 1 1 0.625 1 0.5 0.5 1 0.8 1 1 1 1 1 1 1 0.86 1 1 1 0.625 0.67 0.25 0.5 1 0.8 0.67 1 1 1 0.5 0.5 0.4 1 1 1 1 0.625 0.67 0.25 0.5 1 0.6 1 1 1 0.5 1 1 1 0.86 0.8 0.3 1 0.625 0.67 0.75 0.5 1 0.8 1 1 1 1 0.5 0.5 1 1 0.7 0.5 1 0.625 0.83 0.25 0.5 1 0.6 0.67 1 1 1 0.83 0.5 0.8 0.86 1 0.3 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 1 0.667 1 1 1 0.8 0.67 1 1 1 1 1 1 1 1 1 1 ] penentuan rangking berdasarkan nilai qi tabel .ranking nilai qi n o no induk nama krt ala mat hasi l akh ir rekom 1 33021600000 00033 karso rt 001 rw 002 0,97 9 sangat miskin 2 33021600000 00002 slamet purwadik rama rt 004 rw 006 0,96 13 sangat miskin 3 33021600000 00040 santari rt 006 rw 006 0,96 09 sangat miskin 4 33021600000 00090 kasid kasropi rt 002 rw 004 0,95 31 sangat miskin 5 33021600000 00023 darkam rt 001 rw 006 0,94 97 sangat miskin ... ... … …. …. …… 6 1 33021600000 00011 sarim taryono rt 006 rw 005 0,77 29 rentan miskin 6 2 33021600000 00041 nasum sarjono rt 003 rw 002 0,77 27 rentan miskin 6 3 33021600000 00048 wasito rt 003 rw 004 0,76 61 rentan miskin 6 4 33021600000 00004 wawang nurdianto rt 003 rw 002 0,76 42 rentan miskin 6 5 33021600000 00051 sanrodi slamet rt 004 rw 003 0,75 94 rentan miskin ... ... … …. …. …… 8 5 33021600000 00018 sopiyah rt 002 rw 004 0,66 84 tidak miskin 8 6 33021600000 00056 muharson o rt 002 rw 002 0,66 43 tidak miskin 8 33021600000 sanikrad rt 0,65 tidak 7 00032 005 rw 001 15 miskin 8 8 33021600000 00027 nasam rt 005 rw 004 0,63 7 tidak miskin 8 9 33021600000 00065 warsono rt 003 rw 002 0,59 03 tidak miskin c. code implementasi sistem pendukung keputusan rumah tangga miskin ini dengan menggunakan bahasa java. d. implementasi 1. form halaman data penduduk form data penduduk adalah form yang diakses untuk melihat, menambah, mengubah dan menghapus data penduduk. gambar 2. form data penduduk 2. form halaman data pembobotan kriteria form ini diakses untuk melihat, menambah, mengubah, menghapus dan menentukan bobot kriteria. gambar 3. form halaman pembobotan kriteria http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 202 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 3. form penilaian penduduk pada form penilaian penduduk terdapat dua data yaitu data nilai pemohon dan data hasil normalisasi. pilih tahun periode seleksi kemudian tekan tombol tampil pemohon. setelah data tampil kemudian beri nilai sesuai data yang sudah diperoleh. gambar 4 . form data nilai pemohom setelah semua data nilai penduduk sudah terisi kemudian tekan tombol normalisasi, setelah proses normalisasi selesai tekan tombol simpan data untuk menyimpan data nilai penduduk. gambar 5. form data hasil normalisasi 4. form halaman proses perhitungan waspas halaman ini digunakan untuk menghitung nilai data penduduk. saat form ini diakses maka akan tampil data penduduk beserta nilainya. gambar 6. form data nilai penduduk tekan tombol hitung waspada maka akan tampil data normalisasi waspas dan hasil akhir waspas, kemudian tekan tombol simpan untuk menyimpan hasil perhitungan dengan metode waspas. gambar 7. form data normalisasi waspas berikutt gambar 8, merupakan hasil akhir analisis dengan menggunakan metode waspas, dimana hasilnya dapat ditampilkan diarsir menggunakan warna-warna. gambar 8. form hasil akhir waspas 5. halaman laporan hasil perhitungan halaman laporan hasil perhitungan dengan metode waspas, untuk kebutuhan laporan instansi dapat langsung di cetak kemedia printer. gambar 9. form laporan hasil perhitungan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 4 september 2019 p-issn: 2656-1743 e-issn: 2656-1735 203 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional e. hasil penelitian berdasarkan hasil perhitungan dengan menggunakan metode waspas, dan diimplementasikan dalam bentuk aplikasi sistem, berdasarkan dari 89 sampel yang diteliti terdapat 12 rumah tangga sangat miskin, 28 rumah tangga miskin, 38 rumah tangga rentan miskin dan 11 rumah tangga tidak miskin. hasil yang diperoleh dapat dilihat pada grafik di bawah ini: gambar 15. grafik hasil penelitian kesimpulan berdasarkan dari uraian dan penjelasan serta pembahasan keseluruhan materi pada bab-bab sebelumnya maka dapat diambil kesimpulan antara lain dari jumlah 89 sampel yang dilakukan perhitungan hanya 45 % saja yang layak menerima bantuan program pemerintah. sistem pendukung keputusan ini mampu mengatasi masalah-masalah yang terdapat dalam penentuan rumah tangga miskin di desa cibangkong dan mampu memberikan hasil yang akurat dalam penentuan rumah tangga miskin. dengan sistem pendukung keputusan metode waspas ini pengolahan data akan menjadi lebih efektif dan efisien karena proses penilaian dan pembuatan laporan menjadi cepat. pegawai hanya memasukan data penduduk, data kriteria dan data penilaian penduduk, sedangkan proses perhitungan dan pembuatan laporan dilakukan oleh sistem.penerapan metode waspas untuk sistem pendukung keputusan dapat memberikan hasil yang maksimal dalam hal pengambilan keputusan dengan cara mengurutkan alternatif penduduk dari yang sangat miskin sampai yang tidak miskin. daftar referensi handayani, m., & marpaung, n. (2018). implementasi metode weight aggregated sum product assesment (waspas) dalam pemilihan kepala laboratorium. seminar nasional royal (senar), (september), 253– 258. indrawati, e. s., hyoscyamina, d. e., qonitatin, n., & abidin, z. (2014). profil keluarga disfungsional pada penyandang masalah sosial di kota semarang. jurnal psikologi undip, 13(2), 120–132. https://doi.org/https://doi.org/10.14710/jp u.13.2.120-132 irfansyah, puput, & sunoto, i. (2015). sistem pendukung keputusan seleksi supplier dengan pendekatan metode fis mamdani pada koperasi xyz. faktor exacta, 8(4), 347–358. https://doi.org/10.30998/faktorexacta.v 8i4.505 lestari, u., & targiono, m. (2017). sistem pendukung keputusan klasifikasi keluarga miskin menggunakan metode simple additive weighting (saw) sebagai acuan penerima bantuan dana pemerintah (studi kasus: pemerintah desa tamanmartani, sleman). jurnal tam (technology acceptance model), 8(1), 70–78. retrieved from http://www.ojs.stmikpringsewu.ac.id/index. php/jurnaltam/article/view/97 lubis, p., nadeak, b., & hondro, r. k. (2017). penerapan metode analitical hierarchy process dalam penentuan warga penerima program keluarga harapan ( pkh ) ( studi kasus : kantor lurah tegal sari mandala ii ). media informatika budidarma, 1(1), 17–23. muzakkir, i. (2017). penerapan metode topsis untuk sistem pendukung keputusan penentuan keluarga miskin pada desa panca karsa ii. ilkom jurnal ilmiah, 9(3), 274–281. retrieved from http://jurnal.fikom.umi.ac.id/index.php/ilk om/article/view/156 pardiansyah, a. s. (2015). audit tata kelola teknologi informasi program studi sistem informasi sekolah tinggi manajemen informatika dan komputer ( stmik ) lombok menggunakan framework cobit. ijse indonesian journal on software engineering, 1(1), 17–25. saputra, a., kartini, d., & soesanto, o. (2015). rancang bangun sistem pendukung keputusan penetuan keluarga miskin, 0 10 20 30 40 miskin rentan miskin sangat miskin tidak miskin grafik hasil penelitian http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 4 september 2019 204 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 02(01), 84–95. saputra, a., kartini, d., & soesanto, o. (2016). rancang bangun sistem pendukung keputusan penentuan keluarga miskin. klik kumpulan jurnal ilmu komputer, 2(1), 84–95. https://doi.org/10.20527/klik.v2i1.20 setiani, n. (2017). studi tentang pelaksanaan pendataan kelurga miskin di kelurahan sempaja selatan kecamatan samarinda utara kota samarinda. ejournal administrasi negara, 5, 6709–6723. sianturi, t. n., siburian, l., hutagaol, r. g., & sahir, s. h. (2018). sistem pendukung keputusan pemilihan pegawai bank terbaik menggunakan metode weighted aggregated sum product assessment ( waspas ). sensasi 2018, 625–631. sugiarti, s., nahulae, d. k., panggabean, t. e., & sianturi, m. (2018). sistem pendukung keputusan penentuan kebijakan strategi promosi kampus dengan metode weighted aggregated sum product assesment (waspas), 5(2), 103–108. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 187 sentiment analysis of pedulilindungi applciation reviews using machine learning and deep learning ahmad rais dwijaya1, arif dwi laksito2 program studi informatika, fakultas ilmu komputer universitas amikom yogyakarta yogyakarta, indonesia ahmad. dwijaya@students. amikom. ac. id1, arif. laksito@amikom. ac. id2 (*) corresponding author abstract the covid-19 pandemic that hit the world at the end of early 2020 caused many losses. the indonesian government has established various ways to reduce the path of the covid-19 pandemic by launching the pedulilindungi application to reduce the spread of covid-19. various layers of society responded to the launch of the application with various opinions. this research mainly analyzes public opinion sentimen t toward the pedulilindungi application, as determined by 10,000 reviews on the google play store. this study aims to compare the performance of deep learning and machine learning models in sentiment analysis. the stages of the research method begin with data collection methods, data pre-processing, and sentiment analysis using a machine learning model with the embedding of the word tf-idf, which includes the nave bayes algorithm, decision tree, random forest, k-nearest neighbour, and svm. as for the deep learning model with the fasttext word embedding word representation technique using the lstm algorithm, an evaluation is carried out using the confusion matrix. the results of this study state that deep learning models perform better than machine learning models. keywords: sentiment analysis; machine learning; deep learning; lstm abstrak pandemi covid-19 yang melanda dunia pada akhir awal 2020 menimbulkan banyak kerugian. pemerintah indonesia menetapkan berbagai cara untuk mengurangi dampak dari pandemi covid-19 dengan meluncurkan aplikasi pedulilindungi sebagai alat untuk mengurangi penyebaran covid-19. berbagai lapisan masyarakat menanggapi peluncuran aplikasi tersebut dengan berbagai opini. analisis sentimen opini publik terhadap aplikasi pedulilindungi yang ditentukan dari review di google play store sebanyak 10.000 review menjadi fokus utama penelitian ini. tujuan dari penelitian ini adalah membandingkan performa dari model deep learning dan machine learning dalam melakukan analisis sentiment. tahapan metode penelitian diawali dengan metode pengumpulan data, pre-processing data, dan analisis sentiment menggunakan model machine learning dengan penyematan kata tf-idf yang mencakup algoritma naïve bayes, decision tree, random forest, k-nearest neighbor dan svm. sedangkan untuk model deep learning dengan teknik representasi kata fasttext word embedding menggunakan algoritma lstm kemudian dilakukan evaluasi menggunakan confusion matrix. hasil dari penelitian ini menyatakan bahwa model deep learning mempunyai kinerja yang lebih baik dibandingkan model machine learning. kata kunci: analisis sentimen; machine learning; deep learning; lstm introduction in the midst of the rapid development of various fields in the world, in 2020, the world was shocked by the outbreak of a new virus that spread quickly. indonesia was also attacked by a new virus called coronavirus disease 2019 (covid-19) which is caused by a strain of coronavirus (sars-cov2) (djalante et al., 2020). the 2019 coronavirus is an infectious illness brought on by a brand-new virus that has never been identified. this covid-19 illness can spread from person to person and has flu-like symptoms (sudiarsa & wiraditya, 2020). who and the indonesian government both declared covid-19 to be a disease that causes public health emergencies and non-natural disasters. (keputusan menteri kesehatan republik indonesia, 2020). a new achievement for the government of the republic of indonesia in utilizing technology is the availability of the pedulilindungi application, which is expected to inhibit the spread of the covid19 virus. community involvement is required for p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 188 this software to operate (sudiarsa & wiraditya, 2020). a feature of the pedulilindungi application allows for displaying information on vaccinations, test results for covid-19, statistical data for covid19 cases, and telehealth providers. the government of the republic of indonesia made a policy to facilitate tracing, tracking, and fencing by requiring several places to attach a qr-code at the entrance to check in through the pedulilindungi application. people must install the pedulilindungi application on their devices. it is required for people in indonesia to install the pedulilindungi application on the google play platform or similar platforms. (pribadi, manongga, purnomo, setyawan, & hendry, 2022). community perspectives vary in dealing with government policies regarding the pedulilindungi application. people's perceptions of using the pedulilindungi application vary, as observed on the google play store platform. as time passes, some users of the pedulilindungi application reveal to support it, but others despise it (pribadi et al., 2022). users can voice their opinions in various ways, from subtly complimenting phrases to bluntly disparaging ones. users can also provide reviews of downloaded applications in the form of star ratings (between 1 and 5). this study's main objective is to observe user comments on the pedulilindungi app on the google play store website. many customers have left positive and negative reviews regarding complaints and other opinions. based on this, sentiment analysis of the pedulilindungi application on the google play store platform is needed because it will become test data for application developers, especially the ministry of communication and informatics, to improve application performance. sentiment analysis is a method for automatically classifying several texts into positive or negative attitudes (dashtipour, gogate, adeel, larijani, & hussain, 2021). the lexicon-based approach and the machine learning approach are two methods for sentiment analysis. (onan, 2021). however, deep learning models can also be used to build classification models for sentiment analysis. several studies have used machine learning models for sentiment analysis, such as the knearest neighbours (knn), naive bayes, support vector machine (svm), random forest, and naive bayes algorithms. some studies compare the many methods of machine learning models as well as those that use only one of these algorithms, as in research (deho, agangiba, aryeh, & ansah, 2018) and (stephenie, warsito, & prahutama, 2020) which only conducted research using random forest algorithm. svm, random forest, and decision tree algorithms have been used in the study (steinke, wier, simon, & seetan, 2022). other researchers also used naive bayes, svm, decision trees, random forests, and knn (tran, nguyen, & dao, 2022). the support vector machine approach received the highest accuracy in these two studies compared to other machine learning algorithms. in addition to employing machine learning models, several researchers have begun using deep learning models with convolutional neural networks (cnn), short-term long memory (lstm), and recurrent neural network (rnn) algorithms to carry out sentiment analysis. previous studies by (feizollah, ainin, anuar, abdullah, & hazim, 2019; kilimci, 2020; kilimci & akyokus, 2019; ombabi, ouarda, & alimi, 2020) compared the performance of various sentiment analysis techniques using deep learning models. moreover, an experiment by (feizollah et al., 2019) obtained maximum accuracy in conducting analysis using a combination of cnn and lstm. (kilimci & akyokus, 2019; ombabi et al., 2020) they claimed that the lstm algorithm performs the best. however, using fasttext word embedding to the models (kilimci, 2020; ombabi et al., 2020) concluded that sentiment analysis performed better. recent research found that not all deep learning models are equally successful in various situations (kapočiūtė-dzikienė, damaševičius, & woźniak, 2019). evaluated the support vector machines (svm) and multinomial naive bayes (mnb) models with the lstm and cnn models by conducting a sentiment analysis of the opinions expressed in lithuania on the news portal lietuvos ryta. the highest accuracy results were obtained for classification using machine learning, namely mnb and svm. deep learning with word2vec and fastext works less than optimally, so the accuracy is not as good as machine learning. this study aims to compare the effectiveness of machine learning and deep learning models to identify the most effective model for sentiment analysis on pedulilindungi application reviews on the google play store. research methods in this study, we compared the performance of machine learning models, which include naïve bayes, decision tree, random forest, k-nearest neighbours, and svm, with the tf-idf word embedding technique and lstm deep learning model using fasttext word embedding. the jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 189 dataset was obtained from a scraping review of the pedulilindungi application on the google play store and then evaluated to measure the performance. the framework of this research is shown in figure 1. figure 1. research framework scraping pedulilindungi app review scraping pedulilindungi app review data from the google play store uses python programming, utilizing the google-play-scrapper library with parameters sort = sort.newest means the latest data. country = ‘id’, which means a review from indonesia. lang = ‘id,’ which means review in indonesian, and count = 10000, which indicates the amount of data taken, as many as 10,000. based on the required data, it is filtered and then assigned a sentiment label based on the review rating. ratings 1 and 2 are categorized as having negative sentiment, ratings 4 and 5 have positive sentiment, and rating 3 has neutral sentiment. pre-processing data the data pre-processing process is carried out after collecting and labeling. at this stage, the data is prepared for analysis (pribadi et al., 2022). the preprocessing data stage consists of several subprocesses: symbol/punctuation/emoji removal, case folding, tokenization, filtering, and stemming. during case folding, all letters are reduced to lowercase. then in the tokenization stage, the strings (text) sequence is split into a keyword, word, phrase or other element called a token (dey et al., 2020). words that occur frequently but have meaning in the analysis or stop words are removed during filtering. the stemming stage involves removing affixes or suffixes (both at the beginning and the end of a word) to get to the root word. the stop word remover factory package from sastrawi is used in the last two processes. split dataset data splitting refers to the division of data into two or more parts. this is a crucial machine learning component, especially for building datadriven models. typically, a two-part split is used to train the model, while the other part tests or evaluates the data. in this work, we used the train test split function of the scikit-learn package to split the complete dataset into 80% training data and 20% test data. tf-idf to see the response from the text you have, each word will be weighted with specific rules. the authors used the tf-idf (term frequency — inverse document frequency) method for word embedding in this study. this method calculates the value of the term frequency (tf) and inverse document frequency (idf) for each token in the document in the corpus. in simple terms, the tf-idf method determines the number of times a word appears in a document. tf-idf involves multiplying the idf's size by the tf's size, which has proven to be very strong compared to other models (robertson, 2004). the fasttext word embedding word embedding is a method of representing words as solid vectors that captures the relationships or semantic similarity between words that appear in later paragraphs of the text. (mikolov, grave, bojanowski, puhrsch, & joulin, 2018). proposed fasttext, an enhanced word embedding method with sub-word insertion (ngram characters). fasttext represents words as minor elements using n-gram characters. each word is divided into n-gram characters where 3 ≤ n ≤ 6. for example, with n=3, the word “smart” is then divided into <sm, sma, mar, art, dan re>, and <smart>. fasttext also uses the skip-gram approach with negative samples suggested for word2vec's modified skip-gram loss function. machine learning the author uses traditional machine learning techniques, such as naive bayes, decision tree, random forest, k-nearest neighbours (knn), and support vector machine (svm), to analyze sentiment in this study. the conditional probability model serves as the basis for the naive bayes classifier. sentiment classification involves two vectors, so the classifier assumes the two features' independence and probabilities (zahoor, bawany, & hamid, 2020). a decision tree is a hierarchical p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 190 model for supervised learning in which the decision nodes of the test function identify a local region as a recursive set of subdivisions (bayhaqy, sfenrianto, nainggolan, & kaburuan, 2018). random forest works in two steps. the first step combines n decision trees to create a random forest. then the second step is to make predictions for each tree made in the first step(pribadi et al., 2022). knn is a classification algorithm that forms new data classes using the closest k data (neighbors) as a guide (bayhaqy et al., 2018). the svm algorithm separates class data by finding the most optimal hyperlink (firmansyah, asnawi, hasanah, novian, & pravitasari, 2021). lstm this study uses deep learning models, especially long short-term memory (lstm), to analyze sentiment. researchers have widely explored the lstm method, producing findings superior to previous methods, making it an ideal way to apply sentiment analysis. (romadhoni, fahmi, & holle, 2022). lstm was developed to infer remote dependencies in sequence data. long-term dependencies between data are maintained within the lstm and contain semantic context. this algorithm uses special cells or storage units to store information about dependencies in the remote context. each lstm unit contains input, forget, and output gates to control the information stored, forgotten, and passed on to the next step. lstm units decide what to store and when to allow reading, writing, and erasing through gate bypassing or blocking information through lstm units (kilimci & akyokus, 2019). the network architecture is shown in figure 2. figure 2. lstm network cell the four gate units that make up an lstm are the input gate, forget gate, cell gate, and output gate (romadhoni et al., 2022). gates' job is determining whether the information should be kept or forgotten. equation 1 to 6 shows the calculation for the hidden layer in the lstm cell. 𝑓𝑡 = 𝜎(𝑤𝑓 [ℎ𝑡−1, 𝑥𝑡] + 𝑏𝑓 ) ....................................................... (1) 𝑖𝑡 = 𝜎(𝑤𝑖 [ℎ𝑡−1, 𝑥𝑡 ] + 𝑏𝑖 ) ......................................................... (2) 𝑜𝑡 = 𝜎(𝑤𝑜 [ℎ𝑡−1, 𝑥𝑡] + 𝑏𝑜) (1) ................................................ (3) c̃𝑡 = 𝑡𝑎𝑛ℎ(𝑤𝑐 [ℎ𝑡−1, 𝑥𝑡 ] + 𝑏𝑐 ) (1) ........................................... (4) 𝑐𝑡 = 𝑓𝑡 ∗ 𝑐𝑡−1 + 𝑖𝑡 ∗ c̃𝑡 .............................................................. (5) ℎ𝑡 = 𝑜𝑡 ∗ tanh(𝑐𝑡) ......................................................................... (6) information : 𝑓𝑡 = forget gate 𝑖𝑡 = input gate 𝑐𝑡 = cell gate 𝑜𝑡 = output gate ℎ𝑡 = hidden state c̃𝑡 = intermediate cell state 𝑏 = bias 𝑊 = weight evaluation the last part of this experiment is measuring sentiment analysis performance utilizing deep learning and machine learning, and then the confusion matrix is required to calculate the accuracy or performance value. equations 7 to 10 describe the confusion matrix. 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃+𝐹𝑃 × 100% ..................................................... (7) 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 𝑇𝑃+𝐹𝑁 × 100% ........................................................... (8) 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 × 100% .................................... (9) 𝐹1 − 𝑆𝑐𝑜𝑟𝑒 = 2 × 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛+𝑟𝑒𝑐𝑎𝑙𝑙 × 100% ............... (10) information : tp = true positive tn = true negative fp = false positive fn = false negative this experiment's last stage compares machine learning and deep learning performance. accuracy and f1-score from the confusion matrix are used as matrix evaluation from both approaches. results and discussion the authors employed python version 3.10 to do the experiments, which ran on a windows environment with an amd ryzen 5 2.5ghz processor and 8gb of ram. the dataset used is 10,000 reviews of the pedulilindungi application from the google play store platform. each review jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 191 takes information about the content of the review (content) and the rating given by the reviewer (score), which is used for the labeling process. data labeling in this study is limited to rating review. ratings 1 and 2 are grouped into negative sentiments, while ratings 4 and 5 are grouped into positive sentiments, as shown in table 1. table 1. datasets example content score sentiment semakin kesini semakin berat aplikasinya,..buka aplikasi lama bgt..tolong diperbaiki bug utk scan barcode! 🙏🙏 2 negative sangat membantu setiap melakukan perjalanan. walaupun kadang lemot prosesnya 4 positive mau download sertifikat internasional aja ga bisa ,ga muncul hasil nya, bikin ribet. 1 negative terima kasih kepada pemerintah yang telah memperhatikan masyarakat dengan aplikasi ini 5 positive because of less relevant for the rating 3 content, we ignore this. so, the total data become 9,477 containing positive and negative sentiment; the composition can be seen in figure 3. figure 3. percentage of the amount of data the example of several stages of pre-processing text describes in table 2 below. table 2. data pre-processing example data review semakin kesini semakin berat aplikasinya,..buka aplikasi lama bgt..tolong diperbaiki bug utk scan barcode! 🙏🙏 removal of symbols/punctuation marks/emojis semakin kesini semakin berat aplikasinya buka aplikasi lama bgt tolong diperbaiki bug utk scan barcode case folding semakin kesini semakin berat aplikasinya buka aplikasi lama bgt tolong diperbaiki bug utk scan barcode tokenises [semakin, kesini, semakin, berat, aplikasinya, buka, aplikasi, lama, bgt, tolong, diperbaiki, bug, utk, scan, barcode] filtering [semakin, kesini, semakin, berat, aplikasinya, buka, aplikasi, lama, tolong, diperbaiki, bug, scan, barcode] stemming makin sini makin berat aplikasi buka aplikasi lama tolong baik bug scan barcode p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 192 the dataset is divided into two parts with a ratio of 80:20. 20% of the dataset is used as a data test, and the remaining 80% as a data train. before splitting the datasets, random oversampling (ros) is used to overcome imbalanced data. in a machine learning model experiment, the training data is feature extracted using tf-idf to calculate each word's tf and idf scores. then classification is carried out using algorithms from machine learning models, namely naïve bayes, decision tree, random forest, k-nearest neighbors (knn) with a value of k = 10, and support vector machine (svm) with parameters kernel = linear and c = 5. each algorithm takes less than 1 minute to classify. to evaluate, calculations are performed using recall, precision, and accuracy models based on the confusion matrix. the results of evaluation testing using the confusion matrix from experiments using machine learning models found that the decision tree and svm algorithms perform better than the others with an accuracy of 84.5%, as in table 3. table 3. machine learning model evaluation results algorithm precision recall f1-score accuracy naïve bayes positive 83.6% 86.7% 85.1% 82,8 % negative 81.5% 77.5% 79.5% decision tree positive 94.5% 77.2% 85.1% 84,5 % negative 75.7% 94.1% 83.9% random forest positive 94.2% 77.5% 85.1% 84,3 % negative 75.8% 93.5% 83.7% k-nearest neighbors positive 79.7% 77.3% 78.5% 83,1 % negative 74.5% 92.4% 82.4% support vector machine positive 94.5% 77.2% 85.1% 84,5 % negative 75.7% 94.1% 83.9% in addition to machine learning models, deep learning models were also used in the experiments. a deep learning experiment required 8 minutes and 2 seconds to load the 4.2 gb of trained indonesian word vector data for the training data's word embedding using fasttext word embedding. then classification is carried out using the deep learning model algorithm, namely lstm, with the parameter batch size = 64 and the number of epochs = 200. the lstm technique requires 15 minutes and 34 seconds to train a classification model. an accuracy of 87% is obtained for the classification results by utilizing the lstm 1 layer, as shown in table 4. table 4. deep learning model evaluation results algorithm lstm 1 layer positive negative precision 85 % 89 % recall 90 % 84 % f1-score 87 % 86 % accuracy 87 % figures 4 and 5 show the model accuracy and loss of the estimated training and validation data sets in the lstm network units. figure 4. model accuracy and validation using the lstm network figure 5. loss accuracy and validation using the lstm network conclusion and suggestions in this study, we tested the effectiveness of the machine learning and deep learning models for jurnal riset informatika vol. 4, no. 3. june 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 193 sentiment analysis of the pedulilindungi application review. a total of 9,479 reviews were used. positive reviews reached 56.67% of the total reviews, while negative reviews reached 43.33%. the results show that compared to the machine learning model that uses tf-idf word embedding, the deep learning model that combines the lstm algorithm with the fasttext word embedding word representation technique has the best performance and the highest accuracy. it is noticeable that we are labeling the dataset with the help of a rating review. however, it appears that we could not control the review's sentiment. the upcoming studies should consider the labeling method. moreover, comparing recurrent neural network methods like gate recurrent unit (gru) and lstm will be challenging. reference bayhaqy, a., sfenrianto, s., nainggolan, k., & kaburuan, e. r. 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(2020). sentiment analysis and classification of restaurant reviews using machine learning. proceedings 2020 21st international arab conference on information technology, acit 2020. https://doi.org/10.1109/acit50332.2020.9 300098 jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 129 comparison of saw, wp, and topsis methods in determining the best journalists n.i.s. baldanullah1, febrianti adhania2, desti fitriati3 program studi teknik informatika1,2,3 fakultas teknik universitas pancasila1,2,3 jakarta, indonesia baldanullah55@gmail.com1, febriadh@gmail.com2, desti.fitriati@univpancasila.ac.id3 (*) corresponding author abstract journalists are human resources that have a significant influence on journalistic companies. a system is needed to support the company's decision to select and measure its reporters. pt. inipasti communika is one of the journalistic companies that has never previously measured and assessed its journalists, so it has difficulty assessing and measuring its journalists. this study aims to provide a solution using the decision support system in decision-making using the saw, wp, and topsis methods and provide the final decision results based on comparing these methods. this study uses criteria and criteria values from these companies. the company's data related to its journalists is the privacy of pt. it is a community, so the alternative value used is dummy data that is still by the original standards of the company's data. this study concludes that the three methods can provide the best alternatives with the same results. keywords: decision support system; saw method; wp method; topsis method abstrak wartawan merupakan sumber daya manusia yang memiliki pengaruh besar pada perusahaan jurnalistik. diperlukan suatu sistem dalam mendukung keputusan perusahaan tersebut untuk memilih dan mengukur wartawan mereka. pt. inipasti communika adalah salah satu perusahaan jurnalistik yang sebelumnya belum pernah mengukur serta menilai wartawan mereka, sehingga mengalami kesulitan dalam melakukan penilaian serta pengukuran pada wartawan mereka. penelitian ini bertujuan untuk memberikan suatu solusi menggunakan decision support system dalam pengambilan keputusan dengan menggunakan metode saw, wp dan topsis serta memberikan hasil akhir keputusan berdasarkan perbandingan metode tersebut. penelitian ini menggunakan kriteria dan nilai kriteria dari perusahaan tersebut. data perusah aan terkait wartawan mereka merupakan privasi pt. inipasti communika sehingga nilai alternatif yang digunakan merupakan data dummy yang masih sesuai dengan standar asli data perusahaan tesebut. penelitian ini memberikan kesimpulan bahwa ketiga metode yang digunakan mampu memberikan alternatif terbaik dengan hasil yang sama. kata kunci: sistem penunjang keputusan; metode saw; metode wp; metode topsis introduction a company with high-quality human resources is an advantage for all companies. a company's progress depends on its employees' performance, so it becomes essential to advancing a company(hafiz & ma’mur, 2018). a company certainly has various kinds of benchmarks in assessing their employees’ work results. however, not all companies, especially those still developing, have a tool and system for measuring the performance and quality of their employees. pt. inipasti communika is an incorporated mass media company based online that provides information or news about something in general. media companies are very dependent on their journalists because they rely on the quality and quantity of a piece of news. this company has never previously measured and assessed the journalists they have, making it difficult to measure and assess their reporters. this research tries to provide a method for overcoming the company's problems in deciding to assess and measure the performance and achievements of their journalists so that the conclusions and results will be a reference in assessing the best journalists in the company. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 130 this research uses a decision support system (dss) to provide conclusions supporting a specific goal for the company. a decision support system (dss) is a system that can provide effective problem-solving so that the results can help in decision-making obtained from the results of processing existing information using various methods. this system is designed to support all decision-making stages, identify problems, select the necessary data, determine the models and approaches used in the decision-making process, and evaluate results(aisyah & putra, 2021). the decision support system has various methods that are used; this research uses simple additive weighting (saw), weight product (wp), and technique for order preference by similarity to ideal solution (topsis) methods used to help pt. this is the case for communika in determining a particular decision based on the results of the ranking decision against the company's journalists through the decision support system. simple additive weighting (saw) is a method that seeks the weighted summation of the rating in each alternative on all criteria by normalizing the decision matrix into a scale that compares with all existing alternative ratings (syarief & suwandana, 2018). weight product (wp) is a method of decision-making using multiplication between predetermined criteria values that are previously the value of each existing criterion raised to the rank of a predetermined criterion (rizal et al., 2021). topsis is a method that can provide problem-solving with structured and unstructured conditions. this method provides a prediction and information that can be a benchmark in making a more appropriate decision by choosing alternatives to calculate the nearest value (hutasuhut et al., 2021). this study will compare decisions and provide suggestions regarding the results of which journalists' rankings will be used from the method used as a benchmark for pt. inipasti communika assists the company in achieving specific goals based on what is produced by the decision support system using the method carried out. previously, research has been carried out on accuracy comparison using the saw and wp. topsis methods where this accuracy comparison if the relevant company already has data on the results of previous decisions and made a new decision using existing methods, as in research (supiyan, 2019). this study compares topsis and saw, wp methods in determining bmt el-raushan financing. the research concluded that comparing the simple additive weighting method, weighted product, and topsis methods shows that the wp method is more accurate than the saw and topsis methods. it seems that from the three methods' accuracy level values, the wp method's highest accuracy with an accuracy value of 94%. the research conducted by (kungkung & haryadi kiswanto, 2018)aims to analyze the comparison of saw, wp, and topsis using hamming distance in the case study of the selection of new students at spp negeri kupang with the conclusion that based on these three methods, methods that are close to the results of accurate decisions of related parties are the saw and topsis methods. however, the saw and topsis methods are closer to the results of the school's decision. these three methods are feasible to be used by the kupang state agricultural tuition in processing new student admissions to support obtaining objective verdict results. as for the research using a single method, such as (noval et al., 2020), this research uses the simple additive weighting method to determine the best employees at pt. persada nusantara telekomunikasi with the hope of being able to provide a choice objectively for the company. the results offer options with the best value from several alternatives tested using five criteria and show that selecting the best employees is not only indicated by one criterion. still, some criteria also have a competency value according to existing criteria. research conducted by (salim et al., 2022) uses the topsis method to determine the best employees at pt. regency motor, the company, often experiences several problems, such as the calculation of employee criteria values that experience similarities with each other and take a long time in their calculations. it is not uncommon for errors in the computation of values, so the author uses the topsis method to overcome these problems using six criteria, and the results provide results based on the topsis calculations carried out. research is also conducted by (sihaloho et al., 2022) using the weighted product method in selecting the best employees on the cv. neosoft art medan, in the study, the problem faced was that managers at the company had difficulty assessing employee performance, so the author helped the manager by using the weighted product method with five criteria. the study concluded that using the wp method could speed up selecting the best employee accurately and make it easier for the manager to decide the company's above employees. the research only uses methods in a single decision support system based on various studies jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 131 that have been carried out in determining and choosing decisions related to the best employees in a company. in this study, three methods will be carried out, namely simple additive weighting (saw), weight product (wp), and technique for order preference by similarity to ideal solution (topsis), so that it are expected to provide conclusions where the results can give a ranking comparison that can provide strong confidence based on the calculation results for pt. this is communika. research methods this research was carried out with stages, namely data collection, then continued with the calculation stages on various calculation methods. here is figure 1 flow chart of research methods. figure 1. research method flowchart data acquisition based on data obtained from pt. inipasti communika, here is table 1 related to the criteria for creating a decision support system using existing methods. table 1. criteria and weights code criteria weight attribute c1 number of news releases 35 benefit c2 average of news views 30 benefit c3 violation of the code of ethics 25 cost c4 language skills 10 benefit c5 length of service 5 cost based on the table above, five criteria are criteria for assessing journalists at pt. inipasti communika with the following information: 1. the number of news releases is the total number of news written by journalists and has been published by the release team in 1 entire month 2. average news views are the average person who reads the news written by each journalist in 1 entire month 3. violation of the code of ethics is how many ethical violations have been committed by journalists since becoming journalists. 4. language skills are how much of a language journalists speak. 5. length of service is how long the journalist worked at the pt. inipasti communika. here is table 2 of weight values on each criterion. table 2. weights of each criterion criteria crips weight of crips number of news releases <30 30-50 51-70 >70 10 20 30 40 average of news views <100 101-500 501-1000 >1000 10 20 30 40 violation of the code of ethics 0 1 2 >2 10 20 30 40 language skills 1 2 3 >3 10 20 30 40 length of service 2-6 month 6-12 month 1-2 years >2 years 10 20 30 40 pt. inipasti communika does not want to open alternative data to the public. the company only provides data references, and researchers will use dummy data which can still be used as a standard according to actual company data. table 3 displays alternative data that will be used in this study. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 132 table 3. alternative values alternative c1 c2 c3 c4 c5 a1 a2 a3 a4 a5 a6 a7 56 36 63 24 52 60 44 764 1122 342 648 984 498 1068 2 1 3 2 1 3 1 1 2 2 1 2 1 2 7 15 36 4 16 11 27 simple additive weighting (saw) in the saw method, there are two attributes, such as the benefit criterion and cost criteria (cost). both criteria are the basis for the selection of criteria when making decisions. method saw is a widely used method to complete the retrieval of decisions practically (hermanto & izzah, 2018) the simple additive weighting (saw) method is used to find optimal alternatives from some alternatives with specific criteria. the definition of the simple additive weighting (saw) method is often also known as the weighted summation method. the basic concept of the saw method is to look for a weighted summation of the performance rating on each alternative on all attributes. this method requires normalizing the decision matrix x to a scale that can be compared with all alternative ratings (wijaya & insan, 2018). 𝑟𝑖𝑗 = { 𝑥𝑖𝑗 𝑀𝑎𝑥𝑖 𝑥𝑖𝑗 (𝑏𝑒𝑛𝑒𝑓𝑖𝑡) 𝑀𝑖𝑛𝑖𝑥𝑖𝑗 𝑥𝑖𝑗 (𝑐𝑜𝑠𝑡) ........................ (1) information: rij: normalized performance rating value xij: the attribute value that each criterion has max xij: the most significant value of each criterion min xij: the smallest value of each criterion the preference value for each alternative (vi) can be seen in the following equation: 𝑉𝑖 = ∑ 𝑤𝑗 𝑟𝑗 𝑛 𝑗=1 ............................................. (2) information: vi: rankings for each alternative wj: the weight value of each criterion rij: normalized performance rating value figure 2 is a flowchart of the simple additive weighting (saw) method. figure 2. saw flowchart weighted product (wp) the wp method is called dimensioned analysis because its mathematical structure eliminates units of measure. the wp method is a finite set of decision alternatives described in some respects as decision criteria. so this method does not need to be normalized. this method has several advantages. namely, variable costs and benefits help determine the criteria influencing decisions. this method is more straightforward than others because the calculation is not so complex and easier to understand (novira et al., 2020). the weighted product (wp) method seeks decisions by multiplying to relate attribute ratings, where the attribute must first be raised to the rank of the attribute in question. in the wp method, normalization is performed before multiplying each attribute's value. the value of weights that are profit (benefit), then the value of the lift is positive while the cost (cost) of the lifting is negative (rani et al., 2021). the determination of the normalized weight value with the symbol w can be seen in the following formula: 𝑤𝑗 = 𝑤𝑗 ∑ 𝑤𝑗 ............................................................ (3) jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 133 the determination of the value of the vector s can be seen in the following formula: 𝑆𝑖 = ∏ 𝑥𝑖𝑗 𝑤𝑗𝑛 𝑗=1 ............................................................ (4) information: s: alternative preference by analogy as vector s x: criterion value w: weight of criteria i: alternatives j: criteria n: many criteria the determination of the value of the vector v can be seen in the following formula: 𝑣𝑖 = ∏ 𝑥 𝑖𝑗 𝑤𝑗𝑛 𝑗=1 ∏ 𝑥 𝑗 𝑤𝑗𝑛 𝑗=1 ............................................................... (5) information: v: alternative preference with vector analogy v x: criterion value w: weight of criteria i: alternatives j: criteria n: many criteria figure 3. weight product flowchart technique for order preference by similarity to ideal solution (topsis) designing a decision support system using the topsis method is one of the choices in this study because it can rank selected alternatives, where the best-selected alternatives have the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. a positive ideal solution is defined as a solution that maximizes the profit attribute and minimizes the cost attribute, while a negative ideal solution is defined as a solution that minimizes the profit attribute and maximizes the cost (sugiarto, 2021). here is the formula for forming a normalized decision matrix: 𝑟𝑖𝑗 = 𝑥𝑖𝑗 √∑ (𝑥𝑖𝑗) 2𝑚 𝑖=1 ............................................................. (6) information: rij: normalized attribute values xij: the value of each attribute m: the value of the attributes available for each criterion here is the formula for creating a normalized and weighted decision matrix: 𝑦𝑖𝑗 = 𝑟𝑖𝑗 𝑤𝑗 ........................................................................ (7) information: yij: weighted normalization rij: normalized attribute values wj: criterion value here is the formula for determining the distance between the values of each alternative with a matrix of positive and negative ideal solutions: 𝐷+ = √∑ (𝑦𝑖 − 𝑦𝑖𝑗 +)2𝑚𝑖=1 ............................................... (8) 𝐷− = √∑ (𝑦𝑖 − 𝑦𝑖𝑗 −)2𝑚𝑖=1 ................................................ (9) information: d+: positive ideal d-: negative ideal here is the formula for determining the preference value for each alternative: 𝑉𝑖 = 𝐷𝑖 − 𝐷𝑖 −+𝐷𝑖 + . ..................................................................... (9) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 134 figure 4. topsis flowchart result and evaluation based on the ranking results from the three methods carried out, this stage will combine all the results into one table, and an evaluation will be carried out regarding which method and which results will be a reference for pt. inipasti communika. results and discussion simple additive weighting (saw) based on the research methods that have been carried out, the following is the result of the match rating of each alternative: table 4. result of match rating of each alternative value of saw alternative c1 c2 c3 c4 c5 a1 a2 a3 a4 a5 a6 a7 30 20 30 10 30 30 20 30 40 20 30 30 20 40 30 20 40 30 20 40 20 20 10 20 10 20 20 10 20 30 40 10 30 20 40 after analyzing the suitability of each alternative, then calculate and make a matrix of its normalization. table 5. normalization matrix of saw alternative c1 c2 c3 c4 c5 a1 a2 a3 a4 a5 a6 a7 1 0.66 1 0.33 1 1 0.66 0.75 1 0.5 0.75 0.75 0.5 1 0.66 1 0.5 0.66 1 0.5 1 1 0.5 1 0.5 1 1 0.5 0.5 0.33 0.25 0.1 0.33 0.5 0.25 after creating the normalization matrix, it then determines the preferences of each alternative shown in the following table: table 6. preference for each alternative of saw alternative total a1 a2 a3 a4 a5 a6 a7 83.33333333 80 71.25 57.5 89.16666667 72.5 79.58333333 based on the preference results of each alternative above, an alternative with the highest value, namely the a5 alternative, is obtained. weighted product (wp) the following is a table of normalized weight values: table 7. normalization of weight values criteria total c1 c2 c3 c4 c5 0.35 0.30 0.20 0.10 0.5 total 1 jurnal riset informatika vol. 5, no. 1 december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 135 after normalizing the value of the criterion weight, next, calculate the vector s. table 8 is the result of the value of the vector s: table 8. vector value s of wp s total s1 s2 s3 s4 s5 s6 s7 25.37674499 25.17092464 17.65389941 17.22810213 29.40334233 20.61250884 25.83518789 total 161.2807102 after determining the value and the total number of s values, next determine the vector value v, whose results are shown in the following table 9: table 9. vector value v of wp v values of v v1 v2 v3 v4 v5 v6 v7 0.1573451962 0.1560690339 0.1094606998 0.1068205994 0.1823115876 0.1278051715 0.1601877116 total 1 the results of the vector value v above show the highest value in v5, where v5 represents the alternative a5 with the highest value. technique for order preference by similarity to ideal solution (topsis) based on the value of the criterion weight and alternative values in the research method chapter, the following is the result of the calculation of the divisor value and the calculation of the normalized matrix: table 10. divisor value for normalization criteria divisor c1 c2 c3 c4 c5 131.2135664 2176.311559 5.385164807 4.358898944 51.88448708 the divisor value in table 10 above is the result of calculation by summing each alternative value on a criterion that is ranked two, then the result of the summation is rooted. after performing the divisor calculation, calculate the normalization matrix against the alternate data by dividing the alternate data by the divider. here is table 11 the results: table 11. topsis normalization matrix table alternative c1 c2 c3 c4 c5 a1 a2 a3 a4 a5 a6 a7 0.4267 0.2743 0.4801 0.1829 0.3963 0.4572 0.3353 0.3510 0.5155 0.1571 0.2977 0.4521 0.2288 0.4907 0.3713 0.1856 0.5570 0.3713 0.1856 0.5570 0.1856 0.4588 0.2294 0.4588 0.2294 0.4588 0.4588 0.2294 0.1349 0.2891 0.6938 0.077 0.3083 0.2120 0.5203 divisor 131.21 2176.3 5.3851 4.3588 51.884 next, create a weighted normalization matrix with the results shown in table 12 as follows: table 12. weighted normalization matrix alternative c1 c2 c3 c4 c5 a1 a2 a3 a4 a5 a6 a7 14.937 9.6010 16.804 6.4010 13.870 16.004 11.736 10.531 15.464 4.7140 8.9325 13.564 6.8648 14.722 7.4278 3.713 11.141 7.4278 3.7139 11.144 3.7139 4.5883 2.2941 4.5883 2.2941 4.5883 4.5883 2.2941 0.6745 1.4455 3.4692 0.3854 1.5148 1.0600 2.6019 after carrying out the weighted stages of normalization, it follows to determine the ideal positive and negative solutions with the results spelled out in the following table 13: table 13. positive and negative ideal solution alternative c1 c2 c3 c4 c5 positive 16.804 15.464 3.7139 4.5883 0.3854 negative 6.4017 4.7143 11.141 2.2941 3.4692 based on table 13, the ideal solution on the positive row is calculated by selecting the most significant value of the weighted normalization value on the weighted criteria while the lowest value on the cost attribute. the damaging row is calculated by selecting the smallest value from the weighted normalization value on the criteria that are attributed benefit. in contrast, in the cost attribute, the most significant value is selected on the weighted normalization value on the criteria. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 136 table 14 calculates the distance between the weighted values on each alternative to the positive and negative ideal solutions. table 14. alternative weighted value distance to the ideal solution alternative d+ d a1 a2 a3 a4 a5 a6 a7 6.4588 7.6325 13.427 13.037 3.6831 11.413 6.0345 11.557 13.605 10.652 6.4105 14.080 10.387 13.584 furthermore, it calculates the value of the preference for each alternative. table 15. preference value alternative d+ a1 a2 a3 a4 a5 a6 a7 0.6414 0.6406 0.4423 0.3296 0.7926 0.4764 0.6924 based on the results of the preference values from table 15 above, it can be seen that the alternative with the highest value is the a5 alternative. result and evaluation after carrying out a whole series of stages on the three methods using simple additive weighting (saw), weighted product (wp), and technique for order preference by similarity to ideal solution (topsis), it can be concluded that the three methods provide the same decision regarding the alternatives that get the highest score. here is a comparison table 16 of rankings using these three methods: table 16. ranking comparison alternative saw wp topsis a1 a2 a3 a4 a5 a6 a7 2 3 6 7 1 5 4 3 4 6 7 1 5 2 3 4 6 7 1 5 2 based on the comparison of rankings using the three methods above, it can be seen that the three methods give the highest value decision to the same alternative, a5, as the alternative with the highest value produced by the three methods. using the wp and topsis methods, the resulting ranking is the same. however, it is different from using the saw method, where there are differences in the characteristics of alternatives a1, a2, and a7 to the wp and topsis methods. conclusions and suggestions conclusion from the research conducted and the results obtained, the three methods carried out succeeded in providing the results of the same decision regarding the expectations of pt. inipasti communika and researchers that is to get one of the best alternatives based on the three methods, so the difficulty of pt. inipasti communika in assessing their journalists to be resolved using the methods carried out. although the three methods do not succeed in providing 100% of the same ranking, using these three methods can be a reference and add convenience in deciding on pt. inipasti communika in measuring the performance of their journalists. suggestion based on this study, although the three methods do not provide the same ranking results, this study suggests continuing to use these three methods so that the results can be compared as in this study, then choosing the final decision by looking at the most significant number of methods with the results of the ranking the same as the final result of the decision. references aisyah, n., & putra, a. s. 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(2018). sistem pendukung keputusan penerimaan anggota baru pecinta alam menggunakan metode simple additive weighting (saw) (studi kasus sma negeri 2 subang). jurnal teknologi informasi dan komunikasi stmik subang, 11 (2), 132-146. https://jurnalstmiksubang.ac.id/index.php/jt ik/article/view/133 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.493 jurnal riset informatika vol. 5, no. 1 december 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 138 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 269 k-means binary search centroid with dynamic cluster for java island health clustering muhammad andryan wahyu saputra-1*, muhammad faisal-2, ririen kusumawati-3 magister informatics uin maulana malik ibrahim malang, indonesia 210605210010@student.uin-malang.ac.id1, mfaisal@ti.uin-malang.ac.id2, ri2n.kusumawati@gmail.com3 (*) corresponding author abstract this study is focused on determining the health status of each district/city in java using the k-means binary search centroid and dynamic kmeans algorithms. the research data uses data on the health profile of java island in 2020. comparative algorithms were tested using the davies bound index and calinski-harabasz index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of dbi 1.8175 and chi 67.7868. overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. the test results can be used as one of the best methods in evaluating the level of health in the java island area and a reference for decision-making in determining policies for related agencies. keywords: clustering; binary search centroid; dynamic k-means; java island health profile abstrak penelitian ini difokuskan untuk menentukan derajat kesehatan setiap kabupaten/kota dalam pulau jawa menggunakan algoritma k-means binary search centroid (kbsc) dan dynamic kmeans (dk). data penelitian menggunakan data profil kesehatan pulau jawa tahun 2020. perbandingan algoritma terbaik diuji tingkat validitasnya menggunakan metode davies bound index (dbi) dan calinski-harabasz index (chi) pada algoritma k-means tradisional dan k-means dinamis binary search centroid. berdasarkan pengujian diperoleh 5 jumlah klaster di wilayah sebaran, di antaranya klaster 1 dengan kualitas kesehatan sangat tinggi adalah 11 kabupaten/kota, klaster 2 dengan kualitas kesehatan tinggi adalah 24 kabupaten/kota, dan klaster 3 dengan kualitas kesehatan cukup adalah 28 kabupaten/kota. dan kualitas kesehatan 45 kabupaten/kota untuk klaster 4 dengan kualitas kesehatan rendah serta klaster 5 dengan kualitas kesehatan sangat rendah adalah 11 kabupaten/kota, dengan nilai validasi terbaik dbi 1.8175 dan chi 67.7868. secara keseluruhan optimasi algoritma k-means dinamis berbasis binary search centroid menghasilkan rata-rata kualitas klaster yang lebih baik dan jumlah iterasi yang lebih kecil dibanding algoritma k-means tradisional. hasil pengujian dapat digunakan sebagai salah satu metode terbaik dalam evaluasi tingkat kesehatan yang ada di wilayah khususnya pulau jawa serta acuan pengambilan keputusan dalam menentukan kebijakan bagi instansi terkait. kata kunci: klastering; binary search centroid; dynamic k-means; profil kesehatan pulau jawa introduction indicators of the success of a country's development can be seen from the level of achievement of the country in providing health insurance. in indonesia, the government has set several indicators through the ministry of health that become a benchmark for the progress of health development at all levels of indonesian territorial units (from the province, district, to sub-district). the ministry of health collects and processes population health data annually through health offices across various regions to produce provincial and district/city health rankings. these p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 270 processed results and rankings become essential references. for example, local governments (pemda) formulate intervention plans that are more targeted as promotional materials so that local governments are more motivated to improve their regional health rankings, as the basis for the central government to formulate seriousness or especially for health problem areas (dbkbk), as the basis for determining the allocation of central government health funding to the regions also serves to support the ministry of vulnerable regions (kmpdt) in regional/urban development (l. suryani, 2021). regional clustering is carried out to determine the region's health level, especially java island. the central bureau of statistics notes that java island has 29 regencies and nine cities. so far, the grouping of environmental health indicator data is still based on manual computing techniques, where the calculations still have several problems, especially in data consistency. in terms of improving the health services of the health office, a system is needed that groups healthy areas based on environmental health data so that counselling, services, and assistance can be more accurate and on target. at the time of the condition where the regional grouping data do not want to be determined as needed, but want to produce segmentation from the distribution of the data, then to determine the assumption of the number of clusters it is not expected at the beginning, but the assumption of the number of clusters that will be formed must be sought. to handle this condition, there is the development of the k-means algorithm, namely the dynamic kmeans (dk) algorithm, which has a process to find the number of clusters without having to assume the assumption of the number of clusters (h. santoso et al., 2022). in the research, widiarina and romi proposed a dynamic cluster algorithm on the k-means algorithm to determine the number of clusters (k) to produce optimal cluster quality and provide better and more precise potential segmentation results (w. widiarina & r. s. wahono, 2015). however, this algorithm has some of the same shortcomings as the k-means algorithm, namely the problem of determining the centroid point in the clustering process, which is still randomly selected. based on research from yugar kumar and g. sahoo, the problem of determining the centroid point is done by developing k-means into k-means binary search centroid (kbsc), which has a process in determining the centroid point using a binary search technique approach (akbari gumilar & yusrila kerlooza, 2018). the results of this study prove that the k-means binary search centroid (kbsc) algorithm has better intraand inter-cluster values than the k-means algorithm. however, the k-means binary search centroid (kbsc) algorithm has limitations in determining the number of clusters to form. therefore, it is proposed that combining the dynamic kmeans algorithm with the k-means binary search centroid algorithm can complement the clustering process in the area of assuming the number of clusters and determining the cluster centroid point, to produce the best davies-bouldin index value for case studies of regional clustering based on profiles. health. research methods in carrying out research, a research method is needed to increase the possibility of a research activity running smoothly. the research method acts as a framework for carrying out research so that by following this framework, research can run systematically, and it is hoped that research can be more effective and in a reasonable period (c. kamila et al., 2021). the following is the framework of this research shown in figure 1. figure 1. research flow design jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 271 a. data collection stage data collection for this study cites publications from the central statistics agency and the java island health service. this study uses data from 2020. this study will use datasets from 29 districts and nine cities on the island of java. the variables used in this study are (dinkes & bps pulau jawa, 2020) :  life expectancy  health center ratio  hospital ratio  clinic ratio  percentage of eligible sanitation rt  percentage of drinking water facilities  percentage of babies with lbw  complete basic immunization percentage  percentage of babies who get exclusive breastfeeding  percentage of toddlers with malnutrition  percentage of tuberculosis treatment success  diarrhea pain rate (per 1k population)  covid-19 healing rate  covid-19 death rate  percentage of the population who smokes  percentage of services for diabetes mellitus patients  percentage of public places meet health requirements  b. data pre-processing stage this stage consists of a data selection stage: selecting data from a set of operational data for extracting information, such as summarizing data for each city for each aspect. then perform data cleaning, namely data containing data, removing data duplication, checking inconsistent data, and correcting errors in data. after that, normalize the data, which is the process of making several variables have the same value range, none of which is too big or too small so that it can make statistical analysis easier (c. satria and a. anggrawan, 2021). c. clustering stage in this study, we will combine the dk and kbsc algorithms with the advantages of each algorithm to find the best number of clusters and determine the initial centroid point for each formation of the number of clusters. the combination of the two algorithms is shown in figure 2. figure 2. the flow of merging the two algorithms figure 2 describes the merging flow of the two algorithms. flow chart. this centroid point will use a data search technique with a binary search approach (k. ariasa, 2020). the process will divide the data area based on the number of clusters entered. the following is an explanation of the flowchart flow: 1. enter the number of clusters as much as k (supriyatna et al., 2020). 2. calculate the maximum and minimum values in the data for each data attribute. 3. calculate the range between the centroid points, calculate the range between the centroid points in data in the following way: 𝑀 = max(𝑎𝑖)−𝑚𝑖𝑛(𝑎𝑖) 𝑘 ............................................................ (1) 4. based on formula (1), it is used to calculate a value for the variable m which is the specific distance between the data centroid points before giving the results of the data centroid points. 𝑚𝑖𝑛(𝑎𝑖 ) the minimum value of each data attribute, 𝑚𝑎𝑥(𝑎𝑖 ) is the maximum value of each data attribute, and k is the number of clusters to be formed. 5. determine the data centroid point. to generate the centroid point, use the following formula: 𝐶𝑘 = 𝑚𝑖𝑛(𝑎𝑖 ) + (𝑘 − 1)𝑀 (2) p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 272 6. based on formula (2), ck k is the centroid for cluster k, 𝑚𝑖𝑛(𝑎𝑖 ) the minimum data value, and m range between data centroid points. 7. group all data or objects with similarities by calculating the distance to the nearest centroid point. this distance is defined as the similarity of a data object with a predetermined centroid point. the proximity of two objects is determined based on the calculation of the distance between the objects. the distance theory can be used to calculate the distance of all data to each cluster centroid point. euclidean distance coordinates are formulated as follows (bagus muhammad et al., 2021) : 𝐷(𝑖, 𝑗) = √(𝑥1𝑖 − 𝑥1𝑗 ) 2 + (𝑥 − 𝑥2𝑗 ) 2 + ⋯ + (𝑥𝑛𝑖 − 𝑥𝑛𝑗 ) 2 ...... (3) 8. equation (3) explains that 𝐷(𝑖, 𝑗) is the distance of the data to the center of clusters, then 𝑥𝑛𝑖 is the data to the nth attribute of data and 𝑥𝑛𝑗 is the center point or centroid of the ken attribute. in the k-means dynamic cluster algorithm, intercluster is the minimum distance between the centroid points or the cluster's center with other cluster centers. this inter is usually used to measure the minimum level of similarity in other clusters or the separation between clusters shown in the equation (c. sreedhar et al., 2017). 𝑖𝑛𝑡𝑒𝑟 = 𝑚𝑖𝑛(∑𝑁−1𝑖=0,𝑗=0 )𝑑𝑖𝑠𝑡 (𝑥𝑘 , 𝑥𝑗 ) ∀𝑘 = 1,2, … , 𝑘 − 1 𝑎𝑛𝑑 𝑘 = 𝑘 + 1, … , 𝑘𝑛 ............................ (4) 9. equation (4) explains that 𝑥𝑘 is the distance to the center of the previous cluster, while 𝑥𝑗 is the distance to the center of the next cluster, and k is the distance to the cluster's center. next, in the dynamic cluster algorithm on k-means, there is also an intra-term: the distance between the data and the centroid point in a cluster. intra is used to measure a cluster's maximum similarity level, which is indicated by equation (5) (r. m. alguliyev et al., 2019). √ 1 𝑛−1 ∑ (𝑥𝑖 , 𝑥𝑚 ) 2𝑛 𝑖−1 .......................................................... (5) 10. equation (5) explains that 𝑛 is the amount of data, 𝑥𝑖 is data, and 𝑥𝑚 is the centroid d. analysis stage the analysis was carried out since the determination of the data, the pre-processing process, and the implementation of the algorithm calculations. the evaluation of clustering was tested for validity using several methods, namely cluster variance (v), davies bound index (dbi), and calinski-harabasz index (chi). the equation used by the davies-bouldin index will produce the most optimum value, namely the smallest value produced by equations (6) and (7) (y. a. wijaya et al., 2021). 𝐷𝐵𝐼 = 1 𝑘 ∑ 𝑚𝑎𝑥𝑗≠1{𝐷𝑖,𝑗 } 𝑘 𝑖=1 .......................................... (6) di,j = d̅i+d̅j di,j ........................................................................... (7) based on equations (6) and (7), dbi is the value of the davies-bouldin index, k is the number of clusters, 𝐷𝑖,𝑗 is the value of the distance between clusters 𝑖 and 𝑗. 𝑑𝑖 and 𝑑j are the average distance between the data in the temporary cluster 𝑖 and the cluster 𝑗. the smaller the davies bouldin index (dbi) value obtained (non-negative >= 0), the better the cluster obtained from grouping using the clustering algorithm. the calinski-harabasz (ch) validity index calculates the comparison between the sum of squares. values between clusters (ssb) as separation and the sum of square within-cluster (ssw) value as compactness multiplied by the normalization factor, which is the difference between the number of data and the number of clusters divided by the number of clusters minus one. the more excellent ch value indicates the number of the best clusters [(luthfi et al., 2021). suppose there is a data set with k clusters and n points data. for example 𝐶𝑖 is the l-th cluster where 𝑥𝑖 is the i-th point in the l-th cluster, 𝑁𝑙 is the number of points in the l-cluster, and �̅�𝑖 if the center point of the l-cluster, then the calculation of the ch validity index can be seen in the formula (8), (9), and (10) (a. f. khairati et al., 2019). 𝑆𝑆𝑊 = ∑ ∑ (𝑥𝑖 − �̅�𝑖 )(𝑥𝑖 − �̅�𝑖 ) 𝑇 𝑥𝑖∈𝐶𝑖 𝑘 𝑖=1 ................... (8) 𝑆𝑆𝐵 = ∑ 𝑁𝑖 (𝑥𝑖 − �̅�𝑖 )(𝑥𝑖 − �̅�𝑖 ) 𝑇𝑘 𝑖=1 ............................ (9) 𝐶𝐻 = 𝑡𝑟𝑎𝑐𝑒 (𝑆𝑆𝐵) 𝑡𝑟𝑎𝑐𝑒 (𝑆𝑆𝑊) × 𝑁−𝑘 𝑘−1 .............................................. (10) to determine the evaluation of the model used, several experimental scenarios were carried out to determine the most suitable and accurate algorithm for evaluating algorithm performance. after all cluster results are formed, the algorithms are compared, and conclusions are drawn about which algorithms work optimally, the best algorithm accuracy, and the most optimal number of clusters based on the test data criteria. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 273 results and discussion a. result in knowing the comparison of algorithm performance, the test scenarios are carried out alternately. the traditional k-means test was carried out with clusters ranging from 3 to 6. meanwhile, the dynamic k-means algorithm with binary search centroid uses the results of the ideal cluster number based on intra and inter-cluster calculations of each dynamic cluster algorithm. the assessment of the final evaluation results is determined by the acquisition of experiments that have been carried out previously, then comparing 3 to 6 the number of clusters formed. figure 3 and figure 4 are graphs evaluating the dbi and chi values from testing the two algorithms. figure 3. comparison of the average dbi values of the two algorithms overall the results of the dbi evaluation obtained the best value on the dynamic k-means algorithm with a dbi value of 1.81748915. the addition of the number of clusters affects the results of the dbi evaluation, where the decrease in the value of dbi on the dynamic cluster algorithm is better. it is known that the optimal cluster results with the smallest dbi value or close to the value (non-negative >= 0) indicate the cluster is getting better (siagian et al., 2022). figure 4. comparison of the average calinskiharabasz index (chi) values of the two algorithms in the evaluation test using the calinskiharabasz index (chi), the dynamic k-means algorithm with binary search centroid performs better than the traditional k-means algorithm. this can be seen from the acquisition of the chi value with the best number of clusters indicated by the more excellent chi value (harani et al., 2020). based on figure 4, the calinski-harabasz index (chi) evaluation value, the traditional k-means algorithm, is unstable for each number of clusters formed. based on the evaluation of dbi and chi, it is clear that the change in the value of each algorithm depends on the initiation of the centroid in determining the initial cluster used. the dynamic kmeans algorithm with binary search centroid has the advantage of constant iterations in each test. so that every time the data is grouped, the dynamic kmeans method with binary search centroid always produces the same cluster. initialization of the initial cluster center point value randomly causes the traditional k-means algorithm to obtain different validation values and the number of clusters in each test, making it very difficult to obtain individual initial cluster results. the test results do not necessarily represent a good cluster. as a result, the obtained centroid is not a centroid that is dominated by the grouping of health indicators that have more similarities. the iteration process after the initial cluster generation will produce an inaccurate centroid. determining initial cluster initiation dramatically affects the number of iterations of the clustering algorithm, and the estimated time is not constant. based on the test results, the dynamic k-means algorithm with binary search centroid produces better cluster quality than traditional k-means. to find out the best number of clusters in the dynamic kmeans algorithm with cluster initialization, testing is carried out on the level of validity of each number of clusters. table 1 is a comparison of each evaluation in each number of clusters. table 1. validation value of dynamic k-means cluster k-means with binary search centroid number of clusters vw vb dbi chi 3 3.7282 2.8909 2.8852 44.9747 4 3.6566 4.2032 2.0006 62.6688 5 3.4085 4.0360 1.8174 67.7868 6 3.7151 4.5564 2.1475 69.2945 based on table 1, the vw value of the number of 4 clusters is smaller than the number of clusters 3, while the vb value of the number of clusters 4 is greater than the vb value of the number of clusters 3 4 5 6 traditional k-means 43,45363553 52,86908894 59,75413653 63,48475436 dynamic k-means algorithm with binary search centroid 44,97477344 62,66889519 67,78687766 69,2945078 0 10 20 30 40 50 60 70 80 c h i v a lu e 3 4 5 6 traditional k-means 2,636498 2,329357 2,080675 2,334418 dynamic k-means algorithm with binary search centroid 2,88525484 2,00064044 1,81748915 2,14750858 0 0,5 1 1,5 2 2,5 3 3,5 d b i v a lu e p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 274 3. this causes the dk-kbsc algorithm to add one more cluster to cluster 5 according to the results of the cluster evaluation. when viewed from the dbi and chi values, the dynamic k-means algorithm with binary search centroid has several clusters 5, which is better than the number of clusters 3, 4, and 6. b. analysis based on the results of clustering of java island health profile data in 2020 using the dynamic kmeans algorithm with mean-based initial cluster initialization, the results obtained are 5 clusters in the distribution area, of which cluster 1 with very high health quality is 11 districts/cities, cluster 2 with high health quality is 28 regencies/cities, and cluster 3 with adequate health quality is 24 regencies or cities. moreover, the health quality of 45 districts/cities for cluster 4 with low health quality and cluster 5 with poor health quality in 11 districts or cities. the percentage distribution of clusters is shown in figure 5. figure 5. percentage of dynamic k-means clustering results with binary search centroid the result of the dynamic k-means clustering area distribution with binary search centroid from health profile data is shown in table 2. the clustering results can then be used as a reference to determine the distribution of regional health levels and as evaluation material by the relevant agencies. table 2. clustering results cluster regency/city cluster 1 sukabumi city cirebon city tangerang city cilegon city south tangerang city central jakarta city north jakarta city west jakarta city south jakarta city east jakarta city seribu islands regency cluster 2 pacitan regency bogor regency sukabumi regency cianjur regency bandung regency garut regency tasikmalaya regency ciamis regency kuningan regency cirebon regency majalengka regency sumedang regency indramayu regency subang regency purwakarta regency karawang regency pangandaran regency bogor city bandung city bekasi city depok city cimahi city tasikmalaya city lebak regency pandeglang regency serang regency tangerang regency serang city cluster 3 kediri regency lumajang regency jember regency banyuwangi regency bondowoso regency situbondo regency probolinggo regency pasuruan regency jombang regency nganjuk regency madiun regency tuban regency bangkalan regency sampang regency pamekasan regency kediri city malang city probolinggo city pasuruan city grobogan regency rembang regency pekalongan regency brebes regency banjar city cluster 4 ponorogo regency boyolali regency 9% 24% 20% 38% 9% cluster percentage cluster 1 cluster 2 cluster 3 cluster 4 cluster 5 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.511 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 275 cluster regency/city trenggalek regency tulungagung regency blitar regency malang regency sidoarjo regency mojokerto regency magetan regency ngawi regency bojonegoro regency lamongan regency gresik regency sumenep regency surabaya city batu city cilacap regency banyumas regency purbalingga regency banjarnegara regency kebumen regency purworejo regency wonosobo regency magelang regency klaten regency sukoharjo regency wonogiri regency karanganyar regency sragen regency blora regency pati regency holy regency jepara regency demak regency semarang regency temanggung regency kendal regency batang regency pemalang regency tegal regency surakarta city salatiga city semarang city pekalongan city tegal city cluster 5 blitar city mojokerto city madiun city magelang city kuningan regency bekasi regency kulon progo regency bantul regency gunung kidul regency sleman regency yogyakarta city conclusions the dynamic cluster algorithm on kmeans with binary search centroid initiation produces a better average cluster quality than the traditional kmeans algorithm in evaluating the davies bound index (dbi) and calinski-harabasz index (chi). the algorithm's success can be seen from the resulting 5 clusters with the smallest vw value of 3.408591352 compared to the traditional k-means algorithm. the advantage of the dynamic k-means algorithm with binary search centroid initiation is its constant number of iterations in each test. so that every time the data is grouped, the dynamic kmeans method with binary search centroid initiation always produces the same cluster. meanwhile, the traditional k-means algorithm obtains different validation values and the number of clusters in each test because the initialization of the initial cluster center point value is carried out randomly, so obtaining individual initial cluster results and a small number of iterations is challenging. the number of clusters affects the dbi and chi values. if the number of clusters increases, the chi value will increase. evaluation of the validity of the calinski-harabasz index (chi) the more excellent chi value indicates the best value. the more the number of clusters and the data used, the better the quality of the cluster or grouping. adding the number of clusters affects the results of the dbi evaluation in a better direction. although other algorithms have improved clustering results, the decrease in dbi in the traditional k-means algorithm has not been significant. references a. f. khairati, a. a. adlina, g. f. hertono, and b. d. handari, “kajian indeks validitas pada algoritma k-means enhanced dan k-means mmca,” prisma, prosiding seminar nasional matematika, vol. 2, pp. 161–170, 2019. akbari gumilar and yusrila kerlooza. “peningkatan hasil cluster menggunakan algoritma dynamic k-means dan k-means binary search centroid.” jurnal tata kelola dan kerangka kerja teknologi informasi, vol. 4, no. 2, pp. 25-33, 2018. badan pusat statistik pulau jawa, “statistik kesehatan pulau jawa 2020”, available: https://jatim.bps.go.id/publication/2021/08 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tarihoran, d. rusmana, and r. rahim, “davies bouldin index algorithm for optimizing clustering case studies mapping school facilities,” tem journal, vol. 10, no. 3, pp. 1099–1103, 2021. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 537 pregnancy risk level classification using the crisp-dm method reka dwi syaputra-1*), achmad solichin-2 master of computer science, universitas budi luhur jakarta, indonesia https://www.budiluhur.ac.id/ 1*)2011600075@student.budiluhur.ac.id, 2achmad.solichin@budiluhur.ac.id (*) corresponding author abstract independent midwife practices have the task of reminding and maintaining the quality of standardized reproductive health services for pregnant women. independent midwife practices have had patient visits since the covid-19 pandemic from 2020 to 2021, especially at the yetti puranama midwife, which consists of 320 pregnancy examinations, 130 delivery care, and 50 referrals. the covid-19 pandemic has impacted maternal mortality rates because there are still many restrictions on all services. maternal health services include pregnant women who are routinely unable to go to the puskesmas or other healthcare facilities due to fear of contracting covid-19, which delays the examination of pregnancy gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decisions. so that the problem that occurs is an increase in the risk of pregnancy, resulting in death and increased maternal mortality. in solving this problem, the research takes a machine-learning approach. the research aims to build a classification of pregnancy risk levels that can predict early treatment in this study using the random forest method with cross-validation 2. this study obtained the results of an accuracy value of 98%, precision of 94%, and recalled 100% in the random forest method. keywords: pregnancy risk level, classification, decision tree, random forest. abstrak praktik bidan mandiri memiliki tugas mengingatkan dan mempertahankan kualitas pelayanan kesehatan reproduksi terstandar pada ibu hamil. praktik bidan mandiri memiliki kunjungan pasien sejak pandemi covid 19 dari tahun 2020 sampai 2021 khususnya di bidan yetti puranama yang terdiri dari pemeriksaan kehamilan 320 orang, asuhan persalinan 130 orang dan rujukan 50 orang. masa pandemi covid-19 memberikan dampak angka kematian ibu meningkat disebabkan masih banyak pembatasan ke semua layanan. pelayanan kesehatan ibu seperti ibu hamil yang menjadi rutinitas tidak dapat ke puskesmas atau fasilitas pelayanan kesehatan lainnya disebabkan takut tertular covid-19. sehingga menundakan pemeriksaan kehamilan gravida, abortus, suhu, jarak kehamilan, hemoglobin, tekanan darah, berat badan ideal dan keputusan. sehingga permasalahan yang terjadinya terdapat peningkatan tingkat risiko kehamilan berdampak kematian dan meningkatnya angka kematian ibu. dalam memecahkan masalah ini, penelitian melakukan cara pendekatan dengan machine learning. tujuan penelitian untuk membangun klasifikasi tingkat risiko kehamilan yang dapat memprediksi penanganan secara dini. pada penelitian ini menggunakan metode random forest dengan cross-validation 2. penelitian ini mendapatkan hasil nilai akurasi 98%, precision 94% dan recall 100% pada metode random forest. kata kunci: tingkat risiko kehamilan, klasifikasi, pohon keputusan, random forest. introduction from 2007-2020, the coverage of k4 pregnant women's health services tended to increase. however, there was a decrease in 2020, from 88.55% to 84.60%. this decline is assumed to occur due to program implementation in areas affected by the covid-19 pandemic (kementerian kesehatan ri, 2021). health services for pregnant women (k4) in 2020 in bengkulu province by 87% (badan pusat statistik provinsi bengkulu, 2020). compared to 2021, the coverage of k4 pregnant women's health services tends to fluctuate. in 2021 the k4 rate was 88.8%, which is an increase p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 538 compared to the previous year. new adaptations can influence the increase in k4 coverage of the covid-19 situation in 2021 (ri, 2021). it is because in the previous year, 2020, there were still many restrictions on all routine services, including maternal health services, such as pregnant women who could not go to the health centre or other healthcare facilities for fear of contracting covid-19. thus delaying pregnancy checks and service uncertainty due to a lack of human resources, infrastructure, and personal protective equipment (ppe). health services for pregnant women (k4) in 2021 in the province of bengkulu are 89% (kemenkes ri, 2021). it is because in the previous year, 2020, there were still many restrictions on all routine services, including maternal health services, such as pregnant women who could not go to the health centre or other healthcare facilities for fear of contracting covid-19. so that it delays pregnancy checks and service uncertainty in terms of personnel, infrastructure, and personal protective equipment (ppe). health services for pregnant women (k4) in 2021 in the province of bengkulu are 89% (badan pusat statistik provinsi bengkulu, 2020). while in 2021, the maternal mortality rate increased by 50 maternal deaths, with the cause of 22 pregnant deaths (44%), 11 maternity deaths (22%), and 17 postpartum deaths (34%) (bengkulu, 2021). independent midwife practices have the task of improving and maintaining the quality of reproductive health services for pregnant women, especially in the independent midwife practice of yetti purnama, m.keb, which locate in tanah patah village, ratu agung sub-district, bengkulu city. based on reports from independent midwife practices before the outbreak of the covid-19 pandemic, health services in 1 year could have approximately 650 pregnant women patient visits. when the covid-19 pandemic occurred, maternity health services declined from the beginning of 2020 to the end of 2021. there were 500 pregnant women patients with 320 pregnancy examinations, 130 delivery care and 50 referrals. the problem faced during the covid-19 pandemic is the low level of pregnancy health services. many pregnant patients do not come directly to the independent midwife practice. so there is an increase in pregnancy risk, impacting death and maternal mortality. in solving this problem, researchers took a machine-learning approach. machine learning in understanding how it works aims to make computers learn data. at the same time, data mining plans to extract information from large amounts of data (putra, 2020). this research predicts the risk level by classifying the data set to determine whether it is risky. then collected to build a machine learning model with a supervised learning algorithm model. this prediction occurs in a data set using the random forest method. the research entitled predicting the possibility of diabetes (pavlov, 2018) in the early stages uses a random forest classification algorithm which produces an accuracy value of 97.88% by comparing three machine learning classification algorithms, namely support vector machine. naive bayes and random forest using receiver operating characteristic (roc) curve comparison (apriliah et al., 2021). from the citations of previous research, this research generally has similarities and differences with previous research. in the similarity of this research aims to detect disease early by using the random forest method. also, the differences are the data and the object of research. this research focuses on pregnancy examination data, namely gravida, abortion, temperature, gestational distance, haemoglobin, blood pressure, ideal weight, and decisions so that the research aims to build a classification of pregnancy risk levels using the random forest method to detect early examination by providing an accurate level of risk and no risk during the covid-19 pandemic. this reason makes the design of a random forest method that can predict early detection and provide an accuracy level value, namely risk and not risk. in pregnancy, using the random forest method. with the division of datasets, namely the ratio of training 80% and testing 20% using crossvalidation 2. using the random forest method will get good accuracy results. by paying attention to the results of previous studies, it shows that the random forest method has classification results in a good category. table 1 shows the previous related studies. based on previous literature for solving existing problem concepts is shown in figure 1. research methods this research uses the crisp-dm method (cross-industry standard process for data mining), as seen in figure 2. the stages refer to research entitled classification of pregnancy risk levels using the random forest method. the crsip-dm (crossindustry standard process for data mining) method contains stages carried out sequentially or in a hierarchical process, namely business understanding, data understanding, data prepreparation, modelling, evaluation, and deployment (mulaab, 2017). jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 539 table 1. literature review research no. main objectives method results 1 predicting the likelihood of diabetes (apriliah et al., 2021) support vector machine, naive bayes and random forest using three algorithms obtained good accuracy value results, namely random forest with an accuracy value of 97%. 2 random forest optimization for bank marketing data classification (religia et al., 2021) using random forest optimization with bagging and generic algorithm random forest with 88.30% accuracy 3 detection of patients with diabetes (suryanegara et al., 2021) metode normalization (minmax normalization-rf), model ke 2 (z-score normalization-rf) dan model ke 3 (normalizationrf) (min-max normalizationrf) of 95.45%, model 2 (zscore normalization-rf) of 95%, and model 3 (normalization-rf) of 92%. these results show that model 1 (min-max normalization-rf) is better. 4 clustering the risk level of pregnant women (akbar et al., 2021) k-means method k-means prediction can be modelled on clustering and is categorical. 5 classification of preeclampsia status of pregnant women (kustiyahningsih et al., 2020) k-fold cross validation and id3 algorithm as data clustering k-fold cross validation and id3 algorithm as accuracy data clustering using a confusion matrix 6 pregnancy health diagnosis (hikmatulloh et al., 2019) id3 algorithm this method can produce an accuracy of 80.33% 7 pso optimization to improve the accuracy of the id3 algorithm for pregnant women's disease prediction (byna, 2019) id3 algorithm the id3 classification model produces good accuracy with pso, improving this study's accuracy. 8 detection of pregnancy risk level (wulandari & susanto, 2018) fuzzy mamdani method and simple additive weighting got 88% accuracy using the recognition rate model 9 comparison of random forest and svm classification methods (adrian et al., 2021) random forest and svm methods two data sets will be compared with the first svm algorithm and random forest to get the most accurate sentiment analysis results. 10 classification of human activities (aziz, 2021) metode ensemble stacking ensemble the stacking method as classification. compared to the svm method for accuracy, sensitivity, and specificity. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 540 figure 1. conceptual framework figure 2. crisp-dm model (mulaab, 2017) the stages of the crisp-dm concept are as follows: 1. business understanding this stage is a research object place for initial data collection. 2. data understanding the stages of the data collection process are to build the random forest method and describe the data. the data used is nominal, namely data in the form of categories. 3. data pre-preparation data preparation stage as input to analyze the random forest algorithm. data used in the objective examination. so it uses data processing techniques to overcome data problems and get valid data. all of that uses in data mining techniques. 4. modelling the stage of forming a classification method to predict the category in one object class data, namely risk or no risk. in classifying, research uses the main method in classification, namely the random forest method. at the same time, the decision tree method compares good accuracy rates. the data division of this method has a ratio of 80% as training data and 20% as testing data with a cross-validation pattern of 2. so in the modelling stage of understanding classification in general, there is a system evaluation formula in the deployment and prototype. 5. deployment this stage is the final stage in forming or building an application system. this application answers the formulation of the problem of predicting the level of risk of pregnancy. the model obtained using the basic method is random forest by comparing the decision tree method to find a good level of jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 541 accuracy. there are several steps in problemsolving carried out by researchers as follows: 1. literature study a literature study related to this research topic by conducting related literature objectives that discuss theories around pregnancy, midwives, rstudio, data mining, machine learning, decision tree, and random forest. in addition, also a review of previous studies. researchers conducted a literature study stage, namely looking for various study sources in discussing theories around rstudio, data mining, machine learning, decision tree, random forest, and midwives. also, a review of previous research studies of the literature is in table 1. research literature review. 2. data collection the author collects data through secondary data at the research site, while interviews are not for primary subjects. objective examination underlies secondary data collection, namely gravida, abortion, temperature, gestational distance, haemoglobin, blood pressure, ideal weight, and decision. at the same time, interviews were conducted with midwives for researchers to explore pregnancy knowledge. in addition, the process of collecting data by researchers, namely non-participant observation. observation by observing the visit book of pregnancy patients in independent midwife practices. 3. pre-processing data objective examination data classify by variable names sourced from secondary data and interviews to obtain valid data. at the same time, reconstructing data to form a data structure makes it more suitable for using the random forest method. 4. modelling the classification algorithm modelling in this study uses predictive categories, namely decision tree and random forest. in data mining, there is an evaluation to determine the algorithm's accuracy. comparison of these two algorithms to create patterns of train data and test data with crossvalidation 2 to determine the level of good accuracy. 5. model evaluation comparison of two algorithms to find a good level of accuracy with predictive value or accuracy to evaluate the model requires a method to measure model performance in measuring the performance of the classification model parameters, the designer model, and represented using the confusion matrix formula. table 2. confusion matrix class actual: true actual: false prediction: respondent 1 tp (true positive) fp (false positive) prediction: respondent n fn (false negative) tn (true negative) there is the following explanation of the terms tp, tn, fp, and fn in this study: a. true positive (tp) it shows that predicting pregnancy risk level is risky, and it is true that the risk level is a risk. b. false positive (fp) it shows that predicting pregnancy risk level is not risky, but the model that has predicted it is risky. c. false negative (fn) it shows that the pregnancy risk level is risky, but the model predicts it is not a risk. d. true negative (tn) it shows that predicting the risk level of the pregnancy is not risky, and the model created has predicted the risk level of the pregnancy. 6. drawing conclusions data from the model evaluation results to then conclude that use as an accuracy value and model performance, including proving the theory of the research idea. results and discussion in this trial, training models and processes on the rstudio platform with the main libraries, namely caret, reptree, dplyr, devtools, random forest, tree, rpart, rocr, and related to the main software in prototype development, namely the object detection framework visual code, and mysql database. literature study serves as a source of study in discussing the theory surrounding previous research, as explained in table 1. at the same time, interviews with midwives and patients related to this study. the interview with the midwife is to learn the risk of pregnancy with an objective examination, namely through age, gravida, abortion, temperature, pregnancy distance, haemoglobin, blood pressure, ideal weight, and decision. twenty questions will be given to patients through questionnaires so that the research obtains data to select and make decisions, namely risk and no risk. the objective examination results can build a classification of pregnancy risk levels using the random forest method. below are the types of objective examination names in table 3. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 542 table 3. type name objective examination no name description value 1 age patient age number 2 gravida pregnancy number 3 abortus pregnancy miscarriage number 4 temperature body temperature of the patient celius 5 pregnancy spacing gestational age of the expectant mother (in weeks) number (in weeks) 6 haemoglobin haemoglobin level in pregnant women (gr/dl) gr/dl 7 blood pressure pregnant women's blood pressure (systole/diastole) systole/diastole 8 ideal body weight normal weight gain in pregnant women (9-15 kg during pregnancy) kg 9 decision the conclusion from the data obtained the risk or no risk validation of preparation data with an objective examination of 500 patients, from which the collection is done through secondary data, while the interviews were not with the main subject. the initial data that the type of objective examination name has grouped in table 3 forms the initial data type structure. in rstudio, the data type must first be converted into a factor data type to perform cross-validation two so that the data processing process uses the random forest method. in table 4. the initial data type structure has been categorized as providing risk and non-risk decisions (see table 4). table 4. initial testing data type structure no name data type value 1 age char "above 35 years," "18 to 35 years." 2 gravida char "more than 4x" "below 4x" 3 abortus char "above 2x" "below 2x" 4 temperature char "hyperthermy," "normal" 5 pregnancy spacing char "above two years or 24 months," "below two years or 24 months," "first child"v 6 haemoglobin char "normal" "mild anemia" 7 blood pressure char "normal 100-130," "hypothermic below 100," "normal 100-130" 8 ideal body weight char "less than 18kg" 9 decision char the risk or no risk data processing through rstudio uses a ratio of 80% as training data and 20% as testing data. using a cross-validation two pattern that divides into train and test data with a crossvalidation two ratio of 80% and 20%, which results in initial objective testing data that 52 pregnancy patients are at risk and 48 pregnancy patients are not at risk in the initial objective testing data. while in the initial objective training data, there are 192 pregnancy patients at risk and 208 pregnancy patients who are not at risk in the initial objective training data. the data integration stage will present data analysis and interpretation after collecting data from existing data. the results of data integration analysis can produce a comparison of risk and nonrisk with different variable data compositions. we can see this in table 5. then, in table 5, there is a difference in the composition of the initial dataset. also, this data can experience a change in the variables and composition of each data before modelling and kfold. then the initial dataset must be changed to get the same composition and variables between risk and non-risk for modelling and k-fold. jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 543 table 5. the initial dataset that has not been crossvalidated has two risks and no risks no data name risk no risk 1 validation objective initial data 0,48 0,52 2 objective initial training data 0,52 0,48 3 objective initial testing data 0,48 0,51 table 6. initial dataset with cross-validation 2 risk and no risk no data name risk no risk 1 validation objective initial data 0,50 0,50 2 objective initial training data 0,50 0,50 3 objective initial testing data 0,50 0,50 can be seen in table 6 shows a change in the initial objective data into balanced composition data on risk and non-risk variables. the number of data sets each data as can be seen in table 7. data value results. table 7. data value results no nama data jumlah 1 validation objective initial data 488 2 objective initial training data 384 3 objective initial testing data 96 modelling employs information that has been altered by changing the number of columns and weighted to account for risk and non-risk factors. this data set uses the basic algorithm as a random forest model by considering the decision tree algorithm. these two algorithms will search and provide a good level of accuracy, namely accuracy, precision, recall, and f1-score, to get the results to build a pregnancy risk level classification system that provides accurate information on pregnant women patients experiencing a risk level or not at risk early. there will also be a ratio comparison of 80% as training data and 20% as testing data with a cross-validation pattern of 2. can be seen in table 8. comparison of the accuracy of random forest and decision tree algorithms. table 8. comparison of random forest and decision tree accuracy no algorithm comparison accuracy precision recall f1-score 1 random forest training data 85% 84% 2,95 1,31 2 decision tree training data 77% 79% 1,67 1,08 3 random forest testing data 98% 94% 1 97 4 data testing decision tree 74% 76% 0,68 0,72 can be seen in table 8. comparison of the accuracy of random forest and decision tree methods has different values. there are several explanations in table 8. comparison of the accuracy of random forest and decision tree methods below: 1. accuracy shows that the data from random forest modelling results, namely train and testing data. compared with the data from the decision tree modelling results, namely train and testing data, it produces a conclusion on the accuracy, namely the random forest method has a good level of model accuracy and is superior in classifying correctly, which gets a data value of 85% train and 97% testing. compared to the decision tree. 2. precision shows that the random forest method has a good accuracy between the data requested and the prediction results given by the model, getting a value of 84% train data and 94% testing data. compared to the decision tree, the value obtained by the training data is 79%, and the testing data is 76%. 3. recall shows that random forest modelling gets the model's success in finding good information with a data train value of 2.95 and testing data 1. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 544 at the same time, compared to decision tree modelling getting a data train value of 1.67. 4. f-1 score shows that random forest modelling can provide a good algorithm performance value, namely data train 1.31 and data testing 97. while compared to decision tree modelling, the algorithm performance value gets a data train value of 1.08 and data testing 0.72. testing tools in the form of visual code programs, like those used for constructing webbased models, and a database used for the random forest method, are among the many model implementations available. so that it answers the purpose of this research is to detect early on the risk of pregnancy, both risk and not risk to pregnant women patients. this implementation will provide information on the accuracy level of pregnant women in the form of risk or no risk. in the concept of model implementation, researchers use the sdlc (system development life cycle) waterfall. the waterfall model provides a sequential software flow or cycle approach. we can see this in figure 3. waterfall illustration below: figure 3. modified waterfall model the second analysis stage occurs during the translation of software requirements into a design representation for later incorporation into the program. so this research is built using a use case diagram to classify pregnancy risk levels using the random forest method (figure 4). dataset upload random forest construction single data test figure 4. use case diagram some actors or users have several functions, as shown in figure 4. use-case-diagram there is a process of uploading data sets, building random forests, and testing data, namely single testing data with a cross-validation rule of 2 and displaying accuracy values. the initial view on implementing the random forest model is still empty to run the single data system provided. also has a menu function in the form of data sets to split data and modelling used in random forest models. we can see the initial appearance of the application in figure 5. figure 5. initial view of the menu jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 545 can be seen in figure 5. it is a display of the results of uploading datasets into the system. the data set shown is raw or has no training and test data partitions yet. so, to do random forest modelling, first split the data or divide the training and testing data with a ratio of 80% training data and 20% testing data with cross-validation 2 in the application of pregnancy risk level classification using the random forest method. the above dataset divides training and testing data using the random forest model. use training data to train the algorithm while using test data to determine the performance of a previously trained algorithm when it encounters new, never-before-seen data. figure 6. dataset view figure 7. k-fold value input display table 9. k-fold values 2 to 10 below. no. k-fold sequence testing data accuracy training data accuracy 1 k-fold 2 96% 85% 2 k-fold 3 96% 79% 3 k-fold 4 95% 78% 4 k-fold 5 95% 77% 5 k-fold 6 94% 77% 6 k-fold 7 94% 75% 7 k-fold 8 93% 75% 8 k-fold 9 93% 72% 9 k-fold 10 93% 71% p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i3.xxx jurnal riset informatika vol. 4, no. 3 june 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 546 figure 7 displays the pregnancy risk level classification system using the random forest method. there are several k-folds of 2 to 10, which have different accuracy values for testing and training. the k-fold 2 to 10 is k-fold 2, which has a high accuracy value in both testing and training data in figure 8. figure 8. k-fold 2 result display figure 9. display of pregnancy risk level prediction application jurnal riset informatika vol. 5, no. 1. december 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i1.487 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 547 figure 10. single data result view in the next stage, researchers will predict the classification of pregnancy risk levels using the random forest method from the testing data that has been k-fold 2. display of pregnancy risk level prediction application. in testing the prediction of pregnancy risk level classification applications using the random forest method using single data on testing data with risk status. by having several factors, namely 18 to 35 years, under 4x, above 2x, hyperthermia, under two years or 24 months, mild anemia, normal 100130, and less than 18 kg. to get the results at 87% and not at risk at 13%. can be seen in figure 10. display of single data prediction results. conclusions and suggestions the results of the application of 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(2018). deteksi tingkat risiko kehamilan dengan metode fuzzy mamdani dan simple additive weighting. jurnal teknologi dan sistem komputer, 6(3), 110–114. https://doi.org/10.14710/jtsiskom.6.3.2018. 110-114 pavlov, y. l. (2018). random forests. berlin: de gruyter. retrieved from https://www.degruyter.com/document/doi/ 10.1515/9783110941975/html?lang=en jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 35 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sistem pendukung keputusan karyawan teladan pt. bank rakyat indonesia dengan metode simple additive weighting jodhy prayogo sistem informasi stmik nusa mandiri jakarta www.nusamandiri.ac.id jodhy.prayogo@gmail.com abstract in the decision making of exemplary employees, pt bank rakyat indonesia conducts in several ways, namely, taking an assessment of absenteeism per three months, and individual assignments given by the supervisor. data obtained after that must be processed back into an assessment to produce a model employee's decision. data is collected for collection of types of criteria such as attendance assessment, assessment of individual assignments from the results of the combined assessment data. data will be processed manually with a data processing application from microsoft excel. after processing it into the assessment data, the data will be checked again, the appropriate data will be given directly to the he ad of the department for the selection of quarterly exemplary employees. the existence of the simple additive weighting (saw) method can determine with many criteria in making an exemplary employee assessment decision and the time needed in data processing will also be faster and more efficient. the purpose of this study is to help companies in the process of selecting exemplary employees for the greatest value on attendance criteria is an alternative a4 or employee on behalf of nur hidayatullah, for the largest value on productivity criteria is alternative a27 selected employees are urfani meigasari, while for criteria on individual tasks values the biggest on alternative a26 or selected employees is denny septian, for the biggest value with the criteria of responsibility on the alternative a27 the selected employees are urfani meigasari and for the largest value of the supervisor assessment criteria on the alternative a27 the selected employees are urfani meigasari. keywords: decision support systems, exemplary employees, simple additive weighting abstrakdalam pengambilan keputusan karyawan teladan, pt bank rakyat indonesia melakukan dengan beberapa cara yaitu, mengambil penilaian dari absensi per tiga bulan, dan tugas individual yang diberikan oleh supervisor. data yang diperoleh setelah itu harus diolah kembali menjadi penilaian untuk menghasilkan keputusan karyawan teladan. data dikumpulkan untuk dilakukan pengumpulan dari jenis kriteriakriteria seperti penilaian absensi, penilaian tugas individual dari hasil data-data penilaian gabungan tersebut data akan diolah manual dengan aplikasi pengolah data dari microsoft excel. setelah diolah menjadi data penilaian lalu data tersebut akan di cek kembali, data yang sudah sesuai akan diberikan langsung ke kepala bagian untuk dilakukan pemilihan karyawan teladan pertriwulan. adanya metode simple additive weighting (saw) ini dapat menentukan dengan banyak kriteria dalam membuat suatu keputusan penilaian karyawan teladan serta waktu yang dibutuhkan dalam pemrosesan data juga akan lebih cepat dan efisien. tujuan penelitian ini untuk membantu perusahaan dalam proses pemilihan karyawan teladan untuk nilai terbesar pada kriteria absensi adalah alternatif a4 atau karyawan atas nama nur hidayatullah, untuk nilai terbesar pada kriteria produktifitas adalah alternatif a27 karyawan yang terpilih adalah urfani meigasari, sedangkan untuk kriteria pada tugas individual nilai terbesar pada alternatif a26 atau karyawan yang terpilih adalah denny septian, untuk nilai terbesar dengan kriteria tanggung jawab pada alternatif a27 karyawan yang terpilih adalah urfani meigasari dan untuk nilai terbesar dari kriteria penilaian supervisor pada alternatif a27 karyawan yang terpilih adalah urfani meigasari. kata kunci: sistem penunjang keputusan, karyawan teladan, simple additive weighting pendahuluan dalam pengambilan keputusan karyawan teladan, pt bank rakyat indonesia melakukan dengan beberapa cara yaitu, mengambil penilaian dari absensi per tiga bulan, dan tugas individual yang diberikan oleh supervisor. data yang diperoleh setelah itu harus diolah kembali menjadi penilaian untuk menghasilkan keputusan karyawan teladan. data dikumpulkan untuk dilakukan pengumpulan dari jenis kriteriakriteria seperti penilaian absensi, penilaian tugas http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 36 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. individual dari hasil data-data penilaian gabungan tersebut data akan diolah manual (oktaviani, merlina, & nurmalasari, 2018) dengan aplikasi pengolah data dari microsoft excel (frieyadie, 2016). setelah diolah menjadi data penilaian lalu data tersebut akan di cek kembali, data yang sudah sesuai akan diberikan langsung ke kepala bagian untuk dilakukan pemilihan karyawan teladan pertriwulan. penilaian ini dilakukan subjektif hanya oleh kepala bagian saja terkadang cara tersebut kurang efektif (erwandi, mulyani, & senjaya, 2018) karena data yang didapat harus diolah terlebih dahulu dengan aplikasi pengolah data seperti microsoft excel (frieyadie, 2016) yang dapat menyebabkan kesalahan dalam penginputan nilai yang akan diolah, dan dengan menggunakan proses manual (octavia & yanto, 2014), (ariato & siahaan, 2018) tersebut akan terasa lama (suheryana, sanjaya, & shobary, 2016) dan tidak efisien dalam menentukan karyawan teladan. dengan menggunakan proses manual (priahatin, 2016) tersebut akan terasa lama dan tidak efisien dalam menentukan karyawan teladan. kriteria – kriteria dalam menentukan karyawan teladan pt bank rakyat indonesia di divisi layanan dan contact center yaitu : kriteria absensi, produktifitas, penilaian dari supervisor, penilaian tugas individual untuk melakukan perubahan nilai atau permasalahan dari unit kerja, dan tanggung jawab. kriteria–kriteria tersebut memungkinkan didalam penilaian untuk menentukan peringkat setiap karyawan sesuai dengan kualitas karyawan itu sendiri. dan dengan menggunakan metode simple additive weighting perusahaan dapat melakukan penilaian untuk nilai-nilai karyawan teladan dengan akurat (murtina, 2015). tujuan penelitian ini untuk membantu perusahaan dalam proses pemilihan karyawan teladan. memberikan solusi dengan menerapkan metode simple additive weighting (saw) yang dapat menentukan banyak kriteria dalam membuat suatu keputusan penilaian karyawan teladan. memperbaiki pengolahan data penilaian dan menjadi pertimbangan agar lebih efisien. untuk media penentu dalam memilih dan menentukan karyawan yang berkualitas. bahan dan metode a. instrument penelitian instrument merupakan alat yang digunakan untuk melakukan sesuatu. sedangkan penelitian memiliki arti pemeriksaan, penyidikan, kegiatan pengumpulan, pengolahan , analisis dan penyajian data secara sistematis serta objektif. penelitian ini menggunakan instrument kuesioner yang ditujukan langsung kepada supervisor di divisi layanan pt bank rakyat indonesia untuk penentuan karyawan teladan. pada data kuesioner tersebut berupa kriteria – kriteria dalam penentuan karyawan teladan. setelah melakukan wawancara penulis mendapatkan 5 variabel dalam menentukan karyawan teladan, 5 variabel tersebut yaitu : tabel 1. tabel variabel penelitian kriteria nama kriteria c1 absensi c2 produktifitas c3 tugas individual c4 tanggung jawab c5 penilaian supervisor sumber: (prayogo, 2017) b. metode pengumpulan data dalam pengumpulan data penulis menggunakan metode sebagai berikut: 1. metode pengumpulan data dalam melakukan penelitian dibutuhkan data yang relevan, oleh karena itu penulis melakukan berbagai cara dalam mendapatkan informasi. dan dalam pembuatan skripsi ini metode pengumpulan data yang penulis gunakan adalah : a. data primer data primer adalah data yang langsung dikumpulkan oleh penulis dan data dikumpulkan langsung dari sumber pertama atau tempat objek penelitian dilakukan, data primer juga dapat diartikan sebagai data yang diperoleh dari sumber-sumber asli atau dari sumber pertama kali diperoleh. pengumpulan data primer dalam penelitian ini menggunakan metode obervasi dan wawancara. b. data sekunder dalam pengumpulan data sekunder ini yang menjadi sumber data sekunder adalah buku, jurnal, e-book, dan lain-lain. dan penulis mengumpulkan data sekunder yang didapat melalui study pustaka yang diperoleh dari literatur, jurnal, buku referensi yang berkenaan dengan penelitian yang dilakukan. 2. populasi populasi adalah wilayah generalisasi yang terdiri atas obyek atau subyek yang mempunyai kualitas dan karakteristik tertentu yang ditetapkan oleh peneliti untuk dipelajari kemudian ditarik kesimpulannya (sugiyono, 2015). populasi dalam penelitian ini adalah data pegawai pada divisi layanan dan contact center dalam menentukan karyawan teladan yang terdaftar di pt bank http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 37 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. rakyat indonesia dengan jumlah 35 karyawan periode tahun 2017. tujuan diadakannya populasi adalah agar dapat menentukan besarnya sampel yang diambil dari jumlah populasi. 3. sample penelitian sample pada penelitian ini berdasarkan dari tabel isaac dan michael dengan tingkat kesalahan ditetapkan sebesar 5% diperoleh jumlah sampel adalah sebesar 32 karyawan. sedangkan untuk teknik pengambilan sampel penulis menggunakan probability sampling yaitu memberikan peluang yang sama pada setiap populasi untuk di pilih menjadi anggota sampel. tabel 1. data karyawan pt bank rakyat indonesia divisi layanan periode tahun 2017 no nama pekerja supervisor jenis kelamin tanggal lahir personal number agama status pernikahan 1 wiata prima thasajati darwanto l 27/03/1985 90046821 islam menikah 2 erna widiastuti darwanto p 16/04/1987 90056517 islam menikah 3 muhamad lutfiansyah darwanto l 05/09/1989 90037429 islam belum menikah 4 nur hidayatullah darwanto l 27/11/1989 90085379 islam belum menikah 5 m fahmi rizki darwanto l 12/10/1989 90103467 islam belum menikah 6 anik puji lestari darwanto p 02/08/1992 90046890 islam belum menikah 7 deri arianto darwanto l 27/03/1990 90107652 islam menikah 8 lisa sulistyarini darwanto p 05/02/1991 90101857 islam belum menikah 9 hafizh qalam darwanto l 27/10/1993 90054200 islam belum menikah 10 muhamad budi pamuji irwan s. l 25/07/1991 90106542 islam belum menikah 11 nasfridona irwan s. l 20/04/1989 90122291 islam belum menikah 12 andi riyanto irwan s. l 08/03/1988 90100289 islam belum menikah 13 frengki tumpal irwan s. l 16/04/1983 90047842 kristen menikah 14 rahmat salim rumnie irwan s. l 11/07/1988 90092752 islam belum menikah 15 risky aditya salatin irwan s. l 23/07/1993 90046138 islam belum menikah 16 lesly wuisang irwan s. l 28/08/1989 90079196 kristen belum menikah 17 mohamad andika irwan s. l 17/02/1991 90071468 islam belum menikah 18 achmad faisal adhi c l 23/02/1987 90071458 islam belum menikah 19 mita erlina adhi c. p 02/12/1986 90104736 islam menikah 20 nahlah zafira lestari adhi c. p 24/05/1992 90104737 islam menikah 21 afriza adhi c. l 09/09/1988 90105472 islam menikah 22 irawan sapto aji adhi c. l 19/09/1988 90105665 islam belum menikah 23 zakaria adhi c. l 12/10/1986 90106021 islam menikah 24 denny septian adhi c. l 04/02/1987 90105781 islam belum menikah 25 melisa sitepu heri s. p 13/06/1989 90106809 kristen belum menikah 26 ade febi damanik heri s. l 27/03/1992 90109333 kristen belum menikah 27 urfani meigasari heri s. p 11/06/1990 90106812 islam menikah 28 herman heri s. l 03/08/1986 90050473 islam menikah 29 rahma wulandari heri s. p 24/08/1987 90102070 islam menikah 30 jodhy prayogo heri s. l 02/12/1991 90022201 islam belum menikah 31 putri safrita heri s. p 11/07/1993 90105568 islam belum menikah 32 rena isninna heri s. p 11/07/1994 90090935 islam belum menikah sumber: (prayogo, 2017) c. metode analisis data dalam mencapai tujuan penelitian maka analisis yang digunakan adalah data kualitatif dengan menggunakan metode simple additive weighting (saw), metode ini merupakan metode yang paling sederhana dan paling banyak digunakan dan juga metode yang paling mudah untuk di aplikasikan. hasil dan pembahasan menentukan karyawan teladan di pt bank rakyat indonesia, dengan jumlah data pada penelitian ini 36 karyawan. setiap karyawan disebut sebagai suatu alternatif (a1, a2, a3 dan seterusnya). a. pengolahan data dan perhitungan manual dengan metode saw http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 38 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. dalam proses ini tahapan yang diperlukan dalam pengolahan data adalah sebagai berikut : 1. analisa kebutuhan analisa kebutuhan dapat dibagi menjadi dua bagian, yaitu analisa kebutuhan input dan analisa kebutuhan output. untuk analisa kebutuhan input adalah variabel input yang digunakan untuk penelitian ini adalah: absensi, produktifitas, tugas individual, tanggung jawab, dan penilaian supervisor. sedangkan untuk analisa kebutuhan output pada penelitian ini adalah sebuah alternatif yang memiliki nilai tertinggi dibandingkan dengan alternatif nilai lain. 2. penyelesaian menggunakan metode saw pada tahap ini akan dilakukan pengujian terhadap hasil penelitian dengan menggunakan metode simple additive weighting (saw) yang digunakan dalam pengolahan data menentukan karyawan teladan pada pt bank rakyat indonesia. untuk melakukan perhitungan dalam menentukan karyawan teladan menggunakan metode simple additive weighting (saw) : a. langkah pertama menentukan alternatif, yaitu a1 berikut adalah data alternatif yang akan digunakan dalam perhitungan. tabel 2. data alternatif no alternatif kriteria absensi produktifitas tugas individual tanggung jawab penilaian supervisor 1 a1 3 2 2 3 4 2 a2 3 4 3 2 4 3 a3 2 2 4 4 2 4 a4 5 3 3 4 3 5 a5 3 4 4 3 4 6 a6 4 2 4 3 3 7 a7 3 3 2 2 4 8 a8 5 3 1 1 4 9 a9 3 4 2 2 4 10 a10 4 2 2 1 5 11 a11 2 3 2 3 4 12 a12 4 3 2 1 4 13 a13 3 4 5 2 5 14 a14 4 2 3 2 4 15 a15 3 2 4 2 5 16 a16 4 3 2 2 4 17 a17 3 3 4 2 4 18 a18 4 5 3 2 5 19 a19 5 2 3 3 4 20 a20 4 3 2 3 4 21 a21 4 4 3 2 4 22 a22 2 3 4 1 5 23 a23 2 3 3 2 5 24 a24 3 3 2 1 3 25 a25 3 2 4 2 3 26 a26 4 2 5 3 4 27 a27 4 5 3 4 2 28 a28 3 3 3 1 4 29 a29 2 3 2 3 4 30 a30 4 2 4 2 5 31 a31 3 2 3 1 5 32 a32 3 4 3 2 4 sumber: (prayogo, 2017) b. kriteria dan bobot dalam langkah kedua menentukan kriteria yang akan dijadikan acuan dalam pengambilan keputusan, yaitu c1. dan untuk dalam menentukan penilaian dibutuhkan kriteria, seperti absensi, produktifitas, tugas individual, tanggung jawab, dan penilaian supervisor. 1) absensi 2) produktifitas 3) tugas individual 4) tanggal jawab 5) penilaian supervisor kelima kriteria memiliki nilai dan pembobotan untuk variabel absensi ditunjukan pada tabel 3. tabel 3. absensi bilangan fuzzy nilai http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 39 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. kurang sekali (ks) 1 kurang (k) 2 cukup (c) 3 baik (b) 4 baik sekali (bs) 5 sumber: (prayogo, 2017) c. bobot preferensi (w) dalam proses penentuan ini bobot preferensi atau tingkat kepentingan untuk setiap kriteria. bobot kriteria yang digunakan untuk menentukan karyawan teladan pada pt. bank rakyat indonesia sebagai berikut : tabel 4. bobot kepentingan kriteria bobot (w) keterangan 0,2 kurang sekali 0,4 kurang 0,6 cukup 0,8 baik 1 baik sekali sumber: (prayogo, 2017) tabel 5. tingkat kepentingan (w) kriteria (c) bobot (w) keterangan c1=absensi 1 baik sekali c2=produktifitas 0,6 cukup c3=tugas individual 0,8 baik c4=tanggung jawab 1 baik sekali c5=penilaian supervisor 0,8 baik sumber: (prayogo, 2017) d. nilai rating kecocokan setiap alternatif pada setiap kriteria dalam menentukan rating kecocokan setiap alternatif pada setiap kriteria yang telah ditentukan pada tabel diatas ditunjukan pada tabel 5. tabel 5. rating kecocokan alternatif no alternatif kriteria absensi produktifitas tugas individual tanggung jawab penilaian supervisor c1 c2 c3 c4 c5 1 a1 3 2 2 3 4 2 a2 3 4 3 2 4 3 a3 2 2 4 4 2 4 a4 5 3 3 4 3 5 a5 3 4 4 3 4 6 a6 4 2 4 3 3 7 a7 3 3 2 2 4 8 a8 5 3 1 1 2 9 a9 3 4 2 2 4 10 a10 4 2 2 1 3 11 a11 2 3 2 3 4 12 a12 4 3 2 1 4 13 a13 3 4 5 2 5 14 a14 4 2 3 2 4 15 a15 3 2 4 2 5 16 a16 4 3 2 2 4 17 a17 3 3 4 2 4 18 a18 4 5 3 2 5 19 a19 5 2 3 3 4 20 a20 4 3 2 3 4 21 a21 4 4 3 2 4 22 a22 2 3 4 1 3 23 a23 2 3 3 2 4 24 a24 3 3 2 1 3 25 a25 3 2 4 2 3 26 a26 4 2 5 3 4 27 a27 4 5 3 4 5 28 a28 3 3 3 1 4 29 a29 2 3 2 3 4 30 a30 4 2 4 2 5 31 a31 3 2 3 1 3 http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 40 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 32 a32 3 4 3 2 4 sumber: (prayogo, 2017) e. matriks keputusan setelah nilai pada rating alternatif setiap kriteria sudah ditentukan di langkah selanjutnya membuat matrix keputusan (x) yang dibentuk dari tabel rating kecocokan yang didapat dari semua kriteria. nilai (x) setiap alternatif (a1) pada setiap kriteria (c1) yang telah ditentukan. f. normalisasi keputusan (x) proses selanjutnya adalah proses normalisasi keputusan (x) ke skala yang dapat dibandingkan dengan semua rating alternatif yang telah didapatkan. rij = xij max xij …………………………………………… (1) nilai preferensi (v1) pada proses terakhir adalah menghitung hasil akhir preferensi (v1) yang diperoleh dari penjumlahan dan perkalian elemen baris matriks ternormalisasi (r) dengan bobot preferensi (w) yang bersesuaian dengan elemen kolom matriks (r). 𝑉𝑖 = ∑ 𝑊𝑗 𝑟𝑖𝑗 𝑛 𝑗=1 …………………………………….. (2) bobot preferensi : 1, 0.6,0.8,1,0.8 berikut hasil pengujian dimana nilai awal dari setiap alternatif diproses menggunakan metode simple additive weighting (saw) dan mendapatkan nilai hasil akhir dalam perhitungan seperti diatas, berikut tabel iv.10. tabel 6. hasil pengujian no alternatif kriteria hasil akhir absensi produktifitas tugas individual tanggung jawab penilaian supervisor c1 c2 c3 c4 c5 1 a1 0.6 0.4 0.4 0.75 0.8 2.55 2 a2 0.6 0.8 0.6 0.5 0.8 2.7 3 a3 0.4 0.4 0.8 1 0.5 2.68 4 a4 1 0.6 0.6 1 0.75 3.44 5 a5 0.6 0.8 0.8 0.75 0.8 3.11 6 a6 0.8 0.4 0.8 0.75 0.6 2.91 7 a7 0.6 0.6 0.4 0.5 0.8 2.42 8 a8 1 0.6 0.4 0.5 0.8 2.82 9 a9 0.6 0.8 0.4 0.5 0.8 2.54 10 a10 0.8 0.4 0.4 0.25 1 2.41 11 a11 0.4 0.6 0.4 0.75 0.8 2.47 12 a12 0.8 0.6 0.4 0.25 0.8 2.37 13 a13 0.6 0.8 1 0.5 1 3.18 14 a14 0.8 0.4 0.6 0.5 0.8 2.66 15 a15 0.6 0.4 0.8 0.5 1 2.66 16 a16 0.8 0.6 0.4 0.5 0.8 2.62 17 a17 0.6 0.6 0.8 0.5 0.8 2.74 18 a18 0.8 1 0.6 0.75 0.8 3.18 19 a19 1 0.4 0.6 0.75 0.8 3.11 20 a20 0.8 0.6 0.4 0.75 0.8 2.87 21 a21 0.8 0.8 0.6 0.5 0.8 2.9 22 a22 0.4 0.6 0.8 0.25 0.6 2.13 23 a23 0.4 0.6 0.6 0.5 0.8 2.38 24 a24 0.6 0.6 0.4 0.25 0.6 2.01 25 a25 0.6 0.4 0.8 0.5 0.8 2.46 26 a26 0.8 0.4 1 0.75 0.8 3.23 27 a27 0.8 1 0.6 1 1 3.68 28 a28 0.6 0.6 0.6 0.25 0.8 2.33 29 a29 0.4 0.6 0.4 0.75 0.8 2.47 30 a30 0.8 0.4 0.8 0.5 1 2.98 31 a31 0.6 0.4 0.6 0.25 0.6 2.05 32 a32 0.6 0.8 0.6 0.5 0.8 2.7 sumber: (prayogo, 2017) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 41 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. untuk nilai terbesar pada kriteria absensi adalah alternatif a4 atau karyawan atas nama nur hidayatullah, untuk nilai terbesar pada kriteria produktifitas adalah alternatif a27 karyawan yang terpilih adalah urfani meigasari, sedangkan untuk kriteria pada tugas individual nilai terbesar pada alternatif a26 atau karyawan yang terpilih adalah denny septian, untuk nilai terbesar dengan kriteria tanggung jawab pada alternatif a27 karyawan yang terpilih adalah urfani meigasari dan untuk nilai terbesar dari kriteria penilaian supervisor pada alternatif a27 karyawan yang terpilih adalah urfani meigasari. dari tabel 6, ini untuk nilai terbesar ada pada v27, sehingga untuk alternatif a27 atau karyawan yang terpilih menjadi karyawan teladan adalah urfani meigasari dengan penilaian 3,68. namun pada kasus ini alternatif yang terbaik adalah beberapa karyawan yang mendapatkan nilai pembobotan cukup pada setiap kriteria. kesimpulan berdasarkan uraian dan tinjauan dari pembahasan penelitian yang telah dilakukan, maka kesimpulan yang didapat dari penelitian yang telah dilakukan, proses penentuan karyawan terbaik pt bank rakyat indonesia dengan metode saw dimulai dengan pemberian nilai kriteria, pembobotan, rating kecocokan, normalisasi dan perangkingan. sehingga menghasilkan nilai untuk masing – masing kriteria. hasil dari perhitungan tersebut merupakan perangkingan nilai tertinggi ke nilai terendah, dan nilai tertinggi merupakan hasil yang dibutuhkan untuk menentukan karyawan terbaik. untuk nilai terbesar pada kriteria absensi adalah alternatif a4 atau karyawan atas nama nur hidayatullah, untuk nilai terbesar pada kriteria produktifitas adalah alternatif a27 karyawan yang terpilih adalah urfani meigasari, sedangkan untuk kriteria pada tugas individual nilai terbesar pada alternatif a26 atau karyawan yang terpilih adalah denny septian, untuk nilai terbesar dengan kriteria tanggung jawab pada alternatif a27 karyawan yang terpilih adalah urfani meigasari dan untuk nilai terbesar dari kriteria penilaian supervisor pada alternatif a27 karyawan yang terpilih adalah urfani meigasari metode simple additive weghting (saw) dapat memberikan alternatif keputusan yang terbaik dalam pengambilan keputusan. referensi ariato, e. y., & siahaan, f. b. (2018). analisa penilaian kinerja karyawan dengan metode fuzzy simple additive weighting pada pt. unilever indonesia tbk skin deo factory sbu production tube. jurnal teknik komputer, 4(1), 194–204. https://doi.org/10.31294/jtk.v4i1.2546 erwandi, d., mulyani, e. d. s., & senjaya, a. s. (2018). sistem pendukung keputusan untuk penilaian kinerja guru menggunakan metode weighted product (studi kasus : madrasah ibtidaiyah condong) | erwandi | konferensi nasional sistem informasi (knsi) 2018. in konferensi nasional sistem informasi (knsi) 2018 (pp. 870–876). pangkalpinang: stmik atma luhur. retrieved from http://jurnal.atmaluhur.ac.id/index.php/kns i2018/article/view/463 frieyadie, f. (2016). penerapan metode simple additive weight (saw) dalam sistem pendukung keputusan promosi kenaikan jabatan. jurnal pilar nusa mandiri, 12(1), 37– 45. murtina, h. (2015). sistem penunjang keputusan pemilihan supervisor menggunakan simple additive weighting pada pt nippon indosari corpindo. konferensi nasional ilmu pengetahuan dan teknologi, 1(1), 143–148. retrieved from http://konferensi.nusamandiri.ac.id/prosidi ng/index.php/knit/article/view/77 octavia, e., & yanto, a. h. (2014). penerapan metode fuzzy pada penilaian kinerja karyawan (studi kasus pt. indovisualjakarta). jurnal techno nusa mandiri, 11(2). retrieved from http://ejournal.nusamandiri.ac.id/ejurnal/in dex.php/techno/article/view/96 oktaviani, n., merlina, n., & nurmalasari, n. (2018). pemilihan jasa pengiriman terbaik menggunakan metode simple additive weighting (saw). jurnal sistem dan teknologi informasi (justin), 6(4), 219. https://doi.org/10.26418/justin.v6i4.29126 prayogo, j. (2017). laporan tugas akhir sistem pendukung keputusan karyawan teladan pt. bank rakyat indonesia dengan metode simple additive weighting. jakarta. priahatin, t. (2016). penerapan metode simple additive weighting (saw) untuk penentuan status pengangkatan karyawan. seminar nasional ilmu pengetahuan dan teknologi http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 42 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. komputer, 19–inf.24. retrieved from http://konferensi.nusamandiri.ac.id/prosidi ng/index.php/sniptek/article/view/9 sugiyono. (2015). metode penelitian kuantitatif kualitatif dan r&d. bandu: alfabeta. suheryana, m. d., sanjaya, r., & shobary, m. n. (2016). sistem penunjang keputusan penerimaan pegawai baru pada pt. ebdesk teknologi. seminar nasional ilmu pengetahuan dan teknologi komputer, 63– inf.68. retrieved from http://konferensi.nusamandiri.ac.id/prosidi ng/index.php/sniptek/article/view/17 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 407 developing registration module in health information systems using the design thinking method ratu amalia primadiningsih-1, rahmat izwan heroza2 information system, faculty of computer science, universitas sriwijaya 09031381823067@student.unsri.ac.id-1, rahmatheroza@unsri.ac.id-2 abstract in applications, usability is an essential factor that shows the user experience whether the application is easy to use and fits its function or not. for this reason, this study aims to determine the usability of the registration module on the khanza hospital management information system (simrs) used by rsud banyuasin employees and to find out the difficulties faced, needs, and desires of rsud banyuasin employees as users of simrs khanza. this research is helpful for the agency as a subject of application evaluation and helps discover what opportunities can be applied to overcome user difficulties. the approach used is design thinking. meanwhile, the usability measurement uses the system usability scale (sus) involving five respondents. the initial results of measuring the usability of simrs khanza obtained were 26 with a poor rating. in addition, the interview results show that users have difficulty learning the application, the features and fields are too many, and there is a redundancy of patient data. based on these findings, the researcher recommends a solution design with a high fidelity prototype (hi-fi) with a final value of sus 87. with the implementation of this research recommendation, it is hoped that it can help the performance of rsud banyuasin with an application design that is easy to understand and supports the creation of accurate and consistent data. keywords: information system, design thinking, sus, prototype abstrak dalam aplikasi, ketergunaan merupakan faktor esensial yang menunjukkan pengalaman pengguna apakah aplikasi mudah digunakan dan sesuai fungsinya atau tidak. untuk itu, penelitian ini bertujuan untuk mengetahui tingkat ketergunaan modul registrasi pada sistem informasi manajemen rumah sakit (simrs) khanza yang digunakan oleh pegawai rsud banyuasin dan mengetahui kesulitan yang dihadapi, kebutuhan, serta keinginan pegawai rsud banyuasin sebagai pengguna simrs khanza. penelitian ini bermanfaat bagi pihak instansi sebagai subjek evaluasi terkait aplikasi serta membantu dalam menemukan peluang apa yang dapat diterapkan untuk mengatasi kesulitan pengguna. pendekatan yang digunakan yaitu desig n thinking. sedangkan, pengukuran ketergunaan menggunakan system usability scale (sus) dengan melibatkan lima responden. hasil awal pengukuran ketergunaan simrs khanza yang didapat yaitu 26 dengan rating poor. selain itu, hasil wawancara menunjukkan bahwa pengguna kesulitan mempelajari aplikasi, fitur dan kolom pegisian terlalu banyak, dan terjadi redudansi data pasien. hasil penelitian ini berupa rancangan solusi high fidelity prototype (hi-fi) dengan nilai akhir pengujian sus 87. dengan diterapkannya rekomendasi penelitian ini, diharapkan dapat membantu kinerja pegawai rsud banyuasin dengan desain aplikasi yang mudah dipahami dan mendukung pembuatan data yang akurat dan konsisten. kata kunci: sistem informasi, design thinking, sus, prototype introduction one of the applications of technology in the health sector is a computerized service system. this system allows information to be managed accurately and reliably so that clinics, health centres, hospitals, and various other health institutions have great opportunities to improve service quality. various hospitals in indonesia use a computer-based medical record information system to support hospital operational programs. one is the regional general hospital located in banyuasin regency or the rsud banyuasin. rsud banyuasin must hold medical records. recording of medical records must be made as complete as possible from the beginning until the p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 408 medical record process is complete (hilmansyah, 2021). rsud banyuasin uses a health management information system called the khanza health information system (simrs khanza). the simrs khanza application is open source and frees so that various healthcare institutions can enjoy it. sourced from the results of interviews with users of the simrs khanza application at rsud banyuasin, the application of simrs khanza is deemed not optimal because there are several problems, namely the amount of time needed to learn simrs khanza through manuals, too many fields to fill in and redundancy of patient data. users who are used to interacting with a system will be different from novice users in interacting with the system (astawa, darmawiguna, & sugihartini, 2019). hence, users need a system that is easy to learn because, based on the results of interviews, they have no experience using similar applications and only have little time to learn. in addition, users need a system that can prevent redundancy when updating patient data because users must create accurate and consistent data by the hospital's minimum service standards (putra, dangnga, & majid, 2020). constraints at the medical record management stage at the registration section at the rsud banyuasin can cause the patient waiting time to increase. if that happens, the quality of hospital services will decrease because the waiting time is not by the minimum service standards contained in the regulations of the ministry of health (apriyantini, 2018). previous research conducted by muhammad azmi et al. entitled evaluation of the user experience of the food ordering mobile application with the design thinking method (case study: grabfood) resulted in four solutions to the user experience problem of the grabfood application version 5.27.0, including speeding up the splash screen loading time, performing design improvements in the category section, make design improvements in the recommendations section, and make minor improvements to the address selection section. the solution is then poured into a prototype form in the adobe xd application (azmi, kharisma, & akbar, 2019). another study by suzianti and arafah aimed to redesign the dental clinic's user interface so that the erp system could work optimally. the conclusion is that design thinking has succeeded in redesigning the ui on the erp system according to what is personalized. the needs of each user to help with their work are validated by clinical personnel as users (suzianti & arrafah, 2019). another study by razi et al. entitled the application of the design thinking method to a ui/ux design model for handling losses and finding reports of scattered goods. the kembaliin application is a technology-based media design model specifically designed according to the target user to solve the problem of lost and found items scattered in public places. however, to succeed, the application of kembaliin alone can only be achieved by supporting public participation in holding honesty and respectful culture to help each other in society (razi, mutiaz, & setiawan, 2018). based on the problems above, this study aims to evaluate the usability of the registration module because usability is an essential factor that shows the user experience of whether the application is easy to use and according to its function (suzianti & arrafah, 2019). in addition, this research aims to discover the difficulties faced, needs, and desires of rsud banyuasin employees as users of simrs khanza. design thinking is used as a framework in this research. there are five major stages in design thinking as proposed by the hassoplattner institute of design at stanford university, usa (pratama, rahayu, setiyadi, azhar, & ashshiddiq, 2022). the solutions will then be used to design the prototype simrs khanza as a reference for the agency in developing the system to help the performance of rsud banyuasin with an application design that is easy to understand and supports the creation of accurate and consistent data. research methods types of research this research uses a qualitative descriptive approach to research the application of the simrs khanza. time and place of research the research time for distributing the initial questionnaire was october 17, 2021. interviews were conducted on october 18, 2021, the ideacollection session was held on february 02, 2022, and the testing questionnaires were distributed on mei 08, 2022. data retrieval is done via the zoom application. research target / subject the research target is five people. the target criteria for this research are males or females aged 25-45 years as an employee of the banyuasin hospital and a user of simrs khanza. jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 409 data collection techniques the technique used for data collection in this research include: 1. questionnaire the technique of collecting data by submitting a statement to the respondent is the definition of a questionnaire. the technique used in this study is the system usability scale. the questionnaire is an online questionnaire with statements in the questionnaire measured by a likert scale (karnawan, 2021). 2. interview conducted interviews with employees of the banyuasin hospital with questions and answers via zoom meetings. the selected interview participants are users of simrs khanza who are quite experienced in using simrs khanza and based on time availability. the questions posed to the interviewees were based on the researcher's need to find out the experience of the interviewees in operating the simrs khanza application and the obstacles faced. the researchers tried to allow the interview participants to present the desired solution to their problems. 3. literatur study a literature study is relevant data collected from books, scientific articles, news, and other reliable sources to obtain a theoretical foundation related to the research topic. 4. documentation documents that are analyzed and collected, whether written documents, pictures or electronically, are documentation data collection techniques. documents used by researchers in the form of internal sheets, namely standard operating procedures (sop). design thinking method an approach or research framework that is useful for concise and creative problem-solving with a primary focus on users is known as design thinking. this approach can provide great value for concept exploration. by going through this phase of design thinking, it is hoped that it can be useful in solving problems and manifesting a solution product or application by understanding the needs and desires of users first. the choice is to use this method because the design process can be done by including empathy from the user's side. using user understanding of design thinking can help to question the problem, its assumptions, and implications (darrin & devereux, 2017). there are five stages in design thinking, namely: 1. empathize the first stage is empathy. this stage seeks to understand the experience better and for whom one is designing. the understanding in question is in the context of the product being designed. at this stage, the researcher conducted interviews. then, the researcher processed the interview results into an empathy map and user persona. the empathy map recognizes and understands users' needs, desires, goals, behaviours, and feelings (pileggi, 2021). meanwhile, the creation of user personas aims to provide insight into the needs, wants, and goals of target segments to help make decisions about system features, services, or products. 2. define define which is useful for "defining" the user's point of view, which will be discussed with alternative concept designs. in other words, this second stage aims to analyze the knowledge produced through the first stage as the basis for determining the problem statement to be the primary attention in the research. in this study, point of view is used as a problem statement by utilizing, interpreting, and weighing all findings to allow researchers to create ideas in a goal-oriented way. there are three important pov elements: user, need, and insight (lewrick, link, & leifer, 2018). 3. ideate ideate is intended as a step toward solving problems by exploring alternative solutions as widely as possible (ferdi, putra, ajie, safitri, & jakarta, 2021). in this stage, the ideas generated will be used as a reference in the formation of a new design prototype that will be formed. the collection of ideas at this stage is done by holding a brainstorming session. this session aims to generate many ideas by using the thinking of a group of people who are involved with each other, listen, provide input and build other potential and useful ideas (bonnardel & didier, 2020). after the ideas are collected, then the ideas are selected and grouped into the now-wow-how matrix consisting of three different perspectives. the last is the how perspective, a collection of difficult ideas to apply today (dirman, prayudi, & ramadhani, 2021). now contains various ideas that can be implemented in a short period. then, wow contains an instrument of ideas that can be implemented, and these ideas are innovative. 4. prototype in its implementation, the initial design that is realized will be tested for end users to obtain appropriate feedback or criticism to improve the design. the process carried out by the researcher is first making a wireframe based on the ideas contained in the nwh matrix. then processed into a mockup by applying visual elements. the finished mockup is then integrated to form various interactions called a high-fidelity prototype (hi-fi) . p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 410 at this stage, ideas will be transformed into physical forms for users to experience and interact with the process (rusanty, tolle, & fanani, 2019). 5. test testing is carried out to get user responses or appropriate feedback from the prototypes that have been made to find out whether the solutions have overcome existing problems and explore user needs more deeply. usability measurement at the testing stage uses the system usability scale (sus) technique. system usability scale (sus) sus (system usability scale) is one of the tools most frequently used to assess a system or product's usability. system usability scale is used to measure usability because it has several advantages, including a) can be calculated, and the output is a score of 0-100, so it is easier to understand, b) does not cost in its use, c) with a relative sample size minor is still proven to be valid and reliable (beny, yani, & ningrum, 2019). the list of sus statements is in table 1. the test results are calculated by following the following rules: for each statement with an odd number, the respondent's answer scale is reduced by 1. each piece of information with an even number is reduced by 5, and the respondent's answer scale is reduced. the results of the scale value are 0 to 4 (four being the most positive response). summing up the scale of respondents' answers and multiplying by 2.5. determine the average answer of all respondents (rasmila, 2018). table 1. sus testing instrument instrument skala 1. i think that i would like to use this system frequently 1-5 2. i found the system unnecessarily complex 1-5 3. i thought the system was easy to use 1-5 4. i think that i would need the support of a technical person to be able to use this system. 1-5 5. i found the various functions in this system were well integrated 1-5 6. i thought there was too much inconsistency in this system 1-5 7. i would imagine that most people would learn to use this system very quickly 1-5 8. i found the system very cumbersome to use 1-5 instrument skala 9. i felt very confident using the system 1-5 10. i needed to learn a lot of things before i could get going with this system 1-5 results and discussion evaluation questionnaire distribution using google forms. evaluation at this stage uses a questionnaire method based on the system usability scale (sus) method. based on the sus method, the questionnaire consists of 10 statements (kesuma, 2021). then, the results of the questionnaire the respondent has filled out will be calculated as the final score using the formula determined in the system usability scale (sus) method, carried out on a spreadsheet. table 2 is the final score calculated using the sus formula. table 2. final score sus sum final score mean final score total 130 26 the calculation produces a score which is then interpreted using an adjective rating. figure 1. adjective rating sus score based on the indicators above, the evaluation results later get a value of 26, and the usability of the interface design is classified as poor. after distributing questionnaires to five employees of rsud banyuasin who use simrs khanza, then to find out more specifically what are the difficulties and needs of users, an investigation is carried out on the system with a design thinking approach. 0 50 100 150 adjective rating worst imaginable poor ok good excelent best imaginable jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 411 empathize the first step is to make an interview instrument. table 3 asks questions when conducting individual interviews via the zoom application. table 3. interview questions interview question 1. briefly describe your daily work routine. a working environment conditions b frequency of using gadgets c special needs d work equipment 2. what is the mechanism for managing medical records in rsud banyuasin? 3. what obstacles are faced from the beginning to the end of the medical record management process? 4. what do you feel and do when you experience these problems? 5. in your opinion, what is the ideal medical record system according to your needs and expectations? the next process is making an empathy map using four quadrants: says, thinks, does, and feels. two empathy maps are made as representatives. then the results that have been made can be described in tables 4 dan 5. table 4. empathy map's joni empathy map aspect result 1 says patients sometimes forget to bring identification many fields are not needed in the registration section it takes extra focus to read text that is too small some of the medical record processes are still being recorded on paper 2. thinks users want display features set according to the needs of the profession the design can be made more user friendly 3. feels upset because performance is hampered worried about slowing down the process 4. does ask the it officer use paper-based forms table 5. empathy map's yesi empathy map aspect result 1 says too many features to learn. hard to understand how to use simrs khanza. there is data redundancy. not all units are integrated with simrs khanza 2. thinks users want to reduce data redundancy users want to be able to learn the system easily 3. feels worried that the medical record process will be hampered 4. does ask the it officer. watch tutorials about the application on youtube after making the empathy map, the researcher created a user persona as a fictional character of the target user whose data came from the empathy map. the first user persona is adelio. he studied at the engineering faculty. adelio is currently 40 years old and married. according to adelio, the problems faced by simrs khanza are difficulties in recording medical record information. because of these problems, adelio wants to maximize using the simrs khanza application in recording medical record data. all officers with access rights to the application can access all modules to reduce patient data privacy. then, the officers are expected only to be able to open the application module according to the job description. the second fictional character is adelia, who is cheerful and hardworking. the problem that adelia faces is that it takes much time to learn how to operate simrs khanza and there is data redundancy when registering, and the display of features is too much and does not focus only on the task that staff should do. according to adelia, the ideal hospital management system is that it can be learned easily and can reduce the occurrence of redundancy of patient data. it looks simple and attractive but still provides features and information as needed. define the next process is defined. this stage aims to focus on the problems the researcher will follow up on. the following is the define result which is presented using the point of view table. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 412 table 6. point of view user need insight adelia easy-to-learn system users have no experience using similar applications and only have little time to learn. a system that can prevent redundancy when updating patient data users must create consistent data. ideate after determining the focus of the problem, the next step is to ideate. at this stage, brainstorming is carried out using zoom to conduct online discussions and figma to record the brainstorming results. this stage is carried out with the same respondents as the previous stage. before brainstorming, the questions to be asked are made first using data from the define stage. questions were created using the how might we format. the questions asked are in table 7. table 7. how might we question point of view (pov) how might we adelia needs an easy-to-learn system because she had no experience using similar applications and only had little time to study. how might we support users in registering online efficiently? how might we help new users understand the use of this application easily? how might application users know what to do and how to do it? adelia needs a system that can prevent redundancy when updating patient data because it has to create consistent data. how might we make sure that the data entered is correct? how might a user use an application without making an accidental mistake, such as accidentally pressing a key? brainstorming participants are given 5 minutes for each question answer. then, the question's answers are presented, discussed, and grouped into the now-wow-how matrix. figure 2. result now-wow-how matrix the result of the discussion for the now section is to create structured information to make it easier to double-check, simplify the registration flow, and make additional user confirmations that do not immediately take action. the how matrix section contains ideas that are impossible to implement now, namely the development of prototypes into applications integrated with dukcapil. the second jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 413 idea is to use a help screen that displays patient information. then in the wow section, the idea is to make a short tutorial on the application, visualize the progress of data entry, and add feedback on whether the data entered is correct or incorrect. prototype at this prototype stage, the researcher will apply the ideas collected in the nwh matrix by making designs that can interact directly with the user. the design is made with a high degree of resemblance to the original method. a. user flow when designing a prototype, it is necessary to draw a flow first to make it easier for the designer to know what pages and information are needed in designing the application (rusanty et al., 2019). the first user flow is the user flow in registering new patients. the first step is that the user opens the application, then the user selects a new patient task in the sidebar. the first stage registration page will appear when the user has pressed the new patient button. after the user fills in, the user presses the following button, which the system will then verify whether all the data has been filled in or some are empty. if there is empty data, the progress bar will change colour, and in the empty column, there is a red notification, and the user can continue to the next stage page. the system will repeat the process until the user reaches the fourth stage. if the user has completed the fourth stage and the verification is successful, a notification will appear that the data has been saved. the subsequent user flow shows the user flow in registering old patients. the initial step taken by the user is to open the application and select the old patient registration task in the sidebar menu. then the user is directed to the old patient registration page. after the user fills in the fields on the page, the user must press the save button, which the system will verify. if the verification fails, it will return to the previous page, and if successful, it will display a notification that the data has been saved, and the user can check the data on the table data page. b. wireframe after determining the flow as an illustration of the design, the next step is to create a wireframe as a simple initial design framework without using visual elements such as colour to determine the layout of the information (kenny, regan, hearne, & o'meara, 2021). figure 3 is an example of a wireframe page that has been created. figure 3. example of wireframe c. mockup after making the wireframe, a mockup design displays a more detailed visual design and is closer to the original design than the wireframe. figure 4 is an example of a wireframe page that has been created. figure 4. example of mockup in the mockup design, the design created has been equipped with colors, icons, and other information. it is known that color determines how a user interacts with an application and influences user behavior and awareness. in general, the preferred colors in medical institutions are blue and green. however, in a study by lugwen et al., the most suitable colors for interactive websites are royal blue, slate blue, and dark blue (rusanty et al., 2019). therefore, the primary color used in the application design is blue. the color is then adjusted by performing a contrast test, and the appropriate primary color is obtained, namely blue, with code #164cca. the following is the result of testing the color contrast code #164cca (figure 5). p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 414 figure 5. color contrast test the next visual element is the determination of the icon. icons play an important role in humancomputer interaction because graphic symbols can represent objects in the real world. according to research by zhangfan et al., users who use applications with complex tasks perform better when the icons used are simple and commonly encountered in their daily lives (shen, zhang, li, & liang, 2020). therefore, the selection of icons in this application design is based on the selection of simple shapes and adapts to objects often encountered by participants. after determining the icon, next are typefaces. the typeface used in the design of this application is roboto sans-serif. the size used for heading 1 is 20 pt, and heading 2 is 14 pt. while the button is 16 pt and the body text is 14 pt. reading speed reaches a stable period between medium and bold but decreases when it reaches extra bold (bigelow, 2019). therefore, the thickness of the letters used in this design is regular, medium, and bold unless the text on the logo label uses extra bold thickness. d. high fidelity prototype the finished mockup is then integrated to form various interactions called a high-fidelity prototype (hi-fi). the following is a hi-fi result created using figma software. 1. tooltips page one of the problems faced by respondents when they first used simrs khanza was confusion in understanding the flow contained in the application. users need to see the video tutorials and manuals prepared to know how to use this application. however, the problem is that users only have a little time to learn this application. for this reason, the author makes a short tutorial design or tooltips that can be seen at this point when using the application for the first time. hopefully, these tooltips will make it easier for users to understand how to use the application without the need to open other files and spend much time. the tooltips page is in figure 6. figure 6. tooltips page 2. home page on the home page and other pages, applications that previously did not have a sidebar, there is a top bar that combines the three sections. figure 7 is an image of the previous home page. figure 7. previous home page the application's new design home page can be seen when the user has completed a short tutorial or when he first opens the application. the design of the created application separates the two topbar sections contained in the previous application by moving the first section into the footer section and creating a sidebar for the menu set in the second section so that it is organized. in the new design, the top bar is filled with the home button and the user's name, in which there is access to log out to avoid the wrong button pressing. right below the top bar is a search column directly adjacent to the category selection so that users can immediately filter according to their search needs when searching. figure 8 is a draft of the new design that has been made. jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 415 figure 8. home page recommendation 3. registration page the previous design did not separate registration page displays between new and old patients. to make it easier for users to understand this application, the authors separate the new patient registration page and the registration of old patients or patients who have already registered. moreover, in the application's new design, there is a progress bar to clarify the filling steps that the user must do (silver, 2018). after completing the form in step one, the user can continue filling it out by pressing the next button. the new registration page is in figure 9. figure 9. registration page recommendation in the second phase of the new patient registration page in figure 10, there is an adjustment to the form composition that the user must fill out. the forms are arranged according to the kartu tanda penduduk (ktp) labels. the preparation based on the ktp is expected to make it easier for users to re-check. where in the previous application, the forms were not arranged sequentially. after going through stage one and proceeding to the stage two page, the previous button will appear, which can be used to return to the previous stage if the user wants to correct or recorrect that stage. the button is located on the registration page for stages two to four. figure 10. registration page phase two 4. error description in the previous application, an error description in the form of a pop-up will appear when pressing the save button without changing the colour of the empty form. figure 11 is a description of the error in the previous application. figure 11. previous error description the result of the redesign is that if the data on the form is not filled in, the user can continue filling in, but there is an error statement on the form whose form is not complete, and the colour change in the progress bar becomes red. figure 12 is a page with an error description on the form. figure 12. error description recommendation p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 416 5. pop-up data addition form previously, the user had to press the attachment icon to add or select the data used. meanwhile, the attachment icon functions as a symbol to attach files other than text messages, such as ppt documents, pdf, images, and others. therefore, the user can directly type in the data in the new design, and the system immediately brings up the search results dropdown. if the data does not exist, it will display the words data not found along with a button to add new data. figure 13 is a pop-up data addition form. if the add new button is pressed, a pop-up form for adding data will appear that can be used when the user needs additional data such as unit, doctor, referral origin, and others. users can also check what data has been added to the section by pressing the patient data button, and then all the data that has been added will appear. if the user has finished filling in the pop-up, press saves, and if successful, it will display a notification that the data has been saved. if it fails, it will return to the previous page, and an error notification will appear. figure 13. pop-up recommendation 6. notifications and toast message in the previous application, there was no notification that system had saved the data. moreover, based on the third stage that has been carried out, it is necessary to add application feedback on whether the data entered is correct so that the user becomes confident with the action taken. adding a toast message is helpful to notify the user that the data entered has been saved (megalingam et al., 2019). at the same time, adding a notification on the patient data button is helpful to indicate that users can view previously stored data on the page. notifications and toast messages are in figure 14. figure 14. notification and toast message 7. patient data page figure 15 is the patient data table page in the previous application. to bring up the patient data table, the user must press the call button or the green check icon at the bottom of the application. then, the actions the user can perform on each table row are divided into two, the first is at the bottom of the page, and the second can be seen when the user right-clicks on the cursor and selects the desired action. the changes contained in the new design are layout adjustments by levelling the distance between table columns, placing page selection options into a dropdown located at the top of the table, eliminating the exit button, arranging table data sequences according to changes on the data input page, placing all actions on buttons. the action located on the right side of the table changes the placement of the print, limit, and period buttons to be at the top of the table. figure 15. patient data page jurnal riset informatika vol. 4, no. 4. september 2022 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 417 8. print page figure 16. previous print page changes to the printed page are in the layout adjustment by placing all actions that can be performed on the right side of the screen and focusing on the left as the display of the page to be printed. figure 16 is a view of the previous application, and figure 17 is a new recommended design. figure 17. print page recommendation test the final stage of the design thinking method is testing. the testing phase is carried out by requiring users to test prototypes witnessed by the author via zoom. access to the prototype link is given when participants have entered the zoom meeting. after the trial, participants are given a google form link containing the system usability scale (sus) questionnaire questions. the results of the feedback provided by the user when conducting the test can be seen in figure 18. figure 18. feedback capture grid in the like section, the test participants stated that "the display looks much better," "easy to understand the flow," "the font size is right," and "the design is neat and attractive." then, in the question section, the test participants asked the question, "the table only displays these few columns?", "on the old patient page, can the form data appear directly on the new patient page?". then p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 418 in the wish section, test participants did not give criticism or say something that had to be changed. moreover, in the idea section, the concept that came up during the test was to clarify the shape of the scroll bar and let the user continue to fill in the form to the next stage even though there were still unfilled fields. after testing, the researcher made changes by trying the ideas in the feedback capture grid. the application of the idea is then re-tested by the user. then the author asks the user to fill out the sus questionnaire, and the following table is the final result of the questionnaire calculation. table 7. final score sus sum final score mean final score total 435 87 based on the adjective rating indicator, evaluation results get a value of 87, and then the usability of the interface design is classified as excellent. it can be concluded that the user can accept the results of the redesign (ependi, putra, & panjaitan, 2019). the difference between this study and other studies, such as those that have been carried out by razi et al., is the focus of the research, namely the user experience of the simrs khanza registration module and several techniques used in each process of the design thinking method, such as in the define stage, the researcher uses the point of view technique, the ideate stage uses brainstorming. test results are presented using the now-wow-how matrix. however, this study has limitations. namely, the limited scope includes one company, and the research only reached the prototype stage. conclusions and suggestions conclusion the measurement of the usability level of the simrs khanza application using the system usability scale (sus) shows a final score of 26. furthermore, the results of the adjective rating show a "poor" rating. these results indicate that it is necessary to improve the application's usability. therefore, the researcher conducted interviews with users as a basis for making a redesign in the form of a high-fidelity prototype (hi-fi) for the simrs khanza application using the figma tool. the interface design recommendations have been tested using the sus method with a score of 87, which received an "excellent" rating. based on these results, it means that users can well receive the design. the developer can consider the results of the design recommendations to improve the application's quality to help the performance of rsud banyuasin with an application design that is easy to understand and supports the creation of accurate and consistent data, which is expected to improve the quality of hospital services. suggestion in this study, the recommendations generated are in the form of hi-fi so that the next researcher can conduct further research up to the stage of making the program. then, the next researcher can still make design improvements on other aspects to achieve the best imaginable rating. references 'apriyantini, d. 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(2018). form design patterns unknown binding (pp. 1–386). pp. 1–386. freiburg: smashing media ag. retrieved from https://www.smashingmagazine.com/printe d-books/form-design-patterns/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i4.425 jurnal riset informatika vol. 4, no. 4. september 2022 accredited rank 3 (sinta 3), excerpts from the decision of the minister of ristek-brin no. 200/m/kpt/2020 420 suzianti, a., & arrafah, g. (2019). user interface redesign of dental clinic erp system using design thinking: a case study. icibe 2019: proceedings of the 2019 5th international conference on industrial and business engineering, 193–197. new york: acm. https://doi.org/10.1145/3364335.3364369 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 311 application of fuzzy c means and topsis in warehouse selection at pt warung islami bogor dewi primasari-1, khidir zahid muchtadiabillah-2*), freza riana-3 teknik informatika, fakultas teknik dan sains universitas ibn khaldun bogor bogor, indonesia dewiprimasari9@gmail.com-1, khidirzahid@gmail.com-2*), zarianafre@gmail.com-3 (*) corresponding author abstract pt warung islami bogor needs a warehouse to store goods that come from suppliers. currently, the selection of warehouses is still done manually and is subjective. it is feared that this will lead to inaccuracies in renting the warehouse. so an application is needed to assist companies in choosing a warehouse. the fuzzy c-means method can be used to classify warehouse data based on the characteristics of each group. after obtaining the next group is to make a rating of each group. one method that can be used is the topsis method. the topsis method can be applied to this application to rank the data warehouses that have been grouped. in the selection of this warehouse, there are several criteria. the criteria used are price, building area, distance from the head office (ho), parking area, and number of floors. the calculation process is done by dividing the warehouse data into several groups and ranking them to obtain the best recommendations. this application uses the php programming language with the laravel framework—testing using a black box. fuzzy c-means and topsis calculations show that warehouse ccc is the best warehouse in cluster 1 with a value of 0.797, and the warehouse in front of gas station villa bogor indah is the best in cluster 2 with a value of 0.613. keywords: cluster, warehouse, fuzzy c-means, topsis abstrak pt warung islami bogor membutuhkan sebuah gudang untuk menyimpan barang yang datang dari pemasok. saat ini pemilihan gudang masih dilakukan secara manual dan bersifat subjektif. hal ini dikhawatirkan akan menimbulkan ketidaktepatan dalam memilih gudang yang akan disewa. sehingga dibutuhkan aplikasi untuk membantu perusahaan dalam memilih gudang. metode fuzzy c-means dapat dilakukan untuk mengelompokkan data gudang berdasarkan karakteristik per tiap kelompok. setelah didapatkan kelompok selanjutnya yaitu membuat peringkat dari tiap kelompok. salah satu metode dapat digunakan adalah metode topsis. metode topsis dapat diterapkan pada aplikasi ini untuk membuat peringkat dari data gudang yang sudah dikelompokkan. pada pemilihan gudang ini ada beberapa kriteria. adapun kriteria yang digunakan yaitu harga, luas bangunan, jarak dari head office (ho), luas parkir, dan jumlah lantai. proses perhitungannya yaitu dengan cara membagi data gudang menjadi beberapa kelompok kemudian merangkingnya untuk memperoleh rekomendasi terbaik. aplikasi ini dibangun menggunakan bahasa pemrograman php dengan framework laravel. pengujian menggunakan blackbox. dari hasil perhitungan fuzzy c-means dan topsis didapatkan bahwa gudang ccc adalah gudang terbaik di kluster 1 dengan nilai 0.797 dan gudang depan pom bensin villa bogor indah adalah gudang terbaik di kluster 2 dengan nilai 0.613. kata kunci: cluster, gudang, fuzzy c-means, topsis introduction pt warung islami bogor is one of the developing companies in indonesia with a business that focuses on the trading sector of industrial packaging products. starting from an idea to help fellow business people/producers find it challenging to get product packaging with plastic bottles to cover their production needs, in 2012, pt warung islami bogor pioneered the company from a shop in bogor to become a national company that continues to grow. pt warung islami bogor specializes in distributing plastic bottle product packaging with a distribution network that spreads throughout indonesia through traditional and modern digital and internet-based channels. in its p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 312 distribution, pt warung islami bogor requires warehouses to accommodate goods coming from suppliers, and these goods will be sent back to stores owned by pt warung islami bogor, scattered in various cities in indonesia. a warehouse is a place or building used to hoard, and store goods, either in the form of raw materials, work in process, or finished products (r. widowati & septiawan, 2021). inside the warehouse are essential components of the modern supply chain. the supply chain involves activities in various stages: sourcing, production, and distribution of goods, from handling raw materials and works in progress to finished products. a warehouse is part of a company's logistics system, which stores products and provides information regarding the status and condition of materials/supplies stored in the warehouse so that this information is always up-to-date and easily accessible to anyone concerned (d. widowati & ningtiyas, 2022). selection of the current warehouse without using a specific calculation method. the general affairs (ga) staff conducts a field survey and records the warehouse data into a memorandum, then gives the memo to the ga supervisor, and the ga supervisor forwards it to the operations manager for discussion with the board of directors. the selection of warehouses is only based on the needs criteria at that time, for example, only based on price or building area. warehouse selection must be compared one by one with warehouse data that has been recorded, and the selection is subjective. to optimize the warehouse selection process, the fuzzy method is an alternative that can be used to group warehouse data. first of all, the data must be grouped using fuzzy clustering. fuzzy clustering is a technique for determining optimal clusters in a vector space based on the standard euclidian form for the distance between vectors. several data clustering algorithms exist, including fuzzy c-means (nugraha & riyandari, 2020). fuzzy c-means (fcm) is a data clustering technique in which the existence of each data point in a cluster is determined by the degree of membership (sanusi, zaky, & afni, 2020). the basic concept of fcm is to determine the cluster center that will mark each cluster's average location. with this method, the cluster center and membership degree are always repaired repeatedly so that it can be seen that the cluster center will move toward the correct location (hidayat, nazir, candra, sanjaya, & syafria, 2023; ningtyas, nasution, & syaripuddin, 2022). the fcm method allows dividing part of the data into two or more groups, comparing an object that divides into group members based on the division level (adifia, ulinnuha, & khaulasari, 2023). fcm also has high accuracy and fast computation time (rohmah & saputro, 2020). after the data is grouped, the data must be ranked. there are several data ranking methods, one of which is topsis. topsis is a concept in which the best-chosen alternative has the shortest distance from the positive ideal solution and the longest from the negative ideal solution (nasution & hanum, 2020; syafi’ie, tursina, & yulianti, 2019). this method is widely used in the mcdm concept to solve practical decision problems, and this is because the concept is simple and easy to understand, computationally efficient, and can measure the relative performance of decision alternatives in a simple mathematical form (sembiring & hasugian, 2021). after the data is ranked, the data will be presented by the manager to the management for consideration. to simplify this, we need an accurate system to make decision-making correctly. in this study, four previous studies will be used to support the research that will be carried out. the first study was written by giovan meidy susanto et al. with the title android smartphone selection reference system using the fuzzy cmeans and topsis methods in 2020. this study discusses the fuzzy c-means and topsis algorithms to provide references for selected android smartphones. the results of the fuzzy c-means and topsis calculations can group android smartphones into three clusters, then testing is carried out using white-box testing, and the results are that all functions in the software can run properly. the weakness of this system is that it does not discuss lifestyle needs in choosing android(susanto, kosasi, david, gat, & kuway, 2020). the second study was written by erlita faridatul himah and raden sulaiman, titled implementing fuzzy c-means and topsis methods in evaluating the financial performance of banking companies in indonesia based on financial ratios for 2021. this study discusses financial performance in the banking sector, which needs to be evaluated for company performance. banking in indonesia is based on financial ratios to determine a company's financial condition. the results of the discussion above are cluster rankings using the fuzzy c-means and topsis methods. in calculating company performance evaluations using the fuzzy c-means and topsis methods, a displacement index is obtained: the average number of companies in an inappropriate classification of 1.23 and the error rate of 25.65% (himah & sulaiman, 2021). jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 313 the third study was written by yuliadarnita et al. titled management of distribution of assistance for smes cooperative office using the fcm and topsis methods in 2023. this study discusses the management of aid distribution so that it can be given accurately. fuzzy c-means is used to classify eligible and ineligible recipients, and topsis is used to sort by turnover and the number of employees. the results of calculations using fuzzy c-means and topsis show that smes in bedeng bata deserve assistance (yuliadarnita, dwi wardana, & toyib, 2023). christian sri kusuma aditya wrote the fourth study titled selection of representative sentences with the integration of fuzzy c-means clustering and topsis (fcmtopsis) for document summarization in 2020. in this study, discussing text documents is a muchneeded source of information, but document collections in large numbers can hurt users who need a relatively long time to sort. a summary is needed for the essence without changing the context of information from a text document. the discussion above shows that this study integrates fuzzy c-means with the topsis method to get summary results from text documents (christian s. k. aditya, 2021). what this research has in common with previous research is the method used, namely fuzzy c-means and topsis, and what distinguishes this research from previous research is the research object, namely the selection of warehouses with price criteria, building area, distance from ho, parking area, and floors. research methods the research method applied is by conducting interviews and direct observation of pt warung islami bogor. more details can be seen in figure 1. time and place of research this research starts from march 2022 to june 2022 at pt warung islami bogor. research target / subject in this research, the target/subject is the hrd manager, and then the interview stages are carried out in the information of variables, criteria, and alternatives. procedure the research method is described in figure 1. the analysis begins with the stages of observation, interviews, and literature application. furthermore, the design is carried out using uml. the next stage is coding using the php programming language with the laravel framework. other applications are the google chrome application as a browser, mysql as a database development application, and apache as a web server. the testing phase is carried out using a black box to test the suitability of the application with the functionality or functional capabilities of the system. the fuzzy c-means algorithm generates calculation clusters, and the topsis algorithm generates recommendation values. figure 1. research methods data, instruments, and data collection techniques the data used in this research is warehouse data at pt warung islami bogor. warehouse data can be seen in table 1. table 1. warehouse data no code variable 1 w1 warehouse in front of gas station villa bogor indah 2 w2 warehouse in front of puri nirwana 3 w3 warehouse km 36 4 w4 warehouse 88 5 w5 warehouse welding workshop 6 w6 warehouse ccc 7 w7 warehouse pt. grasindo 8 w8 warehouse kacang garuda 9 w9 warehouse kedung halang 10 w10 warehouse pt. nugratama dayamitra 11 w11 warehouse nurdhin 12 w12 warehouse hj. nasrul p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 314 the criteria used for this research can be seen in table 2, and details can be seen in table 3. table 2. criteria data no code variable 1 c1 price 2 c2 building area 3 c3 distance from ho 4 c4 parking area 5 c5 floor table 3. details of the criteria data no ware house c1 (mon th/ye ar) c2 (m2) c3 (km) c4 (m2 ) c 5 1 w1 200 500 3,3 150 2 2 w2 200 300 1,5 150 2 3 w3 355 600 11,9 300 2 4 w4 460 1500 2,8 500 1 5 w5 180 300 2,8 150 1 6 w6 279 1200 3,3 500 2 7 w7 653 1600 3,2 150 1 8 w8 921 1650 0,8 400 1 9 w9 406 720 11,1 360 2 10 w10 868 1800 10,1 500 1 11 w11 607 600 12,7 700 1 12 w12 962 1400 15,2 546 1 data analysis technique with fuzzy c-means the first stage is warehouse data grouping using fuzzy c-means. 1. input data to be clustered x is a matrix of size n x m (n = number of data samples, m = attributes of each data). xij = sample data i (𝑖 = 1,2, . . . , 𝑛), attribute j (𝑗 = 1,2, . . . , 𝑚). 2. define: a. number of cluster = 𝒄; b. power = 𝒘; c. maximum iteration = maxlter; d. the smallest expected error = 𝜺; e. initial objective function p0 = 0; f. initial iteration = 𝑡 = 1; 3. generate random numbers 𝜇𝑖𝑘 , 𝑖 = 1,2, . . . , 𝑛; 𝑘 = 1,2, . . . , 𝑐; as the elements of the initial partition matrix u. count the sum of each column (attribute): 𝑸𝒋 = ∑ 𝒊𝒌 𝒄 𝒌=𝟏 ............................................................... (1) with 𝑗 = 1,2, . . . , 𝑚. calculate:  𝒊𝒌 = 𝒊𝒌 𝑸𝒋 ......................................................................... (2) 4. compute the cluster center k: 𝑉𝑘𝑗 , with 𝑘 = 1,2, . . . , 𝑐; and 𝑗 = 1,2, . . . , 𝑚. 𝑽𝒌𝒋 = ∑ ((𝒊𝒌) 𝒘∗𝑿𝒊𝒋) 𝒏 𝒊=𝟏 ∑ (𝒊𝒌) 𝒘𝒏 𝒊=𝟏 ................................................... (3) 5. compute the objective function on iteration t, pt. 𝑷𝒕 = ∑ ∑ ([∑ (𝑿𝒊𝒋 − 𝑽𝒌𝒋) 𝟐𝒎 𝒋=𝟏 ](𝒊𝒌) 𝒘)𝒄𝒌=𝟏 𝒏 𝒊=𝟏 ... (4) 6. compute the change in the partition matrix:  𝒊𝒌 = [∑ (𝑿𝒊𝒋−𝑽𝒌𝒋) 𝟐𝒎 𝒋=𝟏 ] −𝟏 𝒘−𝟏 ∑ [∑ (𝑿𝒊𝒋−𝑽𝒌𝒋) 𝟐𝒎 𝒋=𝟏 ] −𝟏 𝒘−𝟏𝒄 𝒌=𝟏 .................................... (5) with: 𝑖 = 1,2, . . . , 𝑛; and 𝑘 = 1,2, . . . , 𝑐. 7. check stop condition: if: (|𝑃𝑡 − 𝑃𝑡 − 1| < 𝜀) or (𝑡 > 𝑀𝑎𝑥𝐼𝑡𝑒𝑟) then stop; if not: 𝑡 = 𝑡 + 1, repeat step 4. data analysis technique with topsis the next step is to rank using the topsis. 1. create a normalized decision matrix; 2. create a weighted normalized decision matrix; 3. determine the positive ideal solution matrix & negative ideal solution matrix; 4. determine the distance between the value of each alternative with the positive ideal solution matrix & negative ideal solution matrix; 5. determine the preference value for each alternative. results and discussion warehouse data grouping using fuzzy c-means the first stage is warehouse data grouping using fuzzy c-means. 1. input warehouse data to be clustered based on table 3 in the form of xij matrix as follows: [ 𝑥11 𝑥21 ⋯ ⋯ 𝑥111 𝑥121 𝑥12 𝑥22 ⋯ ⋯ 𝑥112 𝑥122 𝑥13 𝑥23 ⋯ ⋯ 𝑥113 𝑥123 𝑥14 𝑥24 ⋯ ⋯ 𝑥114 𝑥124 𝑥15 𝑥25 ⋯ ⋯ 𝑥115 𝑥125]  i is warehouse data amounting to 12 (n=12)  j is criterion data amounting to 5 (m=5) 2. initialization : a. number of cluster = 2; b. power = 2; c. maximum iteration = 100; d. the smallest expected error = 10-2; e. initial objective function = 0; f. initial iteration = 1; jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 315 3. generate random numbers (𝜇𝑖𝑘 ) how to calculate the initial 𝜇𝑖𝑘 matrix: a. generate the random value of the partition matrix [ 𝜇11 ⋯ ⋯ ⋯ 𝜇111 𝜇121 𝜇12 ⋯ ⋯ ⋯ 𝜇112 𝜇122] b. calculate the number of each line (attribute) [ 0,316 ⋯ ⋯ ⋯ 0,912 0,365 0,765 ⋯ ⋯ ⋯ 0,179 0,727] → 1,081 ⋯ ⋯ ⋯ ⋯ ⋯ contoh baris ke 1 : 𝜇𝑖1 + 𝜇𝑖2 = 𝑄𝑗 0,316 + 0,765 = 1,081 c. calculate the matrix element. 𝜇𝑖𝑘  𝜇𝑖1 𝑄𝑗 = 𝜇𝑖1 0,316 1,081 = 0,292  𝜇𝑖1 𝑄𝑗 = 𝜇𝑖1 0,765 1,081 = 0,708 𝑄𝑗 is the number of degrees of membership in line = 1: 0,292 + 0,708 = 1 so that the value of the first-row initial partition matrix is obtained: 0,292 0,708 thus, for rows 2 to 12, the initial partition matrix is obtained in table 4. table 4. initial 𝝁𝒊𝒌 matrix 𝝁𝒊𝟏 𝝁𝒊𝟐 0,292 0,125 0,620 0,514 0,112 0,921 0,205 0,545 0,129 0,410 0,836 0,334 0,708 0,875 0,380 0,486 0,888 0,079 0,795 0,455 0,871 0,590 0,164 0,666 4. calculate cluster centroid (𝑣𝑘𝑗 ) for the first cluster center: is known 𝜇11 2 = (0,292)2 = 0,085, and so on until the warehouse to 12 so that ∑ (𝜇𝑖1) 𝑤𝑛 𝑖=1 = 2,944 calculate the first warehouse value for the 1st criterion : 𝜇11 2 × 𝑥11 = (0,292) 2 × 200 = 17,034, and so on until the warehouse to 12 so that ∑ ((𝜇𝑖1) 𝑤 × 𝑥𝑖1) 𝑛 𝑖=1 = 1502,124 calculate the first warehouse value for the 2nd criterion : 𝜇11 2 × 𝑥12 = (0,292) 2 × 500 = 42,584, and so on until the warehouse to 12 so that ∑ ((𝜇𝑖1) 𝑤 × 𝑥𝑖2) 𝑛 𝑖=1 = 3142,911 calculate the first warehouse value for the 3rd criterion : 𝜇11 2 × 𝑥13 = (0,292) 2 × 3,3 = 0,281, and so on until the warehouse to 12 so that ∑ ((𝜇𝑖1) 𝑤 × 𝑥𝑖3) 𝑛 𝑖=1 = 21,273 calculate the first warehouse value for the 4th criterion : 𝜇11 2 × 𝑥14 = (0,292) 2 × 150 = 12,775, and so on until the warehouse to 12 so that ∑ ((𝜇𝑖1) 𝑤 × 𝑥𝑖4) 𝑛 𝑖=1 = 1453,435 calculate the first warehouse value for the 5th criterion : 𝜇11 2 × 𝑥15 = (0,292) 2 × 2 = 0,170, and so on until the warehouse to 12 so that ∑ ((𝜇𝑖1) 𝑤 × 𝑥𝑖5) 𝑛 𝑖=1 = 4,293 calculate the central value for the 1st cluster : 𝑉11 = 510,301 𝑉12 = 1067,708 𝑉13 = 7,227 𝑉14 = 493,760 𝑉15 = 1,458 and so on for the 2nd cluster center, so the cluster center is obtained in table 5. table 5. 1st iteration cluster center 𝑽𝒌𝒋 𝒙𝒊𝟏 𝒙𝒊𝟐 𝒙𝒊𝟑 𝒙𝒊𝟒 𝒙𝒊𝟓 cluster 1 510,301 1067,708 7,227 493,760 1,458 cluster 2 455,540 890,706 5,889 279,577 1,448 5. compute objective function for the 1st cluster, calculate the 1st warehouse value for the 1st criterion. so the objective function is obtained in table 6. p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 316 table 6. 1st iteration objective function [∑ (𝒙𝒊𝒋 − 𝒎 𝒋=𝟏 𝑽𝒌𝒋) 𝟐 ] (𝝁𝒊𝟏 𝒘 ) [∑ (𝒙𝒊𝒋 − 𝒎 𝒋=𝟏 𝑽𝒌𝒋) 𝟐 ] (𝝁𝒊𝟐 𝒘 ) 45715,076 117726,384 12551,624 330051,563 107755,691 13737,417 50103,957 99067,354 10194,818 348477,762 60226,013 1104,689 17662,799 353573,876 153477,066 167159,649 2496,727 28886,814 111846,019 363532,067 189050,165 7669,505 35392,487 260336,977 6. calculate the new partition (𝜇𝑖𝑘 ) matrix how to calculate the new u partition matrix : table 7. 1st iteration partition 𝝁𝒊𝒌 matrix [∑ (𝑥𝑖𝑗 − 𝑚 𝑗=1 𝑉𝑘𝑗) 2 ] −1 𝑤−1 [∑ (𝑥𝑖𝑗 − 𝑚 𝑗=1 𝑉𝑘𝑗) 2 ] −1 𝑤−1 ∑ [∑ (𝑥𝑖𝑗 − 𝑚 𝑗=1 𝑐 𝑘=1 𝑉𝑘𝑗) 2 ] −1 𝑤−1 0,00000186 0,00000426 0,00000612 0,00000124 0,00000232 0,00000356 0,00000357 0,00001052 0,00001408 0,00000528 0,00000238 0,00000766 0,00000122 0,00000226 0,00000349 0,00001407 0,00000570 0,00001977 0,00000237 0,00000179 0,00000416 0,00000194 0,00000124 0,00000317 0,00000668 0,00002625 0,00003293 0,00000151 0,00000096 0,00000246 0,00000369 0,00000352 0,00000721 0,00000315 0,00000170 0,00000486 7. check the stop condition. |𝑃𝑡 − 𝑃0| = |2887796,499 − 0| = 2887796,499 in 1st iteration, the conditions have not been met because (|𝑃𝑡 − 𝑃0| > 𝜀), and (𝑡 < 𝑀𝑎𝑥𝐼𝑡𝑒𝑟), then the process continues to 2nd iteration until the conditions are met. the process stops at the 10th iteration because of the condition. (|𝑃𝑡 − 𝑃𝑡 − 1| < 𝜀) has been met. in other experiments, different cluster positions may be obtained due to the random initial initialization of the partition matrix, but this does not affect the final result. in the 10th iteration, the cluster center and the new 𝜇𝑖𝑘 matrix is obtained in table 8: table 8. 10th iteration cluster center 𝑽𝒌𝒋 𝒙𝒊𝟏 𝒙𝒊𝟐 𝒙𝒊𝟑 𝒙𝒊𝟒 𝒙𝒊𝟓 cluster 1 739,094 1554,222 6,214 427,274 1,074 cluster 2 307,721 521,034 6,791 284,492 1,715 from table 8, the following information is obtained: a. cluster 1 contains warehouse data which has a price of around idr 739,094,000; an average building area of 1,554.222 m2; the distance from ho is about 6,214 km; an average parking area of 427,274 m2; and an average of 1 floor. this cluster is a cluster with a higher price but with a larger building area. b. cluster 2 contains warehouse data which has a price of around idr 307,721,000; an average building area of 521,034 m2; the distance from ho is about 6,791 km; an average parking area of 284,492 m2; and an average of 2 floors. this cluster has a lower price but a smaller building area. table 9. new 𝝁𝒊𝒌 matrix no ware house the degree of data membership in the cluster the most significant degree of membership in the cluster 1 2 1 w1 0,020 0,980 2 2 w2 0,039 0,961 2 3 w3 0,008 0,992 2 4 w4 0,923 0,077 1 5 w5 0,041 0,959 2 6 w6 0,597 0,403 1 7 w7 0,938 0,062 1 8 w8 0,975 0,025 1 9 w9 0,063 0,937 2 10 w10 0,960 0,040 1 11 w11 0,211 0,789 2 12 w12 0,935 0,065 1 the new 𝜇𝑖𝑘 the matrix table shows the degree of membership of the data warehouse in each cluster. the most significant degree of membership shows the highest tendency of the data warehouse to become a member of the cluster. from table 9, the following information is obtained : a. based on the largest membership in cluster 1, there are 6 data warehouses in cluster 1, namely the warehouse: 4, 6, 7, 8, 10, dan 12. b. based on the most significant degree of membership in cluster 2, there are 6 data warehouses in cluster 2, namely the warehouse 1, 2, 3, 5, 9, dan 11. jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 317 warehouse data ranking using topsis after the data is grouped, the data must be ranked. 1. create a normalized decision matrix. table 10. warehouse data cluster 1 no ware house c1 c2 c3 c4 c5 1 w4 460 1500 2,8 500 1 2 w6 279 1200 3,3 500 2 3 w7 653 1600 3,2 150 1 4 w8 921 1650 0,8 400 1 5 w10 868 1800 10,1 500 1 6 w12 962 1400 15,2 546 1 table 11. warehouse data cluster 2 no ware house c1 c2 c3 c4 c5 1 w1 200 500 3,3 150 2 2 w2 200 300 1,5 150 2 3 w3 355 600 11,9 300 2 4 w5 180 300 2,8 150 1 5 w9 406 720 11,1 360 2 6 w11 607 600 12,7 700 1 for cluster 1 calculate the first warehouse value for the 1st criterion: 𝑥11 2 = 4602 = 211.600 calculate the first warehouse value for the 2nd criterion: 𝑥11 2 = 2792 = 77.841 calculate the first warehouse value for the 3rd criterion: 𝑥11 2 = 6532 = 426.409 calculate the first warehouse value for the 4th criterion: 𝑥11 2 = 9212 = 848.241 calculate the first warehouse value for the 5th criterion: 𝑥11 2 = 8682 = 753.424 calculate the first warehouse value for the 6th criterion: 𝑥11 2 = 9622 = 925.444 ∑ 𝑥𝑖𝑗 2𝑚 𝑖=1 = 3.242.959 𝑟11 = 0,255 and so on until the sixth warehouse. to obtain a normalized decision matrix, in table 12. table 12. normalized decision matrix cluster 1 no ware house 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 1 w4 0,255 0,398 0,147 0,451 0,333 2 w6 0,155 0,319 0,173 0,451 0,667 3 w7 0,363 0,425 0,168 0,135 0,333 4 w8 0,511 0,438 0,042 0,361 0,333 5 w10 0,482 0,478 0,530 0,451 0,333 6 w12 0,534 0,372 0,798 0,492 0,333 in the same way for the second cluster, so that in the second cluster, a normalized decision matrix is obtained. table 13. table 13. normalized decision matrix cluster 2 no ware house 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 1 w1 0,228 0,387 0,156 0,170 0,471 2 w2 0,228 0,232 0,071 0,170 0,471 3 w3 0,404 0,465 0,563 0,340 0,471 4 w5 0,205 0,232 0,132 0,170 0,236 5 w9 0,462 0,557 0,525 0,408 0,471 6 w11 0,691 0,465 0,601 0,794 0,236 2. create a weighted normalized decision matrix. the weight to be used is based on table 3 calculate normalized weights : 𝑤𝑖𝑗 = 4+3+2+2+2 = 13 calculate the normalized weight for the 1st criterion: 𝑤1 = 4 ÷ 13 = 0,308 and so on until the 5th criterion calculate the first warehouse value : 𝑦11 = 𝑤1 × 𝑟11 = 0,308 × 0,255 = 0,079 and so on until the sixth warehouse. so we get a weighted normalized decision matrix in table 14. table 14. weighted normalized decision matrix cluster 1 no ware house 𝑦𝑖𝑗 𝑦𝑖𝑗 𝑦𝑖𝑗 𝑦𝑖𝑗 𝑦𝑖𝑗 1 w4 0,079 0,092 0,023 0,069 0,051 2 w6 0,048 0,074 0,027 0,069 0,103 3 w7 0,112 0,098 0,026 0,021 0,051 4 w8 0,157 0,101 0,006 0,055 0,051 5 w10 0,148 0,110 0,082 0,069 0,051 6 w12 0,164 0,086 0,123 0,076 0,051 in the same way, for the second cluster, so that in the second cluster a weighted normalized decision matrix is obtained in table 15. table 15. weighted normalized decision matrix cluster 2 no ware house 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 𝑟𝑖𝑗 1 w1 0,070 0,089 0,024 0,026 0,073 2 w2 0,070 0,054 0,011 0,026 0,073 3 w3 0,124 0,107 0,087 0,052 0,073 4 w5 0,063 0,054 0,020 0,026 0,036 5 w9 0,142 0,129 0,081 0,063 0,073 6 w11 0,213 0,107 0,092 0,122 0,036 3. determine the positive ideal solution matrix & negative ideal solution matrix. if the criterion is a benefit, the most significant value is calculated by calculating the positive ideal solution matrix. if the criterion is cost, then the p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 jurnal riset informatika vol. 5, no. 3 june 2023 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 318 smallest value is taken. the result of the negative ideal solution matrix is taken as the smallest value if the criterion is beneficial. if the criterion is cost, then the most significant value is taken. so we get the matrix of positive and negative ideal solutions in table 16. table 16. matrix of positive and negative ideal solutions cluster 1 𝒚𝒋 + 0,048 0,110 0,006 0,076 0,103 𝒚𝒋 − 0,164 0,074 0,123 0,021 0,051 in the same way, in the second cluster, so that in the second cluster the positive and negative ideal solution matrices are obtained in table 17. table 17. matrix of positive and negative ideal solutions cluster 2 𝒚𝒋 + 0,063 0,129 0,011 0,122 0,073 𝒚𝒋 − 0,213 0,054 0,092 0,026 0,036 4. determine the distance between the value of each warehouse with the positive ideal solution matrix & negative ideal solution matrix. calculate the distance between the values of each warehouse and the positive ideal solution matrix for the first warehouse : 𝐷1 + = 0,065. and so on until the sixth warehouse. calculate the distance between the values of each warehouse and the negative ideal solution matrix for the first warehouse: 𝐷1 − = 0,142 and so on until the sixth warehouse. to get the distance between warehouses, here can be seen in the matrix of positive and negative ideal solutions in table 18. table 18. warehouse distance with positive and negative ideal solution matrix cluster 1 no warehouse 𝐷𝑖 + 𝐷𝑖 − 1 w4 0,065 0,142 2 w6 0,042 0,167 3 w7 0,101 0,113 4 w8 0,123 0,125 5 w10 0,136 0,075 6 w12 0,174 0,056 in the same way for the second cluster, so that in the second cluster, the distance between the warehouse and the positive and negative ideal solution matrices is obtained in table 19. table 19. warehouse distance with positive and negative ideal solution matrix cluster 2 no warehouse 𝐷𝑖 + 𝐷𝑖 − 1 w1 0,105 0,166 2 w2 0,122 0,168 3 w3 0,122 0,113 4 w5 0,127 0,166 5 w9 0,121 0,116 6 w11 0,175 0,110 5. determine the preference value for each alternative for the 1st warehouse : 𝑉1 = 0,142 0,065+0,142 = 0,686 and so on until the sixth warehouse. so that the preference value is obtained which can be seen in table 20. table 20. preference value cluster 1 no warehouse 𝑉𝑖 1 w4 0,686 2 w6 0,797 3 w7 0,528 4 w8 0,503 5 w10 0,357 6 w12 0,244 in the same way as the second cluster, the second cluster gets the preference value in table 21. table 21. preference value cluster 2 no warehouse 𝑉𝑖 1 w1 0,613 2 w2 0,579 3 w3 0,481 4 w5 0,566 5 w9 0,489 6 w11 0,385 6. based on the order of choice, the warehouse with the best preference value is the best. these results can be seen in table 22. table 22. final results ranking cluster 1 no warehouse 𝑉𝑖 1 w6 0,797 2 w4 0,686 3 w7 0,528 4 w8 0,503 5 w10 0,357 6 w12 0,244 jurnal riset informatika vol. 5, no. 3. june 2023 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v5i3.517 accredited rank 4 (sinta 4), excerpts from the decision of the ditjen diktiristek no. 230/e/kpt/2023 319 table 23. final results ranking cluster 2 no warehouse 𝑉𝑖 1 w1 0,613 2 w2 0,579 3 w5 0,566 4 w9 0,489 5 w3 0,481 6 w11 0,385 in table 23, it can be explained that the recommended warehouse data for cluster 1 is the ccc warehouse, with the highest value of 0.797. and for cluster 2, namely the warehouse in front of the villa bogor gas station, with a value of 0.613. system implementation figure 2-5 is the result of the display of the warehouse selection application. figures 2 and 3 show the criteria and warehouse data, while figures 4 and 5 are the calculation process pages. 1. criteria data figure 2. criteria data 2. warehouse data figure 3. warehouse data 3. calculation process figure 4. calculation process 4. calculation result detail page figure 5. calculation result detail page conclusions and suggestions conclusion the conclusion that can be drawn from this research is that this application can group warehouse data into two clusters. with the characteristics of the first cluster, the price is higher but with a larger building area, and the characteristics of the second cluster are cheaper but with a smaller building area. the results of grouping data must be ranked to obtain recommended data warehouses. the warehouse selection application can run according to the needs desired by the company and has been implemented at pt warung islami bogor. suggestion thanks to the newly added warehouse data import capability, companies no longer need to manually enter warehouse data into the system. meanwhile, the subtractive clustering with fuzzy cmeans algorithm is used to increase speed. references adifia, n. n. n., ulinnuha, n., & khaulasari, h. 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(2019). sistem pendukung keputusan pemilihan daerah prioritas penanganan stunting pada balita menggunakan metode topsis (studi kasus : kota pontianak). justin (jurnal sistem dan teknologi informasi), 7(1), 33–39. https://doi.org/10.26418/justin.v7i1.2781 5 widowati, d., & ningtiyas, l. m. w. (2022). analisis penyimpanan dan penempatan barang pada pt bintang dagang internasional (haistar) surabaya dalam mendukung ketersediaan barang di era pandemi. jurnal ilmiah administrasi bisnis dan inovasi, 6(2), 100– 114. https://doi.org/10.25139/jiabi.v6i2.5212 widowati, r., & septiawan, t. r. (2021). administrasi pergudangan pada cv eastern depo jakarta. jurnal akuntansi & bisnis, 7(01), 46–54. retrieved from http://journal.stieyppi.ac.id/index.php/jab/article/view/376 yuliadarnita, dwi wardana, a., & toyib, r. (2023). manajemen pembagian bantuan untuk umkm dinas koperasi menggunakan metode fcm dan topsis. jurnal media infotama, 19(1), 61–68. https://doi.org/10.37676/jmi.v19i1.3423 p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 17 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penerapan transformasi data discrete wavelet transform pada neural network untuk prediksi harga saham indah suryani teknik informatika stmik nusa mandiri jakarta indah.ihy@nusamandiri.ac.id abstrak penelitian mengenai harga saham memang masih menarik bagi para peneliti. seperti halnya dalam penelitian ini, data penutupan harga saham antm dijadikan sebagai set data yang diolah untuk kemudian dilakukan prediksi harga kedepannya. adapun metode neural network merupakan metode yang sangat banyak digunakan peneliti karena berbagai keunggulannya. sedangkan metode discrete wavelet transform digunakan untuk melakukan transformasi data. penggunaan transformasi data menggunakan discrete wavelet transform diharapkan dapat meningkatkan kualitas data sehingga dapat meningkatkan performa neural network. adapun berdasarkan eksperimen yang dilakukan dengan metode neural network dengan fungsi aktivasi binary sigmoid menunjukkan hasil rmse 0,024 sampai dengan 0,022. sedangkan dari hasil eksperimen neural network dengan fungsi aktivasi binary sigmoid yang dilakukan transformasi data dengan discrete wavelet transform, telah menghasilkan rmse yang lebih kecil daripada eksperimen prediksi yang tanpa menggunakan transformasi data dengan discrete wavelet transform yaitu 0,02 sampai dengan 0,018. dari hasil perbandingan rmse tersebut, terdapat selisih nilai rata-rata rmse sebesar 0,0039. artinya penerapan transformasi data menggunakan discrete wavelet transform ini ternyata mampu meningkatkan performa prediksi dengan neural network yaitu dengan menghasilkan nilai error yang lebih kecil atau menghasilkan prediksi yang lebih akurat. kata kunci: prediksi, harga saham, neural network, discrete wavelet transform abstract research on stock prices is still interesting for researchers. as in this study, antm's stock price closing data is used as a data set that is processed and then predicted in the future. the neural network method is a method that is very widely used by researchers because of its various advantages. while the discrete wavelet transform method is used to perform data transformation. the use of data transformation using discrete wavelet transform is expected to improve data quality so that it can improve neural network performance. the experiment based on the neural network method with the binary sigmoid activation function shows the results of rmse from 0.024 to 0.022. while the results of neural network experiments with the binary sigmoid activation function which carried out data transformation with discrete wavelet transform, has produced a smaller rmse than prediction experiments without using data transformation with discrete wavelet transform that is 0.02 to 0.018. from the results of the rmse comparison this, there is a difference in the average value of rmse of 0.0039. this means that the application of data transformation using discrete wavelet transform turns out to be able to improve the performance of predictions with neural networks by producing smaller error values or producing more accurate predictions. keywords: prediction, stock prices, neural network, discrete wavelet transform pendahuluan prediksi pasar saham selalu menjadi hal yang menarik bagi para peneliti (rajput & bobde, 2016). sejalan dengan pendapat (a, adebiyi, k, charles, o, marion, & o, sunday, 2012) bahwa prediksi harga saham dengan teknik datamining adalah salah satu masalah terpenting dalam keuangan yang diselidiki oleh para peneliti di seluruh dunia. data harga saham merupakan data kuantitatif yang termasuk ke dalam data time series. time series merupakan satu set pengamatan kuantitatif yang diatur dalam urutan kronologis (kirchgässner & wolters, 2007). time series plots dapat mengungkapkan pola seperti random, tren, pergeseran tingkat, periode atau siklus, pengamatan yang tidak biasa atau kombinasi dari pola (montgomery, 2008). http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 18 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dalam berbagai bidang penelitian, metode neural network merupakan salah satu metode yang paling banyak digunakan oleh para peneliti. manfaat utama dari penggunaan neural network (bennett, stewart, & lu, 2014) adalah kemampuannya untuk menggeneralisasi, mengidentifikasi hubungan non-linear dan penerapan ke berbagai aplikasi. sebagai approximators dan sistem pembelajaran yang fleksibel, jaringan saraf telah menarik meningkatnya minat dalam menggunakan mereka untuk pemodelan dan peramalan runtun waktu (ouyang & yin, 2014). discrete wavelet transform mirip dengan discrete fourier transform dan banyak digunakan dalam aplikasi praktis. prinsipnya tetap sama dengan transformasi lainnya. sinyal diubah menjadi bentuk yang berbeda menggunakan fungsi dasar yang lebih cocok untuk pemrosesan yang diperlukan, dan transformasi dapat dilakukan secara efisien menggunakan algoritma cepat (sundararajan, 2015). haar discrete wavelet transform adalah wavelet paling sederhana diantara berbagai jenis wavelet. beberapa penelitian terkait dengan topik yang diangkat dalam penelitian ini diantaranya adalah penelitian yang dilakukan oleh (a, adebiyi et al., 2012) yang menyajikan pendekatan hibdrida dari kombinasi variabel terhadap analisis teknis dan analisis fundamental untuk membuat model prediksi feed forward multilayer perceptrom neural network yang dilatih menggunakan algoritma backpropagation untuk meningkatkan akurasi prediksi harga saham. pada hasil empirisnya menunjukkan level akurasi tinggi untuk prediksi harga saham menggunakan pendekatan hibrida ternyata lebih baik dari pendekatan analisis teknis. dalam penelitiannya (anbazhagan & kumarappan, 2014) mengangkat mengenai perlunya proses pra pengolahan data untuk mengekstrak informasi berlebihan dari sinyal asli. dalam penelitiannya dilakukan proses pra pengolahan data dengan menggunakan metode discrete cosine transform untuk meningkatkan efisiensi pembelajaran pada feed forward neural network. hasilnya menunjukan bahwa model ini menunjukkan kompleksitas dan waktu komputasi yang lebih rendah jika dibandingkan dengan 17 model lainnya. selain itu penelitian (suryani, 2015) juga menunjukkan peningkatan terhadap evaluasi nilai root mean square error (rmse) terhadap prediksi harga emas dengan menggunakan metode neural network yang sebelumnya dilakukan transformasi data menggunakan exponential smoothing. salah satu keterbatasan metode penelitian adalah bahwa mereka mengabaikan potensi transformasi untuk meningkatkan perkiraan (beaumont, 2014). maka berdasarkan hasil penelitian terkait, pada penelitian ini dilakukan penerapan transformasi data menggunakan discrete wavelet transform yang diharapkan dapat meningkatkan performa dari metode neural network dalam melakukan prediksi harga saham antm. metode penelitian jenis penelitian penelitian ini meerupakan penelitian kuantitatif berupa penelitian eksperimental. data dan teknik pengumpulan data data yang digunakan dalam penelitian ini adalah data saham perusahaan aneka tambang persero, tbk. data tersebut berupa data historis harga saham antm yang dikumpulkan dari data sekunder yang diambil dari situs web. data saham antm yang digunakan dalam penelitian ini berupa data saham antm sebanyak 2.699 record dari 02 januari 2008 hingga 28 desember 2018. dari data tersebut dipilih entitas berupa tanggal dan harga penutupan saham yang kemudian digunakan dan diolah dalam penelitian ini. data harga saham antm tersebut dapat dilihat pada tabel 1. tabel1. sample data harga saham antm date close 28/12/2018 765 27/12/2018 770 26/12/2018 770 21/12/2018 770 20/12/2018 765 19/12/2018 760 18/12/2018 750 17/12/2018 730 14/12/2018 755 13/12/2018 765 12/12/2018 755 11/12/2018 755 10/12/2018 720 07/12/2018 725 06/12/2018 735 05/12/2018 740 04/12/2018 710 03/12/2018 735 30/11/2018 615 29/11/2018 625 15/11/2018 670 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 19 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional teknik analisis data dari data yang telah dikumpulkan tersebut, kemudian dilakukan proses pengolahan data awal berupa tahapan pra pengolahan data. adapun tahapan pra pengolahan data awal yang dilakukan dalam penelitian ini meliputi: 1. replace missing value pada penelitian ini, pada pengolahan data awal yang pertama kali dilakukan adalah dengan melakukan replace missing value dikarenakan terdapat data yang masih missing value . (hofmann, 2009) replace missing value yaitu salah satu operator yang terdapat di data cleansing pada rapidminer yang membantu menangani nilai null yang mungkin ada dalam data, yang dapat digunakan untuk menemukan nilai-nilai yang hilang dalam atribut atau serangkaian atribut dan merubahnya, dari nilai yang hilang ke nilai yang diinginkan 2. set role pada penelitian ini, set role operator digunakan untuk menjadikan atribut date sebagai atribut spesial yaitu sebagai atribut id 3. normalize windowing merupakan salah satu teknik dalam menentukan data input dan data output dalam prediksi data runtun waktu dengan tipe univariat. data univariat adalah distribusi data dengan melibatkan satu atribut atau variabel (han, kamber, & pei, 2012). pada penelitian ini dilakukan normalisasi data menggunakan fungsi aktivasi binary sigmoid dengan jangkauan 0,1 s/d 0,9. perhitungan untuk binary sigmoid (logsig) didapat dari rumus sebagai berikut: 𝑦′ = 𝑥−𝑥𝑚𝑖𝑛 𝑥𝑚𝑎𝑥−𝑥𝑚𝑖𝑛 × (0.9 − 0.1) + 0.1 .............. ... (1) adapun ilustrasi dari fungsi binary sigmoid dapat dilihat pada gambar 1 berikut. f(x) 0 1 gambar 1. fungsi binary sigmoid 4. windowing teknik windowing dilakukan untuk memecah atribut close pada data harga saham untuk kemudian dipecah menjadi 5 data input dan 1 data output. data input merupakan data 5 hari sebelumnya dan data output adalah data 1 hari berikutnya 5. discrete wavelet transform transformasi data dilakukan dengan mengaplikasikan operator process series yang kemudian dipilih metode tranformasi data dengan discrete wavelet transform berupa haar wavelet. kemudian dari pengolahan data tersebut menghasilkan set data baru dan kemudian diolah dengan metode neural network menggunakan 10 fold cross validation yang dipecah dalam data training dan data testing. kemudian akan didapat nilai rmsenya untuk kemudian dilakukan perbandingan antara metode neural network dengan metode yang diusulkan yaitu metode neural netwotk yang ditambahkan transformasi data dengan discrete wavelet transform. adapun prosesnya dapat dilihat pada gambar 2 sebagai berikut. set data replace missing value set role normalize windowing discrete wavelet transform set data baru perbandingan rmse pra pengolahan data process series x validation neural network data pelatihan rmse data pengujian model evaluasi gambar 2. metode yang diusulkan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 20 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional hasil penelitian dan pembahasan pada penelitian ini, eksperimen yang dilakukan menggunakan tools rapidminer 8.0. tahapan eksperimen yang dilakukan adalah sebagai berikut: 1. menyiapkan set data yang digunakan yaitu berupa data saham antm sebanyak 2.669 record 2. melakukan proses pra pengolahan data dengan melakukan replace missing value, set role, normalize dan windowing 3. melakukan transformasi data menggunakan process series dengan discrete wavelet transform menggunakan haar wavelet dan memilih forward direction 4. menerapkan metode neural network dengan memasukan parameter berupa training cycle, learning rate, momentum dan hidden layer 5. melakukan training dan testing terhadap metode usulan 6. melakukan evaluasi dengan mencatat hasil rmse dari eksperimen yang telah dilakukan. adapun perhitungan hasil rmse didapat dari rumus berikut. 𝐴 = √ ∑ (𝐺𝑖−𝐺𝑖′)𝑁𝑖=1 𝑁 .............. .......................................(2) eksperimen pertama dilakukan dengan melakukan inisialisasi parameter neural network yang terdiri dari training cycle, learning rate, momentum dan hidden layer dan normalisasi neural network menggunakan fungsi aktivasi logsig atau binary sigmoid berupa jangkauan 0,1 sampai dengan -0,9. hasil eksperimen ini kemudian dicatat ke dalam tabel 2 berikut ini. tabel 2. hasil eksperimen metode neural network (fungsi aktivasi binary sigmoid) no perco baan hidd en layer size train ing cycle learn ing rate mom entu m ho riz on rmse 1 1 500 0.3 0.2 1 0,022 2 1 500 0.6 0.3 1 0,023 3 3 1000 0.6 0.3 1 0,023 4 3 1000 0.9 0.6 1 0,023 5 3 500 0.9 0.6 1 0,023 6 1 300 0.5 0.5 1 0,023 7 1 300 0.1 0.3 1 0,024 8 3 500 0.3 0.2 1 0,022 9 3 500 0.6 0.3 1 0,023 10 3 500 0.9 0.6 1 0,023 eksperimen selanjutnya dilakukan dengan menerapkan metode usulan berupa penerapan transformasi data discrete wavelet transform dengan memilih metode haar dan forward direction untuk transformasi discrete wavelet transform nya. selanjutnya menginisialisasi parameter neural network yang terdiri dari training cycle, learning rate, momentum dan hidden layer dan normalisasi neural network menggunakan fungsi aktivasi logsig atau binary sigmoid berupa jangkauan 0,1 sampai dengan -0,9. hasil eksperimen ini kemudian dicatat ke dalam tabel 3 sebagai berikut. tabel 3. hasil eksperimen metode neural network (fungsi aktivasi binary sigmoid)+discrete wavelet transform no perc obaa n hidd en layer size train ing cycle lear ning rate mom entu m horiz on rmse 1 1 500 0.3 0.2 1 0,019 2 1 500 0.6 0.3 1 0,018 3 3 1000 0.6 0.3 1 0,018 4 3 1000 0.9 0.6 1 0,019 5 3 500 0.9 0.6 1 0,02 6 1 300 0.5 0.5 1 0,018 7 1 300 0.1 0.3 1 0,02 8 3 500 0.3 0.2 1 0,02 9 3 500 0.6 0.3 1 0,018 10 3 500 0.9 0.6 1 0,02 gambar 3 di bawah ini merupakan arsitektur eksperimen prediksi harga saham menggunakan metode neural network dengan transformasi data discrete wavelet transform dengan parameter 1 hidden layer, training cycle 300, dan learning rate dan momentum masingmasing 0,5 yang menghasilkan rmse sebesar 0,018.. gambar 3. arsitektur neural network + discrete wavelet transform http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 21 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional dari eksperimen metode neural network dan neural network discrete wavelet transform, terdapat beberapa perbedaan hasil rmse yang kemudian dilakukan pencatatan hasil rmsenya ke dalam tabel 4 sebagai berikut. tabel 4. perbandingan hasil rmse nn dan nn+dwt neural network neural network + dwt 0,022 0,019 0,023 0,018 0,023 0,018 0,023 0,019 0,023 0,02 0,023 0,018 0,024 0,02 0,022 0,02 0,023 0,018 0,023 0,02 berdasarkan data pada tabel 3 di atas, maka dapat digambarkan dalam gambar 4 di bawah ini bagaimana selisih dari perbandingan hasil rmse dari kedua eksperimen tersebut. gambar 4. grafik perbandingan rmse neural network (fungsi aktivasi binary sigmoid dan neural network (fungsi aktivasi binary sigmoid)+ discrete wavelet transform simpulan dan saran simpulan metode usulan pada penelitian ini diterapkan dengan melakukan transformasi data discrete wavelet transform untuk mengembangkan metode neural network dengan fungsi aktivasi binary sigmoid. metode usulan ini digunakan untuk melakukan prediksi terhadap data saham antm yang kemudian berdasarkan eksperimen yang telah dilakukan terbukti dapat meningkatkan hasil prediksi harga saham. hal ini dilakukan dengan melihat perbandingan hasil rmse yang dihasilkan. pada eksperimen menggunakan metode neural network fungsi aktivasi binary sigmoid, dari 10 eksperimen yang dilakukan, menghasilkan nilai rmse terkecil sebesar 0,022 dan rmse rata-rata sebesar 0,0229. sedangkan untuk metode usulan yaitu metode neural network fungsi aktivasi binary sigmoid dengan transformasi data discrete wavelet transform dihasilkan rmse terkecil sebesar 0,018 dan rmse rata-rata sebesar 0,019 sehingga terdapat selisih nilai rmse sebesar 0,0039. saran berdasarkan kesimpulan penelitian, maka untuk penelitian di masa mendatang dapat dilakukan pengembangan terhadap metode neural network dengan metode lainnya untuk dapat lebih meningkatkan performa neural network sehingga menghasilkan prediksi yang lebih akurat lagi. daftar referensi a, adebiyi, a., k, charles, a., o, marion, a., & o, sunday, o. (2012). stock price prediction using neural network with hybridized market indicators. journal of emerging trends in computing and information sciences, 3(1), 1–9. anbazhagan, s., & kumarappan, n. (2014). dayahead deregulated electricity market price forecasting using neural network input featured by dct. energy conversion and management, 78, 711–719. https://doi.org/10.1016/j.enconman.2013.1 1.031 beaumont, a. n. (2014). data transforms with exponential smoothing methods of forecasting. international journal of forecasting, 30(4), 918–927. https://doi.org/10.1016/j.ijforecast.2014.03. 013 bennett, c. j., stewart, r. a., & lu, j. w. (2014). forecasting low voltage distribution network demand profiles using a pattern recognition based expert system. energy, 67, 200–212. https://doi.org/10.1016/j.energy.2014.01.0 32 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 22 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional han, j., kamber, m., & pei, j. (2012). data mining: concepts and techniques. hofmann, m. (2009). data mining and knowledge discovery series. kirchgässner, g., & wolters, j. (2007). introduction to modern time series analysis. https://doi.org/10.1007/978-3-540-732914 montgomery, d. c. (2008). introduction to time series analysis and forecasting. ouyang, y., & yin, h. (2014). a neural gas mixture autoregressive network for modelling and forecasting fx time series. neurocomputing, 135, 171–179. https://doi.org/10.1016/j.neucom.2013.12.0 37 rajput, v., & bobde, s. (2016). stock market forecasting techniques: literature survey. international journal of computer science and mobile computing, 5(6), 500–506. retrieved from www.ijcsmc.com sundararajan, d. (2015). discrete wavelet transform: a signal processing approach. in discrete wavelet transform: a signal processing approach. https://doi.org/10.1002/9781119113119 suryani, i. (2015). penerapan exponential smoothing untuk transformasi data dalam meningkatkan akurasi neural network pada prediksi harga emas. 1(2). http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 37 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional implementasi sistem informasi pemesanan jasa pengiriman barang kargo pada pt.suksema abadi logistik dengan model waterfall ahmad sinnun, intan ayu putri program studi rekaya perangkat lunak1, program studi sistem informasi2, fakultas teknik dan informatika universitas bina sarana informatika ahmad.sinnun@gmail.com1 ; intanayuputri4@gmail.com2 ; abstrak pt. suksema abadi logistik merupakan perusahaan jasa pengiriman barang untuk solusi b2b (bsinis to bisnis) di indonesia. belum adanya sistem informasi, penanganan order masih menggunakan sambungan telepon dan surat electronik yang digunakan pada perusahaan sehingga menjadikan adanya kendala dalam mencatat order pengiriman, monitoring penjadwalan armada, dan laporang status pengiriman yang tidak uptodate. penelitian ini bertujuan untuk menghasilkan perancangan dan implementasi sistem informasi untuk mendorong produktifitas dan pencatatan bisnis proses yang meliputi proses pemesanan, proses transaksi dan proses pengiriman yang bertujuan untuk memudahkan perusahaan pemesan jasa pengiriman melakukan order jasa, memantau jadwal pelaksanaan pengiriman dan mendokumentasikan proses pengiriman dengan dokumen yang terintegrasi. dengan mengimplementasikan metode pengembangan perangkat lunak berbasis metode waterfall, sistem informasi meliputi tiga akses untuk administrator, pelanggan, dan sopir. sistem informasi dapat memperlancar pemesanan jasa ekpedisi dan lebih terdokumentasi dengan data integrasi. kata kunci: sistem informasi, logistik, waterfall abstract pt. suksema abadi logistics is a freight services company for b2b solutions (bsinis to bisnis) in indonesia. the absence of information systems, the handling of orders still using telephone and electronic mail connection used in the company so as to make an obstacle in recording orders delivery, monitoring fleet scheduling, and laporang-status delivery that is not uptodate. this research aims to produce the design and implementation of information systems to encourage productivity and process business records that include the ordering process, transaction process and delivery process that aims to facilitate the company's customer service buyer, monitoring the delivery schedule and documenting the shipping process with integrated documents. by implementing software development methods based on waterfall method, information systems include three access for administrators, customers, and drivers. keywords: information systems, logistics, waterfall pendahuluan sistem informasi merupakan satuan komponen yang saling berhubungan dalam mengumpulkan, memproses, menyimpan dan mendistribusikan informasi (frieyadie, 2014). hal tersebut untuk mendukung pengambilan keputusan dan kendali serta memainkan aturan penting dalam organisasi. rabiatul (2015:181). dalam dunia ekspedisi sebuah perusahaan yang bergerak dalam bidang pengiriman barang semakin pesat pertumbuhannya (hidayat, 2014), banyaknya permintaan client dalam jasa pengiriman membuat seluruh perusahaan dalam bidang ini bersaing untuk memberikan pelayanan terbaik kepada pelanggannya. pt. suksema abadi logistik adalah salah satu perusahaan yang bergerak di bidang jasa pengiriman barang dan fokus pada solusi b2b. perusahaan ini menerima pesanan pengiriman barang dalam skala besar. pelanggan akan melakukan pemesanan yang selanjutnya bagian admin akan mengecek ketersediaan armada. jika armada tersedia, barang yang akan di kirim akan diambil ke tempat pelanggan. jika armada tidak tersedia, pesanan akan ditunda atau dibatalkan sesuai dengan permintaan pelanggan. sebelum adanya sistem informasi yang digunakan pada perusahaan tersebut. proses order armada dari http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 38 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional pelanggan melalui sambungan telepon atau surat electronik, pada saat menjawab telepon petugas administrasi yang menangani order harus mencocokan dokumen armada yang kosong dan jadwal pengiriman hal ini sangat rentan adanya data crash dengan jumlah armada yang mencapai lebih dari 100 unit. melihat permasalahan diatas penelitian ini mewujudkan pada perancangan dan implementasi sistem informasi untuk mendorong produktifitas dan pencatatan bisnis proses (fatayat & frieyadie, 2019) yang meliputi proses pemesanan, proses transaksi dan proses pengiriman yang bertujuan untuk memudahkan perusahaan pemesan jasa pengiriman melakukan order jasa, memantau jadwal pelaksanaan pengiriman dan mendokumentasikan proses pengiriman dengan dokumen yang terintegrasi. metode penelitian jenis penelitian penelitian ini menggunakan metode penelian teknik deskriptif, yakni metode yang digunakan untuk meneliti suatu objek, suatu set kondisi, suatu sistem pemikiran, ataupun suatu kelas peristiwa pada masa sekarang (saifudin & ardani, 2017). metode yang digunakan dalam pengembangan perangkat lunak dalam penelitian ini adalah model waterfall, metode ini melakukan pendekatan secara sistematis dalam proses pengerjaan system (palasara, sinnun, & tabrani, 2018). dengan kata lain metode ini tidak berfokus pada satu tahapan tertentu. tahapan dalam model waterfall ini mencakup tahap analisa kebutuhan perangkat lunak, desain, pembuatan kode program, dan pengujian (kendall & kendall, 2010). waktu dan tempat penelitian penelitian ini dilakukan pada tahun 2019 di pt. suksema abadi logistik. target/subjek penelitian target penelitian dengan metode deskriptif dalam melihat object proses bisnis pada pt. suksema abadi logistik dengan subject penelitian pada bagian pemesanan jasa, pencatatan transaksi pembayaran dan penjadwalan pengiriman. prosedur prosedur penelitian yang digunakan sebagaimana yang telah diuraikan pada bagian jenis penelitian mengikuti tahapan model waterfall sebagai berikut. 1. analisis kebutuhan perangkat lunak analisis kebutuhan berfokus pada kebutuhan sistem informasi mulai dari proses pemesanan jasa, proses pembayaran jasa, dan proses penjadwalan jasa. 2. desain tahapan ini mencakup rancangan basis data, pemodelan rancangan menggunakan unified modeling language (uml), dan rancangan tampilan antar muka. 3. implementasi pengkodean (coding) program menggunakan sublime text sebagai web editor bahasa pemrograman hypertext preprocessor (php) berdasarkan logika yang dirancang pada tahapan desain. teknik pengumpulan data teknik pengumpulan data berfungsi sebagai cara untuk mengumpulkan data dari suatu penelitian (saifudin & ardani, 2017). teknik pengumpulan data yang digunakan, terdiri dari observasi, wawancara dan studi pustaka. adapun penjelasan dari teknik pengumpulan data yang digunakan, diuraikan sebagai berikut: 1. observasi penulis melakukan observasi langsung ke pt. suksema abadi logistik untuk mengamati secara langsung proses pemesanan jasa ekspedisi, transaksi pembayaran, dan penjadwalan armada ekspedisinya. 2. wawancara wawancara merupakan proses tanya jawab langsung dan sistematis dengan karyawan pelaksana dilingkungan pt. suksema abadi logistik. hasil penelitian dan pembahasan a. analisis kebutuhan software a1. skenario kebutuhan admin mengelola data armada; mengelola data pelanggan; mengelola data vendor; mengelola data sopir; mengelola data kota; mengelola data pemesanan; mengelola data laporan pemesanan a2. skenario kebutuhan pelanggan melakukan daftar akun; melihat informasi armada; melakukan pemesanan armada; melihat riwayat pemesanan armada a3. skenario kebutuhan sopir melihat jadwal pengiriman; mengelola status pengiriman analisis kebutuhan sistem 1. admin, pelanggan, dan sopir melakukan login terlebih dahulu untuk dapat mengakses aplikasi ini dengan memasukkan username dan password. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 39 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 2. admin, pelanggan dan sopir melakukan logout setelah selesai menggunakan aplikasi. 3. sistem melakukan pengelolaan data armada, mengelola data sopir, mengelola data pelanggan, mengelola data pemesanan dan melihat laporan pemesanan. b. desain sebelum kode-kode program dibuat langkah selanjutnya penulis merancang dengan gambaran awal dengan use case diagram, aktivity diagram dan entity relation diagram (palasara et al., 2018). model entity relationalship menjelaskan hubungan antar data dalam basis data berdasarkan suatu persepsi object-object dasar yang mempunyai hubungan atau relasi antar objectobject tersebut (tabrani, kholil, & sinnun, 2019). use case diagram gambar 1 diagram use case sistem informasi ekspedisi barang tabel 1. deskripsi use case diagram sistem informasi ekspedisi barang use case narative sistem ekspedisi barang tujuan bagian administrator, pelanggan, sopir armada dapat mengakses dan mengelola data yang ada pada system informasi ekspedisi barang deskripsi aplikasi ini dapat di akses oleh administrator untuk mengelola data armada, pelanggan, vendor, sopir, kota, pemesanan, laporan pemesanan. sedangkan pelanggan dapat melakukan daftar akun, melihat informasi armada, melakukan pemesanan armada, melihat riwayat pemesanan armada. aplikasi ini juga memberikan fasilitas kepada sopir untuk melihat jadwal pengiriman dan mengelola status pengiriman skenario utama aktor administrator, pelanggan, sopir armada kondisi awal administrator, pelanggan, sopir armada harus melakukan login dengan cara mengisi username dan password. aksi aktor reaksi sistem melakukan daftar akun masuk melakukan pemesanan armada melihat informasi armada melihat riwayat pemesanan armada mengelola data armada mengelola data pelanggan mengelola data vendor mengelola data sopir mengelola data kota mengelola data pemesanan mengelola data laporan melihat jadwal pengiriman mengelola status pengiriman keluar pelanggan dapat melakukan daftar akun di dalam system user mengakses sistem pemesanan jasa ekspedisi pelanggan dapat melihat informasi armada pelanggan dapat melakukan pemesanan armada pelanggan dapat melihat riwayat pemesanan armada admin dapat melihat data armada admin dapat melihat data pelanggan admin dapat melihat data vendor admin dapat melihat data sopir admin dapat melihat data kota admin dapat memberi approval data pemesanan admin dapat mengelola laporan pemesanan jasa ekspedisi sopir dapat melihat jadwal pengiriman sopir dapat melihat status pengiriman user dapat keluar dari sistem kodisi akhir jika sesuai perintah maka aplikasi akan menampilkan menu yang dipilih oleh pengguna sumber: (lisnawanty & kurniawan, 2019) activity diagram gambar 2 activity diagram login user act activ ity diagram login masukan username masukan passw ord menentukan hak akses pengguna valid?tidak masuk kedalam sieksbar seasion login aktif masuk kedalam si-eksbar merge valid http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 40 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional rancangan basis data rancangan basis data pada sistem informasi ekpedisi barang pada pt. suksema abadi logistik digambarkan dalam bentuk entity relationship diagram (erd) berikut ini. gambar 5 entity relationship diagram (erd) sistem informasi ekspedisi barang c. desain user interface 1. akses login pengguna pada menu login adalah langkah awal membuka aplikasi, dengan batasan user akses ada didalamnya. gambar 6 akses login 2. halaman informasi armada pengiriman pada menu informasi armada, pelanggan dapat melihat ketersedian armada dan estimasi baiaya. sedangkan pada administrator dapat merubah informasinya berkaitan dengan tarif pengiriman. gambar 7 informasi armada pengiriman 3. halaman riwayat pemesanan armada pengiriman pada halaman ini dari sisi pelanggan bias melihat riwayat pengiriman dan dapat melakukan pesanan armada pengiriman kembali. gambar 8 riwayat pemesanan armada pengiriman 4. halaman penjadwalan pada halaman ini sopir dapat melihat jadwal pengiriman yang harus sopir kerjakan dengan data armada yang sudah tercantum didalam system. gambar 9 tampilan jadwal armada pada halaman sopir http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 41 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 5. halaman pengelola pada halaman ini yang bisa mengakses hanya level administrator, admin dapat mengeksekusi pesanan armada dan menjadwalkan sopir, menyetejui transaksi pembayaran diterima dari pelanggan. gambar 10 halaman pengelolaan pada halaman administrator 6. halaman pembuatan laporan pada halaman pembuatan laporan administrator dapat melakukan pencetakan laporan untuk yang di arsipkan dokumen fisiknya. gambar 11 menu pembuatan laporan d. pembuatan code program dalam pembuatan code program membutuhkan perangkat lunak (software) yang digunakan untuk pembuatan sistem informasi pada pt. suksema abadi logistik. sebagai contoh penerapan code aplikasi pada konfigurasi database ke system sis/config/database.php : <?php defined('basepath') or exit('no direct script access allowed'); $active_group = 'default'; $query_builder = true; $db['default'] = array( 'dsn' => '', 'hostname' => 'localhost', 'username' => 'root', 'password' => 'password', 'database' => 'sisfologistik', 'dbdriver' => 'mysqli', 'dbprefix' => '', 'pconnect' => false, 'db_debug' => (environment !== 'production'), 'cache_on' => false, 'cachedir' => '', 'char_set' => 'utf8', 'dbcollat' => 'utf8_general_ci', 'swap_pre' => '', 'encrypt' => false, 'compress' => false, 'stricton' => false, 'failover' => array(), 'save_queries' => true ); e. testing 1. pengujian laman aplikasi pada jenis aplikasi browser tabel 2. black box testing aplikasi pada browser 1. skenario pengujian buka dengan google chrome test case google chrome (terbuka) hasil yang diharapkan aplikasi dapat diakses dan tampilan sesuai dengan desain hasil pengujian sesuai harapan kesimpulan valid 2. skenario pengujian buka dengan mozila firefox test case mozila firefox (terbuka) hasil yang diharapkan aplikasi dapat diakses dan tampilan sesuai dengan desain hasil pengujian sesuai harapan kesimpulan valid 2. pengujian login aplikasi tabel 2. black box testing aplikasi pada halaman login 1. skenario pengujian jika salah satu kolom dikosongkan test case username: (kosong) hasil yang diharapkan aplikasi menolak akses dan menampilkan pesan “username atau password anda salah, silahkan coba lagi” hasil pengujian sesuai harapan kesimpulan valid 2 skenario pengujian kolom terisi namun tidak sesuai (username, password dan level akses tidak sesuai) test case username:(111111) password: (111111) hasil yang aplikasi menolak akses dan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 42 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional diharapkan menampilkan pesan “username atau password anda salah, silahkan coba lagi hasil pengujian sesuai harapan kesimpulan valid 3 skenario pengujian kolom terisi dan sesuai (username dan password sesuai) test case username: (admin) password: (admin) hasil yang diharapkan aplikasi menerima akses dan mengantarkan pengguna ke halaman dashboard. hasil pengujian sesuai harapan kesimpulan valid f. support kebutuhan perangkat keras dan perangkat lunak dalam mengimplementasikan system informasi pada pt. suksema abadi logistik adalah sebagai berikut: 1. kebutuhan perangkat keras minimal. a. processor : 1.40 ghz b. memory : 8 gb c. harddisk : 500 gb d. monitor : resolusi minimun (1024 x 768) e. keyboard : 86 keys f. mouse : optical 2. kebutuhan perangkat lunak a. sistem operasi : linux server b. web server : apache c. database : mysql d. web browser : mozzila firefox /google chrome simpulan dan saran simpulan semakin besarnya integritas kebutuhan perusahaan maka perlu sebuah sistem yang bisa memenuhinya. sistem informasi pemesanan jasa ekspedisi ini dirancang untuk mengganti proses pemesanan jasa atau armada yang dilakukan secara manual menjadi sistem yang terkomputerisasi. melalui sistem pemesanan jasa ekspedisi berbasis web ini dapat memberikan solusi untuk memudahkan penggunanya dalam melakukan pemesanan jasa atau armada karena dapat dilakukan secara online yang dapat dilakukan dimanapun dan kapanpun, sehingga dapat meningkatkan efektifitas dan efesiensi dalam pengolahan data pemesanan jasa ekspedisi hingga pengolahan data laporan. meminimalisir adanya kesalahan human error yang terjadi. saran setiap perusahaan harus mampu memberikan fasilitas atau media yang dapat menunjang kinerja agar lebih optimal seperti membuat sistem pemesanan jasa ekspedisi barang kargo berbasis web atau online. untuk melindungi data dari kerusakan maka disarankan untuk backup data setiap hari. melakukan pemeliharaan sistem perangkat keras dan perangkat lunak secara berkala, agar dapat beroperasi dengan baik dan lancar. setiap karyawan di harapkan mampu untuk mempelajari setiap perkembangan teknologi, sehingga dapat memberikan kontribusi yang terbaik. daftar referensi fatayat, u., & frieyadie, f. (2019). penggunaan model waterfall dalam perancangan aplikasi penjualan kosmetik berbasis web. jurnal riset informatika, 1(4), 159–166. frieyadie, f. (2014). web sistem informasi berbasis w2000 untuk dukungan pemesanan dan penjualan produk safety. jurnal pilar nusa mandiri, 10(1), 111–116. hidayat, r. (2014). sistem informasi ekspedisi barang dengan metode e-crm untuk meningkatkan pelayanan pelanggan. jurnal sisfotek global, 4(2). kendall, k. e., & kendall, j. e. (2010). systems analysis and design. prentice hall press. lisnawanty, l., & kurniawan, b. (2019). sistem informasi akuntansi penerimaan dan pengeluaran kas berbasis web (studi kasus: pt. sinar kapuas cemerlang). jurnal riset informatika, 1(4), 187–196. palasara, n., sinnun, a., & tabrani, m. (2018). penerapan metode waterfall pada sistem informasi ujian online berbasis web. transistor elektro dan informatika, 3(2), 103–110. rabiatul, a. (2015). perancanaan strategis si/ti menggunakan four stage model pada dinas pendidikan kabupaten kubu raya kalimantan barat. jurnal khatulistiwa informatika, iii(2), 180–198. https://doi.org/10.1145/3132847.3132886 saifudin, s., & ardani, f. p. (2017). sistem informasi akuntansi penerimaan dan pengeluaran kas dalam meningkatkan pengendalian internal atas pendapatan pada rsup dr. kariadi semarang. jurnal rak (riset akuntansi keuangan), 2(1), 123–138. tabrani, m., kholil, i., & sinnun, a. (2019). implementasi rapid application development dalam membangun aplikasi koperasi simpan pinjam (studi kasus koperasi subur jaya mandiri kabupaten subang). sistemasi: jurnal sistem informasi, 8(1), 145–152. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 51 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. implementasi self-regulated learing pada aplikasi pembelajaran manajemen proyek sistem informasi pada perguruan tinggi swasta x di jakarta kelvin febrianto1, yunus fadhillah2 program studi teknik informatika institut bisnis dan informatika kwik kian gie 156140275@student.kwikkiangie.ac.id, 2yunus.fadhillah@kwikkiangie.ac.id abstrak pengerjaan proyek sistem informasi masih mengalami kegagalan dan kerugian dalam beberapa tahun belakangan ini. maka dari itu pemahaman konsep manajemen proyek haruslah diajarkan dalam perkuliahan. dari hasil analisa perkuliahan yang diadakan pada perguruan tinggi swasta x di jakarta selama sepuluh tahun silam ini menunjukkan kesenjangan antara hasil belajar harian dengan nilai ujian yang mahasiswa dapatkan. sehingga harus ada sebuah proses belajar baru yang memperkuat pemahaman mahasiswa terhadap konsep manajemen proyek. metode self-regulated learning (srl) berfokus pada pembelajaran mandiri melalui metakognisi, motivasi, pengaturan diri, dan evaluasi diri. untuk mendorong proses belajar tersebut membutuhkan sebuah alat bantu dalam proses belajar. spesifikasi alat belajar tersebut membutuhkan terciptanya proses metakognisi dengan mengarahkan langkah-langkah belajar secara jelas dan memotivasi siswa dengan penggunaan alat belajar yang intuitif. hasilnya adalah software belajar yang secara eksplisit membantu proses belajar dengan menghasilkan dokumen proyek untuk membantu pemahaman konsep manajemen proyek dan antarmuka pengguna yang interaktif dan mudah digunakan untuk memotivasi proses belajar. kata kunci: manajemen proyek, sistem informasi, pembelajaran berbasis siswa, self-regulated learning , proses pembelajaran abstract failing it projects has kept growing over the past recent years. understanding of project management concepts must be taught in college. recent studies at the conducted courses at college x in jakarta over the past ten years has shown discrepancy between daily exercises with the exam result students received. therefore, developing a new learning process is required to enhance the student’s understanding of project management concepts. self-regulated learning method encompasses independent learning by metacognition, motivation, self-regulation, and self-evaluation. to develop such process would require a learning tools during the learning process. the technical specification for the learning tools in question needs to enable metacognition process by directing the students towards the learning steps and motivates the students by intuitive use of learning tools. the results is a learning software that explicitly help the learning process by generating project documents to help understanding project management concept and interactive user interface that is easy to use and motivates the learning process. keywords: project management, information system, student centered learning, self-regulated learning, learning process. pendahuluan beberapa tahun belakangan ini banyak sekali investasi terhadap proyek-proyek it yang mengalami kegagalan pada saat perencanaan ataupun pada saat implementasi proyek tengah berjalan (marchewka, 2014). hal ini tidak hanya merugikan investor proyek tetapi juga berdampak pada kepercayaan publik terhadap keberhasilan investasi proyek sistem informasi (straub, kerlin, & whalen, 2017). melihat hal ini mendorong keharusan agar manajemen proyek dikuasai oleh mahasiswa dalam studi s1 teknik informatika dan sistem informasi. akan tetapi hasil studi perkuliahan manajemen proyek sistem informasi (mpsi) di perguruan tinggi swasta x di jakarta dalam sepuluh tahun belakangan ini menunjukkan kesenjangan antara hasil belajar harian dengan http://creativecommons.org/licenses/by-nc/4.0/ mailto:56140275@student.kwikkiangie.ac.id mailto:yunus.fadhillah@kwikkiangie.ac.id issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 52 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. nilai ujian yang mahasiswa dapatkan. gambar 1 dan 2 menunjukkan grafik dari perkuliahan ratarata manajemen proyek sistem informasi yang berlangsung di perguruan tinggi swasta x di jakarta selama sepuluh tahun silam. dengan menghilangkan semua asumsi seperti pengaruh dari kemampuan mengajar dosen, kemampuan mahasiswa menerima materi, perubahan kurikulum, pembobotan nilai, dan lain-lain. terdapat penurunan nilai akhir yang cukup terlihat dalam beberapa tahun belakangan ini. penyebabnya dapat terlihat pada nilai uts yang cukup rendah dan nilai uas yang hampir sama rendahnya. akan tetapi terdapat kejanggalan ketika hasil ujian tersebut dibandingkan dengan hasil nilai harian. ini menunjukkan kejanggalan dalam kinerja harian mahasiswa dibandingkan ujian mereka. oleh karena itu proses belajar manajemen proyek harus ditingkatkan. sumber: (febrianto & fadhillah, 2018) gambar 1. nilai akhir rata-rata perkuliahan mpsi di pts x, jakarta sumber: (febrianto & fadhillah, 2018) gambar 2. nilai harian perkuliahan mpsi di pts x, jakarta ranah konsep manajemen proyek konsep manajemen proyek yang diajarkan pada pts x di jakarta mengacu pada buku panduan project management body of knowledge (pmbok) (project management institute, 2017) di dalam panduan pmbok terdapat 10 ranah konsep dari manajemen proyek yang dibagi menjadi 49 proses dan dikelompokkan ke dalam 5 tahapan proses yaitu: (1) inisiasi; (2) perencanaan; (3) eksekusi; (4) pengawasan; dan (5) penutup; penjabaran lengkap dari proses-proses tersebut dapat dilihat pada lampiran tabel i. sistem perkuliahan yang berjalan sumber: (febrianto & fadhillah, 2018) gambar 3. proses perkuliahan mpsi di pts x jakarta pada gambar 5 di atas, pembelajaran merupakan gabungan antara mata kuliah yang disusun sesuai dengan silabus rencana perkuliahan, jadwal pertemuan, dan total kelas yang diajarkan dengan dosen pengajar yang mengajar di perguruan tinggi swasta x di jakarta. setelah melewati proses pembelajaran, mahasiswa kemudian diarahkan untuk menghasilkan output pembelajaran mereka selama berkuliah, seperti tugas harian, uts dan uas. ceramah materi kuliah latihan dalam kelas tugas harian sumber: (febrianto & fadhillah, 2018) gambar 4. proses pembelajaran dalam kelas dalam kelas perkuliahan mahasiswa menerima ceramah konsep soal teori manajemen proyek sesuai dengan rencana perkuliahan. konsep tersebut kemudian diperkuat dengan latihan dalam kelas dan tugas harian. tugas harian yang diberikan merupakan implementasi dari konsep manajemen proyek yang dalam hal ini adalah dokumen proses manajemen proyek dengan urutan sebagai berikut: 0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 100,00 0 10 20 30 40 50 60 70 80 90 100 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 53 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. project charter daftar stakeholders work breakdown structure gantt chart biaya proyek daftar komunikasi proyek penutup proyek sumber: (febrianto & fadhillah, 2018) gambar 5. alur dokumen tugas harian metode penelitian metodologi penelitian untuk menemukan solusi dari permasalahan proses belajar adalah sebagai berikut: metode self-regulated learning metode self-regulated learning adalah metode belajar mandiri dengan terus melakukan penyesuaian terhadap hasil belajar yang dicapai dengan tujuan akhir dari proses belajar melalui perencanaan, pengaturan, dan evaluasi (nilson, 2013) (kizilcec, pérez-sanagustín, & maldonado, 2017) . metode srl terdiri dari 4 parameter yaitu: (1) metakognisi; (2) motivasi; (3) pengaturan diri; dan (4) evaluasi diri; yang direpresentasikan dengan gambar berikut: sumber: (febrianto & fadhillah, 2018) gambar 6. komponen-komponen dalam self-regulated learning manfaat dari metode srl adalah peningkatan dalam performa belajar, percaya diri bertambah, kesadaran akan kemampuan diri, pengendalian stress, dan manajemen diri yang lebih baik (nilson, 2013). melihat akan manfaatnya dan kegunaannya dalam perkuliahan. membuat metode ini sangat cocok untuk menjadi pendekatan utama dalam merancang proses belajar tambahan yang mengkomplemen sistem perkuliahan yang ada dengan harapan mampu meningkatkan peforma perkuliahan manajemen proyek sistem informasi di pts x jakarta. spesifikasi teknis hasil wawancara dengan mahasiswa-mahasiswa yang pernah mengambil mata kuliah manajemen proyek sistem informasi, mengatakan bahwa dikarenakan tidak adanya langkah yang jelas dalam belajar membuat motivasi mereka cukup lemah selama berkuliah. langkah yang jelas dalam belajar dapat diukur sebagai parameter metakognisi, sedangkan motivasi belajar diukur sebagai motivasi dalam srl. dengan demikian mendorong adanya kebutuhan untuk mengembangkan alat bantu belajar dalam bentuk e-learning (clark & mayer, 2016) yang dapat memberikan metakognisi dan motivasi selama proses belajar berlangsung. sumber: (febrianto & fadhillah, 2018) gambar 7. rancangan proses belajar yang diusulkan e-learning akan diimplementasi pada platform web untuk memanfaatkan segala kelebihan yang ditawarkan oleh web ketimbang oleh desktop (broadbent & poon, 2015). tidak hanya itu spesifikasi kebutuhan software lebih lanjut adalah sebagai berikut:  berjalan pada platform web.  mengarahkan mahasiswa dalam langkahlangkah pembuatan dokumen proyek. http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 54 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional.  rancangan antarmuka akan mengacu kepada 8 golden rules of interface design (shneiderman, plaisant, cohen, jacobs, & elmqvist, 2016)  dokumen proyek dalam proses belajar mencakup (1) project charter; (2) daftar stakeholders; (3) gantt chart; (4) wbs; (5) biaya proyek; (6) komunikasi proyek; dan (7) penutup proyek;  dokumen proyek dalam proses akan dihasilkan dalam format .pdf.  membangkitkan motivasi belajar. metode rapid application development dengan spesifikasi software tersebut maka metode rapid application development (rad) dipilih sebagai metode pengembangan software web belajar. metode rad terdiri atas 4 proses yaitu: (1) analisa kebutuhan; (2) rancangan software; (3) coding; serta (4) perpindahan cepat (cutover); dalam rentang waktu yang cukup singkat yang disebut juga dengan timebox (isaias & issa, 2015) sumber: (febrianto & fadhillah, 2018) gambar 8. framework sdlc rapid application development metode sdlc ini dianggap sesuai untuk pengerjaan software dikarenakan tingkat kesulitan dari web belajar cukup sederhana, dan waktu pengerjaan yang relatif singkat. maka dari itu timebox untuk pengerjaan metode rad sendiri akan berkisar sekitar 2-3 minggu. dengan estimasi jumlah timebox yang akan digunakan sebanyak 2 timebox. hasil penelitian dan pembahasan hasil dari rancangan software adalah sebuah web belajar yang dapat diakses dengan menggunakan browser baik secara lokal maupun online dengan rancangan tampilan yang mengikuti pedoman 8 golden rules of interface design (shneiderman et al., 2016). tidak hanya itu web juga mampu melakukan proses pembuatan dokumen manajemen proyek dengan mengandalkan input dari pengguna serta mampu menyimpan data tersebut ke dalam browser. analisis dari implementasi software belajar mengacu kepada spesifikasi software yang telah ditetapkan sebelumnya. beberapa dari spesifikasi tersebut pada tabel 1 berikut: tabel 1. implementasi spesifikasi website belajar spesifikasi website belajar status implementasi berjalan pada platform web. sudah terimplementasi dokumen proyek dalam proses akan dihasilkan dalam format .pdf. sudah terimplementasi dokumen proyek dalam proses belajar mencakup (1) project charter; (2) daftar stakeholders; (3) gantt chart; (4) wbs; (5) biaya proyek; (6) komunikasi proyek; dan (7) penutup proyek; sudah terimplementasi sumber: (febrianto & fadhillah, 2018) untuk lebih lanjut lagi ada beberapa spesifikasi lainnya yang membutuhkan pembahasan lebih mendalam di antaranya adalah: mengarahkan mahasiswa dalam langkahlangkah pembuatan dokumen proyek website alat belajar diawali dengan halaman dashboard yang informatif, dengan memberikan penjelasan sekilas perihal dokumen proyek yang dapat dibuat dan dengan alur halaman web yang sederhana membuat langkah-langkah pembuatan dokumen terarah dengan baik. rancangan antarmuka mengacu kepada 8 golden rules of interface design berikut adalah implementasi rancangan antarmuka dikaitkan dengan 8 aturan emas rancangan antarmuka (shneiderman et al., 2016) tabel 2. kriteria 8 aturan emas rancangan antarmuka dan status implementasinya 8 aturan emas rancangan antarmuka website belajar mempertahankan konsistensi. sudah terimplementasi kejar pengunaan secara universal. belum terimplementasi http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 55 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 8 aturan emas rancangan antarmuka website belajar menawarkan feeback informatif. sudah terimplementasi dialog mengarahkan pada penutupan aksi. sudah terimplementasi mencegah error. sudah terimplementasi memperbolehkan aksi yang reversibel yang mudah. sudah terimplementasi menjaga pengguna dalam kendali. sudah terimplementasi mengurangi beban memori jangka pendek. sudah terimplementasi sumber: (febrianto & fadhillah, 2018) dengan demikian dapat dikatakan bahwa program sudah mengimplementasi 8 aturan emas rancangan antarmuka dengan baik. membangkitkan motivasi belajar dengan langkah-langkah pembuatan dokumen yang terarah dengan baik, maka mahasiswa mampu dengan mudah menetapkan strategi belajar yang cocok untuk mereka gunakan. tidak hanya itu, dengan tampilan sederhana dan menarik pengguna dapat meningkatkan motivasi siswa untuk menggunakan alat belajar. kesimpulan beberapa kesimpulan yang diambil dari penelitian ini adalah mahasiswa dapat memiliki langkah-langkah yang jelas dalam mempelajari manajemen proyek jika mereka diarahkan untuk menyusun langsung dokumen-dokumen manajemen proyek tersebut secara rapi dan terstruktur. alat belajar yang dibangun pada penelitian ini mendukung proses pembelajaran dengan secara eksplisit terlibat dalam proses pembelajaran. dalam hal ini adalah proses pembuatan dokumen manajemen proyek melalui input pengguna. dalam proses pembelajaran perlunya perubahan dalam kurikulum dan penambahan materi praktikum. proses belajar manajemen proyek dapat dibuat menarik dan lebih mudah dipahami mahasiswa dengan membuat proses belajar melalui interaksiinteraksi yang terarah dan panduan yang jelas, disertai dengan estetika tampilan program yang modern, sederhana dan konsisten. referensi broadbent, j., & poon, w. l. (2015). self-regulated learning strategies &amp; academic achievement in online higher education learning environments: a systematic review. the internet and higher education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04. 007 clark, r. c., & mayer, r. e. (2016). e-learning and the science of instruction : proven guidelines for consumers and designers of multimedia learning. new jersey: wiley. febrianto, k., & fadhillah, y. (2018). laporan akhir penelitian implementasi self-regulated learing pada aplikasi pembelajaran manajemen proyek sistem informasi pada perguruan tinggi swasta x di jakarta. jakarta. isaias, p., & issa, t. (2015). high level models and methodologies for information systems. new york, ny: springer new york. https://doi.org/10.1007/978-1-4614-92542 kizilcec, r. f., pérez-sanagustín, m., & maldonado, j. j. (2017). self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. computers & education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.1 0.001 marchewka, j. t. (2014). information technology project management : providing measurable organizational value. new jersey: john wiley & sons. nilson, l. (2013). creating self-regulated learners: strategies to strengthen students? selfawareness and learning skills. virginia: stylus publishing, llc. project management institute. (2017). a guide to the project management body of knowledge (pmbok guide) (6th ed.). pennsylvania: project management institute. shneiderman, b., plaisant, c., cohen, m., jacobs, s. m., & elmqvist, n. (2016). designing the user interface : strategies for effective humancomputer interaction (6th ed.). london: pearson. http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 56 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. straub, j., kerlin, s., & whalen, d. (2017). teaching software project management using project based learning (pbl) and group projects. in 2017 ieee international conference on electro information technology (eit) (pp. 016–021). ieee. https://doi.org/10.1109/eit.2017.8053323 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 207 the work is distributed under the creative commons attribution-noncommercial 4.0 international license inventory information system design at pt. arsen kusuma indonesia jakarta dahlia sarkawi1, dani ramdani2, anggi oktaviani3, deny novianti4 administrasi perkantoran, sistem informasi universitas bina sarana informatika www.bsi.ac.id dahlia.dls@bsi.ac.id, denynov.dov@bsi.ac.id sistem informasi, teknik informatika stmik nusa mandiri www.nusamandiri.ac.id 12170497@nusamandiri.ac.id, anggi.aov@nusamandiri.ac.id abstrak sistem informasi inventory adalah sebuah sistem informasi yang menyediakan informasi dari beberapa proses yang meliputi pengadaan barang, pergudangan dan pelaporan. oleh karena itu perlu dibuat suatu aplikasi sistem informasi inventory untuk meningkatkan proses kinerja perusahaan yang semula manual menjadi terkomputerisasi yang akan menghasilkan laporan yang lebih terinci. pembuatan aplikasi ini dilakukan dengan cara pengumpulan data, perencanaan sistem, analisa sistem, perancangan sistem dan database. pembuatan program aplikasi dikembangkan dengan php dan model waterfall serta di uji menggunakan black-box testing. dan diharapkan pembuatan aplikasi ini dapat mempermudah perusahaan dalam mengolah persediaan barang, maka dalam penelitian ini dibuatkan perancangan sistem informasi inventory pada pt. arsen kusuma indonesia jakarta. kata kunci: sistem informasi; persediaan; model waterfall abstract an inventory information system is an information system that provides information from several processes including procurement of goods, warehousing, and reporting. therefore it is necessary to create an inventory information system application to improve the company's performance process which originally became computerized which will result in more detailed reports. the creation of this application is done using data collection, system planning, system analysis, system design, and database. application program creation was developed with php and waterfall model as well as tested using blackbox testing. and it is expected that the manufacture of this application can facilitate the company in processing the inventory of goods, then in this research was made designing inventory information system at pt. arsen kusuma indonesia jakarta. keywords: information systems; inventory; waterfall model preliminary inventory is an activity concerning activities and transactions in and out of goods in a company. because inventory is so important to the company, the existence of an inventory system is needed to facilitate the recording and processing of transactions in and out of goods at the company. (irfana, 2017). inventory is very important for the survival of the company (salangka, 2013), (barchelino et al., 2016). the company must be able to estimate the amount of inventory it has (suharti & fong, 2018). inventories owned by the company should not be too much and also not too little because it will affect the costs that will be incurred for the inventory (salangka, 2013). without an inventory, the entrepreneur will be faced with the risk that his company at one time will not be able to meet customer requirements (banuwati et al., 2015), who require or request goods/services (kushartini & almahdy, 2016). inventory is the stock of goods or storage of goods (stevenson & chuong, 2014). to the number of items stored which will be used later. by designing the system towards a better direction, it is hoped that it can help and facilitate http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 jurnal riset informatika vol. 2, no. 4 september 2020 208 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the ongoing system process so that it makes it easier to manage data including processing, compiling, storing, and manipulating data which ultimately produces accurate data that can be used for company needs. (irfana, 2017). an inventory information system was created and developed using the waterfall model (frieyadie, 2015). this system is a unit consisting of components or elements that are linked together to facilitate the flow of information, material, or energy to achieve a goal (palit et al., 2015). for that, the system is said to be a good system with characteristics (hutahaean, 2015) namely: components, system boundaries, external environment, system connectors, system inputs, system outputs, system processors, system objectives. the purpose of this research is to make it easier for companies to process inventory so in this study the inventory information system design at pt. arsen kusuma indonesia jakarta. research methods obtain data by conducting research, directly and indirectly, to get it right using the following data collection model: 1. interview, the author conducts direct interviews with the inventory officer or employee. 2. observation, the researcher made direct observations on the existing inventory system at pt. arsen kusuma indonesia. 3. literature study namely by finding and studying relevant books to provide a better understanding of the topic of writing and enrich the writer's knowledge about computer networks. types of research this study uses a qualitative research method approach, which means that data is all information both oral and written, even in the form of pictures or photos, which contribute to answering research problems as stated in the problem formulation or research focus. (rahardjo, 2011). research target/subject the target of this research is to assist and facilitate the ongoing process of the system to facilitate data management including processing, compiling, storing, and manipulating data which ultimately results in inaccurate data that can be used for corporate purposes. research methodology research is often described as a process of the investigation carried out in a planned, orderly, and systematic manner that aims to find, interpret and revise facts. research stages this research was conducted by implementing it in several stages, namely as follows: 1. literature survey in this initial stage, the researcher collected sources and information related to the research. 2. identification of problems identifying problems to be discussed, relating to the inventory information system design at pt. arsen kusuma indonesia jakarta is based on the sources and information that has been obtained. 3. literature review researchers studied theoretical books about inventory information system design which will be used as a theoretical study in research. the information system development model used is the waterfall model. is the model most widely used for the development stage. this waterfall model is also known as a traditional model or classic model (pressman, 2003). waterfall model (waterfall) is often called a linear sequential model (sequential linear) or classical life flow (classic cycle) " (susilo et al., 2018). research results and discussion the method used in developing this software uses the waterfall model method which is divided into several stages, namely:(agus prayitno, 2015). a. software requirements analysis requirements analysis is a requirement that is needed in designing a system designed by the author, this needs analysis is necessary for designing this proposed system and the needs of this system user. 1. user requirements the inventory application has one user who interacts with the system, namely the warehouse admin section. one user chooses the characteristics of the interaction with different systems and has different information needs, such as the warehouse admin needs scenario: a. provide information about the items needed. b. receive and check requests for goods that have been ordered by the supplier. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 209 the work is distributed under the creative commons attribution-noncommercial 4.0 international license c. input reports of incoming goods and goods out of the warehouse to the supplier. 2. system requirements a. users must log in first to be able to access the system by entering their username and password so that the privacy of each user is maintained. b. after the warehouse admin logs in, the warehouse admin can use the incoming goods menu, stock items, demand for goods, incoming and outgoing goods that are in the system according to the level access owned by the warehouse admin. c. the system can store and manage admin data, item data, and reports. d. after the warehouse admin uses the system, it is required to log out so that it can log back in when using the system again. b. design 1. system modeling design the following is a warehouse inventory system software design at pt. arsen kusuma indonesia, which will be described below. a. use case diagram modeling figure 1. use case diagram in figure 1, it is explained that actors have access to log in, manage supplier data, manage goods data, manage incoming goods data, manage outgoing goods data, and manage reports. b. activity diagram modeling figure 2. activity diagram kelola data pegawai figure 2 above, shows the sequence of activities in the process of managing employee data. c. class diagram modeling figure 3. class diagram in figure 3, it is explained that the class diagram connects the existing classes in the design of this system, namely, there are incoming goods, admin, outgoing goods, goods, and also suppliers. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 jurnal riset informatika vol. 2, no. 4 september 2020 210 the work is distributed under the creative commons attribution-noncommercial 4.0 international license 2. display interface a. interface form login figure 4. interface form login figure 4 illustrates the display on the login form. b. interface main homepage figure 5. interface main homepage figure 5 illustrates the display on the main homepage. c. interface all data items figure 6. interface all data items figure 6 illustrates the display form all data items. and also can choose to print data, or save data to excel format. d. interface all employee data figure 7. interface all employee data figure 7 illustrates the display on the all employee data page. where here we can see all employee data, admin data, cashier data, and add employee data. e. items out interface figure 8. items out interface figure 8 depicts the goods out page. it is on this page that all outgoing goods data are input. f. period goods report interface figure 9. period goods report interface http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 211 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 9 illustrates the period goods report page. this report can be printed based on the required period. c. code the following is the source-code for data stocking on the goods in the pt. arsen kusuma indonesia. if(isset($_post['tambah'])){ $kd_pre = $_post['kd_pre']; $kd_trans = $_post['kd_trans']; $kd_barang = $_get['id']; $jumlah = $_post['jumlah']; $subtot = $_post['total']; $stok_barang = $_post['stok']; if($jumlah > $stok_barang){ echo "<script>alert('stok hanya ada $stok_barang')</script>"; } else{ $sql = "select * from table_pretransaksi where kd_transaks i='$kd_trans' and kd_barang='$kd_barang'"; $query = mysqli_query($con,$sql); $row = mysqli_num_rows($query); $data = mysqli_fetch_assoc($query); if($row > 0){ $jumlah = $data['jumlah'] + $jumlah; $subtot = $data['sub_total'] + $subtot; $value = "jumlah='$jumlah',sub_total='$subtot'"; $statement>update_where_2("table_pretransaksi",$value,"kd_barang",$kd_ barang,"kd_transaksi",$kd_trans,"kasir=kelola_transaksi"); } else{ $values = "'$kd_pre','$kd_trans','$kd_barang','$jumlah','$subtot' "; $statement>insert("table_pretransaksi",$values,"kasir=kelola_transaksi"); } d. testing the test used in this study uses black-box testing, as seen in table 1 below: tabel 1. black-box testing no. form uji skenario uji hasil yang diharapkan hasil pengujian 1 tampilan login mengisi username dan password dengan benar berhasil masuk ke menu utama sukses 2 tampilan menu utama membuka menu barang membuka menu barang keluar distributor muncul form data barang muncul form data barang keluar muncul form distributor sukses 3 tampilan data semua barang membuka menu print data membuka menu export excel muncul form print data muncul form export excel sukses 4 tampilan semua data pegawai membuka menu semua data pegawai membuka menu data admin membuka menu data kasir menambah data pegawai muncul form semua data pegawai muncul form data admin muncul form data kasir data pegawai dapat ditambah sukses 5 tampilan tambah barang menambah data barang data barang dapat ditambah sukses 6 tampilan tambah distributor menambah data distributor data distributor dapat ditambah sukses 7 tampilan barang keluar menambah data barang keluar data barang keluar dapat ditambah sukses 8 tampilan laporan barang periode menampilkan laporan barang periode data laporan barang periode dapat ditampilkan sukses e. support describes the use of the software and hardware used in the proposed system and describes it in the configuration form. generally, in a system that has many problems, especially in its maintenance, a system needs to be redeveloped to support the data processing needs by using hardware and software. hardware is a physical factor of a computer that can work optimally and the better the software, the easier it is to process data. the two supporting suggestions constitute a unity that must support each other so that they can be maximally utilized. a. hardware specifications 1) intel (r) core (tm) i5-8250u processor 2) 4gb memory 3) 1 terabyte hard disk 4) 2 gb shared memory 5) mouse 6) keyboard 7) monitor with a minimum screen resolution of 1024x768. b. software specifications the software used to support the proposed program is a. operating system: windows 10 b. application program: sublime text 3 c. database: mysql; d. web server: apache 2.x e. web browsers: google chrome and mozilla firefox. conclusions and suggestions based on the results of the research that has been done, several conclusions can be drawn, namely: the design of this system is to make it easier for users to control data related to the inventory, such as goods data reports, supplier data, incoming goods data, outgoing goods data, and goods stocks. this system built can accelerate performance in the process of data collection of incoming and outgoing goods that are systemized so that it is easier to find the required data and the existence http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.155 jurnal riset informatika vol. 2, no. 4 september 2020 212 the work is distributed under the creative commons attribution-noncommercial 4.0 international license of this inventory system can reduce paper accumulation. the inventory information system that is built can record order transactions, shipments, incoming and outgoing goods, and payment transactions that are computerized and with reports so that they can find out and provide fast, precise, and accurate information every day. suggestions based on the existing discussion, the authors suggest that the inventory information system is still limited and simple so that in the future it is hoped that it can be developed completely, such as auto increment to fill in item numbers automatically. it is necessary to periodically maintain the hardware and software used. development in terms of programming language, namely from php to mobile programming language. it is hoped that this system will continue to be developed with the addition of useful features and more attractive design for users. there is a need for development and better maintenance of the system that has been created so that the system can be used as needed. maintain good performance that has been achieved and implemented by the company. reference list agus prayitno, and y. s. (2015). the research of negative ion test method for fabric. pemanfaatan sistem informasi perpustakaan digital berbasis website untuk para penulis, 1(1), 1–10. https://doi.org/10.4028/www.scientific.net/ amr.756-759.138 banuwati, n., lau, e. a., & rahmawati, i. (2015). analisis pengendalian persedian bahan baku pada pabrik roti sartika disamarinda. ekonomia, 4(2), 152–155. http://ejurnal.untagsmd.ac.id/index.php/ekm/article/view/120 9 barchelino, r., ekonomi, f., bisnis, d., akuntansi, j., sam, u., & manado, r. (2016). the analysis of psak no.14 application toward inventory recording and valuation method at pt. surya wenang indah manado. analisis penerapan psak… jurnal emba, 837(1), 837–846. https://ejournal.unsrat.ac.id/index.php/emb a/article/view/11812 frieyadie, f. (2015). pembangunan sistem informasi inventory menggunakan linear sequential model untuk peningkatan layanan inventory barang. jurnal techno nusa mandiri, 12(2), 209–114. https://doi.org/10.33480/techno.v12i2.45 1 hutahaean, j. (2015). konsep sistem informasi. deepublish. irfana, d. a. (2017). perancangan sistem informasi persediaan barang (inventory application) berbasis web dan bootstrap css [2017]. http://eprints.umpo.ac.id/3027/ kushartini, d., & almahdy, i. (2016). sistem persediaan bahan baku produk dispersant di industri kimia. jurnal pasti, 10(2), 217– 234. https://publikasi.mercubuana.ac.id/index.ph p/pasti/article/view/1590 palit, r. v, rindengan, y. d. y., & lumenta, a. s. m. (2015). rancangan sistem informasi keuangan gereja berbasis web di jemaat gmim bukit moria malalayang. jurnal teknik elektro dan komputer, 4(7), 1–7. https://doi.org/10.35793/jtek.4.7.2015.104 58 pressman, r. s. (2003). software engineering a practitioner’s aproach fifth edition. mc graw hill. rahardjo, m. (2011). metode pengumpulan data penelitian kualitatif. uin maliki malang. https://www.uinmalang.ac.id/r/110601/metodepengumpulan-data-penelitian-kualitatif.html salangka, e. (2013). penerapan akuntansi persediaan untuk perencanaan dan pengendalian lpg pada pt. emigas sejahtera minahasa. jurnal riset ekonomi, manajemen, bisnis dan akuntansi, 1(3), 1120–1128. stevenson, w. j., & chuong, s. c. (2014). manajemen operasi perspektif asia (9th ed.). salemba empat. suharti, s., & fong, r. (2018). analisis akuntansi persediaan barang dagang pada toko cerose home pekanbaru. bilancia : jurnal ilmiah akuntansi, 2(2), 161– 170. http://www.ejournal.pelitaindonesia.ac.id/oj s32/index.php/bilancia/article/view/60 susilo, m., kurniati, r., & kasmawi, k. (2018). rancang bangun website toko online menggunakan metode waterfall. infotekjar (jurnal nasional informatika dan teknologi jaringan), 2(2), 98–105. https://jurnal.uisu.ac.id/index.php/infotekja r/article/view/171 http://creativecommons.org/licenses/by-nc/4.0/ lontar template p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 9 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penerapan metode weighted product sebagai pendukung keputusan seleksi karyawan baru pt. hi-lex indonesia frieyadie1; fariati2 asistem informasi, stmik nusa mandiri jl. damai no. 8, warung jati barat (margasatwa), jakarta 12540, indonesia 1frieyadie@nusamandiri.ac.id (corresponding author) 2fariati1996@gmail.com abstrak penerimaan karyawan dalam setiap perusahaan adalah kegiatan yang senantiasa dilakukan dalam suatu periode tertentu atau secara insidentil. proses perekrutan ini memerlukan proses pengambilan keputusan sebagaimana halnya proses pengambilan keputusan lainnya dalam konteks yang berbeda pada konteks penerimaan karyawan, sejumlah calon karyawan mengajukan diri dengan menyediakan segala berkas yang dipersyaratkan dan mereka juga mungkin diuji secara tertulis atau wawancara. adapun metode yang saya gunakan dalam penelitian ini dengan menggunakan weighted product dan diperhitungkan secara kuantitatif. dari hasil penelitian yang telah dilakukan adalah diperoleh 1 calon karyawan terbaik dari dari jumah sampel sebanyak 10 orang. berdasarkan hasil penelitian yang dilakukan dimana pemilihan bobot penilaian dapat dikembangkan dengan kriteria-kriteria yang lain sesuai dengan kebutuhan perusahaan, pengunaan metode weighted product lebih akurat untuk mendapatkan hasil perhitungan bobot kriteria, hasil perhitungan dengan menggunakan metode weighted product, dengan acuan kriteria tes wawancara, tes psikotes, tes kesehatan, pengalaman kerja, pendidikan terakhir, usia dan penampilan, maka terpilih 1 (satu) orang calon karyawan, yang bernama sukirman yang akan menjadi karyawan di pt.hi-lex indonesia. kata kunci: sistem pendukung keputusan, seleksi karyawan baru, weighted product (wp) abstract acceptance of employees in each company is an activity that is always carried out in a certain period or incidentally. this recruitment process requires a decision-making process as well as other decision-making processes in different contexts. in the context of employee recruitment, a number of prospective employees submit themselves by providing all required files and they may also be tested in writing or interview. the method that i use in this study is using a weighted product and quantitatively calculated. from the results of the research that has been done is obtained the best candidates from 10 households. based on the results of the research conducted where the selection of assessment weights can be developed with other criteria according to company needs, the use of the weighted product method is more accurate to obtain the calculation of criteria weight, the results of calculations using the weighted product method, by reference to interview test criteria, tests psychological test, health test, work experience, recent education, age and appearance, then 1 (one) prospective employee is chosen, named sukirman who will become an employee at pt.hi-lex indonesia. keywords: decision support system, selection of new employees, weighted product (wp) pendahuluan penerimaan karyawan dalam setiap perusahaan adalah kegiatan yang senantiasa dilakukan dalam suatu periode tertentu atau secara insidentil. proses perekrutan ini memerlukan proses pengambilan keputusan sebagaimana halnya proses pengambilan keputusan lainnya dalam konteks yang berbeda. pada konteks penerimaan karyawan, sejumlah calon karyawan mengajukan diri dengan menyediakan segala berkas yang dipersyaratkan dan mereka juga mungkin diuji secara tertulis atau wawancara. pemilihan sejumlah calon karyawan ini terkadang menjadi sesuatu yang sulit dikala jumlah pendaftar itu banyak dari berbagai ragam latar belakang. pt. hi-lex indonesia menyeleksi karyawan baru dari segi tes wawancara, tes psikotes, tes kesehatan, pengalaman kerja, pendidikan terakhir, http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 10 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional usia dan penampilan. saat ini faktor utamanya perusahaan sering kali mendapatkan karyawan yang tidak sesuai dengan bobot yang telah ditentukan disebabkan karena perusahaan tidak memiliki bobot tetap, kesulitan dalam memiliki karyawan yang tidak sesuai dengan kriteria dan masih banyak juga masalah yang lainnya (fajarianto, iqbal, & cahya, 2017) metode weighted product merupakan sebuah metode didalam penentuan sebuah keputusan dengan cara perkalian untuk menghubungkan rating atribut (moenir & budiyanto, 2018), dimana rating setiap atribut harus dipangkatkan terlebih dahulu dengan bobot atribut yang bersangkutan (mirawati, hikmah, & wiguna, 2018). proses admistrasi pada penerimaan karyawan saat ini masih manual (moenir & budiyanto, 2018), (putra & ferdiansyah, 2018) karena perusahaan masih harus memilih curriculum vitae pada masing-masing calon karyawan dan harus mengintputkan data masingmasing calon karyawan satu-persatu kedalam komputer. hal tersebut, terkadang membuat terjadinya subjektifitas (amalia & utami, 2018) pemilihan dan kesalahan pengimputan data (agustin & kurniawan, 2015), (ariani, 2017), maka diperlukan sebuah sistem penudukung keputusan yang dapat digunakan untuk menyeleksi calon karyawan berdasarkan kriteria yang ada dalam menentukan seleksi karyawan (rahmawati & astuti, 2018). data calon karyawan baru belum memiliki kriteria dan bobot yang tepat, proses penerimaan karyawan barunya memerlukan waktu yang cukup lama, dan dalam proses perhitungannya masih belum akurat (ismail & nurjaya, 2016). tujuan penelitian ini diharapkan dapat untuk mendapatkan bobot kriteria kinerja karyawan baru, agar tidak terjadi lagi pemilihan dan kesalahan pengimputan data, dan tidak terjadi lagi kesalahan dalam menginput data karyawan baru metode penelitian tahapan penelitian dalam hal ini akan di uraikan mengenai langkah-langkah yang dilakukan untuk mendapatkan metodologi penelitian yang merupakan suatu tahapan yang harus diterapkan agar penelitian dapat dilakukan dengan terarah dan memudahkan dalam melakukan analisa terhadap permasalahan yang ada. tahapan penelitian tentang sistem pedukung keputusan seleksi karyawan baru di pt. hi-lex indonesia dengan metode weighted product (wp) secara umum dapat digambarkan melalui flowchart dan dapat dilihat pada gambar 1. mulai identifikasi masalah studi pustaka buku teori jurnal terkait menentukan kriteria saran dan kesimpulan mengolah data perhitungan menentukan bobot preferensi mengumpulkan data selesai gambar 1. flowchart tahapan penelitian sumber: hasil pengolahan analisa penulis a. identifikasi masalah mengidentifikasi masalah yang akan dibahas, berkaitan dengan sistem pendukung keputusan seleksi karyawan baru pt. hi-lex indonesia dengan metode weighted product (wp) sesuai dengan informasi yang diperoleh. b. studi pustaka penulis mempelajari buku-buku serta jurnal penelitian sebelumnya yang berkaitan dengan sistem pendukung keputusan metode weighted product (wp). c. mengumpulkan data penulis mengumpulkan data-data dan melakukan wawancara mengenai hal-hal yang berkaitan dengan penelitian. d. menentukan kriteria penulis menentukan kriteria-kriteria dari sistem pendukung keputusan menggunakan metode weighted product (wp) dalam menentukan karyawan baru, diantaranya nilai tes wawancara, tes psikotes, tes kesehatan, pengalaman kerja, pendidikan terakhir, usia dan penampilan e. menentukan bobot preferensi penulis menentukan bobot preferensi atau tingkat kepentingan dari setiap kriteria. f. mengolah data perhitungan penulis mengolah data perhitungan yang sudah di tentukan dengan nilai kriteria dan nilai bobot preferensinya. g. saran dan kesimpulan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 11 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penulis mengambil suatu kesimpulan berdasarkan analisis data-data yang terdapat pada pembahasan sebelumnya dan diperiksa apakah kesimpulan sesuai dengan hipotesis, maksud dan tujuan penelitian. selain itu juga memberikan saran yang dapat digunakan sebagai masukan bagi lembaga sekolah terkait untuk dapat dimanfaatkan lebih lanjut. instrument penelitian adapun jenis instrument yang digunakan dalam penelitian ini, yaitu: a. observasi metode ini diterapkan dengan mendatangi obyek perusahaan, yaitu salah satu pt. hi-lex indonesia di tangerang untuk mendapatkan data-data yang dibutuhkan. b. wawancara pengumpulan data dengan cara wawancara adalah usaha untuk mengumpulkan informasi dengan mengajukan beberapa pertanyaan secara lisan kepada hrd. c. studi pustaka metode ini dilaksanakan dengan melakukan studi kepustakaan melalui membaca bukubuku, jurnal penelitian sejenis meupun e-book yang dapan mendukung penulisan tugas akhir ini, yaitu yang menjelaskan tentang sistem pendukung kepuusan (spk) dengan menggunakan metode weighted product (wp). metode pengumpulan data, populasi, dan sampel penelitian a. metode pengumpulan data metode pengumpulan data yang dilakukan peneliti terbagi menjadi 2 (dua) cara, yaitu: dengan melakukan observasi langsung dan wawancara untuk mendapatkan data primer. data sekunder berasal dari mengumpulkan dan mengidentifikasi serta mengolah data tertulis berbentuk buku-buku dan jurnal yang berkaitan dengan penelitian. b. populasi dan sampel populasi populasi adalah wilayah generalisasi yang terdiri atas: obyek/subyek yang mempunyai kualitas dan karakteristik tertentu yang diterapkan oleh peneliti untuk mempelajari dan kemudian dutarik kesimpulannya.(sugiyono, 2017) sampel adalah bagian dari jumlah dan karakteristik yang dimiliki oleh populasi tersebut.(sugiyono, 2017) dalam penelitian ini, penulis melakuan observasi dan wawancara langsung dengan hrd di salah satu perusahaan pt. hi-lex indonesia di tangerang. populasi yang akan diambil pada salah satu divisi yang sedang memiliki 1 (satu) sampel calon karyawan yang akan diterima oleh perusahaan sebanyak 50 orang. dari populasi tersebut akan diambil 10 (sepuluh) sampel. metode analisis data analisa adalah bagian penting dalam metodologi penelitian ilmiah, dikarenakan dengan melakukan analisis data tersebut dapat diberi arti dan makna yang berguna dalam suatu penyelesaian masalah. metode weighted product (wp) merupakan metode pengambilan keputusan yang diperhitungkan secara kuantitatif (perhitungan matematika sesuai dengan rumus weighted product). langkah-langkah dalam perhitungan metode weighted product (wp) adalah sebagai berukut (sari, 2018): a. menentukun kriteria-kriteria yang akan dijadikan acuan dalam pengambilan keputusan b. menentukan rating kecocokan setia alternatif pada setiap kriteria c. menentukan bobot referensi setiap kriteria d. mengalihkan seluruh atribut bagi sebuah alternatif dengan bobot sebagai pangkat positif untuk atribut keuntungan dan bobot berpangkat negatif untuk atribut biaya e. hasil perkalian tersebut dijumlahkan untuk mengahasilkan nilai vektor v untuk setiap alternatif f. mencari nilai alternatif dengan melakukan langkah yang sama seperti pada langkah 1 (satu), hanya saja menggunakan nilai tertinggi untuk setiap atribut tertinggi untuk setiap atribut manfaat dan nilai terendah untuk atribut biaya g. membagi nilai v bagi setiap alternatif dengan nilai standar h. mencari nilai alternatif ideal yakni dengan merangking nilai vektor v sekaligus membuat kesimpulan sebagai tahap akhir preferensi untuk alternatif ai diberikan sebagai berikut (pratiwi, 2016): 𝑊𝑗 = 𝑤𝑗 ∑ 𝑊𝑗 (1) 𝑆𝑖 = ∏ = 𝑛 𝑗 1 𝑋𝑖𝑗 𝑤𝑗 (2) dimana : s = menyatakan preferensi alternatif dianalogikan sebagai vektor s x = menyatakan nilai kriteria w = menyatakan nilai bobot kriteria i = menyatakan alternatif j = menyatakan kriteria n = menyatakan banyaknya kriteria wj = menyatakan pangkat bernilai positif untuk atribut keuntungan, dan bernilai negative untuk atribut biaya. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 12 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional preferensi relatif dari setiap alternative, diberikan sebagai berikut: 𝑉𝑖 = π𝑗 𝑛=1 𝑋𝑖𝑗 𝑤𝑗 π𝑗 𝑛=1 (𝑋𝑗∗ ) 𝑤𝑗 (3) dimana : v = preferensi alternatif dianalogikan sebagai vektor v x = menyatakan nilai kriteria w = menyatakan bobot kriteria i = menyatakan alternatif j = menyatakan kriteria n = menyatakan banyaknya kriteria * = banyaknya kriteria yang telah dinilai pada vektor s hasil dan pembahasan perhitungan dengan metode weighted product (wp) perusahaan pt.hi-lex indonesia pada bulan april 2019 membuka lowongan untuk 1 (satu) orang. sebagai sampel perhitungan metode weighted product dalam seleksi penerimaan karyawan baru sebanyak 10 (sepuluh) pelamar yang akan diseleksi. menentukan bobot kriteria berikut menentukan bobot kriteria pada penerimaan karyawan baru pt. hi-lex indonesia, terdapat 7 (tujuh) kriteria yang akan ditentukan, antara lain: kriteria tes wawancara, kriteria tes psikotes, kriteria tes kesehatan, kriteria pengalaman kerja, kriteria pendidikan terakhir, kriteria usia, dan kriteria penampilan. tabel 1. data pelamar no nama tes wawancara tes psikotes tes kesehatan pengalaman kerja pendidikan terakhir usia penampilan 1 sarinah cukup sangat baik baik 2 tahun d3 22 tahun sangat baik 2 jilmah cukup buruk baik 2 tahun s1 21 tahun sangat baik 3 nuradiah baik buruk baik 1 tahun s1 21 tahun baik 4 iyan handoko baik sangat baik baik 0 tahun sma 22 tahun buruk 5 nurma cukup buruk baik 0 tahun sma 23 tahun buruk 6 sulaiman sangat baik sangat baik sangat baik 1 tahun d1 27 tahun sangat baik 7 rafli baik baik baik 0 tahun sma 22 tahun sangat buruk 8 nur isnaini baik cukup sangat baik 3 tahun s1 27 tahun sangat baik 9 aprilia assyifa m.n cukup baik baik 1 tahun d3 21 tahun sangat baik 10 sukirman sangat baik sangat baik sangat baik >3 tahun s1 28 tahun baik konversi nilai pelamar menentukan rating kecocokan setiap alternatif pada setiap kriteria. tabel 2. konversi nilai data pelamar no. alternatif nilai kriteria c1 c2 c3 c4 c5 c6 c7 1 a1 3 5 4 3 3 2 5 2 a2 3 2 4 3 4 2 5 3 a3 4 2 4 2 4 2 4 4 a4 4 5 4 1 1 2 2 5 a5 3 2 4 1 1 3 2 6 a6 5 5 5 2 2 5 5 7 a7 4 4 4 1 1 2 1 8 a8 4 3 5 4 4 5 5 9 a9 3 4 4 2 3 2 5 10 a10 5 5 5 5 4 5 4 ket: c1 : tes wawancara c2 : tes psikotes c3 : tes kesehatan c4 : pengalaman kerja c5 : pendidikan terakhir c6 : usia c7 : penampilan tingkat kepentingan penentuan bobot berdasarkan nilai tingkat kepentingan masing-masing kriteria, tingkat kepentingan setiap kriteria dinilai dengan 1 sampai 5. tingkat kepentingan dapat dilihat pada tabel. 3. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 13 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel.3. tingkat kepentingan tingkat kepentingan nilai sangat rendah 1 rendah 2 cukup 3 baik 4 sangat baik 5 nilai bobot atau bobot awal (w) masingmasing kriteria telah ditentukan oleh pihak pt. hilex indonesia pada tabel 4 dibawah ini. tabel .4. pembobotan kriteria kode bobot c1 5 c2 5 c3 5 c4 4 c5 3 c6 3 c7 4 perbaikan bobot perkriteria setelah mendapatkan nilai bobot pada masing-masing kriteria, selanjutnya dilakukan perbaikan bobot dari nilai bobot awal. untuk mendapatkan hasil tersebut dengan menggunakan perhitungan sebagai berikut: a. hasil untuk perhitungan bobot pada kriteria tes wawancara dengan nilai 5 adalah 0,17 b. hasil untuk perhitungan bobot pada kriteria tes psikotes dengan nilai 5 adalah 0,17 c. hasil untuk perhitungan bobot pada kriteria tes kesehatan dengan nilai 5 adalah 0,17 d. hasil untuk perhitungan bobot pada kriteria pengalaman kerja dengan nilai 4 adalah 0,14 e. hasil untuk perhitungan bobot pada kriteria pendidikan terakhir dengan nilai 3 adalah 0,10 f. hasil untuk perhitungan bobot pada kriteria usia dengan nilai 3 adalah 0,10 g. hasil untuk perhitungan bobot pada kriteria penampilan dengan nilai 4 adalah 0,14 berikut merupakan ringkasan dari proses dari perhitungan hasil perbaikan bobot pada setiap kriteria pada tabel 5, dibawah ini. tabel 5. bobot preferensi bobot kriteria nilai (w) c1 0,17 c2 0,17 c3 0,17 c4 0,14 c5 0,10 c6 0,10 c7 0,14 perhitungan nilai vektor s setelah dilakukan perbaikan bobot, dilakukan perhitungan nilai vektor (s), dengan memangkatkan dan mengalikan nilai masingmasing kriteria tersebut dengan bobot yang sudah diperbaiki sebelumnya, dengan perhitungan sebagai berikut: a. hasil perhitungan dari masing-masing kriteria pada alternatif 1 adalah 3,5055 b. hasil perhitungan dari masing-masing kriteria pada alternatif 2 adalah 3,0874 c. hasil perhitungan dari masing-masing kriteria pada alternatif 3 adalah 2,9690 d. hasil perhitungan dari masing-masing kriteria pada alternatif 4 adalah 2,4876 e. hasil perhitungan dari masing-masing kriteria pada alternatif 5 adalah 2,1110 f. hasil perhitungan dari masing-masing kriteria pada alternatif 6 adalah 3,9489 g. hasil perhitungan dari masing-masing kriteria pada alternatif 7 adalah 2,1735 h. hasil perhitungan dari masing-masing kriteria pada alternatif 8 adalah 4,2094 i. hasil perhitungan dari masing-masing kriteria pada alternatif 9 adalah 3,1888 j. hasil perhitungan dari masing-masing kriteria pada alternatif 10 adalah 4,6636 pada tabel 6. merupakan penjelasan preferensi vektor s yaitu hasil nilai dari setiap alternatif serta jumlah total seluruh nilai vektor s. tabel 6. nilai preferensi vektor s preferensi alternatif nilai vektor s s1 3.5055 s2 3.0874 s3 2.9690 s4 2.4876 s5 2.1110 s6 3.9489 s7 2.1735 s8 4.2094 s9 3.1888 s10 4.6636 total 32.3447 menghitung preferensi (vi) untuk perangkingan menghitung preferensi prengkingan untuk mendapatkan nilai hasil dengan melakukan pembagian dengan rata-rata dari nilai setiap perkalian, dengan proses perhitungan sebagai berikut: a. hasil perhitungan vektor v pada alternatif 1 dengan nilai 3.5055 adalah 0,1084 b. hasil perhitungan vektor v pada alternatif 2 dengan nilai 3.0874 adalah 0,0955 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 14 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional c. hasil perhitungan vektor v pada alternatif 3 dengan nilai 2.9690 adalah 0,0917 d. hasil perhitungan vektor v pada alternatif 4 dengan nilai 2.4876 adalah 0,0769 e. hasil perhitungan vektor v pada alternatif 5 dengan nilai 2.1110 adalah 0,0653 f. hasil perhitungan vektor v pada alternatif 6 dengan nilai 3.9489 adalah 0,1221 g. hasil perhitungan vektor v pada alternatif 7 dengan nilai 2.1735 adalah 0,0672 h. hasil perhitungan vektor v pada alternatif 8 dengan nilai 4.2094 adalah 0,1302 i. hasil perhitungan vektor v pada alternatif 9 dengan nilai 3.1888 adalah 0,0986 j. hasil perhitungan vektor v pada alternatif 10 dengan nilai 4.6636 adalah 0,1442 pada tabel.7. merupakan ringkasan hasil nilai vektor v pada setiap alternatif yang telah terhitung diatas. tabel7. preferensi alternatif vektor v preferensi alternatif nilai vektor v v1 0,1084 v2 0,0955 v3 0,0917 v4 0,0769 v5 0,0653 v6 0,1221 v7 0,0672 v8 0,1302 v9 0,0986 v10 0,1442 proses perangkingan proses hasil prengkingan seleksi administrasi yang sudah dilakukan beberapa perhitungan diatas maka didapatkan hasil seperti tabel 8. dibawah ini: tabel 8, hasil perangkingan seleksi administrasi no alternatif nilai vektor v rangking 1 a1 0,1084 4 2 a2 0,0955 6 3 a3 0,0917 7 4 a4 0,0769 8 5 a5 0,0653 10 6 a6 0,1221 3 7 a7 0,0672 9 8 a8 0,1302 2 9 a9 0,0986 5 10 a10 0,1442 1 gambar .2. grafik hasil perangkingan hasil seleksi hasil seleksi calon karyawan baru diambil sebanyak 1 (satu) orang yang dibutuhkan oleh perusahaan pt. hi-lex indonesia, dimana data-data karyawan dihitung bobot penilaianya dijabarkan pada tabel .16. dibawah ini: tabel.16. hasil seleksi no alternatif nilai vektor v rangking 1 a10 0,1442 1 2 a8 0,1302 2 3 a6 0,1221 3 4 a1 0,1084 4 5 a9 0,0986 5 6 a2 0,0955 6 7 a3 0,0917 7 8 a4 0,0769 8 9 a7 0,0672 9 10 a5 0,0653 10 sesuai dengan hasil perhitungan menggunakan metode weighted product untuk penyeleksian karyawan baru terpaparkan pada tabel .16. diatas, selanjutnya didapatkan 1 (satu) alternatif calon karyawan baru terbaik yaitu a10 atas nama sukirman. simpulan dan saran kesimpulan berdasrkan maksud dan tujuan penelitian, pengolahan data dan analis yang telah dilakukan oleh penulis, maka dapat ditarik kesimpulan adalah sebagai berikut: pemilihan bobot penilaian dapat dikembangkan dengan kriteria-kriteria yang lain sesuai dengan kebutuhan perusahaan, dimana perusahaan dapat menyesuaikan dengan kondisi dan iklim perusahaan. berdasarkan beberapa http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 2, no. 1 desember 2019 15 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional jurnal penelitian dengan pemilihan karyawan baru dengan menggunakan metode weighted product, bahwa metode tersebut lebih akurat mendapatkan hasil perhitungan bobot kriteria. hasil perhitungan dengan menggunakan metode weighted product, dengan acuan kriteria tes wawancara, tes psikotes, tes kesehatan, pengalaman kerja, pendidikan terakhir, usia dan penampilan, maka terpilih 1 (satu) orang calon karyawan, yang bernama sukirman yang akan menjadi karyawan di pt.hilex indonesia. saran-saran dari hasil penelitian yang dilakukan oleh penulis, maka dapat diusulkan beberapa saran untuk penelitian lanjutan sebagai berikut: studi penelitian dapat dilakukan juga pada perusahaan lainnya dan dapat dikembangkan dengan metode penelitian seperti topsis. penelitian dapat dikembangkan lebih lanjut dengan kriteria-kriteria yang berbeda sesuai dengan kriteria dan bobot yang ditentukan perusahaan tempat dilakukannya penelitian. semoga apa yang telah dihasilkan dalam penulisan ini dapat bermanfaat dan dapat membantu seorang atasan atau bagian hrd dalam pengambilan keputusan untuk menentukan calon karyawan yang akan diterima menjadi karyawan di pt. hi-lex indonesia. penulisan ini sangat jauh dari kata sempurna, oleh karena itu sangat diperlukan masukan, saran, dan kritik yang membangun dan dapat memperbaiki penelitian ini menjadi lebih baik. references agustin, y. h., & kurniawan, h. (2015). sistem pendukung keputusan penilaian kinerja dosen menggunakan metode weighted product (studi kasus : stmik pontianak) | agustin | seminar nasional informatika (snif). in seminar nasional informatika (pp. 177–182). lppm universitas potensi utama. retrieved from http://ejournal.potensiutama.ac.id/ojs/index.php/snif/article/view /261 amalia, r. m., & utami, d. y. (2018). pemberian reward berdasarkan penilaian kinerja karyawan dengan metode ahp pada pt. anugerah protecindo. jurnal ilmu pengetahuan dan teknologi komputer, 3(2), 181–188. ariani, f. (2017). sistem penunjang dalam penentuan prioritas pemilihan percetakan media promosi menggunakan metode ahp. jurnal informatika, 4(2). https://doi.org/10.31311/ji.v4i2.2122 fajarianto, o., iqbal, m., & cahya, j. t. (2017). sistem penunjang keputusan seleksi penerimaan karyawan dengan metode weighted product. jurnal sisfotek global, 7(1), 49–55. retrieved from http://journal.stmikglobal.ac.id/index.php/si sfotek/article/view/128 ismail, & nurjaya. (2016). seleksi penerimaan karyawan baru menggunakan metode wp (weighted produck) dengan bahasa pemrograman php dan mysql. jurnal informatika universitas pamulang, 1(1), 28– 32. retrieved from http://openjournal.unpam.ac.id/index.php/i nformatika/article/view/1465 mirawati, hikmah, a. b., & wiguna, w. (2018). sistem penunjang keputusan penilaian kinerja kasir lotte mart menggunakan metode weighted product. ijcit (indonesian journal on computer and information technology), 3(2), 186–196. moenir, a., & budiyanto, s. (2018). penerapan metode weighted product (wp) berbasis web untuk pemilihan ketua pada dewan kerja gerakan pramuka kwartir cabang kota tangerang. in seminar nasional informatika dan sistem informasi (pp. 84–96). tangerang selatan: universitas pamulang. retrieved from http://openjournal.unpam.ac.id/index.php/s nisis/article/view/3070 pratiwi, h. (2016). buku ajar sistem pendukung keputusan. yogyakarta: deepublisher. putra, c. i., & ferdiansyah, f. (2018). implementasi dan pembuatan sistem penunjang keputusan penerimaan karyawan di pt genesis indotama teknologi menggunakan metode weighted product berbasis web. skanika, 1(3), 987–994. retrieved from http://jom.fti.budiluhur.ac.id/index.php/ska nika/article/view/2500 rahmawati, a., & astuti, y. (2018). implementasi weighted product untuk penerimaan karyawan. jurnal mantik penusa, 2(1), 28–34. retrieved from http://ejurnal.pelitanusantara.ac.id/index.php/manti http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 1 desember 2019 p-issn: 2656-1743 e-issn: 2656-1735 16 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional k/article/view/321 sari, f. (2018). metode dalam pengambilan keputusan. (s. novidiantoko & h. a. susanto, eds.) (buku). yogyakarta: deepublish publisher. sugiyono. (2017). metode penelitian kuantitatif, kualitatif, dan r&d (buku). bandung: alfabeta. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 3 june 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 101 the work is distributed under the creative commons attribution-noncommercial 4.0 international license analysis of customer satisfaction on service arthaspa service with approach algorithm c4.5 sofian wira hadi1*, wawan kurniawan2, kudiantoro widianto3, ibnu alfarobi4 1,2computer science 1,2stmik nusa mandiri www.nusamandiri.ac.id 114002361@nusamandiri.ac.id ; 214002315@nusamandiri.ac.id ; 3,4accounting information systems 3,4bina sarana informatics university www.bsi.ac.id 3kudiantoro.kdw@bsi.ac.id; 4ibnu.iba@bsi.ac.id (*) corresponding author abstract customer or buyer satisfaction is closely related to how a seller of services or a store serves its visitors. good service for visitors also makes a good impression from visitors, while if the opposite will cause a very bad or unfavorable impression in the eyes of customers, and may also lead to the reluctance of visitors to come back lost the seller's opportunity to get potential buyers to become customers. this study attempts to analyze customer satisfaction with the services provided by arthaspa outlets in grand kemang hotels using the c4.5 algorithm approach. the attributes used are comfort, cleanliness, tidiness, and price. samples taken are customers who have transacted at least once.. keywords: data mining, c4.5, classification, arthaspa abstrak kepuasan pelanggan atau pembeli setuju dengan layanan penjual atau penyedia layanan yang melayani pengunjungnya. layanan yang baik untuk pengunjung yang menarik perhatian kedua pengunjung, sementara sebaliknya akan menarik perhatian yang lebih baik atau kurang di mata pelanggan, dan mungkin juga menarik pengunjung yang datang kembali untuk membeli kesempatan membeli pembeli yang berpotensi mendapatkan pelanggan. penelitian ini mencoba menganalisis kepuasan pelanggan dengan layanan yang disediakan oleh outlet arthaspa yang tersedia di grand kemang hotel menggunakan algoritma c4.5. atribut yang digunakan adalah kenyamanan, kebersihan, kerapian, dan harga. sampel yang diambil adalah pelanggan yang pernah bertransaksi sekali. studi ini mendapatkan kesimpulan paling populer tentang simpul "harga" saat menggunakan microsoft excel dan "kebersihan" saat menggunakan perangkat lunak rapidminer. kata kunci: data mining, c4.5, klasifikasi, arthaspa. introduction the best quality of service to customers, will also affect the trust of customers to the company so that customers are satisfied with the services received and thus the customer will convey satisfaction to others, with this case makes the market share expands and the company will be superior to its competitors (afrizal, 2018). consumer satisfaction is a comparison between the services received and consumer expectations, consumers have assessed satisfaction or dissatisfaction with the level of expectations they create in mind. in a situation of dissatisfaction occurs if consumers after using the product or service purchased feel that performance does not match consumer expectations (alawiyah, 2018). quality of service is very important and closely related to customer satisfaction itself. with good service quality will give satisfaction from customers, while service quality is lacking or not good so it gives uncomfortable effect to the customer (sobandi, 2019) the data visit, and it is also possible that over time can cause customers to switch to other competitors (hartono, 2017)who has a similar business. the current tight competition also forces the seller or buyer to http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 jurnal riset informatika vol. 2, no. 3 june 2020 102 the work is distributed under the creative commons attribution-noncommercial 4.0 international license produce a good service so that it can satisfy the customer (budiarti, 2018). quality of service is very important and closely related to customer satisfaction. with a good quality of service will provide satisfaction from customers, while a quality of service that is lacking or not good will give an uncomfortable effect for consumers who come to visit, and it is also possible that over time it can cause customers to switch to other competitors who have similar businesses . the current competitive conditions also force sellers or buyers to produce good service so that they can satisfy customers. companies in knowledge of the market or customers have a positive correlation in sales performance (noyita, 2019). arthaspa also prioritizes customer satisfaction in marketing strategies for the advancement of its company because it is related to market share and customer retention(puung et al., 2014). besides arthaspa also prioritizes good performance by employees so that the assessment produced by consumers of the service is also good and it is very important for the company, because customer satisfaction is a pleasant response and can be fulfilled, while customer dissatisfaction is an unexpected disappointment (susi et al., nd). in this research, it is expected that the results of customer satisfaction will be analyzed using the data mining approach c4.5 because the c4.5 algorithm is easy to understand and interpreted in its use (eki, 2016). to find out whether artspa has provided services that are in accordance with customer wishes. so the evaluation needs to be done from the customer side considering the attributes of comfort, cleanliness, tidiness, and price are very important in providing satisfaction to customers. research methodology knowledge discovery in database (kdd) data mining or knowledge discovery in database (kdd). the kdd process is the result of minimal data extracting a data pattern, and altering the results so that they are easy to understand(riandari & simangunsong, 2019). in kdd there are six most basic elements in kdd information retrieval techniques, namely: 1. working on data that will be processed with many sources. 2. efficient use of data is required 3. prioritizing statutes 4. requires high level of language usage 5. use several forms of automatic learning 6. produce unique results the knowledge discovery in database (kdd) process can be outlined as follows (yunita, 2018) : 1. data selection is the process for selecting words from a collection of data, data selection is done before the stage of obtaining an information in kdd. the results of the data selection will be stored in a file, separate from the database pre-processing or cleaning is before the data mining process is carried out, it is necessary to clean up the data that is the focus of kdd. the cleaning process includes removing duplicate data, checking for inconsistent data, and correcting data errors; 2. transformation is performed on data that has been selected or selected. so that the data selected is in accordance with the provisions of the data mining process. 3. data mining is the science of finding interesting patterns or information in large amounts of data by using certain techniques or methods. techniques, methods or algorithms in data mining vary widely. the choice of data mining method is very dependent on the kdd process (mardi, 2017). 4. interpretation or evaluation resulting from the data mining process really needs to be defined in a form that is easy to understand by interested parties. this stage is one part of the kdd process called interpretation. this stage as an evaluation of whether the pattern or information found is contrary to the previous hypothesis. data mining data mining is the science of extracting information from big data, in accordance with the purpose of data mining, which is to make a decision from a large data and stored in a database, data warehouse or information stored from a repository (tarigan et al., 2017). data collection data collection method is done by observation directly and use the questionnaire in getting accurate data. 1. observation is done by collecting a number of sales data by visiting directly the hotel in collaboration with cv. artha gemilang to get the information needed related to research. 2. questionnaire where the researcher distributes a list of questions to respondents regarding various aspects related to the value of each customer satisfaction attribute which will later be carried out on the results of research and discussion. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 3 june 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 103 the work is distributed under the creative commons attribution-noncommercial 4.0 international license research population the population that will be used is the total number of customers at outlets that work with cv, artha spa since the beginning of january 2019 until the end of june 2019 who have done at least once. ie as many as 186 people. research sample determination of the number of study samples is calculated by the slovin formula (luvia et al., 2017): n = n 1+ne2 ............................................................................. (1) where: n: number of samples n: total population e: error tolerance limit of 5 percent or 0.05. number of respondents to use: n = 200 1 + (200 ∗ 0,0025) yields n = 133.33, if rounded to 133. so the number of respondents used is 127 people. data analysis method the steps taken in data analysis include: 1 the results of the respondents' answers in the questionnaire were converted into a likert scale 2 obtained likert scale then to make a decision tree with the c4.5 algorithm approach. begin by forming which attribute will become root, or which root attribute will be based on the highest gain gain. if the value of an attribute has not resulted in a unanimous decision, then a recalculation is made by making a new branch under the previous node, but if a unanimous decision has occurred, then the calculation will be stopped and a final conclusion obtained. 3 the results of the algorithm calculation are then represented as a decision tree shape. research results and discussion as for the research on arthaspa using premiere data taken from may to june 2019 for the results of the questionnaire can be seen in table 1: table 1. questionnaire results no attribute number of cases yes not total 50 32 18 1 convenien ce very comfortable 31 27 4 enough 10 5 5 less comfortable 9 0 9 no attribute number of cases yes not total 50 32 18 2 cleanlines s clean 41 32 9 not clean 9 9 9 3 neatness neat 29 23 6 not neatly 21 9 12 4 price affordable 22 22 0 relatively inexpensive 24 7 17 expensive 4 3 1 evaluation and validation evaluation and validation are the results of the classification of data that has been determined based on the process used, henceforth after knowing the evaluation of the classification model based on the number of dataset records that have been predicted correctly and incorrectly in the classification modeling, these results can be known as confusion matrix. after getting a number of attributes, the next step is processing the selection of attributes(santoso, 2014). this attribute selection is done to get the attributes whose values are relevant. the following explanation of the attributes used: 1. convenience is an attribute given to respondents to assess comfort in service and is grouped into 3 categories, namely, very comfortable, quite, less comfortable. 2. cleanliness is an attribute to assess the cleanliness of the environment in the artha spa, and is grouped into 2 categories, clean and unclean. 3. neatness is an attribute that assesses the neatness of arthaspa employees who are grouped into 2 categories, neat and untidy. 4. price is an attribute of the price offered by artha spas to consumers, and is categorized into 3, namely affordable, relatively inexpensive and expensive. in this research the test results can be seen from the following steps: information gain and entrhopy the first step to do is calculate the entropy value and information gain data in table 2: table 2. entrhopy and gain values for all attributes attribute amoun t (s) yes (si) no (si) entrho phy gain total 50 32 18 0.9426 83 convenience 0.398 721 very 31 27 4 0.5547 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 jurnal riset informatika vol. 2, no. 3 june 2020 104 the work is distributed under the creative commons attribution-noncommercial 4.0 international license attribute amoun t (s) yes (si) no (si) entrho phy gain comfortable 78 enough 10 5 5 1 less comfortable 9 0 9 0 cleanliness 0.320 077 clean 41 32 9 0.7592 76 not clean 9 9 9 0 neatness .1022 92 neat 29 23 6 0.7355 09 not neatly 21 9 12 0.9852 28 price 0.459 766 affordable 22 22 0 0 relatively inexpensive 24 7 17 0.8708 64 expensive 4 3 1 0.8112 78 table 3. price attributes attribute amoun t (s) yes (si) no (si) entrho phy gain total 50 32 18 0.9426 83 price 0.459 766 affordable 22 22 0 0 relatively inexpensive 24 7 17 0.8708 64 expensive 4 3 1 0.8112 78 in table 3 the price attributes have 3 categories, namely, affordable, relatively cheap, expensive. with each entrapping value. because the affordable category has a value of 0, what we are looking for in the next node is relatively cheap and expensive, it can be seen in table 4: table 4. relatively cheap attribute amoun t (s) yes (si) no (si) entrho phy gain total 50 32 18 0.9426 83 convenience 0.766 425 very comfortable 10 7 3 0.8812 91 enough 5 0 5 0 less comfortable 9 0 9 0 cleanliness 0.626 299 clean 16 7 9 0.9886 99 not clean 8 0 8 0 neatness 0.581 891 neat 5 0 5 0 attribute amoun t (s) yes (si) no (si) entrho phy gain not neatly 19 7 12 0.9494 52 the results of searching for the relatively inexpensive category price attribute yielded the highest gain value, namely comfort with a value of 0.766425. for further searching for the gain value for the convenience attribute, can be seen in table 5: table 5. comfort attribute total qty (s) yes (si) no (si) entropr gain 50 32 18 0.942683 cleanliness 0.942683 clean 7 7 0 0 not clean 3 0 3 0 neatness 0.766425 neat 0 0 5 0 not neatly 10 7 3 0.881291 the results of looking for comfort attributes produced the highest gain value, namely cleanliness with a value of 0.942683. because the entropy value of the comfort category 0 for the next calculation is to find the gain value from the expensive category, it can be seen in table 6: table 6. expensive categories total attribute number (s) yes (si) no (si) entrapr gain 50 32 18 0.942683 convenience 0.877781 very comfortable 4 3 1 0.811278 enough 0 0 0 0 less comfortable 0 0 0 0 cleanliness 0.942683 clean 3 3 0 0 not clean 0 0 0 0 neatness 0.877781 neat 4 3 1 0.811278 not neatly 0 0 0 0 search results from the expensive category produced the highest gain value, namely cleanliness with a value of 0.942683. because the entrhopy value of the comfort category 0 for the node calculation is complete. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 3 june 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 105 the work is distributed under the creative commons attribution-noncommercial 4.0 international license decision tree figure 1. decision tree results the results of the decision tree modeling in figure 1 shows that all cases are included in the class determined and therefore the decision tree is the final decision tree. after the results of modeling with the decision tree the creation of rules in the decision tree. the rules that can be formulated are as follows: "if cleanliness = not clean then class = not satisfied" "if cleanliness = clean and comfort = very comfortable, class = satisfied" "if cleanliness = clean and comfort = less comfortable then class = not satisfied" "if cleanliness = clean and comfort = enough and price = relatively cheap, class = not satisfied "if cleanliness = clean and comfort = enough and price = affordable, class = satisfied" the results obtained in the sample data that are rooted are the service attributes in the decision tree, while attributes such as comfort, cleanliness and price are good food. from the sample data, the number of rules used formed 5 rules. conclusions and suggestions conclusion from the results obtained in the previous discussion, conclusions can be drawn, with the attributes used such as: comfort, cleanliness, tidiness and price greatly affect the level of customer satisfaction. if cleanliness is not clean then the customer will be dissatisfied if the cleanliness is clean and very comfortable then the customer will be satisfied but if the cleanliness of the comfort level is sufficient and the price is affordable then the customer will be satisfied with the existing services. suggestion it needs to be evaluated and recalculated regularly so that the company can continue to optimize its services to customers. it is recommended to do another algorithm approach, included in the classification category and seen the highest level of accuracy in order to be taken into consideration for analyzing the next level of customer satisfaction. and of course it can also be a constructive suggestion reference list afrizal, b. 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(2019). measuring improvement of service quality and customer trust http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i3.137 jurnal riset informatika vol. 2, no. 3 june 2020 106 the work is distributed under the creative commons attribution-noncommercial 4.0 international license on customer satisfaction with sem analysis method (study at kampoeng spa batam. khazanah ilmu berazam, 2, 62-70. puung, fk, fudholi, a., & dharmmesta, bs (2014). analysis of the effect of service quality on customer satisfaction and loyalty in salons and spas. analysis of the effect of service quality on customer satisfaction and loyalty in salons and spas, 4 (2), 105-110. https://doi.org/10.22146/jmpf.274 riandari, f., & simangunsong, a. (2019). application of c4.5 algorithm to measure student satisfaction level. dikti accredited, 3 (2), 1–7. santoso, tb (2014). analysis and application of c4.5 method for predicting customer loyalty. limi's faculty of engineering scientific journal, 10 (1), 1–6. sobandi, mm (2019). service management in pharmacies p. bhakti kencana towards customer satisfaction. journal of pharmacopolium, 2 (1). https://doi.org/10.36465/jop.v2i1.468 susi, i., situmorang, s., nasari, f., systems, j., university, i., utama, p., & utama, up (nd). analysis of customer satisfaction with spa services with servqual (case study: pt. royal amadeus). december 2018, 521-528. tarigan, hd, destiawati, f., & fitriansyah, a. (2017). implementation of c4.5 algorithm towards customer satisfaction. ethos (journal of research and community service), vol. 6, no, 80–86. yunita, f. (2018). application of data mining using the k-means clustring algorithm on acceptance of new students. systemation, 7 (3), 238. https://doi.org/10.32520/stmsi.v7i3.388 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 2 march 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 55 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. decision support system for provision of natural disaster victim logistic assistance with topsis method enok tuti alawiah, sefrika ¹information systems; 2accounting information system faculty of engineering and informatics, bina sarana informatics university, jakarta, indonesia www.bsi.ac.id 1*enok.etw@bsi.ac.id; 2sefrika.sfe@bsi.ac.id *corresponding author abstract at the beginning of 2020 there was a natural disaster that caused many people to lose their homes, damage to public facilities and infrastructure, as well as the breakdown of transportation links connecting villagers, especially in 3 districts namely sukajaya, nanggung and cigudeg. as a result of the disaster, many villages are isolated and residents need a lot of logistical support to meet their needs. to overcome these problems, we need a system that can help the government and volunteers who are interested in the decision making process so that the provision of assistance for victims of natural disasters can be right on target and in accordance with the urgency of basic needs and logistics needed. decision support systems using the topsis method are used to solve multi-criteria problems by offering various alternative solutions to solve problems. the results obtained a final preference value of 0.68 from c3 criteria in order to prioritize residents with closed transportation access to channel disaster relief funds for victims of natural disasters in bogor regency keywords: decision support system, topsis, multikriteria abstrak awal tahun 2020 terjadi bencana alam yang menyebabkan banyak warga yang kehilangan rumah, rusaknya sarana dan prasarana publik, serta terputusnya jalur transportasi yang menghubungkan warga desa terutama di 3 kecamatan yaitu sukajaya, nanggung dan cigudeg. akibat dari bencana tersebut, banyak desa terisolir dan warga membutuhkan banyak bantuan logistik untuk memenuhi kebutuhan hidupnya. untuk mengatasi permasalahan tersebut, diperlukan sebuah sistem yang dapat membantu pemerintah dan relawan yang berkepentingan dalam proses pengambilan keputusan agar pemberian bantuan bagi korban bencana alam dapat tepat sasaran dan sesuai dengan urgensi kebutuhan pokok dan logistik yang diperlukan. sistem pendukung keputusan dengan metode topsis digunakan untuk menyelesaikan permasalahan multikriteria dengan menawarkan berbagai solusi alternative untuk memecahkan masalah. hasil penelitian mendapatkan nilai preferensi akhir sebesar 0,68 dari kriteria c3 agar memprioritaskan warga dengan akses transportasi tertutup untuk penyaluran dana bantuan bencana bagi korban bencana alam di kabupaten bogor kata kunci: sistem pendukung keputusan, topsis, multikriteria introduction disaster is an event or series of events that threaten and disrupt people's lives. it caused by natural or non-natural factors and themselves. so that many fatalities, environmental damage, property loss, and psychological impact. in law number 24 year 2007 it is defined concerning natural disasters, non-natural disasters, and social disasters. natural disasters are disasters caused by events or a series of events caused by nature such as earthquakes, tsunamis, volcanic eruptions, floods, droughts, hurricanes, and landslides. in bogor regency there are 22 subdistricts and 250 villages, including disaster prone areas. bpbd (badan penanggulangan bencana daerah) bogor regency noted that there were 35 natural disasters including severe landslides that occurred in tenjolaya, west bogor. bnpb noted that 1,092 houses were heavily damaged, 1,625 were moderately damaged, 1,334 were slightly damaged, 10 mosques were heavily damaged, 15 mosques were lightly damaged, 5 schools were http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 jurnal riset informatika vol. 2, no. 2 march 2020 56 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. heavily damaged, 3 schools were moderately damaged, 80 bridges were seriously damaged and 2 bridges were slightly damaged . natural disasters also claimed the lives of 8 people who died, 3 people were missing, 12 seriously injured and 517 minor injuries (bnpb, 2020). the impact of this natural disaster, many villages were isolated. the villagers needed many logistics to full fill their daily life. to prevent this problems, it needs a system that can help the government and the volunteer in processing of taking decision so that the logistics distribution will proper in to the target and appropriate with the urgency of their daily needs and their logistics. the fact is different. when the social assistance distribution system for victims of natural disasters has not been able to run as intended, various problems related to the victim’s daily need is still happens. mechanisms, procedures and the synergy of potential personnel who have not considered the potential of the region, regional characteristics, disaster characteristics as well as the socio-economic and cultural conditions of the community have become factors that have not yet met the needs of victims in a timely, timely and effective manner (regence & prastyowati, n.d.). the natural disaster relief distribution system policy is ultimately oriented towards meeting the needs of victims. fulfillment of the needs of victims of natural disasters will be achieved when there is a match between the operation of the distribution system with regional characteristics, characteristics of disasters, types of disasters, number of victims and losses (rahayu et al., 2015). decision making in an organization is the result of a process of communication and continuous participation of the whole organizationone of the most fundamental management tasks is to maintain existence and develop the organization. one of the most fundamental management tasks is to maintain existence and develop the organization. decision making is a very complex. it is because involving people and information. for this reason management must make decisions regarding the steps that must be taken at both the strategy, tactics and operational levels. decisions to be made to solve problems in order to make better quality decisions, an interactive computer-based system is needed, which can help decision makers utilize data and models to solve unstructured problems.. (rahayu et al., 2015) spk is an interactive information system that provides information, model, and data manipulation. his system is used to assist in decision making in semi structured and unstructured situations (nursalam, 2016, 2013) the support system used uses the topsis method, which is one of the multi-criteria decision making methods that was first introduced by yoon and hwang in 1981. topsis method is widely used for decision making that has multiple criteria or criteria (alawiah & susilowati, 2018). decision support system for flood disaster logistical assistance that is made on a web-based basis simplify for trc to transmit disaster data. this can improve time efficiency so that trc performance becomes more efficient (nursalam, 2016, 2013). the decision support system for reconstruction funds for victims of natural disasters is implemented using the borland delphi 7 programming language, using a mysql database and reports are created using quick report. the system is used to determine the recipient of funds in according with the availability of funds that involve various factors that are used as criteria as the main factor for obtaining the reconstruction funds, namely the condition of the family is a supporting factor. the results of this system will give an alternative assessment for decision makers to determine who will receive reconstruction funds (rahayu et al., 2015). the house has important function for individuals and families. it is not only includes physical aspects, but also mental and social. to support the function of the house as a good place to live, so physical conditions must be fulfilled physical requirements, namely safe as a shelter, mentally fulfills a sense of comfort and can socially protect the privacy of every family member, the house is also become a media for the implementation of family guidance and education. with the fulfillment of one of the basic needs in the form of a decent house, it is expected to achieve family resilience. implementation of poor housing reconstruction program carried out by the social service through bapernas ((badan pemberdayaan masyarakat)) which began with data collection on prospective recipients of reconstruction assistance for poor houses that were known and recommended by the head of district and the village chief. based on the collective data, the social office ranks and makes decisions about prospective recipients of reconstruction assistance for poor houses based on the criteria of poor families determined by the social service (parjono et al., 2015). the research was conducted using the topsis method (technique for order preference by similarity to ideal solution). the topsis method is used because this method can solve http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 2 march 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 57 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. multi criteria problems by offering various alternative solutions to solve the problem. the results obtained a preference value of 0.58 from the c2 criterion of owning a house using their own name. therefore, a decision system that can be implemented to provide assistance to the rs_rutilahu program can consider aspects of home ownership on its own behalf (alawiah & putri, 2019). decision support system is a method used to solve problems accurately, quickly, effectively and efficiently. decision support system is made with the aim to help the decision making process by choosing the various alternative decisions. technique for order preference by similarity to ideal solution (topsis) is a multi-criteria decision making method based on a concept. the concept is that the best alternative not only has the shortest distance from the positive ideal solution but also has the longest distance from the negative ideal solution. this concept is widely used to solve practical decision problems. the aim of this research is to help governments and volunteers who wants to help disaster victims be able to distribute the donation to the target by using the proper decision support system. this study aims to help governments and volunteers who want to help disaster victims be able to channel aid funds on target by using the right decision support system. research methods the research materials used are; 1. data sources he research data is from the national disaster management agency (bnpb) of bogor regency 2. research population population of this research is in 3 districts of bogor regency. the details are: table 1 total study population no target criteria total 1 sukajaya 14.730 2 nanggung 5.310 3 cigudeg 1.702 total 21.742 3. samples and sample selection methods samples were taken from the population, namely victims of disasters in the districts of sukajaya, nanggung, and cigudeg. minimum sampling according to hair in (alawiah & susilowati, 2018) maximum likelihood estimation technique is used because it is more efficient and is used on samples of 100 to 200. samples can also use census methods in this case almost all craftsmen can be used as research samples. 4. research instruments the questionnaire used in this study was the closed question with a limited number of statements and answers in advance. the questions on the questionnaire were closed based on studies from various sources related to research. the questions used in the questionnaire were measured using five likert scales (table 2) adapted from with the provisions of the answers and values as follows. table 2 likert scale answer score positif negatif always 5 1 often 4 2 sometimes 3 3 almost never 2 4 never 1 5 source:(sugiyono, 2017) 5. research methods the research method uses topsis, the research is divided into several stages, namely: a. normalized decision matrix analysis making a normalized decision matrix is a stage in the topsis method that requires a performance rating of each alternative ai on each normalized ci criterion. rij = xij √∑ 𝑋𝑖𝑓 𝑧𝑚 𝑖=1 ........................................................................ (1) under the condition: i = 1,2 ... m j = 1.2 .... n rij = normalized decision matrix xij = the weight of the criteria to j in alternative -i i = alternative to i j = alternative to j b. normalized matrix (r) calculate the normalized matrix (r) with the following formula: rij = xij √∑ xif ²m i=1 ....................................................................... (2) where : i = 1, 2….., m; and j = 1, 2, ….., n http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 jurnal riset informatika vol. 2, no. 2 march 2020 58 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. c. weighted normalized matrix (y) calculate a weighted normalized matrix (y) with the following formula: w = {w1, w2, w3, w4,....wn} ................................... (3) yif = wi rij where i = 1,2, …, m; and j = 1,2, …, n d. positive ideal solution matrix (a +) and negative ideal solution matrix (a-) calculate positive (a +) and negative (a-) ideal solutions with the following formula: a+ = (𝑌1 +, 𝑌2 +, … . . , 𝑌𝑛 + a= (𝑌1 −, 𝑌2 −, … . . , 𝑌𝑛 − where: 𝑌𝑗 + is : max yij , if j is an attribute of benefit min yij , if j is an attribute of cost 𝒀𝒋 − is : min yij if j is an attribute of benefit max yij , if j is an attribute of cost e. distance of ideal negative solution (d-) and positive ideal solution (d +) calculate the distance of a negative ideal solution (d-) and a positive ideal solution (+) by: formula: 𝐷𝑖 +; 𝑖 = 1, 2, … , 𝑚 dj + = √∑ (yif nn j − yif) 2 .................................................... (4) f. preference value for each alternative calculate the preference value for each alternative offered as follows: formula: vi = di di −+ di − ........................................................................... (5) where i = 1, 2, 3, . . . m research resulit and discussion the results of the study are presented in the form of graphs, tables, or descriptive. analysis and interpretation of these results is needed before being discussed. the results of the study to determine the criteria weights are: a. there are fatalities, missing, serious injuries and minor injuries. (c1) b. damage to houses and public facilities (c2) c. covered transportation access (c3) d. logistics availability (c4) table 3 kecamatan data samples rw location criteria sukajaya a1 nanggung a2 cigudeg a3 the research sample was only carried out in 3 districts namely sukajaya, nanggung and cigudeng because the area was the worst location in the natural disaster in the early 2020 that occurred in bogor regency. in these locations there are still isolated local points and still need a lot of help because of closed access due to infrastructure damage. table 4 normalization decision matrix criteria c1 c2 c3 c4 a1 5 4 4 2 a2 5 3 4 4 a3 4 3 3 3 the normalized decision matrix from table 4 above is intended to determine the performance ranking value of each alternative offered by using the topsis method. table 5. results of weight value criteria c1 c2 c3 c4 5 3 4 3 then the value of each normalized data (r) is then multiplied by the weight (w) to get the weighted normalized decision matrix. the following results are in table 6. table 6. normalized values based on criteria c1 c2 c3 c4 a1 5 4 4 2 a2 5 3 4 4 a3 4 3 3 3 criteria rank result 66 34 41 29 root of criteria rank result 8,12 5,83 6,40 5,38 where wj is the rank of positive value for the profit attribute (benefit), and negative value for the cost attribute (cost) the next step is to calculate the normalized matrix for each of the criteria offered. the following is a matrixized matrix for criteria: there are fatalities, missing, serious injuries and minor injuries. (c1) http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 2 march 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 59 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. table 7 normalized matrix c1 criteria c1 rank result a1 5 8,12 0,61 a2 5 8,12 0,61 a3 4 8,12 0,49 the following is a normalized matrix for the criteria for damage to houses and public facilities (c2). table 8 normalized matrix c2 criteria c2 rank result a1 4 5,83 0,68 a2 3 5,83 0,51 a3 3 5,83 0,52 the following is a matrixized matrix for closed transportation access (c3) criteria. table 9 normalized matrix c3 criteria c3 rank result a1 4 6,40 0,62 a2 4 6,40 0,62 a3 3 6,40 0,46 the following is a normalized matrix for the logistical availability criteria (c4). table 10 normalized matrix c4 criteria c4 rank result a1 2 5,38 0,37 a2 4 5,38 0,74 a3 3 5,38 0,55 the results of the performance appraisal of each criterion offered by c1, c2, c3 and c4 will be used for the final assessment in finding the best alternative to solve the problem and choosing the most ideal alternative for the decision making process. the final result is normalized data the table below summarizes the values of each criterion weights calculated for the various alternatives offered. the following results are in table 11. table 11 normalized data criteria c1 c2 c3 c4 a1 0,61 0,68 0,62 0,37 a2 0,61 0,51 0,62 0,74 a3 0,49 0,52 0,46 0,55 normalized matrix is weighted the next stage is to determine the weighted normalized matrix which is calculated based on the normalized data in table 11 multiplied by the results of the criteria weights in table 5, the results are as follows. table 12. weighted normalization criteria c1 c2 c3 c4 a1 3,05 2,04 2,48 1,11 a2 3,05 1,53 2,48 2,22 a3 2,45 1,56 1,84 1,65 weighted normalized matrix shows the value of each criterion that has been calculated with the initial weighting of the alternatives offered. value of positive and negative ideal solutions the value of positive and negative ideal solutions is the value of all the best values achieved from the criteria offered. the following table table 13. positive and negative ideal solutions criteria c1 c2 c3 c4 a1 3,05 2,04 2,48 1,11 a2 3,05 1,53 2,48 2,22 a3 2,45 1,56 1,84 1,65 min 2,45 1,53 1,84 1,65 max 3,05 2,04 2,48 2,22 ideal negative and positive distance solutions distance the negative and positive ideal solutions are the weighted values of each alternative to the positive and negative ideal solutions. the aim is to find out the positive and negative values. the following results are the values of d + and din table 14. table 14. distance of ideal negative & positive solutions criteria d+ d c1 0,60 0,84 c2 0,70 1,03 c3 0,40 0,89 c4 1,24 0,78 preference value the final step in the topsis method is to determine the preference value. the way to calculate it is to look at the results of the values of the ideal and negative ideal solutions. the results of calculating preference values can be used as a reference in the decision making process. the best preference value taken is the highest value for each alternative weight. table 15 final preference values http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i2.118 jurnal riset informatika vol. 2, no. 2 march 2020 60 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. criteria score vi=(di-/(di-+di+) score d c1 (0,84)/( 0,84+0,60) 0.58 c2 (1.03)/( 1,03+0,70) 0.59 c3 (0,89)/( 0,89+0,40) 0.68 c4 (0,78)/( 0,78+1,24) 0.38 based on research that has been found, the final preference value is 0.68 from c3 criteria, namely closed transportation access. providing assistance to disaster victims in bogor regency is recommended to prioritize locations with closed transportation access. conlusions and suggestions conclusion decision support systems using the topsis method can be used to help governments and volunteers with an interest in the decision making process so that the provision of assistance to victims of natural disasters can be right on target and appropriate. the results obtained a final preference value of 0.68 from c3 criteria in order to prioritize residents with closed transportation access to channel disaster relief funds for victims of natural disasters in bogor regency. suggestion research can be used with a variety of other additional variables with a larger number of samples. research can also be used to help make policy in other decision support systems. references alawiah, e. t., & putri, d. a. (2019). sistem pendukung keputusan pemberian bantuan rs. 16(1), 73–78. alawiah, e. t., & susilowati, s. (2018). sistem pendukung keputusan pembelian vending machine dengan metode topsis studi kasus pt . kai commuter jabodetabek. jurnal prosiding seminar nasional pendidikan teknik informatika, 1(1), 256–261. nursalam, 2016, metode penelitian. (2013). 済無 no title no title. journal of chemical information and modeling, 53(9), 1689–1699. https://doi.org/10.1017/cbo978110741532 4.004 parjono, winarno, w. w., & luthfi, e. t. (2015). sistem pendukung keputusan menentukan kelayakan rekonstruksi rumah miskin. rahayu, y. s., farozi, m., & ring, j. (2015). sistem pendukung keputusan dana rekontruksi korban bencana alam ( studi kasus kecamatan piyungan kabupaten bantul ). 6–8. regence, a. c. p., & prastyowati, s. (n.d.). sistem penyaluran bantuan bencana alam dan keterpenuhan kebutuhan korban kasus di kabupaten padang pariaman. sugiyono. (2017). metode penelitian kuantitatif, kualitatif, dan r&d. alfabeta. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 193 the work is distributed under the creative commons attribution-noncommercial 4.0 international license inserting message secret on file data bank using steganography engineering with eof (end of file) method michael sitorus1, deki satria2 department of information system1, departement of system and information technology2 institute of technology and business bank rakyat indonesia (bri institute)1,2 michael.sitorus@bri-institute.ac.id1, deki.satria@bri-institute.ac.id2 abstrak dalam pertukaran informasi tidak disadari ada informasi yang penting. sering beredar informasi yang penting tapi dianggap tidak penting seperti informasi rekening nasabah bank. ancaman keamanan terhadap informasi cukup banyak seperti interruption, interception, modifikasi, dan fabrication. banyak pencurian data informasi oleh pihak yang tidak bertanggungjawab. penelitian ini bertujuan untuk mengamankan data informasi berupa file citra digital yang akan disisipkan pesan rahasia dengan membangun aplikasi untuk sistem keamanan pesan rahasia di bank dengan menerapkan steganografi menggunakan metode end of file (eof). aplikasi steganografi yang mampu menyisipkan pesan rahasia pada data file citra digital. hasil penelitian ini adalah mampu merahasiakan keberadaan dari sebuah pesan tersembunyi. ketika orang lain yang menerima file gambar, mereka hanya dapat melihat gambar saja secara kasat mata. jadi semua pesan tidak terlihat dan tidak akan dapat terbaca oleh hacker atau orang lain sebelum mengetahui sisi keamanannya dan password untuk membuka atau membaca pesan rahasianya. kata kunci: steganografi, end of file (eof), citra digital, keamanan, bank, hacker abstract exchanging information does not realize that is important information. circulates often important information insignificant but such as account bank customer information. the threat of security to information such as interruption, interception, modification, and fabrication. thefts of information data by irresponsible parties. this research aims to secure information data in the form file digital image to be inserted with a secret message on building application for a secret message security system in the bank with applying steganography using the end of file (eof) method. steganography application capable of inserting secret messages in digital image data files. the results of the research can keep the existence of the message hidden. when other people receive image files, they can only see the images with the naked eye. so all messages are invisible and will not be read for hackers or other people before knowing the security side and open of password or reading the secret message. keywords: steganography, end of file (eof), digital image, security, bank, hacker introduction a lot of people do not know that data theft is a threat and easy to do. therefore peoples tend to send their data unsafely through the internet. data shared on the internet, usually public, can be accessed anywhere, anytime, and in any size without looking at its importance. bank, as a business entity, should follow the advancement of technology. therefore data integration became one of the primary concerns in their daily activity. the data need to be integrated, such as client data, business data, and other critical data. these vital data can be in picture format (sa’adah & purqon, 2016). there was a lot of secrets data that need to be encrypted or hide. the business could use steganography as a way to obscure private data. steganography is an art and science about hiding messages into a media such as pictures and sounds. therefore no one knows about the data hidden in the media except the sender and receiver (nurmaesah et al., 2018)(sitorus, 2015a). there were two crucial things needed to implement steganography. they are the message and the media itself. steganography is used to obscure the private data and protect the data (sitorus, 2015b). the media used to obscure the data won’t be changed; therefore, the media will look the same before and after the process (ariyus, 2009). http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 jurnal riset informatika vol. 2, no. 4 september 2020 194 the work is distributed under the creative commons attribution-noncommercial 4.0 international license research method this research using steganography. we can identify excess bit using this method implicitly by forecasting or explicitly using a calculation. we can use this excesses bit to hide our private message in the file. there were four steganography algorithm widely used, which are: 1) least significant bit (lsb); 2) end of file (eof); 3) domain transformation; and 4) spread spectrum encoding. previous research in this area conducted using the eof method and caesar chipper to encrypt data into the file. the application developed based on how substitution work on the caesar chipper method to conduct the encryption and eof to add the message in the end bit of the file (indriyono, 2016). before eof come to light, the lsb algorithm was widely used by the researcher. but the main downside of lsb is when the private/hidden message was larger than the container file. this problem made the steganography impossible to conduct. in eof methods, the hidden message got special marking at the end of the file as an identifier (anggraini & sakti, 2014). we can use this algorithm or technique to hide messages without changing the file size (masri et al., 2019). research time and place this research was conducted in bri pasar minggu and bri institute in august 2020. research population and samples the population of this research is the staff of bri pasar minggu and the bri institute. the details of population and sample is: a. the population is all the staff of bri pasar minggu and bri institute. the total staff is ten people. b. the sample is part of the population we consider to be able to represent the population. the sample was chosen using random sampling. in this research, we used five files with a different extension. research plan this research tried to develop an application that will add a hidden or private message into an image file. this research is a randomized pretest-posttest control group design. we use sdlc waterfall as development methods. the waterfall model steps can be seen in figure 1. figure 1. waterfall model 1) analysis in this step, the user requirement analysis step we conducted. this step was important because we can find what the user needs is. the problem we tried to solve in this research was how to add a hidden message in the image file using jpg, gif, and bmp extension. eof method was used to solve this problem. 2) plan in this step, we tried to solve the problems found in the analysis step. in this step, we made the development model using uml. the models are based on the result of requirement analysis. 3) model development in this step, we will create the ui mockup of the system based on the analysis result. 4) coding the coding process can be seen as a translation from human language to computer language. this is the main process of software development, where the programmer implements the analysis result in the application. 5) testing test steps were conducted to check whether the implementation met the user expectation. this step was also conducted to make sure there were no errors in the coding. data gathering methods we use qualitative methods to gather data, such as interview, observation focus group discussion (fgd), and document result and discussion a. analysis we can use steganography to hide data and enhance the hidden capability of the message (anindyawati & suryani, 2012). from the analysis result, we found that eof is the best method in this case. eof won’t increase the file size event when we add hidden text messages in the fie. this eof methods add a special identifier at the end of the http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 195 the work is distributed under the creative commons attribution-noncommercial 4.0 international license image file as a data header in the file and flag at the end of the file. b. planning system planning needs to have steps that focused on design programs or software, including data structure, software architecture, interface, and code procedure (nasution et al., 2017). we used uml to design the software model. we use three uml models, which are use case diagram, activity diagram, and sequence diagram. 1. use case diagram the use case diagram represents how the user will use the system as an actor. the use case of this system can be seen in figure 2. figure 2. use case diagram in figure 2, users login to the main page to use the application as intended by the user. on the main page, the user can hide the message to the image file, opening the hidden message in the image file, reading the user guide, and log out from the system. 2. activity diagram activity diagram show activity in the system in the form of action set, how each action is executed, decision, and how the action end (suendri, 2018). activity diagram show workflow or activity or business process of the system (hendini, 2016). activity diagram eases the analysis step to determine the activity that needs to be executed. the activity diagram of this application can be seen in figure 3. the activity starts with choosing the image that will become the main file. the next step is to choose “stego image” and insert the password for the hidden message; next, the user inserts the hidden message and then clicking insert to combine the image file and message. figure 3. activity diagram text message insertion figure 4 shows that the insertion of the hidden message starts from click “penyisipan”. after that, the user clicks “stego image” and enter the password for the message. stego image is a process to insert a message into the image file. “sisipkan” use to execute image and data file combinations. figure 4. activity diagram data file insertion 3. sequence diagram sequence diagram is a popular tool in software development, especially in the oop (object oriented programming) approach. the analyst used this diagram to show how objects interacted in the system in some timeline (heriyanto, 2018),(novita & sari, 2015). http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 jurnal riset informatika vol. 2, no. 4 september 2020 196 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 5. sequence diagram text message insertion figure 5 explains text insertion, where the process started from choosing the main image file as the message container. there will be informed if the file has been used as a container from another steganography process. the next step user will choose “stego image” to insert the message and password. figure 6. sequence diagram data file insertion figure 6 shows data file insertion where the process starts from choosing the image as a message container. this step will also check if the container image has been used as a container before, and the message will be added. c. development the result of this research was an application that could be used to add a message to a file. the ui of these apps is divided into five main menus with a specific function. the ui can be seen in figure 7. 1. main page: this page will show all the functionality of the system. 2. insert: in this menu, there was a button to insert the message into the image file. the hidden message in text form. insert button to choose hidden message file in docs extension (doc, docx, pdf, xls) 3. extraction: in this menu, there was an extraction button to extract hidden text messages and file extraction to open secret documents. 4. user guide: users will use this menu when they need the user guide of the application. 5. profile: in this menu, the user can see the information about the steganography and author button to show information about the author. figure 7. home page design figure 8 shows the ui design for text message insertion. on this page, there were master image text book and stego image, which used to display data in folders. message text box used to write the message, and password text box used to insert the password. the insertion process is executed if all text box filled correctly. figure 8.ui design for message insertion in the text message insertion from above, four buttons have different functions: http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 197 the work is distributed under the creative commons attribution-noncommercial 4.0 international license in figure 8, there were four buttons which are: 1. select the button to select the image from the directory in the computer 2. select the button in the “stego image” used to choose the image as the output. 3. insert button used to insert a hidden message and execute the steganography process. 4. exit button to close the systems. figure 9 shows ui for document insertion. the first frame is image frame to show real image file and stego frame to show manipulated image. there were also text boxes in the stego image and real image, which used to display data in the folder. data file text box used to show data in the folder, password text box used to enter the password. the steganography process is executed if all the box filled. in the insertion form, there were five buttons which are: 1. select the button to choose an image from the computer. 2. select button in the stego image button to select an image as output. 3. select buttons in the data file used to choose the file to be inserted into the main image. 4. insert button used to execute the steganography. 5. exit button to exit the process. figure 9. ui design insertion in figure 10, is the interface design of the message extract form in the form of a text message. there is a text box on the stego image to display data from the folder, the text box on the password is used to enter the password, the message text box is to display a secret message. the extraction process can be done when the stego image and password text boxes have been filled in. figure 10 shows the ui design for the message extraction form. there was a text box in the stego image to display the data from the directory, a password text box to show the password of the message, and a message text box to display the hidden message. the extraction process executed if all the text box filled 1. “select” button in the stego image form used to take the manipulated image from the directory. 2. the “extract” button is used to extract the hidden message from the file. 3. exit button to exit the form figure 10. ui design for extraction figure 11 shows the design of the message extraction form in document format. there were text boxes to display data from directory, text box to display password, and a frame to display the stego image. the extraction executes if all the form is filled. figure 11. file extraction ui design in this design, there were three buttons, which are: 1. “select” button in stego image to select the image from the computer directory 2. “extract” button to process the extraction process. 3. “exit” button to close the form. d. coding this process was the implementation process where the design was translated into software code. the code is displayed below. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 jurnal riset informatika vol. 2, no. 4 september 2020 198 the work is distributed under the creative commons attribution-noncommercial 4.0 international license // encrypt text messages if(features== uem || features== cem){ cipher cipher= cipher.getinstance("des"); secretkeyspec spec= new secretkeyspec(password.substring(0, 8).getbytes(), "des"); cipher.init(cipher.encrypt_mode, spec); messagearray= cipher.dofinal(messagearray); messagesize= messagearray.length; } // converts 32-bit message size into byte array tempbyte= new byte[4]; for(i=24, j=0; i>=0; i-=8, j++) { tempint= messagesize; tempint>>= i; tempint&= 0x000000ff; tempbyte[j]= (byte) tempint; } //put 4 bytes messagesize array into master file writebytes(tempbyte); // insert message writebytes(messagearray); dataoutputstream out= new dataoutputstream(new fileoutputstream(outputfile)); out.write(bytearrayout,0,bytearrayout.length); byteout.writeto(out); out.close(); { catch(eofexception e) } { catch(exception e) } message= "oops!!\nerror: "+ e.tostring(); e.printstacktrace(); return false; message= "sukses menyisipkan dalam '"+ outputfile.getname()+ "'."; return true; // insert data file public steganoinformation(file file) this.file= file; isester= false; if(!file.exists()) { starter= null; return; } if(file.getname().equals("sec#x&y")) { isester= true; return; } bytearray= new byte[(int) file.length()]; datainputstream in= new datainputstream(new fileinputstream(file)); in.read(bytearray, 0, (int) file.length()); in.close(); { catch(exception e) } starter= null; return; // get the length of the original file name= new byte[4]; string filename= file.getname(); string fileextension= filename.substring(filename.length()-3, filename.length()); if(fileextension.equalsignorecase("jpg")) inputmarker= steganografi.offset_jpg; else f(fileextension.equalsignorecase("png")) inputmarker= steganografi.offset_png; else inputmarker= steganografi.offset_gif_bmp_tif; retrievebytes(name, bytearray, inputmarker); datalength= 0; for(i=24,j=0; i>=0; i-=8,j++){ temp= name[j]; temp&= 0x000000ff; temp<<= i; datalength|= temp;} inputmarker= datalength; if(datalength<0 || datalength>file.length()){ starter= "invalid"; return; } e. testing in this step, we check the implemented modules, whether it met the requirement or not. after that, we conduct a test to check the ui design, whether it met the intended design or not (guntoro, 2020). figure 12. steganography system ui the test result from figure 12 shows that the ui/ux design met the requirements. the interview and observation conducted to the bri staff show that they feel satisfied with the ui. figure 13.message insertion form the test result of figure 13 shows there was a message hidden in the file. we chose the file and inserted the hidden message to test this form. if we chose the image first and then added the message, the system will display “message been added” automatically. if the system can not add the message, the system will display “message cant be added” in the form, as shown in figure 14. after all http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 199 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the text boxes are filled, the system can execute the algorithm. the result of the system was the same file but with slightly larger file size. figure 14.file insertion form ui figure 15. extract message form ui figure 15, shows that we could only see the hidden message through the form. the first step to see the message is selecting the file with the hidden message. next, the user needs to insert the password and click the extract button. the prerequisite for the extraction is to make sure the file has the hidden message. figure 16, shows that the user needs to make sure the file has a hidden message. in this figure, we test whether we can extract the message or not. figure 16. tampilan form ekstrak file “extract file” form has the same process as the extracted message where the first step is selecting the file, insert the password, and click extract to see the hidden message. conclusion and suggestion conclusion steganography using eof proved to be able to insert a hidden message into an image file, could display the hidden message for example for the banking process. suggestion for the next research, the eof algorithm can be combined with another algorithm, whether to secure the data or the file. the user of the system can be tested more broadly in another conventional bank. reference anggraini, y., & sakti, d. v. s. y. (2014). penerapan steganografi metode end of file (eof) dan enkripsi metode data encryption standard (des) pada aplikasi pengamanan data gambar berbasis java programming. konferensi nasional sistem informasi 2014, 1743. anindyawati, n., & suryani, e. (2012). pembangunan aplikasi penyembunyian pesan menggunakan metode end of file (eof) ke dalam citra digital terhadap pesan yang terenkripsi dengan algoritma rsa. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.153 jurnal riset informatika vol. 2, no. 4 september 2020 200 the work is distributed under the creative commons attribution-noncommercial 4.0 international license itsmart, 1(1), 5–12. https://doi.org/https://doi.org/10.20961/it smart.v1i1.576 ariyus, d. (2009). keamanan multimedia. andi publisher. guntoro. (2020). metode waterfall. materi kuliah si. hendini, a. (2016). pemodelan uml sistem informasi monitoring penjualan dan stok barang (studi kasus: distro zhezha pontianak). jurnal khatulistiwa informatika, iv(2), 107–116. heriyanto, y. (2018). perancangan sistem informasi rental mobil berbasis web pada pt. apm rent car. intra-tech, 2(2), 64–77. indriyono, b. v. (2016). implementasi sistem keamanan file dengan metode steganografi eof dan enkripsi caesar cipher. sisfo, 6(1). kristanto, a. (2004). rekayasa perangkat lunak (konsep dasar). in gava media (1st ed.). gava media. masri, m., masri, m., widya, h., & yuhendri, d. (2019). perancangan aplikasi penyisipan pesan pada pixel citra menggunakan metode end of file. journal of electrical technology, 4(3), 178–184. nasution, y. r., johar, a., & coastera, f. f. (2017). aplikasi penyembunyian multimedia menggunakan metode end of file (eof) dan huffman coding. jurnal rekursif, 5(1). novita, r., & sari, n. (2015). sistem informasi penjualan pupuk berbasis e-commerce. teknoif, 3(2). nurmaesah, n., lestari, t., & retno mariana, a. (2018). aplikasi steganografi untuk menyisipkan pesan dalam media image | nurmaesah | jurnal tam (technology acceptance model). jurnal tam (technology acceptance model) , 8(1), 13–17. http://ojs.stmikpringsewu.ac.id/index.php/j urnaltam/article/view/82 sa’adah, n., & purqon, a. (2016). perbandingan hasil deteksi tepi pada citra kanker payudara dengan menggunakan metode canny dan metode ant colony optimization (aco). simposium nasional inovasi dan pembelaran sains (snips 2016). sitorus, m. (2015a). teknik steganography dengan metode least significant bit (lsb). jurnal ilmiah fakultas teknik limit’s, 11(2), 54–59. https://doi.org/10.13140/rg.2.2.14942.233 62 sitorus, m. (2015b). aplikasi keamanan data dengan teknik steganografi menggunakan metode end of file (eof). proceedings of the 1st informatics conference, 1(1). https://doi.org/10.13140/rg.2.2.28364.006 43 suendri. (2018). implementasi diagram uml (unified modelling language) pada perancangan sistem informasi remunerasi dosen dengan database oracle (studi kasus: uin sumatera utara medan). algoritma: jurnal ilmu komputer dan informatika, 3(1). http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 1 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. metamorphosis visualization with augmented reality using marker-based tracking bayu bagus kencana1, muhammad fathur prayudha2, budi arifitama3 program studi teknik informatika universitas trilogi bagus.bayu@trilogi.ac.id, faturprayuda@trilogi.ac.id, budiarif@trilogi.ac.id abstrak metamorfosis adalah siklus perkembangan biologis dari pertumbuhan hewan mulai dari penetasan hingga mengalami perubahan. siklus metamorfosis dipelajari dalam pembelajaran untuk siswa di sekolah pada mata pelajaran biologi. sayangnya untuk dapat melihat interaksi siklus metamorfosis hewan, membutuhkan waktu dan menemukan hewan yang ingin diamati. penelitian ini memberikan solusi inovatf dalam menjawab permasalahan yaotu dengan menggunakan augmented reality. teknologi augmented reality memvisualisasikan proses metamorfosis dari hewan kedalam bentuk 4 dimensi sehingga siswa menjadi antusias untuk mempelajari bagaimana proses metamorfosis perubahan hewan. metode marker-based tracking digunakan sebagai pendekatan penyelesaian permasalahan dimana lokasi pola tracking pada marker telah ditentukan terlebih dahulu sebagai tempat kemunculan objek augmented reality. hasil dari penelitian ini menunjukan bahwa dengan menggunakan metode marker-based tracking pembelajaran metamorfosis hewan dapat meningkatkan pemahaman metamorfosis siswa karena lebih interaktif. kata kunci: augmented reality, metamorfosis, marker-based tracking abstract metamorphosis is a cycle of biological animal growth. learning metamorphosis is a part of learning for students in schools specifically in the area of biology subjects. unfortunately, the observing activities take time, and finding an animal specimen is limited to study the metamorphosis cycle. this research proposes an innovative solution to overcome these problems which is the implementation of augmented reality technology. the animal metamorphosis cycle process is visualized into 4-dimensional objects to improve interaction for the student on learning metamorphosis during learning sessions. the marker-based tracking method is used as an approach where the location of the tracking pattern on the marker has been determined in advance as the place where the augmented reality object appears. the results of this study indicate that using a marker-based tracking method can improve students' understanding of metamorphosis. keywords: augmented reality, metamorphosis, marker-based tracking introduction metamorphosis is a physical-biological development process of an animal from birth or hatching to develop into a new form physically. the process that occurs in a complete metamorphosis will experience four stages of growth, starting with eggs, larvae, pupa, and adults, while incomplete metamorphosis undergoes three stages of growth starting with eggs, nymphs, and adults (truman & riddiford, 2019),(gonzalez, jiang, & lowe, 2018),(hammer, mcmillan, & fierer, 2014) of learning to recognize the process of animal change is important for a student, especially in biology subject. the process of learning until now uses video learning media to learn biology (nur & nurdiana, 2019) and resulted in an improvement in cognitive learning for the student, but lack of interaction between the video and the student. textbooks are also used as an approach for learning biology in school (wp & supeno, 2018), the approach using textbook on students for biology learning has been analyzed (cimer & coskun, 2018) and resulted that textbooks are still effective for knowledge transfer but lacks the interaction on the student still resulted in a http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 jurnal riset informatika vol. 3, no. 1 december 2020 2 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. problem for improving the learning process of the student. the lack of interaction using books and video learning media from previous research stated makes the learning process less attractive to the student. this research proposes a solution to the problem stated, using augmented reality technology. the use of augmented reality technology for introducing metamorphosis is expected to develop a more attractive and interactive learning process about learning how animal processes can carry out a process of perfect and imperfect metamorphosis. augmented reality is a term to describe the real and virtual world combined to create the illusion that there is no boundary between them (arifitama & syahputra, 2017),(ibáñez & delgado-kloos, 2018). augmented reality technology makes 3d objects that have been created appear to be real(lee, 2012). this technology combines the existing environment in the real world and runs interactively in real-time and creates a mixture between components to be used in three-dimensional media for information and promotion facilities (syahputra & arifitama, 2018). marker-based tracking is used as a marker in this research. marker-based tracking requires a two-dimensional marker that has a unique pattern that will be read by a computer through the camera(gherghina, olteanu, & tapus, 2013),(perwitasari, 2018). users will use a predetermined pattern to be able to display objects and be able to interact with the user through the device. the appearance of animal objects will make students understand how to distinguish between perfect and imperfect metamorphosis change processes. the result of this research that the application of the introduction of the perfect and imperfect metamorphosis process through augmented reality with the marker-based tracking method can help education personnel explain about metamorphosis in a more attractive learning process for the student to understand more clearly. research methods the method used in this research is using the marker-based tracking method(siltanen, 2012). (fleck, hachet, & bastien, 2015). the implementation of the marker-based tracking method in augmented reality requires an image that has a unique pattern to display objects, shown in figure 1. figure 1. marker based tracking mechanism from the image shown in figure 1, the camera will scan the marker created and detected, the desired augmented reality object will appear. the development of augmented reality consists of several development phases which are 3d object design, software development, and augmented reality marker development which uses supporting software namely. sketchup software sketchup is a 3d modeling application that is used for various depictions such as architecture, interior design, video games, and film design (lewis & hampton, 2015). in this research, sketchup is used to create object models for metamorphosis materials as showed in figure 2. figure 2. 3d metamorphosis object modeling unity 3d platform unity 3d is an integrated multi-platform game development technology for game creation(unity technology, 2018), building architecture, and simulation. therefore, this study uses unity to design the display navigation, shown in figure 3. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 3 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. figure 3. unity platform vuforia software development kit vuforia or vuforia sdk (software development kit) is a library for building augmented reality applications(simonetti, alexandro; paredes, 2016). to integrate it requires a marker that has been stored in the vuforia database. figure 4. hierarchy of application display design hierarchy is a sequence between components that describes how the system is connected. with this hierarchy, the application display will be seen where on the top frame is the main page which has two menus, namely the perfect menu which will display perfect metamorphosis and has 4 frames and an imperfect menu that will display imperfect metamorphosis which has 3 frames. result and discussion augmented reality implementation vuforia software development kit (sdk) is used to create marker-based tracking and to test the accuracy and accuracy of markers. the image that is used as a marker in this research is shown in figures 5 and 6. figure 5. perfect metamorphosis augmented reality marker figure 6. incomplete metamorphosis augmented reality marker it can be seen in figures 5 and 6 that each marker has a unique pattern that differentiates it from other patterns as a basic marker pattern used for augmented reality. the results obtained through vuforia are that the markers used have a pattern that can be applied to augmented reality with a value of 5 out of 5 and can be seen in figures 7 and 8. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 jurnal riset informatika vol. 3, no. 1 december 2020 4 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. figure 7. marker value for perfect metamorphosis figure 8. incomplete metamorphosis marker value the chosen markers are imported to unity for the application development process that has been integrated with vuforia, to implement the perfect and imperfect metamorphosis process application showed in figure 9. figure 9. initial appearance this initial display will direct the user to the perfect and imperfect metamorphosis menu. when the user clicks the perfect metamorphosis button, will be directed into another scene shown in figure 10. figure 10. display of the first process of complete metamorphosis the computer will turn on the camera where the camera will scan the markers that have been prepared, then the 3d augmented reality object consisting of butterfly eggs and leaves. next to the object, a description of the phase that occurs in the object will appear. when the user clicks the home screen button, the user will go back to the main menu shown in figure 9. when the user clicks the next button, the object will change when the user scans the same marker as shown in figure 11 or when the previous button is clicked, the object will appear in figure 13. figure 11. display of the second process of complete metamorphosis in figure 11, the object will change from an egg to a larva. there is a description that explains the larval phase in an object. when the user clicks the next button, the object will change to the next phase in figure 12 or when the user clicks the previous button the object will appear in figure 10. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 5 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. figure 12. display of the third process of complete metamorphosis in figure 12, shows the process of changing from a larva into a cocoon. there is a description that explains the cocoon phase. when the user clicks the next button, the object will change to the next phase in figure 13. figure 13. display of the fourth process of complete metamorphosis in figure 13, shows the final process of metamorphosis which is a butterfly. there is a description that explains the final phase. software usability testing software testing is needed as a form of validation regarding the effectiveness of the research. researchers conducted test case validation by distributing test case sheets to 5 application developers. the test case results are as follows : table 1. usability testing no usability test case average result 1 testing movement scene link 80 2 testing the quality of object detection 90 3 testing the stability of object detection 90 4 user interface design 70 5 object visibility 90 6 object detection time responses 80 7 object detection accuracy 80 8 user interface color management 70 9 detailed augmented object 80 10 installation application responses 70 total average 80 based on the results of usability testing performed by the software developer, the average acceptance score is 80, where this value can be concluded that the application is ready for the user. conclusions and suggestions conclusions based on the result, augmented reality can be used to visualize and help educators in explaining the perfect and imperfect of animals’ metamorphosis to the student in a more attractive way. the marker-based tracking used in this research is compatible to be used as a marker. suggestions the suggestions that can be performed for future research are to animate the 3d objects to make it more interactive, the use of different marker like markerless-based tracking to further understand which type of marker is more compatible. reference arifitama, b., & syahputra, a. (2017). cultural heritage digitalization on traditional sundanese music instrument using augmented reality markerless marker method. jurnal teknologi dan sistem komputer. https://doi.org/10.14710/jtsiskom.5.3.2017. 101-105 cimer, a., & coskun, s. (2018). students’ opinions about their ninth grade biology textbook: from the perspective of constructivist http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.168 jurnal riset informatika vol. 3, no. 1 december 2020 6 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. learning approach. journal of education and learning. https://doi.org/10.5539/jel.v7n4p201 fleck, s., hachet, m., & bastien, j. m. c. (2015). marker-based augmented reality. https://doi.org/10.1145/2771839.2771842 gherghina, a., olteanu, a. c., & tapus, n. 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(2018). pengembangan alat peraga edukasi proses siklus air (hidrologi) menggunakan teknologi augmented reality. seminar nasional teknologi dan multimedia (semnasteknomedia), 2-11–1. truman, j. w., & riddiford, l. m. (2019). the evolution of insect metamorphosis: a developmental and endocrine view. philosophical transactions of the royal society b: biological sciences. https://doi.org/10.1098/rstb.2019.0070 unity technology. (2018). unity 3d. in unity technology. wp, h., & supeno, d. (2018). the effectiviness of biology textbook-based mnemonic strategies-assisted method mind mapping against learning retention of students to the study of biology in senior high school. international journal of advanced research. https://doi.org/10.21474/ijar01/6977 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 43 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. optimasi naïve bayes dengan particle swarm optimization dan stratified untuk prediksi kredit macet pada koperasi tati mardiana sistem informasi universitas bina sarana informatika tati.ttm@bsi.ac.id abstract in business, cooperatives have an important role in improving the national economy. the inability of member to pay installments of credit is a major problem that occurs in cooperatives. consequently, it was occurred non-performing loan. cooperatives can avoid non-performing loan by making predictions of cooperative members who are potentially late in paying credit. in several studies have used naive bayes for classification problems because of the efficient calculation and the high accuracy. but naive bayes assumes all the attributes of the class are independent of other attributes. making it especially suitable for the classification problems with large attributes. however, this assumption is often untenable in the real classification problem. so in several documents, naive bayes performance is not perfect. the aim of this study is to optimize the naive bayes method using particle swarm optimization (pso) and sample stratified to improve accuracy in predicting non-performing loan in cooperatives. this study uses data from pusat data koperasi (puskopdit) dki jakarta. the credit set data obtained were 565 records with 15 predictor attributes and 1 class attribute. the test results with confusion matrix and roc curve obtained an accuracy value of 86% and an auc value of 0.867 with a diagnosis of good classification. this study shows that the use pso on nbc to predict non-performing loan in cooperatives increases the accuracy of 21.03% and auc by 0.069. the results of the t-test and anova test showed that the two classification methods tested had significant (significant) differences in the auc values. keywords: particle swarm optimization, sample stratified, naive bayes , cooperatives, bad credit. abstrak dalam bisnis, koperasi memiliki peranan penting dalam meningkatkan perekonomian nasional. ketidakmampuan anggota untuk membayar angsuran kredit merupakan masalah utama yang terjadi pada koperasi. akibatnya, terjadi kredit macet. koperasi dapat menghindari kredit macet dengan membuat prediksi dari anggota koperasi yang berpotensi terlambat membayar kredit. dalam beberapa penelitian telah menggunakan naive bayes untuk masalah klasifikasi karena perhitungan yang efisien, dan akurasi tinggi. tetapi naive bayes mengasumsikan bahwa semua atribut kelas tidak tergantung pada atribut lainnya. naive bayes sesuai untuk masalah klasifikasi dengan atribut besar. namun, asumsi ini sering tidak dapat dipertahankan dalam masalah klasifikasi nyata. dalam beberapa dokumen, kinerja naive bayes tidak sempurna. tujuan dari penelitian ini adalah untuk mengoptimalkan metode naive bayes menggunakan particle swarm optimization (pso) dan untuk meningkatkan akurasi dalam memprediksi kredit macet di koperasi. penelitian ini menggunakan data dari pusat data koperasi (puskopdit) dki jakarta. data set kredit yang diperoleh sebanyak 565 record dengan 15 prediktor atribut dan 1 atribut kelas. hasil pengujian dengan confusion matrix dan kurva roc diperoleh dari nilai akurasi sebesar 86% dan nilai sebesar 0,867 dengan diagnosis klasifikasi baik. penelitian ini menunjukkan bahwa penggunaan pso pada nbc untuk memprediksi kredit macet meningkatkan akurasi 21,03% dan auc sebesar 0,069. hasil uji t-test dan anova menunjukkan bahwa pada dua metode klasifikasi yang diuji memiliki perbedaan yang nyata (signifikan) dalam nilai auc. kata kunci : particle swarm optimization, sample stratified, naive bayes , kredit macet, koperasi. pendahuluan koperasi memiliki peranan yang sangat penting dalam meningkatkan perekonomian nasional melalui kegiatan menghimpun dana masyarakat dan menyalurkan kepada masyarakat dalam bentuk kredit atau bentuk-bentuk usaha lainnya. berdasarkan laporan kementerian koperasi dan usaha kecil dan menengah, jumlah koperasi pada tahun 2014 tercatat sebesar http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 44 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 209.488 unit. kondisi ini meningkat 19% dibandingkan tahun 2009 yang tercatat sebanyak 170.411 unit. pertumbuhan anggota koperasi selama periode tahun 2009-2014 mengalami peningkatan sebesar 19,76% menjadi 7.203.682 orang. seiring dengan peningkatan jumlah unit dan anggota koperasi, sisa hasil usaha yang dibagikan kepada anggota koperasi pada tahun 2014 juga mengalami peningkatan menjadi 64,18% menjadi rp9,5 triliun(kementerian koperasi dan usaha kecil menengah republik indonesia, 2015). saat ini koperasi di indonesia didominasi oleh koperasi kredit yang menguasai antara 5560% dari keseluruhan aset koperasi (ikhsan saputro & mardiana, 2015). koperasi dalam melaksanakan kegiatan usaha kredit harus memperhatikan asas-asas perekreditan yang sehat karena mengandung risiko yang tinggi. analisis terhadap data permohonan kredit anggota dilakukan secara akurat untuk meminimalisasi anggota yang tidak mampu membayar kredit sesuai dengan perjanjian antara kedua belah pihak. namun demikian, permasalahan yang sering terjadi adalah anggota yang telah memenuhi kualifikasi kredit memiliki potensi terlambat membayar kredit (melissa & oetama, 2013) yang pada akhirnya terjadi kredit macet. kredit macet dapat dihindari dengan cara memprediksi anggota koperasi yang berpotensi terlambat membayar kredit (rifqo & wijaya, 2017). prediksi tersebut menggunakan data historis kredit anggota koperasi yang telah disetujui untuk menemukan keteraturan hubungan atau pola tingkat kelancaran pembayaran kredit anggota koperasi. proses ini disebut dengan data mining (antaristi & kurniawan, 2017). proses prediksi tersebut menggunakan teknik klasifikasi (menarianti, 2015). naive bayes telah banyak digunakan untuk membangun classifier karena perhitungannya efisien, memiliki akurasi yang tinggi dan landasan teori yang kuat (li, ding, & li, 2014). naïve bayes merupakan algoritma yang didasarkan pada teorema atau aturan bayes untuk memprediksi probabilitas keanggotaan suatu kelas dengan asumsi nilai dari atribut pada kelas tertentu tidak bergantung (independence) pada nilai atribut lainnya (muhamad et al., 2017). pada metode ini semua atribut akan memberikan kontribusinya dalam pengambilan keputusan, dengan bobot atribut yang sama penting dan setiap atribut saling bebas satu sama lain(fitri cahyanti, saptono, & widya sihwi, 2015). hal ini menyebabkan parameter setiap atribut dapat diperkirakan secara terpisah sehingga lebih tepat diterapkan untuk masalah klasifikasi dengan jumlah atribut yang besar(hasan, 2017). namun, asumsi ini sering tidak dapat dipertahankan sehingga performa naive bayes tidak optimal (li et al., 2014). oleh karena itu, diperlukan metode untuk meningkatkan hasil akurasi naïve bayes pada data dengan jumlah atribut yang besar. beberapa peneliti telah melakukan penelitian terkait optimasi naïve bayes pada data dengan jumlah atribut yang besar. hasan mengusulkan optimasi seleksi atribut pada naïve bayes menggunakan forward selection untuk meningkatkan akurasi naïve bayes untuk memprediksi tingkat kelancaran pembayaran kredit (hasan, 2017). hasil penelitian menunjukkan nilai akurasi naive bayes berbasis forward selection mencapai nilai akurasi sebesar 71,97%. sedangkan rinawati mengusulkan optimasi bobot atribut pada naïve bayes classifier menggunakan particle swarm optimization untuk memprediksi penilaian kredit. hasil penelitian menunjukkan nilai akurasi yang lebih tinggi yaitu sebesar 75,90% dan nilai auc sebesar 0,773. sehingga dicapai peningkatan akurasi sebesar 3,5%, dan peningkatan auc sebesar 0,008 (rinawati, 2017). berdasarkan penelitian sebelumnya, penelitian ini menggunakan pendekatan hybrid yang mengkombinasikan antara algoritma particle swarm optimization dan naïve bayes (pso-nb). metode menerapkan particle swarm optimization untuk optimasi bobot atribut pada data historis pembayaran kredit anggota koperasi kemudian membangun model klasifikasi dengan naïve bayes. dalam penelitian ini digunakan metode sample stratified untuk pengacakan data sehingga menghasilkan data pelatihan dan pengujian yang proposional. tujuan penelitian ini adalah mengoptimasikan algoritma naïve bayes menggunakan particle swarm optimization (pso) dan sample stratified agar lebih akurat kinerjanya dalam memprediksi kredit macet pada koperasi. bahan dan metode objek penelitian ini adalah pusat koperasi kredit (puskopdit) dki jakarta. puskopdit dki jakarta bertujuan membina dan mendampingi penyelenggaraan koperasi kredit primer yang ada di jakarta. saat ini puskopdit dki jakarta beranggotakan 134 koperasi kredit primer. fokus penelitian ini adalah mengoptimasi metode naïve bayes sehingga kinerjanya lebih akurat dalam memprediksi kredit macet pada koperasi. jenis penelitian yang dilakukan merupakan penelitian eksperimen yang melibatkan penyelidikan tentang http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 45 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. perlakuan pada parameter atau variabel tergantung dari penelitinya dan menggunakan tes yang dikendalikan oleh si peneliti itu sendiri. data yang mendukung dalam melakukan penelitian ini, sebagai berikut : a. data primer data primer adalah data yang diperoleh secara langsung dari sumber, misalnya datadata hasil wawancara dan diskusi langsung dan pengamatan langsung dengan puskopdit dki jakarta. b. data sekunder merupakan data yang diperoleh secara tidak langsung, misalnya dari dokumentasi, literatur, buku, jurnal, dan informasi lainnya yang ada hubungannya dengan masalah yang diteliti. adapun teknik pengumpulan data yang dilakukan dalam penelitian ini, sebagai berikut : a. observasi observasi dilakukan dengan mengumpulkan data dengan melakukan pengamatan secara langung dan sistematis terhadap proses penyaluran kredit kepada anggota. b. wawancara wawancara dilakukan kepada ketua puskopdit dki jakarta dan bagian analis kredit untuk mmeperoleh informasi lebih mendalam terkait proses penyaluran kredit kepada anggota. c. studi pustaka studi pustaka dilakukan dengan mengumpulkan dan mempelajari literatur yang berkaitan dengan pemecahan masalah kredit macet menggunakan metode naïve bayes. sumber literatur berupa buku teks, karya ilmiah,dan situs-situs penunjang. kegunaan metode ini diharapkan dapat mempertegas teori serta keperluan analisis dan mendapatkan data yang sesungguhnya. tahapan yang dilakukan dalam eksperimen menggunakan metodologi crossstandard industry process for data mining atau crisp-dm (fadillah, 2015), sebagai berikut : 1. pemahaman bisnis (business understanding) pada tahap ini diperlukan pemahaman tentang latar belakang dan tujuan pada proses bisnis yang berhubungan dengan penyaluran kredit, antara lain : a. menentukan tujuan bisnis (determine business objectives) tujuan bisnis yang ingin dicapai puskopdit dki jakarta adalah meminimalkan risiko terjadinya kredit macet. b. menilai situasi (asses the situation) penyaluran kredit kepada anggota koperasi melibatkan data anggota koperasi, data permohonan kredit, data pembayaran kredit dan data partisipasi anggota. c. tujuan data ming tujuan dilakukannya data mining adalah mengoptimasi metode naïve bayes sehingga kinerjanya lebih akurat dalam memprediksi kredit macet pada koperasi. 2. pemahaman data (data understanding) pada tahapan pemahaman data, ada beberapa hal yang dilakukan antara lain, mengumpulkan data awal, mendeskripsikan data, mengeksplorasi data, dan memverifikasi kualitas data terkait proses bisnis. 3. persiapan data (data preparation) pada tahapan persiapan data, ada beberapa hal yang dilakukan antara lain, deskripsi data set, memilih data, membangun data, mengintegrasikan data dan membersihkan data. data pembayaran kredit yang diperoleh sebanyak 565 record dengan 15 atribut prediktor dan 1 atribut kelas. atribut prediktor pada dataset koperasi kredit terdiri dari usia, jumlah pinjaman, jangka waktu pinjaman, angsuran, jenis pekerjaan, masa kerja, penghasilan, status rumah, lama tinggal, jumlah tanggungan, nilai agunan, tujuan pinjaman, jumlah simpanan, prestasi pengembalian kredit sebelumnya, dan partisipasi mengembangkan koperasi kredit, sedangkan atribut kelas adalah status pembayaran kredit. 4. pemodelan (modeling) pada tahapan pemodelan dilakukan pemilihan metode klasifikasi dan membangun model klasifikasi untuk memprediksi kredit macet pada koperasi. adapun metode yang digunakan untuk membangun model klasifikasi adalah particle swarm optimization (pso) untuk meningkatkan bobot atribut (attribute wighting), sample stratified untuk pengacakan data sehingga menghasilkan data pelatihan dan pengujian yang proposional dan naïve bayes untuk memprediksi probabilitas keanggotaan suatu kelas. pada gambar. 1 menunjukkan proses klasifikasi dengan naïve bayes kemudian dioptimasi dengan menggunakan metode pso-nb dengan sample stratified. pengujian model klasifikasi yang diusulkan menggunakan aplikasi rapidminer. model klasifikasi yang terbentuk diuji untuk mengukur tingkat akurasi dan area under curve (auc) dengan http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 46 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. data yang secara acak dipisahkan dengan kfolds cross validation dengan k=10. akurasi diukur dengan menggunakan confusion matrix dan nilai auc akan diukur dengan menggunakan roc curve. 5. evaluasi (evaluation) evaluasi dilakukan untuk memvaliditas model yang terbentuk. evaluasi dilakukan dengan membandingkan nilai akurasi dan nilai auc serta menguji signifikan perbedaan antara sebelum dan sesudah dilakukan optimasi pembobotan atribut dengan pso dan sample stratified pada klasifikasi naïve bayes menggunakan metode t-test dan anova. hasil uji t-test dan anova didapatkan suatu model yang signifikan lebih akurat kinerjanya dalam memprediksi kredit macet pada koperasi. dataset pembayaran kredit anggota koperasi pso sample stratified sampling optimasi bobot atribut label 1 (lancar) naïve bayes label 2 (macet) 10 folds xvalidation pengujian confusion matrix dan roc curve evaluasi label 1 (lancar) naïve bayes label 2 (macet) 10 folds xvalidation pengujian confusion matrix dan roc curve evaluasi t-test dan anova komparasi model klasifikasi naïve bayes model klasifikasi pso-nb hasil naïve bayes yang dioptimasi dengan pso dan stratified signifikan lebih akurat untuk prediksi kredit macet pada koperasi sumber: (mardiana, 2018) gambar 1. model yang diusulkan hasil dan pembahasan model yang telah dikembangkan diuji keakuratannya dengan memasukkan sejumlah data ke dalam model. eksperimen yang dilakukan dalam penelitian ini menggunakan aplikasi rapidminer. data uji diambil dari histori pembayaran kredit anggota koperasi pada tahun 2012-2014. data pembayaran kredit yang diperoleh sebanyak 565 record dengan 15 atribut prediktor dan 1 atribut kelas. atribut prediktor pada dataset koperasi kredit terdiri dari usia, jumlah pinjaman, jangka waktu pinjaman, angsuran, jenis pekerjaan, masa kerja, penghasilan, status rumah, lama tinggal, jumlah tanggungan, nilai agunan, tujuan pinjaman, jumlah simpanan, prestasi pengembalian kredit sebelumnya, dan partisipasi mengembangkan koperasi kredit, sedangkan atribut kelas adalah status pembayaran kredit. eksperimen pertama dilakukan pengujian model klasifikasi menggunakan metode naïve bayes. desain pengujian model klasifikasi dengan metode naïve bayes disajikan pada gambar 2. sumber: (mardiana, 2018) gambar 2. pengujian model klasifikasi dengan metode naïve bayes pada tabel 1 dijelaskan hasil pengujian model klasifikasi dengan metode naive bayes pada confusion matrix didapatkan hasil dari 565 data, 294 diklasifikasikan lancar sesuai dengan prediksi yang dilakukan dengan metode naive bayes, lalu 114 data diprediksi lancar tetapi ternyata hasilnya macet, 67 data diprediksi macet ternyata hasil nya lancar dan 90 diklasifikasikan macet sesuai dengan prediksinya. hasil perhitungan divisualisasikan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 47 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. dengan kurva roc. perbandingan kedua class disajikan pada gambar 3 yang merupakan kurva roc untuk algoritma naïve bayes. kurva roc pada gambar 2 menunjukkan confusion matrix dari tabel 1. garis horizontal adalah false positives dan garis vertikal true positives. menghasilkan nilai auc sebesar 0.747 dengan diagnosa klasifikasi cukup (fair classification). tabel 1. hasil confusion matrix model klasifikasi dengan metode naïve bayes accuracy : 67.97% +/4.635(mikro: 67.96%) true lancar true macet class precisio n pred. lancar 294 114 72,06% pred. macet 67 90 57,32% class recall 81,44% 44,12% accuracy : 67.97% +/4.635(mikro: 67.96%) true lancar true macet class precision pred. lancar 294 114 72,06% pred. macet 67 90 57,32% class recall 81,44% 44,12% sumber: (mardiana, 2018) sumber: (mardiana, 2018) gambar 3. kurva roc dengan metode naïve bayes pada ekperimen kedua, akan dilakukan uji coba dengan memberi nilai pada parameter population size antara 10-100 dan maximum number of generation antara 60-100. dalam pengacakan data pelatihan dan pengujian ini ada kemungkinan akan menghasilkan data yang tidak proporsional dalam tiap klasifikasi. oleh karena itu digunakan metode sample stratified untuk pengacakan data sehingga menghasilkan data pelatihan dan pengujian yang proposional. desain pengujian model klasifikasi dengan metode psonb disajikan pada gambar 4. sumber: (mardiana, 2018) gambar 4. pengujian model klasifikasi dengan metode pso-nb hasil pengujian pada eksperimen kedua disajikan pada tabel 2. berdasarkan hasil pengujian diperoleh akurasi tertinggi terjadi pada saat population size bernilai 50 dan maximum number of generation bernilai 100 dengan nilai akurasi sebesar 89,00%, auc 0,879 dalam waktu 35 detik. hasil pembobotan atribut dengan pso seperti yang disajikan pada tabel 3 menunjukkan bahwa hanya 11 atribut variabel predictor yang berpengaruh terhadap prediksi kredit macet, antara lain : usia, jangka waktu pinjaman, angsuran, jenis pekerjaan, penghasilan, lama tinggal, jumlah tanggungan, nilai agunan, tujuan pinjaman, simpanan, dan prestasi pengembalian kredit yang lalu. tabel 2. hasil akurasi model klasifikasi dengan metode pso-nb populat ion size maximum number of generation 60 70 80 90 100 10 80.00 80.00 84.00 80.00 80.0 0 20 84.00 84.00 84.00 84.00 84.0 0 30 82.00 82.00 82.00 81.00 82.0 0 http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 48 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 40 84.00 86.00 88.00 85.00 84.0 0 50 87.00 87.00 87.00 87.00 89.0 0 60 85.00 87.00 88.00 83.00 83.0 0 70 84.00 83.00 83.00 84.00 84.0 0 80 84.00 86.00 86.00 85.00 84.0 0 90 85.00 84.00 83.00 86.00 85.0 0 100 84.00 83.00 86.00 84.00 86.0 0 sumber: (mardiana, 2018) tabel 3. hasil pembobotan atribut atribut bobot usia 0.216 jumlah pinjaman 0 jangka waktu pinjaman 0.429 angsuran 1 jenis pekerjaan 0.150 masa kerja/usaha 0 penghasilan 0.037 status rumah 0 lama tinggal 1 jumlah tanggungan 0.303 nilai agunan 0.664 tujuan pinjaman 0.267 simpanan 1 prestasi pengembalian kredit yang lalu 0.365 partisipasi mengembangkan koperasi kredit 0 sumber: (mardiana, 2018) tabel 4 menjelaskan dari 100 data yang proposional untuk digunakan dalam klasifikasi dengan naïve bayes, 60 diklasifikasikan lancar sesuai dengan prediksi, lalu 7 data diprediksi lancar tetapi ternyata hasilnya macet, 4 data diprediksi macet ternyata hasilnya lancar, 29 diklasifikasikan macet sesuai dengan prediksi. dari hasil perhitungan tersebut, kemudian divisualisasikan dengan kurva roc. garis horizontal adalah false positives dan garis vertikal true positives. dari gambar 5 disajikan grafik roc dengan nilai auc sebesar 0.879 dengan diagnosa klasifikasi baik (good classification). tabel 4. hasil confusion matrix model klasifikasi dengan metode pso-nb accuracy : 89.00% +/11.36(mikro: 89.00%) true lancar true macet class precisio n pred. lancar 60 7 89.55% pred. macet 4 29 87.88% class recall 93.75% 80.56% sumber: (mardiana, 2018) sumber : (mardiana, 2018) gambar 5. kurva roc dengan metode pso-nb berdasarkan hasil pengujian, dilakukan evaluasi dengan mengamati perbandingan nilai akurasi dan auc dari setiap metode seperti yang disajikan pada tabel 5. hasil evaluasi menunjukkan bahwa nilai akurasi dan pso-nb lebih tinggi dibandingkan naïve bayes tunggal. penggunaan pso untuk optimasi bobot atribut dan sample stratified pada klasifikasi naïve bayes menghasilkan peningkatan akurasi sebesar 21,03% dan auc sebesar 0,069. tabel 5. perbandingan hasil metode nb dengan pso-nb metode akurasi auc naïve bayes 67.97% 0.747 pso-nb 89% 0.879 sumber: (mardiana, 2018) evaluasi lebih lanjut dilakukan untuk menguji validitas performansi model klasifikasi menggunakan uji t-test dan anova. desain uji ttest dan anova pada aplikasi rapidminer disajikan pada gambar 6. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 1 desember 2018 issn: 2656-1743 49 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sumber: (mardiana, 2018) gambar 6. uji t-test dan anova model klasifikasi naïve bayes dengan pso-nb hasil uji te-test disajikan pada gambar 7 yang menunjukkan bahwa metode pso-nb memiliki perbedaan nilai yang sangat baik atau signifikan karena memiliki probabilitas < 0.05 yaitu 0.000 terhadap metode naive bayes. sumber: (mardiana, 2018) gambar 7. hasil uji t-test model klasifikasi naïve bayes dengan pso-nb hasil uji anova pada gambar 8 menunjukkan bahwa dkantar kelompok(pembanding) = 1, dkdalam residual (penyebut) = 18, pada alfa=0.05 maka nilai f tabelnya adalah f0,05(1,18) = 4,41. sedang f hitung = 27.485. nilai fhitung > ftabel, 27.485> 4,41, maka pada dua metode klasifikasi yang diuji memiliki perbedaan yang nyata (signifikan) dalam nilai auc. sumber: (mardiana, 2018) gambar 8. hasil uji anova model klasifikasi naïve bayes dengan pso-nb hasil evaluasi menunjukkan bahwa pso-nb dengan sample stratified merupakan kombinasi metode yang cukup baik dalam pengklasifikasian data. penggunaan pso untuk optimasi pembobotan atribut mampu menunjukkan atribut yang berpengaruh terhadap prediksi kredit macet. dengan demikian metode pso-nb dengan sample stratified menjadi solusi pemecahan permasalahan dalam memprediksi kredit macet pada koperasi. kesimpulan penggunaan pso sangat efektif untuk metode naïve bayes yang mengasumsikan nilai dari atribut pada kelas tertentu tidak bergantung (independence) pada nilai atribut lainnya. hasil pembobotan atribut dengan pso seperti yang disajikan pada tabel 3 menunjukkan bahwa hanya 11 atribut variabel predictor yang berpengaruh terhadap prediksi kredit macet, antara lain : usia, jangka waktu pinjaman, angsuran, jenis pekerjaan, penghasilan, lama tinggal, jumlah tanggungan, nilai agunan, tujuan pinjaman, simpanan, dan prestasi pengembalian kredit yang lalu. optimasi metode naïve bayes menggunakan pso dan sample stratified memberikan nilai akurasi sebesar 89% 21,03% dan nilai auc sebesar 0.879 dengan diagnosa klasifikasi baik (good classification). performansi kinerja metode pso-naïve bayes dengan sample stratified menghasilkan peningkatan akurasi sebesar 21,03% dan auc sebesar 0,069 dibandingkan dengan metode naïve bayes tunggal. hasil uji t-test dan anova menunjukkan pada dua metode klasifikasi yang diuji memiliki perbedaan yang nyata (signifikan) dalam nilai auc. dengan demikian metode pso-nb dengan sample stratified menjadi solusi pemecahan permasalahan dalam memprediksi kredit macet pada koperasi. penelitian lanjutan dapat dikembangkan pendekatan hybrid untuk optimasi bobot atribut http://creativecommons.org/licenses/by-nc/4.0/ issn: 2656-1743 jurnal riset informatika vol. 1, no. 1 desember 2018 50 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sehingga kinerja naïve bayes lebih akurat dalam memprediksi kredit macet pada koperasi. referensi antaristi, m., & kurniawan, i. (2017). aplikasi klasifikasi penentuan pengajuan kartu kredit menggunakan metode naive bayes di bank bni syariah surabaya. jurnal teknik elektro, 9(2), 45–52. fadillah, a. p. (2015). penerapan metode crispdm untuk prediksi kelulusan studi mahasiswa menempuh mata kuliah ( studi kasus universitas xyz ). jurnal teknik informatika dan sistem informasi, 1, 260– 270. https://doi.org/10.1016/s2214109x(14)70029-4 fitri cahyanti, a., saptono, r., & widya sihwi, s. (2015). penentuan model terbaik pada metode naive bayes classifier dalam menentukan status gizi balita dengan mempertimbangkan independensi parameter. jurnal teknologi & informasi itsmart, 4(1, juni), 28. https://doi.org/10.20961/its.v4i1.1754 hasan, m. (2017). prediksi tingkat kelancaran pembayaran kredit bank menggunakan algoritma naive bayes berbasis, 9, 317–324. ikhsan saputro, m., & mardiana, t. (2015). agen cerdas untuk penentuan kelayakan pemberian kredit koperasi simpan pinjam, i(2), 245–252. kementerian koperasi dan usaha kecil menengah republik indonesia. (2015). building cooperatives and smes as national economic resilience. jakarta. li, j., ding, l., & li, b. (2014). a novel naive bayes classification algorithm based on particle swarm optimization. the open automation and control systems journal, 6(1), 747–753. https://doi.org/10.2174/187444430140601 0747 mardiana, t. (2018). laporan penelitian penelitian mandiri model klasifikasi prediksi kredit macet menggunakan pso-naïve bayes pada koperasi. jakarta. melissa, i., & oetama, r. s. (2013). analisis data pembayaran kredit nasabah bank menggunakan metode data mining. ultima infosys, iv(1), 18–27. menarianti, i. (2015). klasifikasi data mining dalam menentukan pemberian kredit bagi nasabah koperasi. jurnal ilmiah teknosains, 1(1), 1–10. muhamad, h., prasojo, c. a., sugianto, n. a., surtiningsih, l., cholissodin, i., ilmu, f., … optimization, p. s. (2017). optimasi naive bayes classifier dengan menggunakan particle swarm optimization pada data iris. teknologi informasi dan pendidikan, 4(3), 180–184. rifqo, m. h., & wijaya, a. (2017). implementasi algoritma naive bayes dalam penentuan pemberian kredit. jurnal pseudocode, 4(2), 120–128. rinawati. (2017). penentuan penilaian kredit menggunakan metode naive bayes berbasis particle swarm optimization. jurnal sains komputer & informatika, 1(1), 58–58. https://doi.org/10.30645/j-sakti.v1i1.28 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 233 the work is distributed under the creative commons attribution-noncommercial 4.0 international license implementation of data mining to determine the association between body category factors based on body mass index desti fitriati1, bima putra amiga2 program studi teknik informatika1,2 universitas pancasila1,2 desti.fitriati@univpancasila.ac.id1, bimaamg@gmail.com2 abstrak perkembangan arus globalisasi yang semakin meningkat dalam bidang ilmu pengetahuan dan teknologi serta peningkatan pendapatan telah memberikan dampak pada berkurangnya aktivitas fisik masyarakat yang mengakibatkan penyimpangan pola makan dan aktivitas fisik yang membuat seseorang tidak memperhatikan bentuk tubuhnya. metode perhitungan indeks massa tubuh ini dapat digunakan untuk menentukan bentuk tubuh seseorang. terdapat beberapa faktor yang dapat mempengaruhi nilai indeks massa tubuh diantaranya adalah faktor individu, pola konsumsi, dan kurangnya aktivitas fisik yang mengarah pada pola hidup sedentaris (sedentary lifestyle). faktor-faktor tersebut dijadikan menjadi 69 itemset yang akan dijadikan dasar pertanyaan dalam kuesioner untuk mengumpulkan dataset yang nantinya akan diolah menggunakan algoritma fp-growth dan dicari aturan asosiasi yang memiliki nilai support x confidence tertinggi. dari 490 data hasil perhitungan dikategorikan menjadi 10 masing-masing adalah laki-laki dengan indeks massa tubuh (imt) sangat kurus dengan nilai support x confidence tertinggi sebesar 39,56%, laki-laki dengan imt kurus sebesar 55,90%, laki-laki dengan imt normal sebesar 70%, laki-laki dengan imt gemuk sebesar 49,23%, laki-laki dengan imt obesitas sebesar 41,34%, perempuan dengan imt sangat kurus sebesar 41,37%, perempuan dengan imt kurus sebesar 37,21%, perempuan dengan imt normal sebesar 68,83%, perempuan dengan imt gemuk sebesar 41,65%, dan perempuan dengan imt obesitas sebesar 42,91%. kata kunci: algoritma fp-growth, data mining, asosiasi, indeks massa tubuh, imt abstract the development of the increasing flow of globalization in the field of science and technology as well as increased income has resulted in reduced physical activity of the community which results in diverging diet and physical activity which makes a person not pay attention to his body shape. this method of calculating the body mass index can be used to determine a person's body shape. several factors can affect the value of the body mass index, including individual factors, consumption patterns, and lack of physical activity which leads to a sedentary lifestyle. these factors are made into 69 itemsets which will be used as the basis for questions in the questionnaire to collect a dataset that will later be processed using the fp growth algorithm and looking for association rules that have the highest support x confidence value. from the 490 calculation data, the results are categorized into 10, each of which is men with a very thin bmi with the highest support x confidence value of 39.56%, men with a thin bmi of 55.90%, men with a normal bmi of 70%, men with a fat bmi of 49.23%, men with an obese bmi of 41.34%, women with a very thin bmi of 41.37%, women with a thin bmi of 37.21%, normal bmi is 68.83%, women with obese bmi are 41.65%, and women with obese bmi are 42.91%. keywords: fp-growth algorithm, data mining, associations, body mass index, imt pendahuluan the development of the increasing flow of globalization in the field of science and technology as well as increasing incomes have had an impact on changes in lifestyle, behavior, and situations in the community (ayuni, suharso, & sukidin, 2019). these changes have an impact on reduced physical activity (suryadinata & sukarno, 2019) a society which results in divergent eating patterns and physical activities that make a person not pay attention to his body shape (rahmayanti, 2016) some people feel their body is fat when in fact they are not fat or vice versa (ariati, gumala, & http://creativecommons.org/licenses/by-nc/4.0/ mailto:desti.fitriati@univpancasila.ac.id1 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 jurnal riset informatika vol. 2, no. 4 september 2020 234 the work is distributed under the creative commons attribution-noncommercial 4.0 international license nursanyoto, 2017). this perception arises because a person does not know how to categorize his body shape (nahdiyah, 2015) in the right way. one way to find out a person's body shape is by calculating the body mass index method. this method of calculating the body mass index can be used to determine a person's nutritional status (kusuma & pinandita, 2011), (syahputra & muhathir, 2018). the results of the calculation of the body mass index can be categorized into very thin, skinny, normal, overweight, and obese body shapes. (putra & solichathi rizqi, 2018). among the body mass index categories, obesity is a problem (dewi & mahmudiono, 2013) where people who are categorized by body shape have excess nutrition (dewi & mahmudiono, 2013). in public life, the term obesity is usually called overweight. this obesity phenomenon is the result of an unbalanced excessive intake of nutrients (mahdali, 2019) by expending energy and can cause health problems (hasanah & febrianti, 2012). a high body mass index (bmi) value is indicated by a bodyweight above the average due to the accumulation of excess fat in the body over a long period. (jannah & utami, 2018) in 2016, who stated that the prevalence of obesity in the world has more than doubled since 1980. in 2014, more than 1.9 billion adults aged 18 years and older were overweight, and 600 million among them are obese. some of the factors associated with high body mass index include high dietary habits and lack of physical activity which leads to a sedentary lifestyle such as watching television and playing computer/video games. this study aims to examine the relationship between the factors that influence body shape based on a person's bmi which includes individual factors, physical activity, eating habits, sedentary behavior, and stress factors at work using the fpgrowth algorithm data mining method. also, this study provides guidelines and guidelines to determine what factors influence body shape based on bmi. research methods types of research this research is a type of experimental quantitative research, where the conclusion of this study is based on the results of the calculation trial which is selected the best. time and place of research the data collection time in this study was october 2019. research target / subject in this study using non-physical data by distributing questionnaires that have been compiled based on the results of a literature study. then the questionnaire was distributed to general people using google form. the completed questionnaires will be collected in a file with the xls extension. subjects were taken randomly to get various types of body categories. procedure the fp-growth algorithm uses the concept of building a tree, which is commonly called the fptree, in searching for frequent itemsets instead of generating candidates as is done in the apriori algorithm. by using this concept, the fp-growth algorithm is faster than the apriori algorithm (anggraeni, iha, erawati, & khairunnas, 2019). the following are the steps for the fp-growth algorithm. 1. determine the minimum support and minimum confidence. 2. create an fp-tree based on the sorted itemset. fp-tree is formed by a root labeled null, a group of trees whose members are certain items. each node in the fp-tree contains three important information, namely the item label, informing the type of item the node represents, support count, representing the number of transaction paths that go through the node, and a pointer (muliono, 2017). 3. create a conditional pattern base. conditional pattern base is a sub-database that contains a prefix path and a pattern suffix. generating the conditional pattern base is obtained through the previously built fp-tree. the conditional pattern base is obtained from the fp-tree. 4. create a conditional fptree. at this stage, the support count of each item in each conditional pattern base is added up, then each item that has a support count greater than the minimum support count will be generated with a conditional fp-tree. the conditional fp.tree was obtained from the established fp.tree (lestari, 2015). 5. create a frequent pattern. the frequent itemset search stage if the conditional fp-tree is a single path, then the frequent itemset is obtained by doing a combination of items for each conditional fp-tree. if it is not a single track, a frequent pattern will be generated. frequent patterns are obtained from the conditional fp-tree 6. create association rules. the results of the association rules are obtained from the conditional frequent pattern which is entered http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 235 the work is distributed under the creative commons attribution-noncommercial 4.0 international license into the support, confidence, lift ratio formula. lift ratio is an important parameter besides support and confidence in the association rule. the lift ratio measures how important the rule is based on the value of support and confidence. the lift ratio is a value that indicates the validity of the transaction process and provides information on whether product a has a relationship with product b. if the calculation results are looking for lift ratio> 1, then we can determine a valid rule. the results of the association rules are obtained from the conditional frequent pattern. and to find out the validation of the results of the association rules, the support, confidence, and lift ratio values can be calculated. data, instruments, and data collection techniques the data used in this study are primary, that is, the data is taken and processed by the researcher. the data collection technique used was a randomly distributed questionnaire form, where the instrument used was the google form. data analysis technique the results of data processing using the fp growth algorithm by setting a minimum support value which is determined based on the upper quartile calculation of the itemset frequency of each gender and bmi, and minimum confidence of 80%. where the minimum 80% confidence indicates the certainty that the data will appear in the item set. results and discussion the results of the research are presented in graphical, tabular, or descriptive form. analysis and interpretation of these results are required before the discussion. data processing the data that will be used to predict this is obtained based on the results of the questionnaire that the respondents have filled in. the results of the questionnaire can be divided into 10 types of respondent characteristics based on gender and bmi category. the following is the distribution in table 1 below. table 1. data grouping by gender and bmi no. gender bmi 1 lakilaki sangat kurus 2 lakilaki kurus 3 lakilaki normal no. gender bmi 4 lakilaki gemuk 5 lakilaki obesitas 6 perempuan sangat kurus 7 perempuan kurus 8 perempuan normal 9 perempuan gemuk for the itemset used, there were 69 itemsets, among others, as shown in table 2 below. table 2. list of itemset categories no. code itemset 1 a1 remaja 2 a2 dewasa 3 b1 jawa dan bali 4 b2 sumatra 5 b3 kalimantan 6 b4 papua 7 b5 sulawesi 8 b6 maluku 9 b7 nusa tenggara 10 b8 pendatang 11 c1 tidak berpendidikan 12 c2 pendidikan terakhir sd atau yang sederajat 13 c3 pendidikan terakhir smp atau yang sederajat 14 c4 pendidikan terakhir sma atau yang sederajat 15 c5 pendidikan terakhir d3 16 c6 pendidikan terakhir d4 17 c7 pendidikan terakhir s1 18 c8 pendidikan terakhir s2 19 c9 pendidikan terakhir s3 20 d1 perokok aktif 21 d2 perokok pasif 22 d3 mantan perokok 23 d4 tidak rokok 24 e1 waktu tidur ideal 25 e2 waktu tidur tidak ideal 26 f1 makan pagi sering 27 f2 makan pagi sebagian besar waktu 28 f3 makan pagi jarang 29 f4 makan pagi tidak pernah 30 g1 makan siang sering 31 g2 makan siang sebagian besar waktu 32 g3 makan siang jarang 33 g4 makan siang tidak pernah 34 h1 makan malam sering 35 h2 makan malam sebagian besar waktu 36 h3 makan malam jarang 37 h4 makan malam tidak pernah 38 i1 konsumsi susu sering 39 i2 konsumsi susu sebagian besar waktu 40 i3 konsumsi susu jarang 41 i4 konsumsi susu tidak pernah 42 j1 konsumsi buah sering 43 j2 konsumsi buah sebagian besar waktu 44 j3 konsumsi buah jarang http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 jurnal riset informatika vol. 2, no. 4 september 2020 236 the work is distributed under the creative commons attribution-noncommercial 4.0 international license no. code itemset 45 j4 konsumsi buah tidak pernah 46 k1 konsumsi fast food sering 47 k2 konsumsi fast food sebagian besar waktu 48 k3 konsumsi fast food jarang 49 k4 konsumsi fas food tidak pernah 50 l1 konsumsi makanan kecil sering 51 l2 konsumsi makanan kecil sebagian besar waktu 52 l3 konsumsi makanan kecil jarang 53 l4 konsumsi makanan kecil tidak pernah 54 m1 konsumsi minuman berasa sering 55 m2 konsumsi minuman berasa sebagian besar waktu 56 m3 konsumsi minuman berasa jarang 57 m4 konsumsi minuman berasa tidak pernah 58 n1 konsumsi soft drink sering 59 n2 konsumsi soft drink sebagian besar waktu 60 n3 konsumsi soft drink jarang 61 n4 konsumsi soft drink tidak pernah 62 o1 sedentary behavior rendah 63 o2 sedentary behavior tinggi 64 p1 aktivitas fisik ringan 65 p2 aktivitas fisik kuat 66 p3 aktivitas fisik sedang 67 q1 stress ringan 68 q2 stress sedang 69 q3 stress berat system design the architecture of this system contains four kinds of data which can be seen in figure 1, namely login, category management, management dataset, and calculation of fp growth. this login process is only done by the admin. admin is required to log in before entering the system. the admin in this system is only one condition and cannot be added so that it does not require user management. there are two data management systems for this system, namely the management data category and the management dataset. categorical data that contains factors that affect the value of the body mass index. while the dataset contains data from the distribution of questionnaires filled out by respondents. finally, the calculation of fp growth, which contains the process of calculating fpgrowth from the tb_relation data, which contains data from the results of the respondent's questionnaire and is related to tb_data and tb_category. the calculation of fp-growth is carried out to see the results of the association between the factors that affect the body mass index value. figure 1. software architecture the following is a snippet of the fp-growth program: <?php class fpg extends ci_controller { public function __construct() { parent::__construct(); if(!$this->session >userdata('login')) redirect('user/login'); $this->load->model('data_model'); $this->load >model('kategori_model'); $this->load->helper('fpgrowth'); $this->load->model('relasi_model'); } public function index() { $data['title'] = 'perhitungan fp growth'; $this->form_validation->set_rules( 'min_support', 'minimal support', 'required' ); $this->form_validation->set_rules( 'min_confidence', 'minimal confidence', 'required' ); if ($this->form_validation->run()=== false) { load_view('fpg', $data); } else { $data['data'] = $this->relasi_model > get_relasi_filter($this->input->post('jk'), $this->input->post('imt')); $data['kategori'] = $this->kategori_model>tampil(); $this->load->view('header', $data); $this->load->view('fpg'); $this->load->view('fpg_hasil'); $this->load->view('footer'); } } } http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 237 the work is distributed under the creative commons attribution-noncommercial 4.0 international license implementation program 1. fp-growth calculation page figure 2. fp-growth calculation page interface figure 2 is the initial entry before calculating the fp growth according to the selected category, where the entries include setting gender, bmi category, the minimum value of support, and minimum confidence. in this study, we will look for the associated value of the factors that affect the value of bmi based on gender and bmi category. 2. the interface of fp-growth for ordered itemset stage figure 3 detailed interface implementation after making the initial settings, then the item combination process is carried out as seen in figure 3 above. 3. fp-growth calculation interface for conditional fp-tree stage figure 4 fp-growth calculation interface for conditional fp-tree stage after obtaining an ordered itemset, the next step is to calculate the conditional pattern base and conditional fp-tree to get the rules as shown in figure 4. 4. fp-growth calculation interface frequency pattern stage figure 5 formation interface for fp-growth stage frequency pattern after getting the rules, then this algorithm starts making frequency patterns. where at this stage you will see several itemsets that are always close together and the number of occurrences is calculated simultaneously. the frequency pattern stage can be seen in figure 5 above. 5. fp-growth calculation interface association rules stage http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 jurnal riset informatika vol. 2, no. 4 september 2020 238 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 6 the interface of fp-growth calculation of association rules stage the last step to take is to find association rules that match the predetermined minimum support and minimum confidence. the results of the final association are taken for itemset that has a lift ratio value of more than 1. figure 6 shows the results of the rules taken, where the itemset that is blocked or colored orange are itemsets that do not meet the requirements. results analysis based on the test results by setting a minimum confidence value of 80%, the following results were obtained: 1. the results of system testing for the male sex group with a very thin bmi category with a minimum support value of 16, the highest support x confidence results are 39.56% in the association rule "if you eat dinner often then ideal sleep time". 2. the results of the system test for the male sex group with the thin bmi category with a minimum support value of 20, the highest support x confidence results are 55.90% in the association rules "if the last education is a high school or equivalent, then the adolescent age" 3. the results of system testing for the male sex group with the normal bmi category with a minimum support value of 20, the results obtained support x confidence of 70% in the association rules "if the age is adolescent then the last education is high school or the equivalent" 4. the results of the system test for the male sex group with the fat bmi category with a minimum support value of 22, the highest support x confidence results are 49.23% on the association rule "if lunch is frequent, then adult age" 5. the results of system testing for the male sex group with the category of bmi obesity with a minimum support value of 23, the highest support x confidence results are 41.34% in the association rules "if you are a teenager then the last education is high school or equivalent" 6. the results of system testing for the female sex group in the very thin bmi category with a minimum support value of 17, the highest support x confidence results are 41.37% in the association rule "if sedentary behavior is high, then stress is moderate" 7. the results of system testing for the female sex group with the normal bmi category with a minimum support value of 19, the highest support x confidence results are 37.21% in the association rule "if you consume milk most of the time then moderate stress" 8. the results of the system testing for the female sex group with the thin bmi category with a minimum support value of 20, the highest support x confidence results are 68.83% in the association rules "if the last education is a high school or equivalent, then the teenage age" 9. the results of system testing for the female sex group with the fat bmi category with a minimum support value of 22, the highest support x confidence results are 41.65% in the association rules "if you are an adult then don't smoke" 10. the results of system testing in the female sex group with the bmi category of obesity with a minimum support value of 24 obtained the highest support x confidence results of 42.91% in the association rule "if it is ideal sleep time then the adult age" conclusions and suggestions conclusion from the 490 calculation data, the results are categorized into 10, each of which is men with a very thin bmi with the highest support x confidence value of 39.56%, men with a thin bmi of 55.90%, men with a normal bmi of 70%, men with a fat bmi of 49.23%, men with an obese bmi of 41.34%, women with a very thin bmi of 41.37%, women with a thin bmi of 37.21%, normal bmi is 68.83%, women with obese bmi are 41.65%, and women with obese bmi are 42.91%. suggestion it is necessary to distribute questionnaires more widely to add more data to obtain the results of the association rules which have a higher support x confidence value in each grouping based on the gender and body mass index category. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 239 the work is distributed under the creative commons attribution-noncommercial 4.0 international license reference anggraeni, s., iha, m. a., erawati, w., & khairunnas, s. 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(2019). http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.159 jurnal riset informatika vol. 2, no. 4 september 2020 240 the work is distributed under the creative commons attribution-noncommercial 4.0 international license pengaruh aktivitas fisik terhadap risiko obesitas pada usia dewasa. in the indonesian journal of public health (vol. 14). https://doi.org/10.20473/ijph.v14i1.2019.1 04-114 syahputra, d., & muhathir, m. (2018). perhitungan metode fuzzy sugeno dan antropometri dalam memprediksi status gizi indeks massa tubuh. journal of informatics and telecommunication engineering, 2(1), 16. https://doi.org/10.31289/jite.v2i1.1676 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 227 the work is distributed under the creative commons attribution-noncommercial 4.0 international license decision support system for selection of exemplary employees at pt. sinar asia perkasa syahriani, nurmah, luthfi indriyani informatics engineering, information systems, sekolah tinggi manajemen informatika dan komputer nusa mandiri www.nusamandiri.ac.id syahriani.yii@nusamandiri.ac.id, nurmahn15@gmail.com computer technology universitas bina sarana informatika www.bsi.ac.id luthfi.lfy@bsi.ac.id abstrak pt. sinar asia perkasa merupakan perusahaan manufaktur, dimana perusahaan ini selalu dituntut untuk melakukan inovasi dan meningkatkan mutu serta kualitas produknya. karena perihal tersebut, maka perusahaan pt. sinar asia perkasa harus berbenah diri agar mendapatkan karyawan yang memiliki kualitas dan produktivitas kerja yang tinggi. karyawan merupakan salah satu bagian terpenting dalam perusahaan yang harus dikelola secara baik. untuk mendapatkan karyawan dengan kualitas terbaik, dibutuhkan proses yang dapat secara langsung memberikan rekomendasi dalam memilih karyawan teladan pada pt. sinar asia perkasa yaitu dengan dibuatkannya sistem pendukung keputusan. sistem pendukung keputusan ini, diharapkan dapat membantu dalam pemilihan karyawan teladan dengan penilaian secara objektif. pembuatan sistem pendukung keputusan ini menggunakan metode profile matching dengan beberapa kriteria yaitu aspek disiplin, aspek integritas, aspek kerjasama, dan aspek prestasi kerja. kemudian untuk tahapan akhir dari metode ini adalah perankingan. kata kunci: profile matching, karyawan teladan, sistem penunjang keputusan abstract pt. sinar asia perkasa is a manufacturing company, where this company is always required to innovate and improve the quality and quality of its products. because of this, the company pt. sinar asia perkasa must improve itself to get employees who have high quality and work productivity. employees are one of the most important parts of a company that must be managed properly. to get employees of the highest quality, a process is needed that can automatically provide recommendations in selecting exemplary employees at pt. sinar asia perkasa, namely by establishing a decision support system. this decision support system is expected to assist in objectively selecting employees. making this decision support system using the profile matching method with several criteria, namely aspects of the discipline, aspects of integrity, aspects of cooperation, and aspects of work performance. then for the final stage of this method is ranking. keywords: profile matching, exemplary employees, decision support system introduction employees have an important role and are the spearhead for the development of a company or institution. a company that is successful in running its business cannot escape the hard work done by its employees. therefore, for the smooth running and development of a company, the quality and productivity of its employees need to be improved and maintained properly. pt. sinar asia perkasa has problems in selecting exemplary employees, where the company still uses a subjective one-way assessment(purwanto, 2017), (ricki & devitra, 2019) because it does not have a systematic method or indicator that is applied. pt. sinar asia perkasa needs to improve itself in the process of managing its human resources. if human resources can be well organized, it is hoped that the company can carry http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 228 the work is distributed under the creative commons attribution-noncommercial 4.0 international license out all its business processes properly(penta, siahaan, and sukmana, 2019). therefore the company pt. sinar perkasa requires a decision support system for selecting exemplary employees in its company. decision support systems are part of a computer-based information system including knowledge-based or knowledge management systems that are used to support decision making in an organization or company. (pareda, mongi, and montolalu, 2019). the method used in the decision support system process is the profile matching method. (sudrajat, 2018), (mashyur & frieyadie, 2019). the concept of the profile matching method is to compare individual competencies into job competencies so that differences incompetence can be found (called gap). (triandi, 2011), the smaller the resulting gap, the greater the weighted value, which means that there is a greater chance for someone to occupy that position (susilo, 2018). also, according to atmanagara et al, this method is very suitable for use in human resource management efforts, because in the process of the profile matching method in outline is a process of comparing individual abilities to competencies that must be achieved in a position (atmanagara, putri, and sutrisno, 2017). research on decision support systems for selecting exemplary employees at pt. sinar asia perkasa is made based on data and special criteria used to support employee quality and productivity. the gap calculation process is carried out to determine the points of each criterion and match the weighting of each criterion. then the results of the calculation process will produce employee rankings. research methods this study uses the profile matching method. according to merlina and hidayat, the stages and processes of searching for ranking values using the profile matching method are as follows (merlina and hidayat, 2012): 1. weighted value the weight value is used to find the gap value or the difference between the value of the employee profile and the value of the best employee profile, which can be written with the formula: 𝐺𝐴𝑃 = 𝑁𝑖𝑙𝑎𝑖 𝐾𝑎𝑟𝑦𝑎𝑤𝑎𝑛 – 𝑁𝑖𝑙𝑎𝑖 𝑆𝑡𝑎𝑛𝑑𝑎𝑟 (𝑀𝑖𝑛𝑖𝑚𝑢𝑚) ......... (1) 2. calculation and grouping of core and secondary factors after determining the weight of the gap value, the next step is to calculate and group the values based on the core factor and secondary factor. a. core factor value calculation the calculation of the core factor value is shown in the formula below: 𝑁𝑅𝐶 = 𝛴𝑁𝐶 𝛴𝐼𝐶 ...................................................................... (2) information: nrc: average value core factor nc: the total number of the scores core factor ic: amount of core factor items b. calculation of secondary factor value the calculation of the second-factor value is shown by the formula below: 𝑁𝑅𝐶 = 𝛴𝑁𝐶 𝛴𝐼𝑆 ...................................................................... (3) information: nrf: average value secondary factor ns: the total number of scores secondary factor is: amount of secondary factor item 3. total value calculation from the results of the weighted value calculation and the grouping of core factor and secondary factor, then the total value is calculated based on the percentage of cores and secondary factors that are estimated to affect the performance of each profile. the formula is as follows: 𝑁𝑇 = [𝑥]%𝑁𝐶𝐹 + [𝑥]% 𝑋 𝑁𝑆𝐹 ................................... (4) information: nt: the total value of the variable (x)%: the percent value entered ncf: average value core factor nsf: average value secondary factor 4. ranking determination calculations the final result of the profile matching process is ranking. ranking refers to the results of certain calculations, which are shown in the formula below: 𝑅𝑎𝑛𝑔𝑘𝑖𝑛𝑔 = [𝑥]% 𝑋 𝑁1 + [𝑥]% 𝑋 𝑁2 + [𝑥]%𝑋 𝑁3 + [𝑥]% 𝑋 𝑁4 ................................................... (5) source: (adhar, 2014) information: n1, n2, n3, n4: the total calculated aspect value (x)% : the percent value entered likert scale the likert scale is a scale used to measure the attitudes, opinions, and perceptions of a person http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 229 the work is distributed under the creative commons attribution-noncommercial 4.0 international license or group of people regarding a symptom or phenomenon that will be used as a benchmark or to test how strong a statement consisting of 5 points, in table 1 including (sugiyono, 2016) table 1. likert scale no. information score 1 very agree / always / very positive 5 2 agree / often / positive 4 3 indecisive / occasional / neutral 3 4 disagree / almost never / negatively 2 5 strongly disagree / never 1 research procedure the procedures or stages of this research are: figure 1. procedure or research stages data collection technique the research methods used by the authors in conducting this research are: a. observation the data and information obtained related to the selection process for the best employees is direct research at pt. sinar asia perkasa, penjaringan, north jakarta. b. interview this study conducted interviews with resource persons, namely mr. stevanus vincent susanto, se, as the hrd manager at the company. c. literature review the author is looking for various references such as books, online journals, previous works, ebooks, literature studies, and others related to the theme the author takes. d. questionnaire the questionnaire is designed in the form of a likert scale based on the profile matching method, namely, there are 5 sub-criteria response values. value 5 for a very good response, score 4 for a good response, score 3 for an adequate response, score 2 for poor response, and value 1 for very poor response. data analysis technique sampling technique the sample was determined by a population at pt. sinar asia perkasa uses the quota sampling technique, which is part of the nonprobability sampling which consists of the information technology, finance & accounting, promotion, shipping and security sections, along with the data (taken for example the it department) in table 2. table 2. sample it department employees no. name position 1. ahmad suhelji it hardware 2. galant fadhila it design 3. andi sulasikin it design 4. hifsar septiyawan creative design 5. muhammad albanjaari digital account executive 6. nadya alwin digital account executive criteria and sub criteria the criteria and sub-criteria are determined in table 3 as follows. table 3. criteria and sub criteria no. criteria sub criteria 1. discipline aspects responsible be on time appearance polite manners 2. aspects of integrity professional consistent 3. cooperation aspects group work participate & contribute active & productive helping colleagues 4. aspects of work performance complete tasks above standard complete tasks based on standards perform performance improvements research results and discussion classification of core factor and secondary factor after determining the gap from each predetermined aspect, namely the aspect of discipline, aspects of integrity, aspects of cooperation, and aspects of work performance, then next is to determine the core factor and http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 230 the work is distributed under the creative commons attribution-noncommercial 4.0 international license secondary factor of each of these sub-criteria can be seen in table 6. a. core factor (main aspect), is the most favored aspect and has a high value to get the maximum results from this best employee data management. b. secondary factor (supporting aspects), is an aspect that supports the main aspects. table 4. core factor and secondary factor no. aspect core secondary 1. discipline a. responsible b. be on time c. appearance d. politeness 2. integrity a. professional b. consistent 3. cooperation a. group work b. participate & contribute c. active & productive d. helping colleagues 4. work performance a. complete tasks above standard b. complete tasks based on standards c. perform performance improvements information on the value of the aspect sub criteria then it can be seen for the sub-criteria aspect values in table 5 below: table 5. value of sub criteria aspects sub criteria value 1 = less 2 = enough 3 = good 4 = satisfactory 5 = very satisfying gap value calculation table 6 is the calculation result from the aspect of discipline for the information technology section. table 6. discipline aspects of gap value weighting results in part ti no name disciplinary aspects ket d1 d2 d3 d4 1 ahmad suhelji 3 1 2 2 2 galant fadhila 2 1 2 3 3 andi sulasikin 2 2 3 4 4 hifsar septiyawan 3.5 3 3 3 5 muhammad albanjaari 2 2 3 5 6 nadya alwin 3 3 3 4 standard value 5 5 4 4 1 ahmad suhelji -2 -4 -2 -2 gap 2 galant fadhila -3 -4 -2 -1 3 andi sulasikin -3 -3 -1 0 4 hifsar septiyawan -1.5 -2 -1 -1 5 muhammad albanjaari -3 -3 -1 1 6 nadya alwin -2 -2 -1 0 convert value to weight 1 ahmad suhelji 3 1 3 3 gap 2 galant fadhila 3.5 1 3 4 3 andi sulasikin 2 2 4 5 4 hifsar septiyawan 4 3 4 4 5 muhammad albanjaari 2 2 4 4.5 6 nadya alwin 3 3 4 5 calculation and grouping ncf and nsf table 7 below is the calculation and grouping of ncf and nsf from disciplinary aspects in the field of ti: table 7. core dan secondary factor of discipline aspects in part ti name disciplinary aspects cf sf d1 d2 d3 d4 ncf=(d1+d2)/2 nsf=(d3+d4)/2 ahmad suhelji 3 1 3 3 2 3 galant fadhila 3.5 1 3 4 2.25 3.5 andi sulasikin 2 2 4 5 2 4.5 hifsar septiyawan 4 3 4 4 3.5 4 muhammad albanjaari 2 2 4 4.5 2 4.25 nadya alwin 3 3 4 5 3 4.5 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 231 the work is distributed under the creative commons attribution-noncommercial 4.0 international license calculation of total value table 8 is a calculation of the total value of the disciplinary aspects of the ti: table 8. total value of disciplinary aspects section ti name cf sf result % n1 ncf (60%) nsf(40%) nt = 60%ncf+ 40%nsf ahmad suhelji 2 3 1.2 1.2 2.4 galant fadhila 2.25 3.5 1.5 1.4 2.9 andi sulasikin 2 4.5 1.2 1.8 3 hifsar septiyawan 3.5 4 2.1 1.6 3.7 muhammad albanjaari 2 4.25 1.2 1.7 2.9 nadya alwin 3 4.5 1.8 1.8 3.6 ranking calculation for the determination of ranking results obtained from the formula: ranking = (25% x discipline) + (20% x integrity) + (25% x cooperation) + (30% x job performance) table 9. final results of ranking section ti name n1 n2 n3 n4 n1 n2 n3 n4 final result 25% 20% 25% 30% ahmad suhelji 2.4 3.8 4.22 3.4 0.6 0.76 1.055 1.02 3.47 galant fadhila 2.9 4.4 4.4 4.4 0.725 0.88 1.1 1.32 3.91 andi sulasikin 3 4.4 4.4 4.4 0.75 0.88 1.1 1.32 4.05 hifsar septiyawan 3.7 5 4.58 4.4 0.925 1 1.145 1.32 4.39 muhammad albanjaari 2.9 4.4 4.4 4.4 0.725 0.88 1.1 1.32 4.03 nadya alwin 3.6 4.4 4.4 4.4 0.9 0.88 1.1 1.32 4.20 table 10. section ranking results in ti name the final result ranking hifsar septiyawan 4.39 1 nadya alwin 4.2 2 andi sulasikin 4.05 3 muhammad albanjaari 4.03 4 galant fadhila 4.03 5 ahmad suhelji 3.44 6 from table 10 can already be seen the final result to select exemplary employees in the field of it, where the first rank is occupied by hifsar septiyawan, the second level is occupied by nadya alwin, the third place occupied by andi sulasikin, the fourth-place occupied by muhammad albanjaari, the fifth-place occupied by galant fadhila and the last rank occupied by ahmad suhelji. conclusions and recommendations conclusion based on the research that has been carried out, it can be concluded that to determine the capacity of employees using profile matching method can be used the results of individual values converted to weight values i.e. by comparing between individual competencies into the competencies of the best employees so that it can be known the difference in competencies (called gaps) which if the resulting gap is smaller then the opportunity to become the best employee is more wide open. researchers create a decision support system as a tool in making decisions by collecting data, conducting interviews with the parties concerned, analyzing from the data generated by profile matching method, gap calculation, core and secondary factor grouping, calculation of yield value to stamping in the place used as a research site namely pt. sinar asia perkasa. addressing the assessment conducted subjectively is by determining several criteria for the assessment using the profile matching method by giving questionnaires to the boss of each division and the questionnaire results will be calculated so that the questionnaire results can produce accurate data. recommendation from the results of research conducted on the decision support system (spk) of the best employees in pt. sinar asia perkasa, then researchers proposed several suggestions including developing the system by adding methods such as simple additive weighting, analytical hierarchy process, or others so that results may be more accurate. implement this method by building a web or desktop-based http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 232 the work is distributed under the creative commons attribution-noncommercial 4.0 international license decision support system so that decision-makers find it easier and the system more efficient reference adhar, d. 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(2011). sistem pendukung keputusan untuk kenaikan jabatan menggunakan metode profile matching modeling. jurnal digit, 1(2), 143–152. retrieved from https://jurnaldigit.org/index.php/digit/arti cle/view/6 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 65 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. sistem deteksi plat kendaraan dengan menggunakan metode k-nearest neghbour (knn) farida 1, zahir zainuddin 2 , supriadi sahibu 3 1 sistem informasi stmik bina adinata faridahvaryd4@gmail.com 2 teknik informatika universitas hasanuddin zainuddinzahir@gmail.com 3 pascasarjana sistem komputer stmik handayani makassar http://pps.handayani.ac.id/ supriadi.dit@gmail.com abstrak sistem pemantauan dan manajemen kendaraan berbasis plat nomor kendaraan telah berkembang, identifikasi dan pengenalan plat nomor menjadi aplikasi utama dalam bidang lalu lintas. terlalu banyak kejadian dalam berlalu lintas, salah satunya, seperti melewati batas stop di jalan sehingga mengganggu para pengendara lainnya, hal ini paling banyak terjadi dan bahkan sering terjadi di kota-kota bahkan daerah sekalipun. tujuan dari penelitian ini adalah untuk mengekstraksi dan mengenali plat nomor dari citra kendaraan yang melakukan pelanggaran sehingga dapat digunakan sebagai data set dalam membuat laporan penentuan sanksi yang sesuai dengan jenis pelanggaran kendaraan tersebut. penelitian ini menggunakan proses ekstraksi pengolahan citra gambar sehingga metode yang sesuai dengan penelitian ini adalah k-nearest neighbour, dengan menggunakan metode tersebut akan memudahkan dalam proses pendeteksiannya. karena metode ini tidak menggunakan perulangan. prose ini dimulai dengan menyiapkan dari citra latih tahap kemudian melakukan tahapan pengenalan citra. pengenalan citra merupakan proses mencocokan data citra digital yang sudah dirubah menjadi sebuah matriks kemudian dicocokan dengan dataset yang ada sehingga dapat diketahui hasilnya yang berupa laporan. kata kunci: open cv, k-nearest neighbour abstrack the monitoring system and managemen of vehicle have developed. identification and number plat recognition in the field of traffic one of them is like passing the stop line on the road so that is disturbs other drivers, this is the most and even often occurs in big city cities even regions. the purpose of this study is to extract and recognize license plates from the image of the vehicle, that infringes so that it can be used as a dataset in making reports on determining sanctions that are appropriate to the type of violation of the vehicle. this study uses an image processing extraction process so that method according to this method is knearest neighbour, using this method will facilitate the detection process because this method does not use looping, this process begins by preparing a training image and then performing the image recognition stage. image recognition is a process of matching digital image data that has been converted into a matrix and then matched with existing datasets so that the result can be known in the form of reports keywords: open cv, k-nearest neighbour, pendahuluan deteksi plat kendaraan yang merupakan sistem pemantauan dan manajemen kendaraan berbasis plat nomor kendaraan telah berkembang, identifikasi dan pengenalan plat nomor menjadi aplikasi utama dalam bidang lalu lintas. terlalu banyak kejadian dalam berlalu lintas, salah satunya, seperti melewati batas stop di jalan (fadila, 2017) sehingga pelanggaran lalu lintas (ruslianto & harjoko, 2013) ini http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 66 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. mengganggu para pengendara lainnya, hal ini paling banyak dan bahkan sering terjadi di kota-kota bahkan daerah sekalipun. olehnya itu didesign pengelolaan citra pendeteksi plat kendaraan. tujuannya adalah untuk mengekstraksi dan mengenali plat nomor dari citra kendaraan yang melakukan pelanggaran sehingga dapat digunakan sebagai data set dalam membuat laporan penentuan sanksi yang sesuai dengan jenis pelanggaran kendaraan tersebut. telah banyak penelitian mengenai sistem pengenalan plat nomor dengan pengolahan citra namun, dalam penelitian ini dikembangkan sistem pengenalan citra plat kendaraan setelah melewati batas stop dan hal ini mampu menjadi terobosan baru dalam ketertiban lalu lintas sehingga para pengendara mampu menjadi lebih tertib dan disiplin. penelitian ini menggunakan proses ekstraksi pengolahan citra gambar sehingga metode yang sesuai dengan penelitian ini adalah knearest neighbour (sari, 2011), (budianto, ariyuana, & maryono, 2018), dengan menggunakan metode tersebut akan memudahkan dalam proses pendeteksiannya. metode penelitian metode k-nearest neighbour (knn) merupakan salah satu metode untuk melakukan klasifikasi terhadap objek berdasarkan data pembelajaran yang jaraknya paling dekat dengan objek tersebut. tujuannya adalah untuk mengklasifikasikan objek baru berdasarkan atribut dan data training. klasifikasi dilakukan tanpa menggunakan model tetapi hanya berdasarkan memori. algoritma k-nearest neighbour menggunakan klasifikasi ketetanggaan sebagai prediksi terhadap data baru (fauziah, sulistyowati, & asra, 2019). pada fase training, algoritma ini hanya melakukan penyimpanan vektor-vektor fitur dan klasifikasi data training sampel a. rancangan sistem perancangan ini terdapat beberapa komponen yang saling berkaitan serta saling mendukung dan membentuk sebuah rangkaian. sistem ini rencananya akan dipasang di lampu merah lalu lintas dan juga mampu mendeteksi plat kendaraan, ketika kendaraan tersebut melakukan pelanggaran dalam artian melewati batas stop lampu merah lalu lintas maka sistem akan langsung mendeteksi plat kendaraan tersebut kemudian dimasukkan ke dalam daftar kendaraan yang melakukan pelanggaran lalu lintas. gambar 1. rancangan sistem jenis penelitian penelitian ini menggunakan pendekatan kuantitatif waktu dan tempat penelitian penelitian ini direncanakan akan dilakukan di wilayah bulukumba target/subjek penelitian target atau subject dari penelitian ini adalah plat kendaraan yang berwarna hitam baik kendaraan roda dua maupun roda empat. data, intrumen, dan teknik pengumpulan data 1. penelitian ke perpustakaan (library research), yaitu pengumpulan data dengan cara membaca buku melalui literature, tutorial-tutorial maupun artikel dari internet yang bersifat ilmiah yang ada hipatitisnya dengan materi pembahasan. 2. penelitian lapangan (field research), yaitu dilakukan dengan cara mengumpulkan data secara langsung kepada objek penelitian yaitu pada pembina atau guru yang bersangkutan dengan penelitian. teknik analisis data penelitian ke perpustakaan (library research), yaitu pengumpulan data dengan cara membaca buku melalui literature, tutorial-tutorial maupun artikel dari internet yang bersifat ilmiah yang ada hipatitisnya dengan materi pembahasan. penelitian lapangan (field research), yaitu dilakukan dengan cara mengumpulkan data secara langsung kepada objek penelitian yaitu pada pembina atau guru yang bersangkutan dengan penelitian. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 67 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. hasil penelitian dan pembahasan setelah citra telah disiapkan dari citra latih tahap selanjutnya perlu dilakukan tahapan pengenalan citra. pengenalan citra merupakan proses mencocokan data citra digital yang sudah dirubah menjadi sebuah matriks kemudian dicocokan dengan data set yang ada sehingga dapat diketahui menjadi laporan (output) berupa text. keberhasilan suatu pengujian dipengaruhi juga dengan jumlah data set yang dimiliki sehingga hasilnya sangat bergantung. data set memiliki banyak varian sangat bagus karena tinggak kegagalan akan menjadi berkurang. proses dimana dilakukan untuk menghitung tingkat akurasi dari hasil pengidentifikasian citra plat nomor kendaraan pada mobil pribadi (plat warna hitam) dengan menggunakan metode knearest neighbour seberapa besar terjadinya kesalahan atau berapa besar terjadinya kebenaran dalam proses identifikasian tersebut.adapun cara menghitung tingkat akurasi adalah sebagai berikut: 𝐴𝑘𝑢𝑟𝑎𝑠𝑖 = 𝐽𝑢𝑚𝑙𝑎ℎ 𝑝𝑟𝑒𝑑𝑖𝑘𝑠𝑖 𝑏𝑒𝑛𝑎𝑟 𝑗𝑢𝑚𝑙𝑎ℎ 𝑡𝑜𝑡𝑎𝑙 𝑝𝑟𝑒𝑑𝑖𝑘𝑠𝑖 𝑥 100% (1) jumlah prediksi benar adalah jumlah record data uji yang diprediksi kelasnya menggunakan metode klasifikasi dan hasilnya sama dengan kelas sebenarnya. sedangkan jumlah total prediksi adalah jumlah keseluruhan record yang diprediksi kelasnya (seluruh data uji). metode klasifikasi berusaha untuk mencari model yang memiliki tingkat akurasi yang tinggi ketika model tersebut diterapkan pada data uji. a. tahapan pra pengolahan pra pengolahan melibatkan dua proses yaitu merubah resolusi dan mengkonversi ruang warna. selain itu pengolahan juga melibatkan lokalisasi, lokalisasi ini berfungsi untuk memproses binarisasi dan mengubah citra kedalam bentuk black white, proses pengolahan inilah yang disebut sebagai thresholding atau tingkat kemiripan gambar. 1. merubah citra warna dalam proses ini citra yang berwarna akan di ubah ke citra grayscale ( keabu-abuan ) tujuannya adalah untuk mempermudah dalam pengidentifikasian citra plat nomor yang dibaca. gambar 2. citra plat asli gambar 2 tersebut merupakan gambar citra asli sebelum di ubah ke bentuk grayscale ( keabuabuan ) bentuk hitam putih dari hasil pengolahan tersebut dapat dilihat pada gambar di bawah ini. gambar 3. citra plat keabuan grayscaling adalah proses awal yang banyak dilakukan dalam image processing, hal ini dilakukan bertujuan untuk menyederhanakan model citra. pada awalnya citra rgb umumnya terdiri dari 3 layer matrik yaitu r-layer, g-layer dan b-layer. bila setiap proses perhitungan dilakukan menggunakan tiga layer, berarti dilakukan tiga perhitungan yang sama. sehingga konsep itu diubah dengan mengubah 3 layer di atas menjadi 1 layer matrik grayscale dan hasilnya adalah citra grayscale. citra ini tidak mempunyai elemen warna seperti citra sebelum diubah, melainkan mempunyai derajat keabuan. gambar 4. citra hasil konversi biner setelah proses grayscaling maka dilanjutkan ke proses pengkonversian citra binary, setelah itu yang dapat menghasilkan pencarian nilai threshold atau tingkat kemiripan. b. proses pengenalan k-nearest neighbour (knn) merupakan metode yang bersifat supervised dimana hasil dari query distance yang baru diklasifikasikan berdasarkan mayoritas kategori pada knn. pada vase training sample. pada fase ini fitur-fitur yang sama di hitung untuk testing data ( jneis klasifikasinya belum diketahui ). jarak dari vector yang baru ini terhadap seluruh vector training data sample dihitung. dan sejumlah k buah yang paling dekat diambil beberapa analogi metode knn : a. analogi metode klasifikasi knn ( k – nearest neihgbour ) dimana k = 1 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 68 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. gambar 5. analogi knn dengan k=1 dari gambar 5 di atas jelas terlihat bahwa data uji huruf e memiliki euclidean 1 dengan huruf e yang berada di dalam dataset sehingga ketika melakukan pengujian data maka akan ditemukan hasil euclidean distance antara data uji dengan data set yaitu 1. gambar 6. analogi knn dengan k =2 dari gambar 6 di atas jelas terlihat bahwa data uji huruf e memiliki euclidean 2 dengan huruf e yang berada di dalam dataset sehingga ketika melakukan pengujian data maka akan ditemukan hasil euclidean distance antara data uji dengan data set yaitu 2. gambar 7. analogi knn dengan k=3 dari gambar 7 di atas jelas terlihat bahwa data uji huruf e memiliki euclidean 3 dengan huruf e yang berada di dalam dataset sehingga ketika melakukan pengujian data maka akan ditemukan hasil euclidean distance antara data uji dengan data set yaitu 3 dari proses ekstraksi menggunakan metode knn didapatkan hasil pengenalan sebagai berikut: gambar 8. deteksi plat dengan gambar langsung . gambar 9. deteksi plat secara langsung pengujian dilakukan pada plat kendaraan dengan kondisi baik dan plat kendaraan dengan kondisi baik dan plat kendaraan dengan kondisi yang kurang baik, dimana setiap plat diuji 4 kali berdasarkan jarak, sudut dan threshold yang diberikan. pengujian dilakukan menggunakan kamera webcam dengan raspberry pi spesifikasi prosesor mini. plat kendaraan yang baik adalah plat kendaraan yang karakter dan bentuk plat tidak rusak (sempurna) dan hanya terdapat sedikit noise ( kotoran ). sedangkan plat kendaraan yang kurang baik adalah plat kendaraan ang karakter dan bentuk plat tidak sempurna serta memiliki banyak noise atau kotoran pada plat. b. hasil pengujian plat kendaraan table 1. hasil pengujian jarak 40 cm no no. plat hasil pengenalan keterangan 1 b 457uty b457uty sukses 2 dn5269vk dn5269vk sukses 3 dd6656hk dd6656hk sukses 4 dn4505vt dn4505vt sukses 5 dd 5154 ku dd 5154 ku sukses 6 dn3742md dn3742m0 gagal 7 dd 3333 yh dd 3333 yh sukses 8 dd 1473 ya dd 1473 ya sukses 9 dn 4112 ye dn 4112 ye sukses 10 dd 5804 cj dd 5804 cj sukses 11 dn 4879 yc dn 4879 yc sukses http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 69 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. no no. plat hasil pengenalan keterangan 12 dd 4353 xy dd 4353 xy sukses 13 dn 6330 vm dn6330vm gagal 14 b 6703 wjf b 6703 wjf sukses 15 dd 9412 xu dd 9412 xu sukses 16 dd 5678 nt dd 5678 nt sukses 17 dd 6565 by dd6565by sukses 18 dd 1719 ni dd1719ni sukses dari hasil pengujian pada table 4.1 dapat diketahui bahwa persentase rata-rata keakuratan system pengenalan plat kendaraan dengan jarak 40 cm ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑠𝑢𝑘𝑠𝑒𝑠) ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑆𝑎𝑚𝑝𝑒𝑙) = 16 18 x 100% = 88,88% tabel 2 hasil pengujian dengan jarak 50 cm no no. plat hasil pengenalan keterangan 1 b 457uty b457uty sukses 2 dn5269vk dn5269vk sukses 3 dd6656hk dd6656hk sukses 4 dn4505vt dn450svt gagal 5 dd 5154 ku dd 5154 ku sukses 6 dn3742md dn3742m0 gagal 7 dd 3333 yh dd 3333 yh sukses 8 dd 1473 ya dd 1473 ya sukses 9 dn 4112 ye dm 4112 ye gagal 10 dd 5804 cj dd 5804 cj sukses 11 dn 4879 yc dn 4879 yc sukses 12 dd 4353 xy dd 4353 xy sukses 13 dn 6330 vm dn 6330 vm sukses 14 b 6703 wjf b 6703 wjf sukses 15 dd 9412 xu dd 9412 xu sukses 16 dd 5678 nt dd 5678 nt sukses 17 dd 6565 by dd6565by sukses 18 dd 1719 ni dd1719ni sukses dari table di atas di tentukan tingkat akurasinya dengan menggunakan langkah berikut. ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑠𝑢𝑘𝑠𝑒𝑠) ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑆𝑎𝑚𝑝𝑒𝑙) = 12 18 x 100% = 85,71 % table 3 hasil pengujian dengan jarak 60 cm no no. plat hasil pengenalan keterangan 1 b 457uty b457uty sukses 2 dn5269vk dn5269vk sukses 3 dd6656hk dd6656hk sukses 4 dn4505vt dn450svt gagal 5 dd 5154 ku d0 5154 ku gagal 6 dn3742md dn3742m0 gagal 7 dd 3333 yh dd 3333 yh sukses 8 dd 1473 ya d0 1473 ya gagal 9 dn 4112 ye dm 4112 ye gagal 10 dd 5804 cj dd 5b04 cj gagal 11 dn 4879 yc dn 4879 yc sukses 12 dd 4353 xy dd 4353 xy sukses 13 dn 6330 vm dn6330vn gagal 14 b 6703 wjf b 6703 wjf sukses 15 dd 9412 xu dd 9412 xu sukses 16 dd 5678 nt d0 5678 nt sukses 17 dd 6565 by dd6565by sukses 18 dd 1719 ni dd1719ni sukses dari table di atas dapat di tentukan tingkat akurasinya dengan menggunakan persamaan berikut : ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑠𝑢𝑘𝑠𝑒𝑠) ∑(𝑗𝑢𝑚𝑙𝑎ℎ 𝑆𝑎𝑚𝑝𝑒𝑙) = 10 18 x 100% = 55,55% adapun faktor-faktor lain yang menyebabkan kegagalan proses pengenalan plat kendaraan adalah noise yang ada pada plat kendaraan. penggunaan threshold yang kecil belum mampu menghilangkan noise dengan baik sehingg menyebabkan proses karakter tidka tersegmentasi dengan baik. sedangkan penggunaan threshold yang tinggi juga mempengaruhi proses pengenalan karakter, walaupun noise dapat dihilangkan akan tetapi berpengaruh pada penitisan lebar dari karakter, sehingg akan menyebabkan prubahan bentuk karakter sperti pada karakter n dan h, s dan 5, b dan 8 dan d dan 0. simpulan dan saran simpulan dari hasil peneltian yang telah dilakukan pada citra plat nomor kendaraan pribadi (plat nomor warn hitam ) dengan menggunakan metode knearest neighbour dapat diperoleh kesimpulan sebagai berikut: citra dapat diidentifikasi jika cahaya yang ada pada citra tidak terllau besar maupun kecacatan tidak terllu parah karena akan menimbulkan kesalahan dalam identifikasi. dalam pengidentifikasian citra dibutuhkan banyak data set sehingga dalam pengidentifikasian tidak terjadi kesalahan karena dalam proses pengidentifikasian ini mencari kemiripam dengan data set yang mirip dengan data set yang sudah ada jika tidak ditemukan maka akan terbaca sebagai data set yang lain sehingga terjadi kesalahan. hal ini merupakan karakter dari metode k-nearest neighbor. dalam proses ini dperoleh tingkat akurasi sebesar 88 % dengan jarak 40 cm dan akurasi 85 % dengan jark 50 cm dan 55 % dengan jarak 60 cm. penggunaan metode k-nearest neighbor lebih tepat karena metode ini tidak tidak melakukan perulangan dalam proses pencocokan sehingga lebih efisien dalam melakukan pengujian. saran perlu adanya penetapan nilai threshold yang baik untuk menangani tingkat noise yang ada pada plat serta pencahayaan pada plat sehingga karakter pada plat kendaraan dapat tersegmentasi dengan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 70 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. baik. sistem ini perlu dikembangkan lebih lanjut sehingga nantinya mampu memberikan hasil yang lebih baik dalam pengenalan karakter sehingga dapat diimplementasikan pada system transportasi daftar referensi budianto, a., ariyuana, r., & maryono, 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(2018). perbandingan k-nearest neighbor (knn) dan support vector machine (svm) dalam pengenalan karakter plat kendaraan bermotor. jiptek : jurnal ilmiah pendidikan teknik dan kejuruan, 11(1). https://doi.org/10.20961/jiptek.v11i1.18018 fadila, a. (2017). tingkat kesadaran berlalu lintas pengendara sepeda motor di surabaya selatan. kajian moral dan kewarganegaraan, 5(3), 1036– 1051. retrieved from https://jurnalmahasiswa.unesa.ac.id/index.php/ju rnal-pendidikankewarganegaraa/article/view/22055 fauziah, s., sulistyowati, d. n., & asra, t. (2019). optimasi algoritma vector space model dengan algoritma knearest neighbour pada pencarian judul artikel jurnal. jurnal pilar nusa mandiri, 15(1), 21–26. https://doi.org/10.33480/pilar.v15i1.27 ruslianto, i., & harjoko, a. (2013). pengenalan karakter plat nomor mobil secara real time. ijeis (indonesian journal of electronics and instrumentation systems), 1(2), 101–110. https://doi.org/10.22146/ijeis.1967 sari, m. i. (2011). desain segmentasi dan pengenalan karakter pada plat nomor kendaraan. in konferensi nasional ict-m politeknik telkom (pp. 250–253). politeknik telkom. retrieved from http://journals.telkomuniversity.ac.id/knip/articl e/view/556 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 57 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. implementasi rapid application development sistem informasi pendidikan sma dalam menghadapi industri 4.0 yunus fadhillah sistem informasi institut bisnis dan informatika kwik kian gie yunus.fadhillah@kwikkiangie.ac.id abstrak pengembangan sumber daya manusia yang berfokus pada steam (science, technology, engineering, the arts and mathematics) dalam menghadapi industri 4.0 pada sistem pendidikan sekolah menengah atas (sma) harus dipersiapkan dan adaptif terhadap kesiapan dan kemampuan berintegrasi dengan baik dengan teknologi 4.0 terutama infrastruktur perangkat keras, jaringan internet dan manajemen sistem informasi. rapid application development (rad) meruapakan metode yang digunakan untuk merancang dan memodifikasi sistem informasi pendidikan agar bisa beradaptasi dan berkolaborasi dengan teknologi 4.0 seperti machine learning, artificial intelligent, biometric, user behaviour, media social api and collaboration, dan lain sebagainya. kata kunci: sistem informasi pendidikan, rapid application development (rad), industri 4.0, manajemen sistem informasi, sekolah menengah atas (sma), teknologi informasi, sistem informasi abstract the development of human resources focused on steam (science, technology, engineering, and mathematics) in the face of industry 4.0 in the high school education system must be prepared and adaptive to readiness and ability to integrate well with 4.0 technology, especially device infrastructure. hard, internet network and information system management. rapid application development (rad) is a method used to design and modify educational information systems in order to adapt and collaborate with 4.0 technologies such as machine learning, artificial intelligent, biometric, user behavior, social media api and collaboration, and so on. higher education management system, rapid application development, industry 4.0, management information system, information tehcnology, information system. pendahuluan sejak tahun 2011, dunia memasuki era industri 4.0 dimana manusia, mesin dan sumber daya lainnya terhubung satu sama lain. indonesia di tahun 2018 ini meluncurkan roadmap implementasi industri 4.0 oleh kementrian industri. industri 1.0 dimulai pada tahun 1784 dimana manusia mulai menggunakan teknologi mesin uap dalam industri. tahun 1870 dimulainya industri 2.0 dengan teknologi mesin secara massal dalam industri dan ditandai juga dengan dimulainya penggunaan listrik dan bahan bakar minyak sebagai sumber energi. industri 3.0 mulai tahun 1969 dengan penggunaan teknologi informasi dan otomasi mesin industri. info grafis dapat lihat gambar 1 dibawah ini. gambar 1. tahapan industri dalam indonesia making 4.0, indonesia akan mendorong 10 prioritas nasional dalam inisiatif “indonesia making 4.0”, yaitu: 1. perbaikan alur barang dan material 2. desain ulang zona industri http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 58 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. 3. mengakomodasi standar-standar keberlanjutan (sustainability) 4. memperdayakan usaha mikro, kecil dan menengah (umkm) 5. membangun infrastrukur digital nasional 6. menarik minat investasi asing 7. peningkatan kualitas sumber daya manusia (sdm) 8. pembangunan ekosistem inovasi 9. insentif untuk investasi teknologi 10. harmonisasi aturan dan kebijakan point peningkatan kualitas sdm adalah hal yang penting untuk mencapai kesuksesan pelaksanaan making indonesia 4.0. berencana untuk merombak kurikulum pendidikan dengan lebih menekankan pada steam (science, technology, engineering, the arts, dan mathematics), menyelaraskan kurikulum pendidikan nasional dengan kebutuhan industri di masa mendatang. indonesia akan bekerja sama dengan pelaku industri dan pemerintah asing untuk meningkatkan kualitas sekolah kejuruan, sekaligus memperbaiki program mobilitas tenaga kerja global untuk memanfaatkan ketersediaan sdm dalam mempercepat transfer kemampuan. pendidikan menengah atas merupakan titik tolak seorang pelajar untuk melanjutkan ke jenjang kuliah atau pendidikan yang lebih tinggi atau tidak dan memutuskan untuk memasuki tahapan kehidupan lainnya seperti bekerja, menikah, menjadi pengangguran dan lain sebagainya. jumlah sekolah menengah atas khususnya non sekolah menegah kejuruan (smk) tahun 2017 pada gambar 2 mencapai 502 sekolah yang terdiri dari 124 sma negeri dan 357 sma swasta. pada gambar 3 menunjukkan jumlah siswa sma negeri mencapai 88319 siswa, sedangkan sma swasta mencapai 83321 siswa. gambar 2. jumlah sma di dki jakarta tahun 2017 gambar 3. jumlah siswa sma di dki jakarta tahun 2017 banyaknya jumlah sekolah yang belum memiliki sistem informasi (susanti, 2016) dan tata kelola yang baik dan terintegrasi menyebabkan terjadinya masalah dalam pengelolaan sumber daya, pembelajaran dan bahkan terhadap kualitas pelajar yang dihasilkan. tata kelola yang baik dan adaptif harus didukung dengan teknologi informasi yang tepat mulai dari proses awal hingga proses akhir sesuai dengan proses perjalanan pendidikan manusianya. pengelolaan dan peningkatan mutu sma harus mengimplementasikan teknologi informasi yang adaptif. berdasarkan penelitian prasojo (prasojo, 2010)bahwa model sma abad 21 ada beberapa hal pokok yang harus diperhatikan dalam meningkatkan mutu pendidikan, yaitu: 1) pembaharuan sma yang berkelanjutan dalam meningkatkan mutu pendidikan 2) implementasi sistem manajemen berbasis sekolah 3) implementasi sitem manajemen dalam konsep balanced scorecard dan sinkronisasi terhadap manajemen berbasis sekolah 4) implementasi sistem manajemen dalam kerangka good governance 5) implementasi sistem manajemen yang adaptif dengan perkembangan teknologi informasi empat dari lima hal yang diperhatikan dalam meningkatkan mutu pendidikan sekolah menengah adalah implementasi sistem manajemen dengan konsep terkait dan adaptif terhadap perkembangan teknologi informasi. hal tersebut mengindikasikan bahwa sistem informasi manajemen yang baik dan sesuai dengan kebutuhan merupakan salah syarat kritis yang harus dipenuhi dalam peningkatan mutu pendidikan di sekolah menengah. rapid-application development (rad) rapid-application development (rad) merupakan pendekatan pengembangan perangkat http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 59 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. lunak yang adaptif dikemukan oleh james martin (martin, 1991) merupakan kelompok pengembangan perangkat lunak secara meningkat (incremental). pengembangan perangkat lunak ini menekankan pada siklus pengembangan yang pendek, singkat dan cepat. pengembangan di tahap awal menggunakan purwa rupa untuk menetapkan kebutuhan pengguna dilakukan secara iteratif dan selanjutnya jika sistem sudah selesai maka purwa rupa akan dihilangkan. tahapan-tahapan dalam metode rapidapplication development (rad) adalah: 1. requirement planning phase, tahapan analisa dan perencanaan mudali dari kebutuhan bisnis, ruang lingkup, hambatan dan kebutuhan sistem. 2. user design phase, tahapan analisa dan penemgembangan model dengan menggunakan purwa rupa proses sistem, masukan dan keluaran. 3. construction phase, tahapan yang fokus pada pogram dan aplikasi yang dibangun termasuk pemograman, pengujian unit dan sistem. 4. cutover phase, tahap akhir merupakan tahapan implementasi termasuk konversi data, pengujian, perubahan sistem dan pelatihan pengguna. tahapan diatas dapat digambarkan dengan gambar 4 dibawah ini: gambar 4. tahapan rapid application development teori entitas teori entitas diperkenalkan oleh codd (codd, 2009) untuk manajemen basis data dan bentuk logikanya oleh chen (chen & pin-shan, 1976)dengan model relasi entitas (entity relationship model) yang menggambarkan secara abstrak bentuk basis data beserta notasinya (brady & loonam, 2010) entitas merupakan objek yang mewakili dunia nyata, bersifat abstrak, nyatadan dapat dibedakan dengan objek lainnya. entitas memiliki karakteristik khusus yang disebut dengan attribut dan relasi. attribut merupakan ciri khusus dari entitas yang berupa data atau informasi aktual yang dapat disimpan. terdapat 4 jenis attribut, yaitu: 1. key, untuk menentukan suatu entitas secara unik 2. simple, attribut yang bernilai tunggal 3. multivalue, kelompok nilai attribut 4. composite, attribut yang bernilai lebih kecil dan mempunyai arti tertentu pemetaan jenis-jenis dan contoh attribut dapat dilihat pada gambar 5 dibawah ini. gambar 5. jenis-jenis attribut. selain attribut ada juga relasi, yang mewakili hubungan yang terjadi antar attribut dan entitas dan memiliki derajat relasi. ada 3 jenis derajat relasi yang terjadi, yaitu: 1. derajat 1 (unary degree) 2. derajat 2 (binary degree) 3. derajat 3 (ternary degree) pada gambar 6. menunjukkan dan mencontohkan derajat-derajat relasi pada entitas. gambar 6. derajat relasi pada entitas http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 60 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. teori entitas ini diterapkan untuk membantu pengembangan sistem dalam merancang basis data. tahapan dalam perancangan basis data dimulai dengan: 1. penentuan entitas 2. penentuan attribut dan key dari masingmasing entitas 3. penentuan derajat relasi 4. diagram relasi entitas 5. logical record structure maka dari tahapan diatas terbentuklah pemodelan basis data yang sesuai dengan kebutuhan sistem. unified model language (uml) uml merupakan alat bantu untuk pemodelan sistem dan perancangan program berorientasi objek (object oriented programming) yang diperkenalkan oleh object management group. menjadi standar pengembangan sistem berbasis objek sejak tahun 1980 dan merupakan rancangan dasar dari ibm (kroenke, 2009). adapun jenis-jenis diagram (miles & hamilton, 2006) yang dapat dibuat dalam uml pada tabel 1 dibawah ini: tabel 1. diagram uml jenis diagram pemodelan uml versi use case 1.x iteraksi sistem dengan pengguna atau dengan sistem ekternal lainnya. membantu dalam pemetaan untuk persyaratan sistem activity 1.x urutan dan kegiatan pararel di dalam sistem class 1.x hubungan antar kelas, jenis dan antarmuka object 1.x instan objek dari kelas yang telah ditentukan pada class diagram dalam konfigurasi penting di sistem sequence 1.x interaksi antar objek dimana urutan interaksi menjadi sangat penting communication 1.x cara objek berinteraksi dan koneksi yang dibutuhkan untuk mendukung interaksinya timing 2.0 interaksi antar objek dimana waktu menjadi perhatian penting interaction overview 2.0 digunakan untuk mengumpulkan sequence, communication dan timing diagram bersamaan untuk mendapatkan interaksi penting yang timbul composite structure 2.0 bagian internal class atau component dan dapat didefinisikan sebagai class relationship dalam konteks yang diberikan. component 1.x komponen penting di dalam sistem dan antarmuka yang digunakan untuk berinteraksi satu dengan yang lainnya package 2.0 struktur organisasi kelompok class dan component state machine 1.x keadaan suatu objek selama siklus hidupnya dan peristiwa yang keadaannya dapat berubah deployment 1.x bagaimana sistem dibangun dalam dunia nyata secara garis besar uml membentuk pandangan yang saling melengkapi seperti dapat dilihat pada gambar 7 dibawah ini: gambar 7. diagram uml penggunaan pemodelan dengan menggunakan uml dalam indutri terus meningkat dan menjadi standar terbuka dalam industri perangkat lunak dan pengembangan sistem. metode penelitian kerangka yang dilakukan peneliti dalam kerangka kerja rapid application development (rad) dapat digambarkan pada gambar 8 dibawah ini. gambar 8. metode penelitian analisa kebutuhan sistem dengan melakukan observasi, pengumpulan data dan dokumen serta kebutuhan sistem yang dikombinasikan dengan memenuhi standar industri 4.0. kapital, cambria-10 bold, rata kiri. sebagai contoh dapat dilihat berikut. waktu dan tempat penelitian penelitian dilakukan selama kurun waktu 3 bulan di smp dan sma wilayah jakarta timur. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 61 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. target/subjek penelitian objek penelitian adalah salah satu sekolah swasta yang mengelola pendidikan tingkat sekolah menengah pertama (smp) dan sekolah menengah atas (sma) dengan dan atau tanpa sistem penginapan (boarding school). prosedur sistem berjalan saat ini adalah sistem sekolah yang berjalan secara umum digambarkan pada rangkaian diagram alur dibawah ini secara umum dan sederhana dalam sistem akademik di sekolah menengah atas pada gambar 9. gambar 9. diagram alur sistem akademik sma. pendaftaran calon siswa, dilakukan dengan memberikan bukti dokumen kelulusan yaitu ijazah terakhir atau pendidikan setingkat dengan lanjutan tingkat atas dan kartu keluarga sebagai syarat administratif utama pendaftaran. seleksi calon siswa, dilakukan untuk menyeleksi jumlah siswa yang mendaftar sesuai dengan kapasitas sekolah, dimulai dari pengurutan berdasarkan nilai tertinggi sampai dengan terendah termasuk penerima undangan dan beasiswa. daftar ulang siswa baru, dilakukan sebagai penyaringan siswa yang telah dinyatakan diterima ntuk melakukan konfirmasi dan kesanggupan mengikuti peraturan, tata tertib, kegiatan dan biaya sekolah. jika tidak melakukan pendaftaran ulang maka akan dianggap mengundurkan diri dan jika masih berminat maka akan dipertimbangkan dengan melihat kapasitas kursi yang masih tersedia sesudah daftar ulang dan mengikuti prosedur pendaftaran dari awal kembali. pembayaran spp (sumbangan biaya pendidikan & pembangunan), dibebankan kepada siswa yang telah melakukan daftar ulang dan menyelesaikan semua biaya administratif sekolah. jika tidak melakukan pembayaran maka statusnya akan dipertimbangkan dengan penyelesaian tertentu atau mengundurkan diri. kehadiran di kelas, pembelajaran dimulai dan sesuai dengan kurikulum sekolah. selama mengikuti pembelajaran akan dilakukan penilaian kehadiran di kelas, nilai kehadiran dan sikap serta keaktifan di kelas yang diikuti. penilaian mata pelajaran, dilakukan penilaian diakhir pembelajaran atau tahapan pembelajaran yang diikuti untuk nilai harian. penilaian ektrakurikuler, jika siswa mengikuti kegiatan ektrakurikuler maka akan ada penilaian terhadap kegiatan ektrakurikuler yang diikuti. penilaian ujian, penilaian ujian dilakukan diakhir masa pembelajaran untuk menilai secara menyeluruh terhadap materi yang diterima oleh sisa. raport, merupakan hasil akhir dari penilaian mata pelajaran, kegiatan ketrakurikuler, sikap dan keaktifan siswa dalam mata pelajaran yang diikuti.dari sini penilaian akhir dan kesimpulan bahwa siswa berhasil mengikuti mata pelajaran yang diikuti atau tidak berhasil. data, intrumen, dan teknik pengumpulan data observasi non-participant teknik pengamatan langsung terhadap objek yang diteliti tetapi tidak menjadi bagian dalam objek tersebut dan hanya sebagai pengamat independen. wawancara wawancara merupakan teknik wawancara terstruktur dimana dilakukan melalui tatap muka dan tanya jawab langsung antara penulis dengan narasumber atau sumber data. penulis telah mengetahui dengan pasti apa informasi yang ingin digali dari responden. narasumber atau sumber data yang diambil merupakan sumber data primer dimana data diperoleh secara langsung dari pihak utama. studi literatur/studi pustaka teknik ini dilakukan dengan mengumpulkan informasi yang relevan dengan topik atau masalah yang sedang diteliti oleh penulis. sumber informasi diperoleh dari bukubuku ilmiah, karya-karya ilmiah, jurnal, laporan data pada perusahaan, dan sumber-sumber tertulis lainnya yang ada pada media cetak maupun elektronik lainnya. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 62 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. hasil penelitian dan pembahasan hasil analisa lingkungan industri 4.0 dalam era industri 4.0 memiliki dan mempunyai ciri-ciri yang membentuk lingkungan industri 4.0, yaitu:  transparansi informasi (ti) sehingga ketersediaan informasi dapat diakses dan data dapat di analisa  interkoneksi (ik), semua lingkungan dan peralatan terhubung satu sama lain dalam bentuk kolaborasi dengan standar dan tingkat keamanan tertentu.  teknologi komputer (tk), teknologi komputer sangat berperan dalam menghasilkan keputusan yang tepat dan informasi yang akurat dengan sedikit atau tanpa bantuan manusia hasil dari pengolahan data dan informasi di atas.bentuk teknologi mulai dari internet of thing (iot), artificial intellegent (ai), machine learning (ml), virtual machine (vm), grid computing dan lain sebagainya. analisa kesenjangan untuk mengetahui jarak yang ada pada kondisi saat ini dengan kebutuhan sistem yang mendukung industri 4.0 yang akan datang maka dilakukan analisa kesenjangan. hasil bisa dilihat pada tabel 1 dibawah ini. tabel 1. analisa kesenjangan unsur/ur aian transpar ansi informasi (ti) interkon eksi (ik) teknolog i kompute r (tk) analisa guru para guru harus memiliki kemampu an dalam menguasai tik (teknologi dan ilmu komputer ) informasi perkemba ngan siswa sangat transpara n hubunga n antar guru, orang tua dan siswa terjalin baik penguasa an teknologi hanya sebatas gadget dan aplikasiny a belum sesuai, kurangn ya penguas aan guru terhada p tik prasarana koneksi internet tidak ada fasilitas wifi tidak ada jaringan komputer tidak ada peralatan untuk koneksi internet belum sesuai, tidak ada koneksi umum internet sarana komputer, laptop dan gadget hanphone hanphon e aplikasi sosial dan pesan belum sesuai, perangk at milik pribadi masingmasing orang tua/ siswa penguasaa n & penggunaa n teknologi gadget penguasaa n aplikasi di hanphone aplikasi sosial dan pesan berbasis hanphone belum sesuai, hanya mengua sai palikasi sosial dan pesan sistem akademik sistem akademik yang mendukun g industri 4.0 bentuk hardcopy stand alone pc/lapto p sistem akademik berbentuk komputer isasi belum sesuai, sistem tidak ada intekone ksi, teknolog i data prosessi ng. rancangan sistem gambar 10.use case sip sma rancangan alur sistem dibuat hanya secara garis besarnya saja dari use case pendaftaran, use case pendidikan dan use case dashboard dimana use case-use case lainnya adalah sub use case yang sudah termasuk dalam aktivitas use case besar tersebut. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 63 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. gambar 11. diagram activitypendaftaran gambar 12. diagram activity pendidikan gambar 13. diagram activity dasboard rancangan basis data gambar 14. basis data simpulan dan saran simpulan kurang memadainya sarana dan prasarana yang mendukung dan kelanjutan penerapan teknologi informasi yang dapat mendukung teknologi 4.0. kurangnya penguasaan dasar teknologi informasi seperti office sehingga penggunaan dan penguasaannya tidak optimal untuk pengembangan sistem pendidikan yang lebih baik. pengelolaan sistem dan manajemen informasi masih sangat sederhana masih dibutuhkan pengembangan pada tahap-tahap lanjutan. terutama infrastuktur, dan manajemen informasi. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 64 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional. saran mengadopsi kebutuhan dan sistem pegelolaan sekolah menengah atas sesuai dengan permendikbud no 53 tahun 2015. infrastruktur dan manajemen informasi yang akan dan sudah dibangun nanti, masih harus dilengkapi dengan teknologi 4.0 dalam pegelolaan data ataupun hasil keluaran yang diharapkan. membutuhkan pengembangan lanjutan dan bantuan dari berbagai pihak dalam percepatan penerapan implementasi teknologi informasi untuk mengetahui masalah dan hambatan nyata di lapangan. daftar referensi brady, m., & loonam, j. 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(2010). model manajemen sekolah menengah atas abad xxi. cakrawala pendidikan, 29(3), 379–391. retrieved from http://www.jpnn.com/index.php?mib=b susanti, m. (2016). perancangan sistem informasi akademik berbasis web pada smk pasar minggu jakarta. jurnal informatika, 3(1). https://doi.org/10.31311/ji.v3i1.304 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 1 the work is distributed under the creative commons attribution-noncommercial 4.0 international license sentiment analysis of digital wallet service users using naïve bayes classifier and particle swarm optimization alvie delia cahyani1, tati mardiana2, laela kurniawati3 information system1.3; information technology2 stmik nusa mandiri1.3; universitas bina sarana informatika2 http://www.nusamandiri.ac.id1.3, http://www.bsi.ac.id2 e-mail : adeayu.aar@gmail.com1, tati.ttm@bsi.ac.id2, laela@nusamandiri.ac.id3 abstrak— layanan dompet digital memberikan banyak kemudahan dan keuntungan kepada para penggunanya. namun, tidak semua pengguna layanan dompet digital memiliki opini yang positif terhadap layanan tersebut. oleh karena itu, perusahaan layanan transportasi online perlu melakukan analisis untuk mengetahui sentimen masyarakat terhadap produknya. metode naïve bayes classifier merupakan metode yang sederhana, cepat, berakurasi tinggi, dan mempunyai performa yang cukup baik untuk melakukan klasifikasi data. namun, metode naïve bayes classifier mengasumsikan atributnya indepensi sehingga dapat menyebabkan akurasinya kurang optimal. tujuan penelitian ini adalah mengoptimalkan metode naïve bayes classifier menggunakan particle swarm optimization dalam klasifikasi polaritas percakapan layanan dompet digital. penelitian ini menggunakan data yang dari twitter sebanyak 490 data tweet. hasil pengujian dengan confusion matrix dan kurva roc menunjukkan peningkatan akurasi metode naïve bayes classifier dompet digital dana dari 60.00% menjadi 91.67% dan dompet digital isaku dari 53.23% menjadi 85.00%. hasil uji t-test dan anova menunjukkan hasil pengujian pada kedua metode klasifikasi memiliki perbedaan yang nyata dalam nilai accuracy. kata kunci: dompet digital; twitter; analisis sentimen; naïve bayes classifier; particle swarm optimization. abstract— digital wallet services adequately provides many benefits to its users. however, not all users of digital wallet services obtain a positive opinion about the service. therefore, online transportation service companies need to carry out an analysis to determine general sentiment towards their products. the naïve bayes classifier method represents a method that is simple, fast, excellent accuracy and obtains a comparatively excellent performance for classifying data. however, the naïve bayes classifier method assumes that the attributes are independent so that it can cause the accuracy to obtain less than optimal. this study aims to improve the accuracy of the naive bayes classification for the classification of public opinion on digital wallet services using particle swarm optimization. this study manages data from twitter as much as 490 tweet data. the test results with confusion matrix and roc curves show an increase in the accuracy of the naïve bayes classifier method for the dana digital wallet from 60.00% to 91.67% and the isaku digital wallet from 53.23% to 85.00%. the results of the t-test and anova test show that the test results of both classification methods provide significant differences in the accuracy value. keywords: digital wallet; twitter; sentiment analysis; naïve bayes classifier; particle swarm optimization. introduction in recent years, the popularity of digital wallet services in indonesia has experienced significant growth. digital wallets represents electronic money that introduces people to easy, safe, and profitable cashless or non-cash payment methods (aaputra, didi rosiyadi, windu gata, & syepry maulana husain, 2019). based on bank indonesia data, there was an increase in the number of electronic money transactions including digital wallets at the end of 2019 soaring 79.3% to 5.2 billion compared to 2018 of 2.9 billion transactions. moreover, the total nominal value of electronic money transactions experienced a drastic increase of 208.5%, reaching145 trillion rupiahs. this figure has increased to 98 trillion rupiahs or almost three times compared to 2018 of 47 trillion rupiahs (budiansyah, 2020). as of april 2020, 49 e-money companies have received official licenses from bank indonesia, including banks, technology, and communication companies (indonesia, 2020). http://creativecommons.org/licenses/by-nc/4.0/ mailto:adeayu.aar@gmail.com1 mailto:tati.ttm@bsi.ac.id2 p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 2 the work is distributed under the creative commons attribution-noncommercial 4.0 international license not all digital wallet services acquire several active users. this occurs due to a lack of public trust in the security of these services, and they still consider this as something new. based on the research results of iprice group and app annie, the ten digital wallet applications that have most significant number of monthly active users in the second quarter of 2019 are gopay, ovo, dana, linkaja, jenius, go mobile, isaku, sakuku, doku, and paytren, while the most downloaded digital wallet applications in the second quarter of 2019 are gopay, ovo, dana, linkaja, isaku, jenius, go mobile, paytren, sakuku, and doku (devita, 2019). digital wallet services adequately provides many benefits and advantages to their users. however, not all users of digital wallet services obtain a positive opinion of the service. in this digital era, people can provide opinions about a service, product, politics, and other topics through social media like twitter (mahendrajaya, buntoro, & setyawan, 2019). the number of daily twitter users jumped up in the 3rd quarter of 2019, daily users on twitter increased by 17% to 145 million users. one of the countries experiencing the most massive growth in active daily twitter users in indonesia (clinten, 2019). the number of tweets sent to twitter is up to 500 million daily tweets and 200 billion tweets (maulana, 2016). companies need to do sentiment analysis to find out what the public thinks about their products or services (pertiwi, 2019). sentiment analysis can extract public opinion on certain topics, politics, products, or services contained in unstructured texts (saidah & mayary, 2020). the previous research related to the analysis of the sentiment of digital wallet service users, namely research conducted by mahendrajaya, buntoro, and setyawan using the lexicon based and support vector machine methods. the sentiment labeling of gopay user comments from twitter is 923 positive comments and 287 negative comments. the sentiment labeling implements the lexicon based method. this study implements the svm method for classification of comparing two kernels. the linear kernel classification of comments as many as 1109 reviews got an accuracy value of 89.17% of the results. while the polynomial kernel with a comment classification of 1021 reviews got an accuracy value of 84.38%. the results of this study concluded that the classification of tweet data on twitter for gopay user comment reviews using the svm method and linear kernel is good (mahendrajaya et al., 2019). based on the description above, the researchers researched analyzing the sentiment of users of the dana and isaku digital wallet services, because not many have researched these digital wallets. we humbly propose implementing particle swarm optimization to improve the naïve bayes classifier method in the classification of public opinion on digital wallet services. this study aims to increase the accuracy value and auc value in the sentiment analysis of digital wallet service users by adding attribute weights using pso to the naïve bayes classifier method. materials and methods the stages carried out in this study used the cross standard industry process for data mining or crisp-dm methodology, as follows (pratama, pradnyana, & arthana, 2020): 1. business understanding business understanding is a stage in recognizing a business process in a company or organization in the form of business objectives, assessing conditions and field problems, and their needs. in this case, the business objectives being carried out are to find out the opinions given by users of the digital wallet services dana and isaku and apply the naïve bayes classifier and pso methods to analyze the sentiment of digital wallet service users. 2. data understanding data understanding is the stage of collecting data, understanding data, and identifying problems related to data quality. this study manages tweet data in indonesian from 19 june 2020 to 27 june 2020. the data collection process is by crawling tweet data from social media twitter using the twitter search operator on rapid miner 9.1. the keywords used are @danawallet and isaku. the data was taken from 19 june 2020 to 27 june 2020. from the results of the crawling, the data obtained were 490 tweets consisting of 272 tweet keywords @danawallet and 218 tweets with isaku keyword. 3. data preparation data preparation is the stage of preparing data for the next data mining process by changing the data from unstructured text documents to structured data. the application of data preparation in analyzing the sentiment of users of the dana and isaku has several stages, including (kurniawan & susanto, 2019): a. transform case the process of changing all capital letters into lowercase letters contained in tweet data so that they are uniform. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 3 the work is distributed under the creative commons attribution-noncommercial 4.0 international license b. tokenize the process of tokenization is to cut sentences into sections or words based on punctuation such as commas, periods, and other punctuation as needed. c. stopword removal this process stop removal is to eliminate unnecessary words. omitting the word will not change the information in it. d. stemming the process stemming is to remove the affixes contained in words so that all words return to their root words. 4. modeling modeling is the stage of processing data from preparation results to obtain the required information. this processing process uses the naive bayes classifier method to predict the probability of class membership and the addition of attribute weighting (attribute weighting) using pso to increase accuracy in the classification of the naïve bayes classifier. testing the proposed classification model uses the rapidminer 9.1 application. we tested the classification model that was formed to measure the level of accuracy and auc with randomly separated data with k-folds cross validation with k = 10. measurement of accuracy using confusion matrix and auc values using the roc curve. 5. evaluation evaluation is to validate the formed model. this evaluation uses a confusion matrix and roc curve and tests the significant difference between the weighting before and after weighting the attributes with pso on the naïve bayes classifier using the t-test and anova methods. figure 1. proposed model results and discussion this chapter will explain the results and discussion of the research on the sentiment analysis of digital wallet service users. 1. business understanding in this study, the business objectives carried out were to determine the opinions given by users of dana and isaku digital wallet services, apply the naïve bayes classifier and pso methods to analyze the sentiment of digital wallet service users, and find out the increase in the value of accuracy and auc value in sentiment analysis users of digital wallet services with additional weight attributes use pso to the naïve bayes classifier method. 2. data understanding data understanding of the data collection stage in this study is to retrieve tweet data with the crawling method from twitter social media using the twitter search operator on rapid miner 9.1 and enter the data in the form of ms. excel to facilitate data processing. the data taken is only tweets in indonesian. the keywords used are @danawallet and isaku . the data was taken from 19 june 2020 to 27 june 2020. from the results of the crawling, the data obtained were 490 tweets consisting of 272 tweet keywords @danawallet and 218 tweets with isaku keyword. the design of the tweet data crawling model is presented in figure 2. figure 2. tweet data crawling model design the flow of the data collection process in figure 2 is to search for tweet data on twitter based on the entered keywords. the use of this keyword as a sentiment analysis dataset, removes duplicates from the dataset based on the specified attributes. the attribute in this research is text. duplicate text represent the selected attribute has the same value in it. select the attributes you want to display and delete other attributes and convert the obtained dataset into ms file. excel. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 4 the work is distributed under the creative commons attribution-noncommercial 4.0 international license 3. data preparation (initial data processing) the data from the crawling that has been previously obtained is still raw data, where the data still contains a lot of noise and is unstructured. therefore, a preprocessing stage is needed to remove noise in words and data to make it more structured to facilitate research. preprocessing in this study uses the gataframework which can be accessed through http://www.gataframework.com/textmining.gataf ra-mework is an alternative in the pre-processing text to process words in indonesian because in the rapidminer application the dictionary to change indonesian acronyms and stopwords are still not available (hermanto, mustopa, & kuntoro, 2020). data from preprocessing results using gataframework will be used as a dataset for testing models in the rapidminer application. figure 3. display of gataframework text mining tools 4. modeling after carrying out the preprocessing stage, the next step is the classification process modeling process to determine a sentence as a member of the positive class or negative class based on the probability calculation value of the naïve bayes classifier formula. if the probability value in a sentence having a positive class is greater than the negative class, then the sentence is included in the positive class. and if the probability of the sentence having a positive class is smaller than the negative class, then the sentence is included in the negative class. the overall results of the classification data can be seen in table 1. table 1. amount of data class positive and negative digital wallets no digital wallets positive negative 1. dana 152 120 2. isaku 104 114 5. evaluation evaluation is done to validate the formed model. evaluation is done by using the confusion matrix and roc curve and testing the significant difference between before and after weighting the attributes with pso in the naïve bayes classifier using the t-test and anova methods. the results of the t-test and anova tests obtained a significant and more accurate model. a. testing the fund sentiment model using the naive bayes classifier the first experiment was to test the classification model using the naive bayes classifier method. the test design of the fund sentiment classification model using the naive bayes classifier method is presented in figure 4. figure 4. design of sentiment classification model funds using the naïve bayes classifier http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 5 the work is distributed under the creative commons attribution-noncommercial 4.0 international license in table 2, it is explained that the results of testing the 10 fold cross-validation model of dana's sentiment were obtained as many as 272 data consisting of 152 positive data and 120 negative data. a total of 88 data were predicted to be class positive according to the positive class prediction and 64 data predicted class positive turned out to be in the negative class, 75 data were predicted to be class negative accordingly, namely included in the negative class prediction and as many as 45 data predicted to be class negative turns into a positive class. the results obtained by using the naïve bayes classifier algorithm using rapid miner 9.1 get the accuracy value = 60.00% and auc = 0.599. table 2. results of the confusion matrix fund sentiment using the naïve bayes classifier accuracy : 60.00% +/11.53% (micro: 59.93%) true positive true negative class precision pred. positive 88 45 66.17% pred. negative 64 75 53.96% class recall 57.89% 62.50% figure 5. roc fund sentiment curve using the naïve bayes classifier b. sentiment model testing isaku uses the naive bayes classifier the first experiment was to test the classification model using the naive bayes classifier method. the test design of the sentiment classification model isaku using the naive bayes classifier method is presented in figure 6. figure 6. design of sentiment classification model isaku uses the naïve bayes classifier in table 3, it is explained that the test results of the model 10 fold cross-validation sentiment isaku obtained as many as 218 data consisting of 104 positive data and 114 negative data. a total of 30 data were predicted to be class negative according to the class negative prediction, and 84 data predicted class negative turned out to be in a positive class, 86 data were predicted to be class positive according to the class positive prediction, and 18 data predicted class positive turned out to be in the class positive. negative. the results obtained by using the naïve bayes classifier algorithm using rapid miner 9.1 get the accuracy value = 53.23% and auc = 0.520. table 3. results of confusion matrix sentiment isaku uses the naïve bayes classifier accuracy : 53.23% +/5.51% (micro: 53.21%) true negative true positive class precision pred. negative 30 18 62.50% pred. positive 84 86 50.59% class recall 26.32% 82.69% figure 7. roc sentiment curve isaku using the naïve bayes classifier http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 6 the work is distributed under the creative commons attribution-noncommercial 4.0 international license c. testing the fund sentiment model using the naive bayes classifier and pso the second experiment was to test the classification model using the naive bayes classifier and pso methods. the test design of the fund sentiment classification model using the naive bayes classifier and pso methods is presented in figure 8. figure 8. design of a fund sentiment model using nbc + pso based on the results of trials changing the value of inertia weight on the pso, the highest accuracy and auc values are located at population size 7, the maximum number of generation value is 100, and the inertia weight value 0.2 produces an accuracy value of 91.67% and an auc value of 0.750. table 4 explains that from a sample of 0.1 used in the classification with the naïve bayes classifier and pso, 14 of the fund's digital wallet service users were predicted to be class positive, which was included in the positive class prediction and 1 data predicted class positive turned out to be in the negative class, 11 the data predicted in the negative class was by the negative class prediction, and 1 data predicted the negative class turned out to be in a positive class. the results obtained by using the naïve bayes classifier algorithm and pso using rapid miner 9.1 get an accuracy value = 91.67% and auc = 0.750. table 4. funds sentiment confusion matrix results using nbc + pso accuracy : 91.67% +/-17.08 % (micro: 92.59 %) true positive true negative class precision pred. positive 14 1 93.33% pred. negative 1 11 91.67% class recall 93.33% 91.67% figure 9. roc fund sentiment curve using nbc + pso d. sentiment model testing isaku uses the naive bayes classifier and pso the second experiment was to test the classification model using the naive bayes classifier and pso methods. the test design of the sentiment classification model isaku using the naive bayes classifier and pso methods is presented in figure 10. figure 10. design of sentiment model isaku uses nbc + pso based on the results of trials changing the inertia weight value on the pso, the highest accuracy and auc values are located at population size 7, the maximum number of generation value is 100, and the inertia weight value 0.1 results in an accuracy value of 85.00% and an auc value of 0.800. table 5 explains from a sample of 0.1 which is used in the classification with the naïve bayes classifier and pso users of the digital wallet service i. as much as 8 data is predicted to be a negative class according to that is included in the negative class prediction and 3 data predicted the negative class turns into the positive class, 10 the data was predicted to be class positive according to the positive class prediction, and as many as 0 data was predicted to be class positive, it turned out to http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 7 the work is distributed under the creative commons attribution-noncommercial 4.0 international license be in the negative class. the results obtained by using the naïve bayes classifier algorithm and pso using rapid miner 9.1 get an accuracy value = 85.00% and auc = 0.800. table 5. results of confusion matrix sentiment isaku use nbc + pso accuracy : 85.00% +/22.91% (micro: 85.71%) true negative true positive class precision pred. negative 8 0 100.00% pred. positive 3 10 76.92% class recall 72.73% 100.00% figure 11. roc sentiment curve isaku using nbc + pso based on the test results, an evaluation was carried out by observing the comparison of the accuracy and auc values of each method as presented in table 6. the evaluation results showed that the accuracy and auc values of nbc + pso were higher than single nbc. the use of pso for attribute weights can increase the accuracy of the naïve bayes classifier algorithm in analyzing the sentiment of digital wallet service users. table 6. comparison of experimental results using nbc and nbc + pso naïve bayes classifier naïve bayes classifier dan pso accura cy auc accurac y auc dana 60.00 % 0.599 91.67% 0.750 isaku 53.23 % 0.520 85.00% 0.800 further evaluation was carried out by researchers to test the validity of the classification model performance using the t-test and anova test. e. t-test and anova test for fund sentiment the t-test and anova test designs for fund sentiment on the rapidminer 9.1 application are presented in figure 12. figure 12. t-test model design and anova model classification of nbc with nbc + pso fund sentiment the results of the t-test are presented in table 7 shows that the nbc + pso method has a significant difference in value because it has a probability of <0.050, namely 0.000 on the naive bayes classifier method. table 7. t-test results of naïve bayes classification model with nbc + pso fund sentiment t-test significance a b c 0.607 +/ 0.094 0.917 +/ 0.171 0.607 +/ 0.094 0.000 0.917 +/ 0.171 the anova test results are presented in table 8 showing that dk between groups (comparison) = 1, dk in residuals (denominator) = 18, and alpha = 0.050. then the f table value is f0.050 (1.18) = 4.41. and f count = 25,245. the value of fcount> ftable = 25,245> 4.41, then the two classification methods tested have a significant difference in the accuracy value. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 8 the work is distributed under the creative commons attribution-noncommercial 4.0 international license table 8. anova test results for naïve bayes classification model with nbc + pso fund sentiment anova test source square sums df mean squares f prob between 0.480 1 0.480 25.245 0.000 residuals 0.342 18 0.019 total 0.823 19 the evaluation results show that nbc + pso is a fairly good combination of methods in classifying data. the use of pso for attribute weighting affects increasing the value of accuracy in analyzing the sentiment of users of dana's digital wallet services. f. t-test and anova test for sentiment isaku the t-test and anova test design for sentiment isaku in the rapidminer 9.1 application are presented in figure 13. figure 13. design of the t-test and anova model test for classification of nbc with nbc + pso sentiment isaku the results of the t-test are presented in table 9 shows that the nbc + pso method has a significant difference in value because it has a probability of <0.050, namely 0.000 on the naive bayes classifier method. table 9. t-test results for naïve bayes classification model with nbc + pso sentiment isaku t-test significance a b c 0.523 +/ 0.053 0.850 +/ 0.229 0.523 +/ 0.053 0.000 0.850 +/ 0.229 anova test results are presented in table 10 showing that dk between groups (comparison) = 1, dk in residual (denominator) = 18, and alpha = 0.050. then the f table value is f0.050 (1.18) = 4.41. and f count = 19,377. the value of fcount> ftable = 19,377> 4.41, then the two classification methods tested have a significant difference in the accuracy value. table 10 anova test results for naïve bayes classification model with nbc + pso sentiment isaku anova test source square sums df mean squares f prob betwee n 0.536 1 0.536 19. 377 0.000 residu als 0.497 18 0.028 total 1.033 19 the evaluation results show that nbc + pso is a fairly good combination of methods in classifying data. the use of pso for attribute weighting affects increasing the value of accuracy in the sentiment analysis of isaku digital wallet service users. it can also be ignored that the digital wallet that has the best service is dana because most of its users give a positive opinion on the digital wallet service. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 2, no. 4 september 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 9 the work is distributed under the creative commons attribution-noncommercial 4.0 international license conclusion the results of the classification of 490 tweet data using the naive bayes classifier show that the dana digital wallet has 152 positive sentiments and 120 negative sentiments, as well as the isaku, has 104 positive sentiments and 114 negative sentiments. testing tweet data using pso can increase the accuracy value and the auc value. the increase was significant, previously the sentiment test using nbc only produced 60.00% and an auc value of 0.599 after adding the pso, the accuracy value was 91.67% and the auc value was 0.750, while for sentiment testing isaku using nbc only produced 53.23% and a value auc 0.520 after adding the pso accuracy value to 85.00% and 0.800 auc value. weighting the attributes using pso has a major effect on the accuracy results obtained, giving an increase of 31.67% for fund sentiment and 31.77% for sentiment isaku. the results of the t-test and anova test show that the two classification methods tested have significant differences in the accuracy value. nbc + pso is a fairly good combination of methods in classifying data. the use of pso for attribute weighting affects increasing the value of accuracy in analyzing the sentiment of users of the dana and isaku digital wallet services. reference aaputra, s. a., didi rosiyadi, windu gata, & syepry maulana husain. 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(2016). twitter rahasiakan jumlah pengguna di indonesia. retrieved april 24, 2020, from cnn indonesia website: https://www.cnnindonesia.com/teknologi/2 0160322085045-185-118939/twitterrahasiakan-jumlah-pengguna-di-indonesia pertiwi, m. w. (2019). analisis sentimen opini publik mengenai sarana dan transportasi mudik tahun 2019 pada twitter menggunakan algoritma naïve bayes, neural network, knn dan svm. inti nusa mandiri, 14(1), 27–32. pratama, k. a., pradnyana, g. a., & arthana, i. k. r. (2020). pengembangan sistem cerdas untuk prediksi daftar kembali mahasiswa baru dengan metode naive bayes (studi kasus: universitas pendidikan ganesha). sintech journal, 3(1), 22–34. saidah, s., & mayary, j. (2020). analisis sentimen pengguna twitter terhadap dompet http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v2i4.162 jurnal riset informatika vol. 2, no. 4 september 2020 10 the work is distributed under the creative commons attribution-noncommercial 4.0 international license elektronik dengan metode lexicon based dan k – nearest neighbor. jurnal ilmiah informatika komputer, 25(1). http://creativecommons.org/licenses/by-nc/4.0/ 277 predicting the bitcoin price using linear regression optimized with exponential smoothing indah suryani 1*), hani harafani 2 informatika universitas nusa mandiri www.nusamandiri.ac.id indah.ihy@nusamandiri.ac.id 1*), hani.hhf@nusamandiri.ac.id 2 (*) corresponding author abstrak bitcoin merupakan salah satu mata uang kripto yang paling popular saat ini. di dalam kondisi pandemic yang melanda dunia saat ini akibat covid-19, maka bitcoin diharapkan dapat dijadikan sebagai sebuah investasi ketika tingkat ketidakpastian ekonomi sedang tinggi. pada penelitian ini, data yang digunakan adalah data harga bitcoin yang termasuk ke dalam data deret waktu. salah satu metode yang umum digunakan untuk prediksi dalam deret waktu adalah metode regresi linear. untuk dapat mengembangkan hasil prediksi tersebut, digunakan teknik transformasi data menggunakan metode yang popular yaitu exponential smoothing. pada metode exponential smoothing, dilakukan optimasi parameter alpha untuk dapat mendongkrak hasil prediksi dari regresi linear. dan dari hasil ekperimen yang dilakukan, terbukti bahwa optimasi parameter alpha pada exponential smoothing mampu meningkatkan performa prediksi regresi linear dengan hasil perbandingan rmse dengan uji t telah menghasilkan hasil perbedaan yang signifikan. kata kunci: bitcoin; linear regresi; exponential smoothing abstract bitcoin is one of the most popular cryptocurrencies today. in the current pandemic conditions that hit the world due to covid-19, bitcoin is expected to be used as an investment when the level of economic uncertainty is high. in this study, the data used is bitcoin price data which is included in time series data. one of the commonly used methods for prediction in time series is the linear regression method. to be able to develop the prediction results, a data transformation technique is used using the popular method, namely exponential smoothing. in the exponential smoothing method, optimization of the alpha parameter is carried out to be able to boost the prediction results from linear regression. and from the experimental results, it is evident that the optimization of the alpha parameter in exponential smoothing can improve the prediction performance of linear regression with the results of the comparison of rmse with the t-test which has resulted in significant differences. keywords: bitcoin; linear regression; exponential smoothing introduction bitcoin having received increased levels of attention from the media and investors alike in recent years (kalyvas et al., 2020). so making bitcoin one of the most popular among all cryptocurrencies. in line with (jareño et al., 2020), they stated that in recent years, cryptocurrency markets have become much more popular, so cryptocurrencies may have moved to the category of investment assets. much research on bitcoin has focused on the price discovery process and market efficiency in the bitcoin market (tsang & yang, 2020). there are several bitcoin exchanges and the price difference between them is large and changes over time (tsang & yang, 2020). the global covid-19 pandemic has disrupted normal business and affected sustainable economic development in many countries. however, it seems that the economic uncertainty following the covid-19 containment measures is supporting the cryptocurrency market signal (sarkodie et al., 2021). in line with (kalyvas et al., 2020), their findings indicate that bitcoin may possess hedging http://creativecommons.org/licenses/by-nc/4.0/ 278 properties against economic uncertainty; therefore, it may be beneficial for investors to consider this cryptocurrency as an investment when economic uncertainty is high. the linear regression model is representative of the most well-known family of regression models, this model consists of a linear function that underlies the class of hypotheses (vercellis, 2009). linear regression is a statistical technique that describes a linear relationship between two variables, namely the dependent variable and the independent variable (aslanyan, 2021; mondal & rehena, 2020). linear regression (lr) can be useful not only for discovering patterns in experimental data but also as a baseline for benchmarking and validating new analysis techniques (zakeri et al., 2020), especially novel or unfamiliar ones. linear regression is also one of the prediction methods in machine learning that is quite popular for researchers to develop, as done by (huang & hsieh, 2020), (matiz & barner, 2020), and (patel & kiran, 2019). a relevant problem that often faced by practitioners regarding the dynamic nature of time series is the selection of a particular exponential smoothing model. for example, the choice between adopting a local linear trend and simple exponential smoothing is usually driven by the detection (or absence) of a trend in the data (sbrana & silvestrini, 2014). however, during the course of a business cycle, the trend dynamics of a series are sometimes not constant over time and may vary (sbrana & silvestrini, 2013). data transformation can be in the form of smoothing, aggregation, generalization, normalization, and attribute construction or feature construction. one of the functions of the smoothing technique is to remove noise from the data. and exponential smoothing is one of those smoothing techniques (han & kamber, 2006). another advantage of exponential smoothing is that it can consider trends and seasonal effects of the data so that it can produce estimates with simple formulas (tratar, 2015). in addition, exponential smoothing also can beat many other advanced methods (beaumont, 2014). therefore, exponential smoothing is also widely used to develop time series prediction models, as was done by previous research in (yager, 2013),(koehler et al., 2012), and (suryani, 2015). based on the literature, it is interesting in this study to be able to predict the price of bitcoin by developing a linear regression method which developed by transforming the data using exponential smoothing. which in previous studies, efforts to improve performance with exponential smoothing used for the gold price dataset and were directly carried out on the neural network method without first comparing with other machine learning methods. while in this study, optimization with exponential smoothing carried out after comparing the rmse values between the three machine learning methods and used to predict bitcoin prices. research methods types of research this type of research is currently being carried out in the form of quantitative research in the form of experimental research. time and place of research this research used secondary data from https://www.investing.com/crypto/bitcoin/histor ical-data. these data records collected from 01 march 2017 until 05 march 2021 procedure in this study, the dataset in the form of bitcoin closing prices was processed first with data pre-processing techniques such as set roles, normalize and windowing. the role set is used to define labels and id. normalize is used to normalize the data and windowing is used to break the closing price attribute into 5 parts, namely 5 input data and 1 output data. modeling in this research is the optimization of the alpha parameter in exponential smoothing to improve performance on prediction results using linear regression as shown in figure 1. the first thing to do is to process a dataset in the form of bitcoin closing prices with pre-processing techniques such as set roles, normalize and windowing. the role set is used to define labels and id. normalize is used to normalize data using binary sigmoid activation function and windowing is used to break the closing price attribute into 5 parts, namely 5 input data and 1 output data. furthermore, exponential smoothing will used to optimize the performance of linear regression by optimizing its alpha parameter. after that, the new data will be produced and then processed by linear regression method using 4 future selection options in the form of t-test, m5prime, greedy, and iterative-test. the processing is carried out using the 10 fold cross-validation technique, namely by dividing the training and testing data. then the rmse value will obtained from each experiment carried out and then a comparison is made. http://creativecommons.org/licenses/by-nc/4.0/ 279 figure 1. proposed method data, instruments, and data collection techniques the data collected is in the form of historical data on bitcoin prices which includes the attributes of date, opening price, highest price, low price, closing price, volume_btc, volume_currency, and weighted prices. the attributes used to be processed are only the attributes of the date and closing price. which is contains 1.170 records. data analysis technique the data used in this research is time-series data in the form of historical data from bitcoin prices. wherefrom the bitcoin price data, only one price data attribute is used in the form of the closing price data only. as shown in table 1. below. table 1. samples of bitcoin prices data date closing price 05/03/2021 56.826 05/02/2021 57.016 05/01/2021 57.700 4/30/2021 56.803 4/29/2021 53.006 4/28/2021 54.456 4/27/2021 55.067 4/26/2021 53.297 4/25/2021 48.075 4/24/2021 50.955 based on this data, there are two attributes, namely the date and closing price. then made arrangements to determine the id and label attributes. we specify the date attribute as the id attribute and the closing price attribute as the label. furthermore, the data normalization was carried out using the activation function of the binary sigmoid. then the windowing technique is carried out because the data used is in the form of univariate data. after that, the data is ready to be processed using machine learning. from the experiments conducted, the performance of several methods in machine learning was tested, namely using k-nearest neighbor, neural network, and linear regression. based on the rmse results generated from the three methods, the method that produces the highest average rmse is chosen and then optimized with exponential smoothing. results and discussion evaluation the data that is ready to use after preprocessing is then predicted by experimenting with three methods. then an evaluation will be made of the average rmse value generated from each of these methods. first, the experiment was carried out using the knn method. in this experiment, the k parameter optimization was carried out on the knn with 4 experimental samples. the results can be seen in table 2 below. the average rmse value generated from the knn method is 0.608. table 2. experiments result using knn no. k rmse 1 0.7 0.5 2 0.5 0.48 3 0.3 0.478 4 0.1 0.974 average 0.608 the next experiment is to use the neural network method. in this experiment, optimization was carried out on the learning rate and momentum parameters with 4 experiments. the result obtained is to get an average rmse value of 0.497 as shown in table 3 below. table 3. experiments result using neural network no. lr mom rmse 1 0.01 0.9 0.507 2 0.001 0.9 0.435 3 0.01 0.5 0.454 4 0.001 0.5 0.59 http://creativecommons.org/licenses/by-nc/4.0/ 280 average 0.497 the third method that was tested is linear regression. this experiment was carried out with 4 different feature selections as listed in table 4. the resulting average rmse value was 0.451. and it turns out that this method produces the lowest rmse value, which means that this method produces better predictive results. table 4. experiments result using linear regression no. feature selection rmse 1 t-test 0.451 2 m5prime 0.452 3 greedy 0.449 4 iterative-t test 0451 average 0.451 from the experiments that produced the best average rmse value, then it was made to improve performance by using the exponential smoothing method. then an experiment was carried out by optimizing the results of linear regression with exponential smoothing. the experiment was carried out by optimizing the alpha value in exponential smoothing with 4 feature selections in linear regression. and the results of these experiments can be seen in table 4 below. table 5. experiments result using linear regression + exponential smoothing no. alpha feature selection rmse 1 0.5 t-test 0.229 2 0.3 t-test 0.157 3 0.1 t-test 0.175 4 0.5 m5prime 0.229 5 0.3 m5prime 0.157 6 0.1 m5prime 0.178 7 0.5 greedy 0.229 8 0.3 greedy 0.157 9 0.1 greedy 0.178 10 0.5 iterative-t test 0.229 11 0.3 iterative-t test 0.157 12 0.1 iterative-t test 0.178 average 0.188 by performing optimization using exponential smoothing in the linear regression method, it turns out that it can produce a lower rmse value with an average rmse value of 0.188. this value is generated over 12 experiments. and it can be seen that the choice of features does not affect increasing the rmse value, while the optimization of the alpha value on exponential smoothing has a sufficiently good impact on the increase in the rmse value. validation to prove whether there is a difference, and how significant the difference is between the usual linear regression method and the proposed method, in the form of optimizing linear regression using exponential smoothing, so it validates with a t-test. table 6. rmse comparison between lr and lr+es using t-test variable 1 variable 2 mean 0.45075 0.18475 variance 1.58333e-06 0.0009562 5 observations 4 4 pearson correlation 0.070674182 hypothesized mean difference 0 df 3 t stat 17.14049415 p(t<=t) one-tail 0.000216309 t critical one-tail 2.353363435 p(t<=t) two-tail 0.000432618 t critical two-tail 3.182446305 from the results of the t-test in table 6, it produces a t-table value of 17.14049415, and tcount value of 3.182446305, then the t-table value is greater than the t-count value. this means that there is a difference in alias h1 is accepted and h0 is rejected. this difference also shows a significant value. this can be seen at the p-value which is less than 0.05 yaitu semester 0.000432618. conclusions and suggestions conclusion based on the experiments conducted, this study uses three machine learning methods, namely k-nearest neighbor, neural network and linear regression. from the three methods, it is known that the linear regression method shows the highest average rmse value of 0.451. after that, efforts were made to improve linear regression performance with exponential smoothing. it is known that the optimization of the alpha parameter in exponential smoothing can provide an average rmse value of 0.188 and can provide a significant difference in the classical linear regression method. where in previous studies, efforts to increase performance with exponential smoothing were directly carried out on the neural network method without first comparing what machine learning method has a better rmse. that study also used another dataset, which was the gold price dataset. meanwhile, in this study, optimization with exponential smoothing was carried out after http://creativecommons.org/licenses/by-nc/4.0/ 281 knowing the method that produced the most superior rmse for predict the bitcoin prices. and it can be concluded that exponential smoothing can improve the performance of linear regression to be able to predict bitcoin prices. suggestion from the results of the research conducted, it turns out that exponential smoothing can provide increased performance in predictions using the linear regression method. so in future research, it is hoped that the use of exponential smoothing will be developed as a method in pre-processing data to improve the performance of other machine learning methods. other experiments are also expected to be carried out with different datasets. references aslanyan, t. k. 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(2013). exponential smoothing with credibility weighted observations. information sciences, 252, 96–105. https://doi.org/10.1016/j.ins.2013.07.008 zakeri, z., mansfield, n., sunderland, c., & omurtag, a. (2020). cross-validating models of continuous data from simulation and experiment by using linear regression and artificial neural networks. informatics in medicine unlocked, 21(july), 1–6. https://doi.org/10.1016/j.imu.2020.100457 http://creativecommons.org/licenses/by-nc/4.0/ 37 development of manufacturing inventory management system using material requirement planning method ami rahmawati1*), rizal amegia saputra2, ita yulianti3 sistem informasi universitas nusa mandiri ami.amv@nusamandiri.ac.id1*) sistem informasi akuntansi kampus kota sukabumi universita bina sarana informatika rizal.rga@bsi.ac.id2, ita.iyi@bsi.ac.id3 (*) corresponding author abstrak inventori memiliki peranan penting dalam aktivitas bisnis. hal ini dikarenakan inventori berpengaruh terhadap perubahan pasar produksi serta mengantisipasi perubahan harga dalam permintaan barang yang banyak. pt. barkah jaya mandiri merupakan perusahaan yang bergerak dalam bidang manufaktur dimana pengelolaan inventory barang pada perusahaan tersebut masih dilakukan secara konvensional. hal tersebut menyebabkan timbulnya berbagai masalah seperti terjadinya selisih pada stok barang, ketidaksesuaian data dan laporan akhir serta kendala pada proses produksi apabila terjadi kekurangan maupun kelebihan bahan baku.oleh karena itu, melalui penelitian ini akan dibuatkan sistem berbasis desktop menggunakan bahasa pemrograman java dengan metode mrp (material requirement planning) agar dapat mengatasi permasalahan yang terjadi pada perusahaan tersebut. penggabungan antara model sdlc dan teknik pengumpulan data meliputi observasi, interview dan studi pustaka juga dilakukan pada penelitian ini guna tercapainya sistem yang akan dibangun agar sesuai dengan kebutuhan yang ditargetkan. dengan adanya sistem tersebut, pengelolaan data inventory pada perusahaan ini dapat dilakukan dengan mudah dan akurat serta mengefektifkan waktu dibandingkan dengan sistem berjalan sebelumnya, sehingga aktivitas pengadaan bahan baku manufaktur menjadi optimal dan kinerja karyawanpun menjadi lebih baik. kata kunci: inventori, pemrogramman java, material requirement planning, sdlc abstract inventory has an important role in business activities. this is because inventory has an effect on changes in the production market and anticipates price changes in the demand for many goods. pt. barkah jaya mandiri is a company engaged in manufacturing where the management of inventory at the company is still done conventionally. this causes various problems such as the occurrence of discrepancies in the stock of goods, discrepancies in data and final reports as well as obstacles in the production process in the event of a shortage or excess of raw materials. (material requirement planning) in order to overcome the problems that occur in the company. the combination of the sdlc model and data collection techniques including observation, interviews and literature study were also carried out in this study in order to achieve the system that will be built to suit the targeted needs. with this system, the management of inventory data at this company can be done easily and accurately and save time compared to the previous system, so that the procurement of manufacturing raw materials is optimal and employee performance is better. keywords: inventory, java programing, material requirement planning, sdlc introduction inventory can be interpreted as items stored for use or sale in the future or period (prasetya & prakoso, 2020). inventory has an important role in business activities (nur fazli & jumaryadi, 2019). this is because inventory affects changes in the production market and anticipates price changes in demand for large quantities of goods (mufida et al., 2019). inventory management information system is a system to collect and maintain data that mailto:rizal.rga@bsi.ac.id2 38 explains the inventory of goods and converts the data into information and report to users (indrian & sudarmadi, 2015). activities on inventory management in general consist of the data collection of overall inventory, expenditure, procurement and transfer of goods and so on (rahmadi & yusmiarti, 2016). the tighter business competition in the business world, speed and accuracy in acting is a major thing (pahlevi et al., 2018). strategies in data processing and supporting facilities are needed to help process data quickly and produce the necessary reports of the company (hasan, 2017). by utilizing the development of existing information technology, data processing is done easily and produce information that is needed accurately and effectively time, as well as costs incurred more efficiently (hengki & suprawiro, 2017). pt. barkah jaya mandiri is one of the companies in sukabumi regency engaged in manufacturing. inventory management of goods in this company is still done conventionally, especially on the recording of in and out of goods, demand for procurement of raw materials and reports of purchases and use in the quantity of items still using the receipt book. this causes various problems such as differences in the stock of goods, data discrepancies and final reports between warehouse heads and warehouse admins even when the data search takes a relatively long time (bustami, 2021). in addition, the planning of procurement of raw materials is still carried out based on estimates only, so it is not surprising if there is a shortage or excess of raw materials that can slow down the production process which leads to incorrect target delivery time to customers if there is a shortage of raw materials, and vice versa if excess will impact the high cost of storage and the risk of loss and damage to raw materials (pratama & latipah, 2019). based on these problems, it is proposed the creation of an application system for inventory management of goods as an alternative solution to the current problems. there are several similar studies that have previously implemented such systems as inventory information systems at riau university hospitals (nasution & frianti, 2019), cv. limoplast (nurlaela et al., 2019) and ud. berkah jaya (ariani & taufik, 2021). the three studies both utilize php programming language to build a webbased inventory information system in managing inventory or stock of goods so as to facilitate employees in making reports. but in this study, the system will be created desktop-based using java programming language to provide newness compared to previous research. on the other hand, the application of mrp (material requirement planning) method is also widely used in various research related to inventory because it is considered capable of controlling the amount of raw materials that must be prepared or purchased according to production needs (susmita & cahyana, 2018)(rishmaya, 2020). therefore, to provide novelty compared to previous research, the system will be built by implementing the mrp method into desktop-based programs using the java programming language to support the management of production processes, especially in controlling the supply of raw materials at pt. barkah jaya mandiri. the purpose of this research is to improve the weaknesses in the existing system by building a program that can cover the need to manage raw material inventory in pt. barkah jaya mandiri. with the system is expected to provide convenience and benefits for the company in optimizing procurement activities ranging from the management of raw material data, scheduling purchases, and making reports. research methods the data used as a means of supporting the achievement of this research was obtained by observation, interview and literature review. 1. observation this observation was carried out at pt. barkah jaya mandiri with the aim of being able to produce a better picture of the research that will be made. 2. interview this data collection technique is done by interacting and conducting questions and answers regarding the inventory management system to the warehouse admin at pt. barkah jaya mandiri. 3. literature review literature study was carried out by looking for various references including books and e-books related to making applications and supporting articles obtained from the internet. types of research this type of research is in the form of qualitative research. this research is a research that emphasizes on understanding the problems in social life based on the conditions of reality, a holistic, complex and detailed natural setting (anggito & setiawan, 2018). therefore, a case study approach was carried out to achieve the objectives of this study supported by the main data collection techniques including observation, interviews and literature study. 39 time and place of research this research was conducted in january 2021 at pt. barkah jaya mandiri which is located in the sukabumi industrial center complex (sentris) block d4 cipancur, cisaat kab. sukabumi west java province. research target / subject this study aims to update the system that is currently running at pt. barkah jaya mandiri by utilizing computer technology and building a desktop program in the inventory management system as a solution in dealing with problems that occur so as to improve and optimize employee performance. this was chosen for the reason that the development of computer technology today has very important and useful contributions in various fields of life, ranging from education, economics, geography, health, and others (prasanti & indirani, 2018). procedure the design of the program in this study was carried out using the waterfall model. this method is sometimes called a linear sequential model or classical life flow model which provides a sequential or sequential software lifeflow approach starting from requirements analysis, design, coding, testing and support stages (s. & shalahudin, 2018). data, instruments, and data collection techniques in addition to data collection techniques, sdlc model is also implemented as a method of software development in creating programs that will be built in this research. 1. need analysis this stage begins with an analysis of the specifications of the system requirements to be built such as goods data, incoming goods data, outgoing goods data and others, which will later be involved in making inventory management applications. 2. design design representation implemented in the form of erd and uml with the aim of describing the design of the system to be built so that the applications created can run optimally. 3. coding at this stage, the results of the design are translated into software programs with the java programming language in netbeans applications. in addition, the mrp method is also added in the program built in order to provide predictions of raw materials that must be prepared on the next production scheduling. material requirements planning (mrp) is a scheduling method for purchased plans and manufactured planned orders submitted for further analysis with regard to capacity availability (susmita & cahyana, 2018). the output generated from the mrp method is information that can be used to carry out production control in the form of orders arranged based on the timing of each component/item (pratama & latipah, 2019). 4. testing testing is done by testcomplete to know if the application used can run as needed. testcomplete is an automated testing tool used for functional testing and automated scripts can be composed from python, c++script, vbscript, jscript, or javascript languages as well. it supports different testing types and methods, for example; functional testing, unit testing, and gui testing (okezie et al., 2019). testcomplete has evaluated to test program in context to factors i.e. load performance, response time etc. with respect to the number of users or threads (lenka et al., 2018). 5. maintenance the maintenance phase can repeat the development process ranging from specification analysis to existing software changes, but not to creating new software. results and discussion system requirements analysis the stages of system requirements analysis in this study are described with the help of erd and uml. a. entity relationship diagram the following is a display of the erd designed for system development in this research: 40 figure 1. entity relationship diagram apa?? based on the erd, databases and tables that have been designed are then created using mysql. b. use case diagram there are two use case diagrams designed in this study, namely for warehouse admin and warehouse head. the following is a display of the warehouse admin use case: figure 2. warehouse admin use case diagram if the warehouse admin will manage goods data, division data, category data, supplier data, incoming goods data, outgoing goods data, procurement data, purchase report data, and goods usage report data, they must first log in. after logging in, the warehouse admin can enter the main menu. a warehouse head can only manage purchase report data and goods usage report data. to manage the data, the head of the warehouse is required to login first to the system. c. activity diagram the following is a figure of the activity diagram made in this study: figure 4. activity diagram 41 from the diagram (see figure 4.), it can be seen that the design of the proposed system is based on the system's previous procedures. d. class diagram in addition to use cases and activity diagrams, class diagrams were also made in this study which are presented in the following figure: figure 5. class diagram the diagram shows the design of the system structure/scheme that describes the classes, attributes, methods, and relations of each object. 1. implementation a. login page the initial view on the built system is a login page that can be seen in the following figure: figure 6. login page in this inventory program, if you log in as a warehouse admin, you can access all parts of the program that have been created such as item data, category data, division data, supplier data, incoming goods data, outgoing goods data, procurement of goods and reports. but, if you are logged in as the head of the warehouse, you cannot access all existing data, you can only access report data. b. main menu page furthermore, if the login is successful, the master menu will appear as shown in figure 7. figure 7. main menu page on the main menu page, various menus from the inventory application will appear that can be accessed by the warehouse admin and warehouse head. c. item data form page figure 8. shows a item data form page. in this form, warehouse admin can processing data including input, edit and delete inventory items contained in pt. barkah jaya mandiri. figure 8. item data form page 42 d. incoming goods data form page figure 9. shows a incoming goods data form page. this form can processing incoming goods data, such as the first item arrives, the item is inputted by the warehouse admin. figure 9. incoming goods data form page e. report page figure 10. shows a report page on the purchase of goods reported to the management. this report can be accessed on the system by warehouse admin and warehouse head. figure 10. report page f. planning form page the following is a display of the planning form page created using mrp method: figure 11. planning form page in this form, the mrp method is applied by dividing the time of product manufacture into 4 periods so that information can be obtained regarding the inventory of raw materials that are available and which must be purchased according to production needs. conclusions and suggestions conclusion by making an inventory management application in general, it can help simplify and optimize the performance of inventory management activities at pt. barkah jaya mandiri. the application of the mrp method in the system is also capable of producing information and scheduling the availability of raw materials needed in the production process. suggestion added security for applications made to avoid unwanted things such as human error. then make automatic data backups such as to the cloud to keep the data safe. references anggito, a., & setiawan, j. 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(2019). perancangan aplikasi perencanaan bahan baku menggunakan metode mrp ( material requirement planning ) pada pt. e-t-a indonesia. insand comtech, 4(2), 10–11. rahmadi, l., & yusmiarti, k. (2016). perancangan sistem informasi inventory di amik lembah dempo pagaralam. semnasteknomedia online, 4(1), 133–138. https://ojs.amikom.ac.id/index.php/semnast eknomedia/article/view/1246/0 rishmaya, r. (2020). sistem informasi pengendalian bahan baku menggunakan metode material requirement planning pada pd. samijaya sukabumi berbasis web. jurnal sistem komputer, 10(1), 17–23. https://doi.org/10.14710/jsk.v10i1.177 s., r. a., & shalahudin, m. (2018). rekayasa perangkat lunak terstruktur dan berorientasi objek (revisi). informatika bandung. susmita, a., & cahyana, b. j. (2018). pemilihan metode permintaan dan perencanaan kebutuhan bahan baku dengan metode mrp di pt. xyz. seminar nasional sains dan teknologi 2018 fakultas teknik universitas muhammadiyah jakarta, 10. 44 microsoft word 14_236-jri-32-187-194_tyas 187 implementation of inventory information system design using economic order quantity method frieyadie1,*, tyas setiyorini 2 1 sistem informasi, teknik informatika universitas nusa mandiri, jakarta, indonesia www.nusamandiri.ac.id 1*) frieyadie@nusamandiri.ac.id, 2 tyas.setiyorini@gmail.com *) corresponding author abstrak permasalahan penelitian yang dihadapi adalah diantaranya biaya pemesanan barang yang selalu berubah-ubah setiap ada pemesanan. pendataan order produk yang kurang baik dan kurang optimalnya penanganan order produk hal ini dapet merugikan perusahaan. untuk memecahkan ketidak baikan pengangan manajemen inventory ini, maka menggunakan metode economic order quantity (eoq) dimana terbukti efektif dalam mengatasi permasalahan tersebut. kontribusi yang dihasilkan dengan membangun sistem informasi manajemen inventory, supaya permasalahan yang dihadapi tidak terulang lagi. tujuan dari penelitian ini supaya biaya pemesanan barang menjadi lebih stabil dan menajad lebih optimal dalam penanganan order produk. kata kunci: sistem informasi; economic order quantity (eoq) model waterfall; persediaan barang abstract the research problems faced are among others the cost of ordering goods which always changes every time there is an order. poor product order data collection and less than optimal handling of product orders can harm the company. to solve the problem of controlling inventory management, the economic order quantity (eoq) method is used, which is proven to be effective in overcoming these problems. contribution is generated by building an inventory management information system so that the problems faced are not repeated. the purpose of this study is to make the cost of ordering goods more stable and more optimal in handling product orders. keywords: information systems; economic order quantity (eoq) waterfall model; inventory introduction pt. msj is a company engaged in manufacturing. where in the process of ordering raw materials, incoming or outgoing goods still use a manual process, namely by using paper to record them. likewise with the stock items still using paper. of course, this can create big risks that will occur. based on observations at pt. msj has several obstacles, namely difficulties in finding data on a raw material stock (ramdhany & fitriasih, 2019) (humaeni, muanas, & sudradjat, 2019), data processing goods, supplier data, and raw material purchase data. in the process of making reports, the company still takes a long time to prepare reports on raw material stock, incoming goods, and outgoing goods that still use paper records which at any time can be lost and damaged by rain and dirty in uncertain conditions. the current purchasing and inventory processes are deemed not going well and efficiently, where all processes are still running manually (cahyana, yuwono, & asmoro, 2012)(pudjiarti, puspitasari, & septyawati, 2019)(atikah, 2017), do not have good control, and do not have good data collection (apriani, aisyah, & anggraini, 2019). as for some of the problems faced, including the cost of ordering goods which always changes every time there is an order (tarsono & khotimah, 2018). this ordering cost is also influenced by the type of goods, the origin of the purchase of the goods, the amount of stock. the process of making documents that still use paper documents. frequent document loss (fatmawati & munajat, 2018)(sidik, waluyo, & susilawati, 2018), damage to documents, and the creation of double documents. difficulty in searching for historical data, and difficulty in making inventory reports (setiyanto, nurmaesah, & rahayu, 2019). the existing system has not been able to provide accurate stock information. based on previous research using the eoq method to reduce errors in data collection of ordered products (mujiastuti, meilina, & rully, 2018), so that the optimal ordering frequency can be found (anwar, mikhratunnisa, & cahyono, 2019). the method proposed in this study is the economic jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.174 188 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional order quantity (eoq) method. this method can answer the problems faced by pt. msj, which can calculate the exact reorder point (nasution & indriya, 2020). optimally, it can calculate the reorder quantity. based on the trend of using the eoq method (figure 1) in 1 (one) year in 2020. the eoq method still ranks as a widely used method for solving inventory management problems. figure 1. trends in the use of the eoq method in 2020. this inventory information system development model adopts the waterfall model, where the flow of system development is clearer based on the phases in the waterfall model. the current research contribution refers to the application system designed and implemented at pt. msj. the purpose of this research is to avoid document loss, make it easier to produce inventory reports, and be able to see accurate and correct stocks. given the breadth of the root of the raw material inventory information system, the boundaries or coverage will be made to be analyzed. the system discussed starts from the ordering process to suppliers, warehouse stock, product categories, goods data, and releasing goods. in the process of inputting incoming goods acceptance transactions, goods releasing transactions, and printing of goods stock data reports. research methods data collection technique in collecting data and information, the author researches to obtain the necessary data, by methods, including: a. observation the observation process was carried out by observing directly at pt. msj is a system process that runs, both from the initial purchase submission until the goods being ordered are received by the warehouse so that the research process can run well and find out the needs and shortcomings. b. interview the interview method is a direct question and answer process to the executive, general administration, and logistics administration to find out problems that exist in the raw material supply process. c. literature review the literature study process carried out and various reference sources both internal media, journal articles related to purchasing and inventory systems. economic order quantity (eoq) one of the methods used to control goods in a company (zahirah & arista, 2019). the existence of this eoq method makes inventory planning better. some things that need to be considered in the procurement of goods, namely ordering costs and storage costs. this ordering cost will continue to increase because usually, the ordering costs will continue to change, along with the number of orders for goods from suppliers. storage costs are also a burden on the company because storage costs are also constantly changing based on the number of items stored. eoq has a calculation using a formula like the following: 189 ��� � ��� � � .................................................................... (1) from equation 1, calculating the economic order quantity, where: r: ordering fee (order preparation and machine storage) per order s: estimated use or demand per period p: storage cost per unit per year eoq method can be implemented several questions can be done in the following conditions. a. the demand for a product is constant, uniform, and known (deterministic). b. the price per unit of product is constant. c. storage cost per unit per year (p) is constant. d. order cost per order (r) is constant. e. the time between order placed and goods received (lead time, l) is constant. f. there is no shortage of goods or "back orders". system development model the waterfall model is sometimes called the classic life cycle (pressman & maxim, 2015), suggests a systematic and sequential approach to software development starting with the specification of customer requirements and progressing through planning, modeling, construction, and deployment, culminating in the ongoing support of completed software. figure 2. waterfall model the following is an explanation of the waterfall model which is in. a. communication (project initiation & requirements gathering) time to start technical work, the developer must meet with customers to establish communication, to understand and achieve the goals of the system to be achieved. the result of this communication is the initialization of the project, such as the need for functional characteristics of each user in the system, analyzing the problems faced, and collecting the necessary data. b. planning (estimating, scheduling, tracking) the 2nd (two) next step is the planning stage which describes the estimation of the technical tasks to be carried out, the risks that may occur, the resources needed to create the system, the work products to be produced, the work plans that will be carried out, and the tracing of the system work processes. c. modeling (analysis & design) step 3 (three) is the stage of designing and modeling system architecture, with a focus on designing data structures, software architecture, interface displays, and program algorithms. the goal is to better understand the overall picture of what will be done. d. construction (code & test) stage 4 (four) of the process of translating the design form into program code or changing the result of the design to a machine-readable language. after the coding is complete, testing is carried out on the system and also the code that has been created. the goal is to find errors that might be corrected later. e. deployment (delivery, support, feedback) the fifth stage (five) stages of software implementation to customers, software repair, software evaluation, and software development are based on the feedback given so that the system can continue to run and develop according to its function. results and discussion the following is a discussion of system development using the waterfall model. each stage of the discussion will follow the flow of the waterfall model stage. communication (project initiation & requirements gathering) the following is a specification of the needs of each user of the raw material inventory information system. 1. purchasing section a. can log into the system b. can manage product data b. can manage supplier data c. can manage purchase order data 2. warehouse section a. can log into the system b. can manage incoming items b. can manage outgoing goods planning (estimating, scheduling, tracking) 1. estimating implementation of estimates in planning the technical tasks to be carried out, the risks that may jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.174 190 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional occur, the resources needed to create the system, the work products to be produced  analyst system. in charge of carrying out system analysis to be built  programmer. in charge of translating the results of analysis and design that have been made in code with a programming language that suits the needs and objectives to be made  tester system. in charge of testing the application that is built whether it runs by the planned functional needs 2. scheduling dan tracking figure 3 is the implementation of work plans that will be carried out in the construction of an inventory information system and also tracking work. implementation for 16 (sixteen weeks). figure 3. inventory information system design modeling (analysis & design) some modeling for information systems analysis using uml will be discussed by describing use case diagram modeling. a. use case diagram inventory information system figure 4. use case diagram inventory information system table 1 explains, one of the use case manage eoq data in figure 4. table 1. use case manage eoq data scenarios use case name : manage eoq data use case description : purchasing dept. manage eoq data, by adding, update, deleting, and printing data. actors : purchasing dept. pre-condition : the system must be connected to an intranet network. purchasing dept. must be logged in post condition : after successful login, a successful message is displayed for eoq data main scenarios serial no. step purchasing dept 1 click add to input eoq data 2 click view data to display material data 3 click edit data to change material data 4 click clear data to change material data 5 permit accessing system by access rights extensions 1a the added eoq data form appears 2a the eoq data view form appears 3a the eoq data edit form appears 4a clear eoq data message appears 5a print eoq data will appear uc use case inv entory information system inventory information system purchasing dept. login manage material data manage supplier data manage eoq data manage purchasing order manage incoming material warehouse manage outgoing material create a material requirements letter 191 b. eoq data browse design figure 5 is an eoq data browse plan, which displays the overall eoq data. purchasing dept. can manage eoq data, such as: add, view, edit, delete and print data. figure 5. eoq data browse design c. eoq data input design figure 6 is the form used to add eoq data, some field items that must be inputted by the purchasing dept. figure 5. eoq data browse design construction (code & test) a. economic order quantity algorithm design the following algorithm is written for an inventory information system application. begin number r, s, p, l input r input s input p input l input hpt (2*r*s)/p  eoq (p*eoq/2)+(r*s*eoq) tc s/eoq  frq hpt/frq  dh_eoq l*d/hpt  rop output eoq output tc output frq output dh_eoq output rop end b. economic order quantity function the following functions will be used in the inventory information system. jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.174 192 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional public function eoq($r,$s,$p){ $eoq=round(sqrt((2*$r*$s)/$p)); return $eoq; } public function tc($r,$s,$p,$eoq){ $tc = round(($p*$eoq/2)+($s*$r/$eoq)); return $tc; } public function frq($r,$eoq){ $frq = round($r/$eoq); return $frq; } public function durasi($jhk,$frq){ $durasi = round(($jhk/$frq)); return $durasi; } public function rop($l,$r,$jhk){ $rop = round(($l*$r)/$jhk); return $rop; } c. system performance testing figure 6. system performance testing testing system performance in figure 6, users involved as many as 8 users. the response time from the initial minute to the last minute of testing was set for 10 minutes of listening testing, averaging 0.92 seconds, reading pdf data files an average of 1.03 seconds. average response time with page reading of 0.92 seconds. deployment (delivery, support, feedback) the main purpose of this stage is to explain how software is implemented into hardware, also to visualize how software interacts with hardware to carry out its functionality.. a. delivery after completing the development of the inventory information system, the developer then submits it to the end user to be installed on the company's server. figure 7. system performance testing in figure 7, it can be concluded that the minimum specification for running an inventory information system application is that on the server side it requires a webserver, namely apache web server, with the application installed on the webserver by accessing index.php. the database management system used by mysql database. on the client-side, deployment deployment diagram inv entory information system «devi ce» webserv er index.php «device» web serv er mysql database «device» user dev ice «appl ication» web brow ser port 80 193 using devices that are not limited to pcs, can also be tablets, smartphones, laptops. when accessing it with a web browser. b. feedback feedback in table 2, in the form of a user acceptance test, is information on the results of the end user's reaction to the inventory application, someone's performance on an assignment, etc. which is used as a basis for improvement. tabel 2. user acceptance test no a list of questions percentage 1 is the eoq data management dashboard easy to operate 91% 2 is eoq data input understandable and easy to use? 93% 3 is change eoq data understandable and easy to use 93% 4 does appear data eoq understands and is easy to use 95% 5 is the calculation process as expected? 93% 6 is the process of accessing this inventory application fast? 92% 7 is the data access process in this inventory application fast? 92% 8 is this inventory application sufficient? 96% table 2, is the stage for testing user acceptance of, in general, users who test the inventory information system accept this application. conclusion in general, this inventory information system can help users implement the company's existing inventory of incoming and outgoing goods. the application of 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(2019). pengendalian persediaan dengan menggunakan metode economy order quantity pada distributor makanan. computer and science industrial engineering (comasie), 11(01), 32–41. retrieved from https://ejournal.itn.ac.id/index.php/valtech/ article/view/209 395 identification of water turbidity with turbidity sensor based on arduino hafdiarsya saiyar1, mohammad noviansyah2 program studi teknologi komputer1, program studi sistem informasi2 universitas bina sarana informatika hafdiarsya.hyr@bsi.ac.id1, mohammad.mnh@bsi.ac.id2 (*) corresponding author abstrak air merupakan kebutuhan yang penting bagi semua makhluk hidup, terutama manusia. manusia membutuhkan air dengan kualitas yang memenuhi persyaratan fisik, mikrobiologi, kimia, dan radiologi yang terdapat dalam parameter wajib dan parameter tambahan. pemilihan parameter tersebut sangat penting untuk memenuhi persyaratan air yang baik, yaitu tidak berasa, tidak berbau, dan berwarna. sedangkan parameter yang digunakan untuk pengidentifikasian air ada tiga,yakni parameter ph, tingkat kekeruhan, dan parameter suhu. dari permasalahan tersebut penulis meneliti pendeteksian kualitas air, khususnya tentang kekeruhan air. penulis mencoba membuat suatu alat yang mampu mendeteksi tingkat kekeruhan air dengan turbidity sensor sebagai pendeteksi tingkat kekeruhan air, arduino uno sebagai pemroses hasil data yang telah dideteksi, dan lcd 16x2 sebagai penampil hasil pengukuran tingkat kekeruhan yang berupa nilai kekeruhan dan keterangan air yang diujikan. rentang pengukuran yang dapat dideteksi oleh alat ini yaitu dari 0 – 3000 ntu. metode penelitian yang digunakan adalah observasi langsung terhadap objek yang terpilih yaitu dilingkungan rumah penulis serta melakukan studi pustaka yang berkaitan dengan mikrokontroler arduino. tujuan penelitian ini adalah untuk mengetahui dan mendeteksi tingkat kualitas air yang ada di masyarakat. sebagai salah satu tools atau alternatif bagi masyarakat untuk mengetahui atau mendeteksi tingkat kualitas air secara dini. kata kunci: arduino uno, kekeruhan air, lcd, ntu, turbidity sensor abstract water is an important need for all living things, especially humans. humans need water with quality that meets the physical, microbiological, chemical, and radiological requirements contained in the mandatory and additional parameters. the selection of these parameters is very important to meet the requirements of good water, namely tasteless, odorless, and colorless. meanwhile, there are three parameters used for water identification, namely ph parameters, turbidity levels, and temperature parameters. from these problems, the authors examine the detection of water quality, especially water turbidity. the author tries to make a tool that can detect the level of turbidity of water with a turbidity sensor as a detector of the level of turbidity in the water, arduino uno as a processor for the data results that have been detected, and a 16x2 lcd as a display of turbidity level measurement results in the form of turbidity values and descriptions of the water being tested. the measurement range that can be detected by this tool is from 0 – 3000 ntu. the research method used is direct observation of the selected object, namely the author's home environment, and conducting library research related to the arduino microcontroller. the purpose of this study was to determine and detect the level of water quality in the community. as one of the tools or alternatives for the community to find out or detect the level of water quality early. keywords: arduino uno, water turbidity, lcd, ntu, turbidity sensor introduction humans, animals, and plants both need water to grow and develop. however, it certainly requires water with each capacity. to continue to live and develop, humans always need water every day. however, it's not just water that humans need. the water that humans need has a certain standard or quality. good water quality can be seen from various things depending on its use. the water needed for human drinking needs is different in quality from the water used for sanitation needs. according to the regulation of the minister of health number 492/menkes/per/iv/2010 concerning drinking water that is safe for health is if it meets the physical, microbiological, chemical, mailto:hafdiarsya.hyr@bsi.ac.id 396 and radioactive requirements contained in the mandatory and additional parameters. the selection of important parameters in this measurement to meet the requirements of good water, namely, tasteless, odorless, and colored. there are three types of parameters used for water identification, namely, ph parameters, turbidity level parameters, and temperature parameters. (amani & prawiroredjo, 2016). the level of turbidity in the water can raise concerns that in the water used some substances are harmful to the human body and cause disease (sutrino, 2004). turbidity is an optical property of a solution that causes light through it to be absorbed and refracted (kadir, 2013). turbidity is expressed in units of ntu (nephelometric turbidity units) and measured using a standard measuring instrument, namely a turbidimeter (joko, 2010). however, this tool is only owned by certain parties, so the development of various water turbidity measuring instruments is carried out by utilizing electronic circuits (suliyani, suciyati, pauzi, & surtono, 2021). water turbidity is a measure that uses the effect of light as a basis for measuring the state of raw water on the ntu (nephelometric turbidity unit) or jtu (jackson turbidity unit) or ftu (formazin turbidity unit) scale. turbidity of water is expressed in units of turbidity, which is equivalent to 1 mg/liter of sio2. this turbidity is caused by the presence of mixed objects or colloidal objects in the water(manaor, efendi, & informatika, 2017)(effendi, 2003). to make it easier to determine the level of water turbidity, the parameters used are ntu (nephelometer turbidity unit) units. (wardhana, 2004). in this study, the author only discusses the second parameter, namely, the level of water turbidity. water turbidity is a form of measurement of scattered light from the interaction of suspended and dissolved materials in water samples, this is used as an indicator of water quality. (kautsar, rizal isnanto, & didik widianto, 2015). the purpose of this study was to determine and detect the level of water quality in the community. as a tool or alternative for the community to find out or detect the level of water quality early. research methods in collecting data and information, the author uses several research methods, including: 1. observation method the author makes direct observations of the selected object, namely in the author's home environment, where the author observes the types of water that exist in the community where the author lives. 2. literature study conducting library research to support all the collection of the required information. information collection is done by looking for references related to the tools that the author will make. these references are obtained from books, journals, articles, and the internet. a. block diagram picture 1. block diagram the explanation of this tool circuit block is: 1. input this input component is the input component that will be processed. the input components consist of: a. a power supply that supplies +12 volts to the appliance circuit b. turbidity sensor which functions to detect the level of turbidity of water poses 2. process the process is an important component in a tool because it functions as a data manager that has been detected by the sensor which then the processed results will be displayed by the output. in this process, the author uses arduino uno r3. 3. output output is the output of all processes that have been carried out. in this tool, the author uses a 16x2 lcd as the display output of the resulting process. a. schematic of tool circuit power supply (input) turbidity sensor (input) arduino uno r3 (proses) lcd 16 x 2 (output) 397 picture 2. set of tools the circuit schematic consists of a series of inputs, processes, and outputs. the input itself consists of a power supply obtained from a 12-volt adapter and a turbidity sensor which functions as a detector of turbidity in the water. the process in this tool is carried out by arduino uno. arduino uno processes and processes data that has been previously known by the turbidity sensor. which then arduino uno displays the results to a 16x2 lcd. the output circuit consists of a 16x2 lcd which has been connected to i2c. the use of i2c is to simplify the pins that will be connected to the arduino uno. with i2c pins used in arduino uno only 4 pieces, namely the ground pin, vcc, sda, and scl. b. how the tool work 1. power supply picture 3 power supply scheme a power supply is a pin that provides voltage for components or circuits that are connected to arduino. (galih, v., purnomosari, e., 2019). in this tool, the author uses a 12-volt power supply with a scheme like a picture above. this power supply uses a 12 volt 1a ct stepdown type transformer, also uses a 1n5401 diode in the form of a bridge which functions as a full-wave rectifier. 2 capacitors function as voltage filters. and also use a 1n4001 diode so that the output current is 1a. 2. turbidity sensor sensors are generally defined as devices capable of capturing physical (physical) or chemical phenomena and then converting them into electrical signals, either electric current or voltage. physical phenomena that can stimulate sensors to produce electrical signals include temperature, pressure, force, magnetic field, light, and so on. (suryono & pramusinto, 2016) turbidity sensor that can detect water turbidity by reading the optical properties of water due to light and as a comparison of light to be reflected with future light (noor, supriyanto, & rhomadhona, 2019). on the turbidity sensor, that the higher the level of water turbidity will be followed by a change in the sensor output voltage(wadu, ada, & panggalo, 2017). picture 4 turbidity sensor turbidity sensor consists of 2, namely adapter dimension and probe dimension. turbidity sensor works when given a voltage of 5 volts. there are two-pin sockets in the turbidity sensor adapter. the socket pin on the right (as shown above) is connected to the probe which is used to detect the turbidity value. while the socket pin on the left (as shown above) is connected to the ground, vcc, and a0 pins on the arduino uno. after the turbidity value is known, the probe then transmits data to the turbidity sensor adapter which is then sent to arduino uno to be processed and processed. 398 3. arduino uno picture 6 arduino uno arduino is an open-source single-board microcontroller, arduino microcontroller hardware is programmed using a wiring-based programming language based on syntax and libraries.(dinata & sunanda, 2015). arduino uno in this tool is used as a controller and processor of the results of the input. arduino uno consists of an atmega 328 microcontroller circuit and has 20 pins. arduino uno itself can operate in a voltage of 7-12 volts. in this tool, arduino uno functions as a data processor and processor that has been detected by the turbidity sensor. the pins in the turbidity sensor are connected to the ground pin, 5v, and the a0 pin on the arduino uno. which then the results that have been processed are displayed by a 16x2 lcd. 4. output circuit picture 7 output circuit schematic lcd is a tool that serves to display size or number so that it can be seen and known through the crystal screen display (budiyanto, 2012). when the water turbidity data has been detected by the turbidity sensor which is then processed and processed using arduino uno, the data in the form of turbidity numbers and water information are then displayed by a 16x2 lcd. in making this tool the author uses i2c to simplify the pins that will be connected to the arduino uno. with the help of i2c, only 4 pins are used, namely ground, vcc, sda, and scl. flowchart diagram picture 8 flowchart diagram research results and discussion in this section, the author will conduct several test schemes, which consist of input experiments, output experiments, and the results of all experiments or conclusions from these experiments. start turbidity sensor reading water turbidity arduino processes and converts data lcd displays data results finish 399 a. input experiment result table 1 the experiment results of input no voltage water type turbidity arduino ntu description sensor uno 1 5 volt mineral water true true 2,11 sensors and arduino uno are working fine pam water true true 2,40 well water true true 3,20 sand water true true 11,75 groundwater true true 3000 2 12 volt mineral water true true 2,56 sensors and arduino uno are working fine pam water true true 2,86 well water true true 3,35 sand water true true 17,57 ground water true true 3000 table 1, the test results above show that there are several differences in the ntu values of the water tested. the voltage difference between the 5volt adapter and the 12-volt adapter is different. the arduino uno voltage needed is 7-12 volts, based on previous test results arduino uno can still work well with a 5-volt adapter, but the ntu test results are different, with a lower value. this value is lower because the power supply required by arduino uno is less than the recommended voltage limit. and also do not use a voltage that exceeds the required component (more than 12 volts) because it can damage the component. therefore, for more accurate results, it is better to use a voltage source that is needed by the arduino uno, namely 7 to 12 volts. b. experimental results output table 2 output result table water type turbidity arduino ntu lcd description sensor uno mineral water true true 2,56 clear sensors and arduino uno are working fine pam water true true 2,86 clear sensors and arduino uno are working fine well water true true 3,35 clear sensors and arduino uno are working fine sand water true true 17,57 dirty sensors and arduino uno are working fine ground water true true 3000 very dirty sensors and arduino uno are working fine from the test results above, the lcd component that is used as an output can work well according to the program that is input. c. overall experiment result table 3 overall experiment result no voltase water type turbidity arduino ntu lcd description sensor uno 1 5 volt mineral water true true 2,11 cleat sensor, arduino uno and lcd are working fine pam water true true 2,40 cleat well water true true 3,20 cleat sand water true true 11,75 dirty ground water true true 3000 very dirty 2 12 volt air mineral true true 2,56 clear sensor, arduino uno and lcd are working fine air pam true true 2,86 clear well water true true 3,35 clear sand water true true 17,57 dirty ground water true true 3000 very dirty 400 conclusions and suggestions conclusions based on the results of data testing using a water turbidity detector using a turbidity sensor with arduino uno, the design of this tool consists of an input block, a process block, and an output block. the input block consists of a power supply and a turbidity sensor, the process block is carried out by arduino uno and the author's output block uses a 16x2 lcd. the level of water turbidity can be detected using the turbidity sensor, then the data obtained will be processed and processed by arduino uno, which then results in the form of turbidity numbers and water information displayed on a 16x2 lcd. with a water turbidity detector using a turbidity sensor with arduino uno, the level of water turbidity can be known more quickly. suggestions in testing with a water turbidity detector using a turbidity sensor with arduino uno, there are still shortcomings. namely in testing this tool to be more thorough in taking data samples so that the data obtained is more accurate, and this tool should be equipped with a voltage source that does not require direct electrical power such as batteries or power banks to facilitate outdoor use. reference amani, f., & prawiroredjo, k. 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(2004). dampak pecemaran lingkungan. yoyakarta: andi. 105 classification of student satisfaction with online lecture nanang ruhyana1*), tati mardiana2, fachri amsury3, daning nur sulistyowati4 sains data1*,2, sistem informasi3,4 fakultas teknologi dan informasi, universitas nusa mandiri www.nusamandiri.ac.id nanang.ngy@nusamandiri.ac.id, tati.ttm@nusamandiri.ac.id, fachri.fcy@nusamandiri.ac.id, daningnur.dgs@nusamandiri.ac.id (*) corresponding author abstrak covid-19 memberikan dampak yang signifikan dalam kehidupan masyarakat, mengakibatkan lumpuhnya hampir seluruh perekonomian dan pendidikan, terutama pada bidang pendidikan mengakibatkan banyak siswa dan mahasiswa tidak bisa melakukan aktifitas kegiatan belajar mengajar di sekolah atau universitas. berdasarkan itu kementrian pendidikan dan kebudayaan memberikan himbauan untuk menghentikan kegiatan belajar mengajar secara tatap muka di sekolah maupun perguruan tinggi dan digantikan dengan melakukan pembelajaran jarak jauh atau daring. mengakibatkan kegiatan belajar mengajar menjadi kurang maksimal untuk siswa ataupun mahasiswa, terdapat ketidakpuasan terhadap sistem pembelajaran jarak jauh atau daring, tujuan dari penelitian ini adalah mengukur tingkat kepuasan mahasiswa terhadap perkuliahan secara daring dengan menerapkan teknik data mining, melakukan klasifikasi tingkat kepuasan pembelajaran secara daring menggunakan pendekatan algoritma k-nn dan decision tree dengan 100 data kuesioner yang telah dikumpulkan dari mahasiswa aktif yang melaksanakan perkuliahan secara daring dengan tingkat akurasi dari algoritma k-nn sebesar 96.00% serta nilai presisi puas sebesar 95.51%, nilai recall puas sebesar 98.84% pada nilai presisi kelas tidak puas sebesar 90.91%, nilai recall kelas tidak puas 71.43%. sedangkan hasil akurasi menggunakan pendekatan algoritma decision tree lebih rendah dengan akurasi sebesar 95.00%. berdasarkan hasil penelitian bahwa tingkat kepuasan mahasiswa terhadap pembelajaran jarak jauh atau daring cukup tinggi. kata kunci: covid 19, data mining, daring, k-nn, decision tree abstract abstra covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. based on this, the ministry of education and culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k nn algorithm and decision tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k -nn algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. while the accuracy results using the decision tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high. keywords: covid 19, data mining, online, k-nn, decision tree introduction covid-19 has an impact on all lines of economic, social and educational life (damanik, sumijan, & nurcahyo, 2021). in the world of education there has been a significant change in teaching and learning activities in connection with the policies of the ministry of education and culture give an appeal to stop teaching and learning activities face-to-face at school and 106 college and replaced by doing distance learning or online. so it requires carrying out. so that requires carrying out lectures at home by applying distance learning by utilizing the internet as well as virtual applications that can help teaching and learning activities, but sometimes there are obstacles in the process, both technically and non-technically (mulyana, rainanto, astrini, & puspitasari, 2020). barriers to the online process are related to the readiness of the government and the community in the educational environment to quickly adapt to changes in the existing learning system (putu et al., 2021). online learning is the implementation of distance education in higher education aimed at equalizing access to quality learning (napaswati, 2020). some universities have implemented an online learning system, but there are pros and cons in the process, one of which is experienced by students in online learning, there are things to complain about, mainly because of inadequate internet or unsupported devices, causing the teaching and learning process to be disrupted and ineffective. (susilowati, sabariah, & gozali, 2015). the effect of changing the learning model from face to face to online causes less effective teaching and learning activities due to the lack of direct interaction, it is necessary to re-adapt to find out the compatibility of learning media with learning methods (musu, simpen, & samsie, 2021). related research to measure the level of student satisfaction with online lectures using the nave bayes algorithmic approach (natuzzuhriyyah & nafisah, 2021). determining the level of activeness of students participating in online training using the k-mean algorithm (rohman & mujiyono, 2020). prediction of online learning outcomes during the pandemic using the c4.5 . method (fitriani, defit, & nurcahyo, 2021). analysis of public opinion on policies for handling the covid-19 pandemic using the k-nn algorithm (aulianita, 2016). this study aims to classify student satisfaction with distance or online learning, by applying data mining techniques and the k-nn algorithm and decision tree approaches. research methods research using the concept of knowledge discovery in database (tsytsarau & palpanas, 2012). to classify student satisfaction with online lectures at the university of nusa mandiri with the following stages: types of research this study uses quantitative data in the form of questionnaires distributed to 6th and 7th semester students at nusa mandiri university. research target / subject the population in this study were 6th and 7th semester students at nusa mandiri university by randomly distributing 100 questionnaires. the sample used is a questionnaire that has been filled out by students from the period 21 march 2021 14 june 2021 with 100 data. the sampling technique used is snowball sampling, which is a method to get a sample where the first selected sample is asked to choose and forward it to the group that matches the criteria to be used as a sample. (susilowati et al., 2015). procedure the following is a framework that is carried out based on the steps and procedures of this research process (takdirillah, 2020). figure 1. knowledge discovery in database figure 1 describes the kdd process that is used as a way to find and identify a pattern in the data where in this study the data came from a questionnaire where the data was selected and cleaned of noise, then the selected data was transformed so that the data mining process could be carried out. the data mining process is carried out by searching for patterns or information from data using data mining methods or algorithms, finally at the interpretation/evaluation stage is the process of translating patterns that have been generated from data mining and presented in a form that is easily understood by interested parties. 107 1. data the data used comes from questionnaires that have been distributed to students of nusa mandiri university semesters 6 and 7, to classify satisfaction with the distance or online learning system. 2. data selection & cleaning aims to process data derived from the results of questionnaires and select data that can be used in research. 3. data transformation data that has gone through the selection and cleaning process will be transformed into a separate file with the xls format so that the data is ready for the mining process. 4. data mining the transformed dataset will look for the right algorithm approach by comparing the two algorithms, namely k-nn and decision tree to see the level of accuracy generated from the two algorithms using rapidminer studio tools. 5. interpretation / evaluation the data that has been modeled on the k-nn algorithm and decision tree will be visualized to make it easier to compare the best accuracy levels of the two algorithms. data, instruments, and data collection techniques this study uses quantitative data in the form of questionnaires distributed to students of nusa mandiri university semester 6 and 7 from the period 21 march 2021 14 june 2021 as much as 100 data, in the process using rapid miner studio version 9.8.001 tools. data analysis technique this study uses a quantitative research approach because it uses a lot of numbers from the beginning of data collection, then transforms the data and performs data visualization (palupi, 2021). this study uses quantitative data analysis techniques by conducting interviews with students who do distance or online learning and observing activities during learning and distributing questionnaires via google form to 6th and 7th semester students participating in online learning. there are 100 students who fill out the questionnaire and the resulting data is 100 data. based on that, a study was designed to classify student satisfaction with distance or online learning systems. results and discussion the following is a sample of the data used in the study. table 1. student questionnaire data sample name jk smt a b c d e andi muhamad yusup l 7 23 19 30 30 25 kandai l 7 25 20 30 30 25 andry stevanus p l 7 18 13 19 23 18 rian drevianto l 7 19 15 23 23 19 achyar ibnu hadjar l 7 18 15 22 21 11 santoso l 7 25 29 27 30 25 heny p 6 21 19 24 25 20 thesalonika apresa p 6 22 18 25 29 21 yuliana dwi puspitasari p 7 21 15 23 29 21 yoshua kristiawan l 7 19 16 26 28 22 table 1 is a sample of research data consisting of name column, jk column is gender, smt column is semester, column a is tangibles value, column b is reliability, column c is responsiveness, column d is assurance value and column e is empathy value. k-nearest neighbor the basic concept of k-nn is the search for the shortest distance between the data to be evaluated with the k closest neighbors, then the value of the distance between the test data and the training data is sorted from the lowest value, the sorting process is carried out to select a minimum distance of k (nasution, khotimah, & chamidah, 2019). the value of k used in this study is k = 3. the equation for the calculation is as follows: 𝑑(𝑥, 𝑦) = √∑ (𝑥𝑖 − 𝑦𝑖 ) 2𝑚 𝑖=1 …………………...………... ( 1) description:: d : sample distance xi : knowledge sample data pj : data input var to i n : number of samples figure 2. k-nn algoritma algorithm validation process 108 figure 2 is a validation process by applying the k-nn algorithm modeling to get an accuracy value, with a value of k=3. figure 3. the results of the accuracy of the k-nn algorithm figure 3 explains that the results of the accuracy of the k-nn algorithm are 96.00% with the results of class precision predictions being dissatisfied at 100.00%, class precision predictions being satisfied 95.56% and class recalling true being satisfied at 100.00%, class recalling being being dissatisfying 71.43%. based on the results of the questionnaire data, it shows that the classification for distance or online learning satisfaction is 86 data and the questionnaire data referred to in the dissatisfied prediction is 4 data. questionnaire data satisfied with predictions of dissatisfied as much as 0 data and questionnaire data dissatisfied with predictions of dissatisfied as much as 10 data. decision tree generally, the decision tree algorithm is used to build a decision tree by selecting an attribute that will be the root attribute and each branch will represent the result for the attribute value, break down the cases on the branch and repeat the process until all cases on the branch have received the same class. (rosandy, 2016). the equation for the calculation is as follows: 𝐸𝑛𝑡𝑟𝑜𝑝𝑦(𝑆) = ∑ − 𝑝𝑖 ∗ 𝑙𝑜𝑔2 𝑝𝑖 𝑛 𝑖−1 …………............. (2) description: s : case set; n : number of partitions s ; pi : the proportion of si to s figure 4. decision tree algorithm validation process figure 4 is a validation process by applying decision tree algorithm modeling to get the accuracy value. figure 5. decision tree algorithm accuracy results figure 5 explains that the results of the decision tree algorithm accuracy are 95.00% with class precision predictions satisfied 95.51%, class precision predictions dissatisfied 90.91% and class recall true results satisfied 98.84%, class recall true dissatisfied 71.43%. based on the results of the questionnaire data, it shows that the classification for distance or online learning satisfaction is 85 data and the questionnaire data referred to in the dissatisfied prediction is 4 data. the questionnaire data is satisfied with the prediction of being dissatisfied with 1 data and the questionnaire data is not satisfied with the prediction of being dissatisfied with as much as 10 data. conclusions and suggestions conclusion qualification results from this study using the k-nn algorithm get an accuracy value of 96.00% with a class precision prediction of dissatisfied at 100.00%, class precision prediction is satisfied 95.56% and class recall true results are satisfied 100.00%, class recall true is not satisfied 71.43%. while the results of the accuracy of the decision tree algorithm are 95.00% with the results of class precision prediction satisfied 95.51%, class precision prediction dissatisfied 90.91% and class recall true results satisfied 98.84%, class recall true dissatisfied 71.43%. based on the results obtained, the accuracy level using the k-nn algorithm has a higher level of accuracy compared to the accuracy results generated by the decision tree algorithm. it can be concluded that the k-nn algorithm with k=3 can be used as an appropriate approach model for predicting student satisfaction with distance or online lectures with a high level of accuracy, so that it can help universities to see the level of 109 student satisfaction with distance or online learning systems. suggestion future research is expected to further explore varied data and a larger number of samples, can also optimize for the level of accuracy by adding features and applying optimization techniques as well as conducting trials with different algorithmic approaches to see the advantages of the algorithm in the case of the classification of student satisfaction levels. towards distance or online learning. references aulianita, r. 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(2012). survey on mining subjective data on the web. data mining and knowledge discovery, 24(3), 478– 514. https://doi.org/10.1007/s10618-0110238-6 225 university service website quality measurement with webqual 4,0 (case study: faculty of business and economics islamic university of indonesia) nur hamid sutanto1, kusrini2, asro nasiri3 1,2,3master of informatics engineering, faculty of computer science, universitas amikom yogyakarta ringroad utara condong catur, depok, sleman, diy, indonesia 1hamid.alsa@gmail.com 2kusrini@amikom.ac.id 3asro@amikom.ac.id abstrak implementasi website sebagai media layanan menjadi sangat populer digunakan dalam perkembangan di perguruan tinggi khususnya di masa pandemi covid-19 yang menerapkan protokol kesehatan. dengan adanya sistem informasi layanan berbasis website memungkinkan untuk tetap menjalankan proses bisnis dalam pelayanan terhadap penggunanya tanpa harus datang dan menghidari interaksi langsung atau tat ap muka. fbe uii mengimplementasikan sistem informasi website layanan untuk melayani mahasiswa secara daring yang menggantikan sistem manual yang dilayani secara langsung dan tatap muka. pertama diimplementasikan sampai dengan sekarang belum pernah dilakukan pengukuran terhadap kualitas layanan yang berasal dari penggunanya. pengukuran menggunkan metode webqual 4,0 yang mengukur penilaia n pengguna terhadap 3 area yaitu kegunaan (usability), kualitas informasi (information quality), kualitas interaksi layanan (service interaction quality) yang berpengaruh terhadap kepuasan pengguna (user satisfaction) dengan parameter pengguna mau menggunakan website layanan. penelitian ini dilakukan di fbe uii. pengumpulan data penelitian menggunakan kuesioner. populasi adalah mahasiswa fbe uii yang pernah menggunakan website layanan. hasil penelitian ini berupa kesimpulan penerapan website sukses untuk menggantikan sistem yang sebelumnya digunakan, kualitas layanan berbasis website pada sistem informasi baik, persepsi penilaian pengguna mudah dalam berinteraksi, dan rekomendasi langkah strategis yaitu melakukan migrasi layanan lain dengan memanfaatkan website layanan terpadu. kata kunci: kualitas website; perguruan tinggi; website layanan; webqual 4.0 abstract the implementation of the website as a service medium has become very popular in the developments of universities, especially during the covid-19 pandemic, which implements health protocols. with the existence of a website-based service information system, it is possible to continue running business processes in service to users without having to come and avoid direct or face-to-face interactions. fbe uii implements a website service information system to serve students online, which replaces the manual system that is served directly and face-to-face. firstly, it is implemented until now, there has never been a measurement of the quality of service that comes from its users. the measurement used the webqual 4.0 method which measures the user's assessment of 3 areas, namely usability, information quality, service interaction quality which affects user satisfaction with the parameter of the users using the service website. this research was conducted at the fbe uii. research data collection used a questionnaire. the population was fbe uii students who had used the service website. the results of this study were the conclusions of successful website implementation to replace the previously used system, the quality of website-based services on information systems was good, the perception of user ratings was easy to interact with, and recommendations for strategic steps, namely migrating other services by utilizing integrated service websites. keywords: website quality; university, service website; webequal 4.0 introduction the development of online service information systems using advances in websitebased information technology is the demand of every institution or organization is facing the current digital era. most institutions and organizations are already providing services by utilizing information systems. stakeholders have a great influence on the success rate of implementing http://creativecommons.org/licenses/by-nc/4.0/ 226 information systems in an institution or organization. stakeholders or person who has interest play an important role in the design, development, and implementation of information technology, both directly and indirectly in decision making (hermanto, rahmat, & riyanto, 2018). this becomes important because the development of information technology is very dependent on stakeholder policies. in addition, it needs have a measurement that is used to evaluate the success of the implementation of an information system implemented in an institution or organization, especially regarding the positive impact that can be felt by service users (sapty rahayu, apriliyanto, & sigit purnomo wuryo putro, 2018). one of the solutions in the development of the digital era that demands easy access is a service information system using a website in the application of a user service information system. many websites are found that do not provide satisfaction from their users because they are not by the original purpose of the website being created (santiari & rahayuda, 2018). it means that the application is not for the original purpose of the website. in other research, it explained that the initial role of the website is as an information medium for an institution or organization. along with technological developments, the website has turned into a service media that has proven to be able to provide facilities for users to get services quickly and easily (tristiyanto, saputri, & iqbal, 2020). it is in line with the need for special measurements of website parameters. the important stage in website development is the stage of measuring the quality of the website by using the appropriate parameters to obtain recommendations for implementation (hidayat & suryani, 2020). to carry out the evaluation, researchers used the standards of the webqual 4.0 method framework, which measures the criteria for usability, information quality, and quality of service interactions that have an influence on user satisfaction to be used in evaluating the implementation and recommendations of system development. in other research, it is stated that webqual 4.0 can be used to measure and analyze the quality of the website through stages such as questionnaire data collection, data processing, data testing, validation process, and analysis (rerung, fauzan, & hermawan, 2020). in the end, it will show an assessment of the quality of a service website that comes from stakeholders and users. it explains that webqual is one of the methods that can be used in evaluating service websites that come from user assessments (tristiyanto et al., 2020). the business process carried out by universities with the use of information systems is something that cannot be avoided (sutanto, 2021). thus, the application of information systems becomes an important part of running the business processes of university organizations (sutanto, setiawan, rakhman, utami, & mustafa, 2020). the faculty of business and economics, islamic university of indonesia uses information system for student letters to provide active student letters at the undergraduate level for both regular and international students. in its development, this information system is developed to be used by students of diploma, masters, and doctoral programs. this system was built in august 2019 and began to be effectively used in a transitional period on january 1, 2020. within three months, the program was officially in full use and replaced the manual system. in this case, there are considerations in evaluating the quality and success of the service website as well as capturing the perceptions of the assessment from the aspect of service utilization that comes from its users. this research measures the success of the application or implementation of the use of the information system for student letters at fbe uii and at the same time recommendations for system development using the webqual 4.0 measurement method approach. this method is used to evaluate the website-based service information system which sees at the 3 assessment areas of the user. from the model used, it is hoped that conclusions can be drawn in the form of measurement analysis of the implementation of web-based service information systems as well as recommendations for future information systems. research methods the webqual 4.0 method is a method based on the assessment of user satisfaction in the process of measuring the quality of website services. the analysis of the results of webqual 4.0 leads to 3 (three) areas, namely the usability area, the information quality area, and the service interaction quality area (barnes & vidgen, 2002). in assessing website user satisfaction, the quality of the website can be described by the usability aspect which is manifested in webqual 4.0 because this aspect emphasizes the assessment from the user's point of view and perceptions related to two other related aspects namely the quality of information and the quality of service interactions (kurniawati, kusyanti, & mursityo, http://creativecommons.org/licenses/by-nc/4.0/ 227 2018). furthermore, webqual 4.0 is described in figure 1. figure 1. the relationship of webqual 4.0 area measurement model this type of research is a quantitative study that includes assessment data in the form of numbers. to obtain this data, the researchers used surveys or data collection or information from a predetermined population (priyono, 2016). a descriptive approach is also used to describe the real conditions that exist in the research object during the implementation of the system. sampling and population method a method used in sampling data is usually called a sampling technique. in this research, researchers used the purposive sampling technique or subjective sampling. purposive sampling is a method of sampling that uses criteria and assessments from the researchers themselves in the process of selecting members of the population to be used in the research. purposive sampling is included in the non-probability criteria (sugiyono, 2015). in this case, the researcher determines the criteria for respondents, namely fbe uii students who have used the website service information system for student letters. the slovin method is used to determine the size or number of samples to be used in the research. slovin formula (sujarweni, 2014) can be seen in below: 𝑛 = 𝑁 1+(𝑁𝑥𝑒2) .............................................. (1) description: n : sample size n : population e : detachment looseness percentage because of error in taking sample wanted validity test the validity test is used to test the accuracy of the test equipment or size in evaluating data. data is said to be accurate and has high validity in measurement if it can fulfill and describe the measured variables as expected (ashari, pradana, & wahyuni, 2020). in this test, researchers used the ibm spss software. reliability test a reliability test is a test that aims to measure the consistency of the questionnaire data (kurniawati et al., 2018). in this test, researchers used the ibm spss software. data analysis in this research, researchers used user satisfaction assessment aspects which included three assessment aspects or independent variables, namely usability, information quality, and service interaction quality, and one dependent variable, namely user satisfaction (roliani, tristiyanto, & heningtyas, 2018). the hypothesis used to measure the quality of website services is determined as follows:  h1: there is a relationship between usability and user satisfaction.  h2: there is a relationship between information quality and user satisfaction.  h3: there is a relationship between service interaction quality and user satisfaction. research flow in this research, several stages will be done based on the research flow as seen in figure 2 as follows: figure 2. research flow http://creativecommons.org/licenses/by-nc/4.0/ 228 results and discussion in collecting questionnaire data, researchers compiled several questions using the webqual 4.0 standard. as a result, the list of questions can be seen in table 1 below. table 1. webqual 4,0 questionnaire no. question score a. usability (x1) 1 the fbeuii.id/siso-fe website is easy to operate 1-5 2 interaction with the fbeuii.id/siso-fe website is clear and understandable 1-5 3 the fbeuii.id/siso-fe website has easy navigation (easy to find menus on the website) 1-5 4 the fbeuii.id/siso-fe website address is easily accessible 1-5 5 the fbeuii.id/siso-fe website has an attractive appearance 1-5 6 the information layout on the fbeuii.id/siso-fe website is appropriate 1-5 7 the fbeuii.id/siso-fe website has complete facilities or features 1-5 8 the fbeuii.id/siso-fe website provides a positive experience for users 1-5 b. information quality (x2) 1 the fbeuii.id/siso-fe website provides clear enough information 1-5 2 the fbeuii.id/siso-fe website provides reliable information 1-5 3 presentation of information on the fbeuii.id/siso-fe website is always up to date 1-5 4 the fbeuii.id/siso-fe website provides relevant information 1-5 5 the fbeuii.id/siso-fe website provides accurate data information 1-5 6 the fbeuii.id/siso-fe website provides detailed information 1-5 no. question score 7 information on the fbeuii.id/siso-fe website is presented in the appropriate format 1-5 c. interaction quality (x3) 1 the fbeuii.id/siso-fe website has a good reputation 1-5 2 the fbeuii.id/siso-fe website is safe from viruses and the like 1-5 3 any data inputed on the fbeuii.id/siso-fe website is kept confidential 1-5 4 the fbeuii.id/siso-fe website provides space for personalization 1-5 5 the fbeuii.id/siso-fe website provide convenience to communicate with the faculty 1-5 6 the fbeuii.id/siso-fe website guarantees a high level of trust in the information presented 1-5 d. entirety (y) 1 has the information system of student letter website or fbeuii.id/sisofe successfully replaced the manual student letter service that was previously used? yes, no, possible 2 the information system of student letter website or fbeuii.id/siso-fe is overall good 1-5 the questionnaire was distributed via email to the respondents and conducted online using google form media to make it easier to reach. sampling dan population method the population of respondents is students of the faculty of business and economics uii from the undergraduate level with the criteria of having used a service website during the month of january 2021. thus, the calculation is carried out as follows: n = 100 e = 0.10 𝑛 = 100 1 + (100𝑥0.102) http://creativecommons.org/licenses/by-nc/4.0/ 229 from this formula, the size or number of samples that will fill out the questionnaire is 50 respondents. the hypothesis used to measure the quality of website services is determined as follows: h1: there is a relationship between usability and user satisfaction. h2: there is a relationship between information quality and user satisfaction. h3: there is a relationship between service interaction quality and user satisfaction. validity test in conducting the validity test, researchers used 30 respondents with 21 questions in the questionnaire. the significance level for the test was 0.2960 which was carried out with the ibm spss statistics software. the result can be seen in table 2 as follows: table 2. validity test question rxy rtabel conclusion pa1 .371* 0.2960 valid pa2 .792** 0.2960 valid pa3 .710** 0.2960 valid pa4 .484** 0.2960 valid pa5 .739** 0.2960 valid pa6 .771** 0.2960 valid pa7 .729** 0.2960 valid pa8 .506** 0.2960 valid pb1 .746** 0.2960 valid pb2 .619** 0.2960 valid pb3 .672** 0.2960 valid pb4 .711** 0.2960 valid pb5 .793** 0.2960 valid pb6 .812** 0.2960 valid pb7 .662** 0.2960 valid pc1 .876** 0.2960 valid pc2 .530** 0.2960 valid pc3 .562** 0.2960 valid pc4 .908** 0.2960 valid pc5 .624** 0.2960 valid pc6 .703** 0.2960 valid based on the results of the validity test conducted on 21 questions to 30 initial respondents, it can be concluded that all questions presented were valid or accepted. reliability test the reliability test results can be seen in table 3 and the reliability test statistics can be seen in table 4 as follows: table 3. reliability test rxy n % cases valid 30 100.0 excludeda 0 .0 total 30 100.0 table 4. reliability test statistics cronbach's alpha n of items .941 21 based on the results of the reliability test carried out, it can be concluded that all the questions presented are reliable. data collection after the validity test results and reliability test results showed valid and reliable results, the presentation of the data provided was based on the results of distributing the questionnaires obtained which were quantitative. the data collection process is carried out by distributing questionnaires in the form of online to students who use the service website. the demographics of the respondents' profiles can be seen in table 5. table 5. respondents' profile sample size amount percentage (%) study program accounting 18 33.96 management 22 41.51 economics 13 24.53 total 53 100 batch 2014 1 1.89 2015 1 1.89 2016 3 5.66 2017 20 37.74 2018 14 26.42 2019 8 15.09 2020 6 11.32 total 53 100 gender man 19 35.85 woman 34 64.15 total 53 100 data analysis in analyzing the data, previously the independent variable was correlated with the dependent variable. the correlation coefficient aims to see the strength of the linear relationship and the direction of the relationship between two http://creativecommons.org/licenses/by-nc/4.0/ 230 random variables. if the correlation coefficient is positive, the two variables have a unidirectional relationship. it means that, if the value of the x variable (independent) is high, the value of the y variable (dependent) will be high. based on multiple linear regression analysis using ibm spss software, the output used for analysis is as follows: figure 3. correlation analysis output based on figure 3, it can be said that the correlation between the user satisfaction and usability variables was 0.499. the correlation between user satisfaction with information quality was 0.350. the correlation between user satisfaction with service interaction quality was 0.685. the average correlation was 0.511. this indicated that the relationship between the x variable and y variable was moderately positive or it can be interpreted that the two variables were dependent. figure 4. table of goodness of fit model figure 4 shows the measure of the goodness of fit model, which is shown by the value of adjusted r square. if the simple linear regression is shown by the value of r square, multiple linear regression analysis should be measured through the value of adjusted r square. in figure 3, it can be seen that the adjusted r square value was 0.710, which means that the y diversity could be explained by x1, x2, and x3 of 71% or it could be said that user satisfaction could be explained by usability, information quality, and service interaction quality of 71 %. while the remaining 29% was in other aspects or areas outside the model used. h1: there is a relationship between usability and user satisfaction based on the results of hypothesis test 1, it can be concluded that usability (x1) affects customer satisfaction (y) of the fbe uii student letter service website. it is proven by the positive relationship between usability and user satisfaction. respondents assumed that the http://creativecommons.org/licenses/by-nc/4.0/ 231 usability aspect of the website influences customer satisfaction with the perception of its design. in this usability aspect, users perceived that the website appearance is attractive so that it is considered easier to use compared to the previous service system which was done manually. h2: there is a relationship between information quality and user satisfaction. based on the results of hypothesis test 2, it can be concluded that the information quality (x2) influenced customer satisfaction (y), the website of fbe uii student letter services. this was proven by the positive relationship between information quality and user satisfaction. respondents assumed that it would be better if a website could be assessed by the quality of the content, have a level of consistency, accuracy, and attractiveness in providing information. h3: there is a relationship between service interaction quality and user satisfaction. based on the results of hypothesis test 3, it can be concluded that service interaction quality (x3) influences customer satisfaction (y) on the website of the fbe uii student letter. it is proven by a positive relationship between information quality and user satisfaction. respondents assumed that service interaction quality is an important factor for users because it provides security in using the service website. thus, the user which was the fbe uii students felt secure and did not worry about using the service website again in proposing student letter. conclusions and suggestions conclusions based on the data analysis above it can be concluded that 3 areas influenced the satisfaction of website users which included usability, information quality, and service interaction quality. it was proven by the positive influence between the dependent variables, namely user satisfaction and independent variable in the 3 areas of the webqual 4.0 method in the analysis. positive results were also shown from the hypothesis carried out between the x variable and y variable. it can be said that the service website was successful in replacing the previous system because it had a positive influence, the user's assessment of the service website was good, and users felt secure and easy; thus, users decided to reuse the service website. suggestions based on the results of the assessment derived from user perceptions and analysis and hypotheses that showed a positive relationship toward the service website, the use of the service website to serve the integrated organization service activities in one integrated service entrance was named the integrated service portal website. references ashari, m. r., pradana, d. s., & wahyuni, e. d. 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(2020). implementasi metode webqual dan customer satisfaction index untuk mengevaluasi website perguruan tinggi negeri di bandar lampung. klik kumpulan jurnal ilmu komputer, 7(1), 57. https://doi.org/10.20527/klik.v7i1.303 http://creativecommons.org/licenses/by-nc/4.0/ 377 e-information system design of student admission in american indonesian educational institution cijantung branch anggi oktaviani1*), dahlia sarkawi2, deny novianti3, achmad chaerul amri4 teknik informatika universitas nusa mandiri anggi.aov@nusamandiri.ac.id1*) administrasi perkantoran / sistem informasi universitas bina sarana informatika dahlia.dls@bsi.ac.id2, denynov.dov@bsi.ac.id3, achmadchaerul29@gmail.com4 (*) corresponding author abstrak saat ini perusahaan, organisasi dan institusi sangat membutuhkan perkembangan teknologi dan sistem informasi khususnya teknologi informasi melalui media internet.. lembaga pendidikan indonesia amerika cabang cijantung yang sampai saat ini masih belum terkomputerisasi. saat ini, proses registrasi menjadi peserta didik di lembaga pendidikan indonesia amerika cabang cijantung tergolong masih manual baik dari pengisian formulir yang disediakan oleh lembaga pendidikan indonesia amerika cabang cijantung sampai menjadi peserta didik. biasanya proses pendataan sedikit terhambat dikarenakan jumlah pendaftar lebih banyak dibandingkan customer service dan terkadang customer service kesulitan dalam membaca formulir calon peserta didik. hal ini menyebabkan pelayanan dari customer service lembaga pendidikan indonesia amerika cabang cijantung tidak optimal. penelitian ini menggunakan menggunakan model waterfall sebagai model pengembangan perangkat lunak. dari penelitian yang dilakukan diharapkan dapat menghasilkan sebuah sistem pengendalian sistem penerimaan peserta didik baru yang dapat membantu customer service dalam pendataan calon peserta didik dan mengelola formulir calon peserta didik. perancangan sistem penerimaan peserta didik baru diharapkan menjadi lebih efektif dan efisien. sehingga baik saat proses pendaftaran dapat diselesaikan dalam waktu yang tidak lama. sistem ini juga diharapkan bisa meminimalisir kesalahan pendataan yang dilakukan oleh customer service. kata kunci: perancangan sistem informasi, sistem penerimaan peserta didik, waterfall abstract currently, companies, organizations and institutions are in dire need of the development of technology and information systems, especially information technology through the internet. the american indonesian education institute, the cijantung branch, which until now has not yet been computerized. currently, the registration process to become a student at the american indonesian education institute cijantung branch is still manual, both from filling out forms provided by the american indonesian education institute cijantung branch to becoming a student. usually the data collection process is a bit hampered because the number of applicants is more than customer service and it is too difficult for customer service to read prospective student forms. this causes the service from the customer service of the american indonesian education institute cijantung branch is not optimal. this study uses the waterfall model as a software development model. from the research conducted, it is expected to produce a new student acceptance control system that can assist customer service in collecting data on prospective students and managing prospective student forms. the design of the new student admission system is expected to be more effective and efficient. so it's good when registration can be completed in a short time. this system is also expected to minimize data collection errors made by customer service. keywords: information system design, student admission system, waterfall 378 introduction technological developments from time to time are increasingly rapid, marked by the number of existing innovations such as smart devices that can be carried anywhere by hand (sidik & rahmawati, 2018). therefore, the development of technology that has accelerated we must make good use to be used in facilitating human work in any field. the internet is a medium that can bring negative and positive changes, it depends on how we take benefits to support work patterns so that they can produce better changes (ramdhan & wahyudi, 2019). one of the positive changes of the internet is that it makes it easier for people to know things in real-time and also quickly. we must make good use of the internet today, not to be misused for things that are not important. this convenience and sophistication are often used in everyday life (purnia et al., 2019), one of which is in the world of education which is one of the aspects that most take advantage of technological advances. admission of new students is one of the processes that exist in the educational environment that is used to screen suitable new students who will later become students (jimmie et al., 2018). the process of accepting new students using the sophistication of the internet provides convenience for parents, and also students when going to apply to the intended school. since everything is already online, there is no need to come to school and queue to register new students (ismi et al., 2020). so that this creates competitive competition between every educational institution, including private educational institutions (witanto & solihin, 2016). there are many ways to get an education, ranging from formal and non-formal education. informal education, learners must follow the selection of new student admissions held by the school to be able to study at the school (sarwosri et al., 2020). there are many ways to take education, ranging from formal and non-formal education. informal education, students must take part in the selection of new student admissions held by the school to be able to study at the school (sarwosri et al., 2020). there is also non-formal education that comes as a complement to formal education such as courses or training institutions. the purpose of implementing psb online is to create transparency in new student admissions and ease the selection of new students. in terms of processing data and information, these schools have indeed used a computerized system, but this is only limited to certain parts, such as typing reports related to schools or making education letters only (astuti et al., 2014). one of the conveniences felt from the acceptance of new students online is that students and parents no longer need to come to school and queue while carrying some files to register their children. because with the acceptance of students online, parents and students can register from home or from anywhere as long as there is internet access. this manual process of admitting new students is considered less effective and efficient, both for the new student admissions committee and for prospective new students themselves. this is because the process of implementing the acceptance of new students can take time and energy (alaih et al., 2020). the acceptance of new students manually will certainly be more draining time and energy, because the number of students who will apply and also with the deadline given by the government makes the school overwhelmed when the process of accepting new students. gap analysis (gap or gender analysis pathway) is a comparison of actual performance with potential or expected performance. this method is a business evaluation tool that focuses on the gap in the current performance of government agencies with previously targeted performance, for example, those already listed in the business plan or annual plan for each function of the government agency. the gap analysis also identifies what actions are needed to reduce the gap or achieve the expected performance in the future (nugraha & yuniawan, 2017). by using this method as an evaluation tool of the program that has been created, it is expected that it can hereby find out whether the program is appropriate or not with the expected target. the american indonesian education institute, the cijantung branch, was established several years ago, which is located on jalan haji hasan baru, pasar rebo, east jakarta. currently, the registration process to become a student at the cijantung branch of the american indonesian education institute is still manual, both from filling out the forms provided by the american indonesian education institute, the cijantung branch, to becoming a student. the problem that occurs is that the new student admissions process (psb) is still using the conventional system, so that prospective students sometimes have difficulty getting information to carry out the registration process (rosmiati, 2020). this causes the customer service of the american indonesian education institute cijantung branch to be not optimal. likewise, the process of making new student admission selection reports takes longer (arum ningtyas et al., 2018). another problem that often arises is the existence of a long document 379 administration process, less integrated or even lost. this is because of the number of documents that must be handled during the admission of new students, then the loss of documents can occur. from the problems faced, the american indonesian education institute cijantung branch requires an information system that has been computerized, because it is very useful, especially in the acceptance of prospective new students. acceptance of new prospective students using a computerized system can help students, including the american indonesian education institute, cijantung branch because data and information for prospective new students can be accessed anywhere via the internet so that it can save time and energy. the process of accepting new students is expected to be more effective and efficient. this system is also expected to minimize data collection errors made by customer service. research methods the data collection methods used in this study are: 1. observation method in this study, the authors observed and recorded directly regarding the activities of new student admissions carried out at the american indonesian education institute. 2. interview the author carried out data collection techniques by holding direct questions and answers which were carried out face-to-face with mrs. santi mayasari, s.sos as branch manager at the american indonesian education institute regarding the acceptance of new students. 3. literature review the author collects data from books, journals, and articles on the internet which are intended to obtain materials that are theoretical in nature and serve as the basis for thinking and comparison with practical matters in writing this research obtained from several sources including through the library and the internet. types of research this research uses a qualitative method and case study approach. the main techniques in collecting data are observation and in-depth interviews. the results obtained show that verbal communication used by family members who have not studied indigo more deeply prefer one-way communication and verbal communication is commanding (arkandito et al., 2019). another reason to use qualitative methods is that they can generally be used in research on people's lives and also they can understand the hidden from a phenomenon that is sometimes difficult to understand. time and place of research this research was conducted at the american indonesian education institute, the cijantung branch, which was established several years ago, which is located on jalan haji hasan baru, pasar rebo, east jakarta. research target / subject the purpose of this study is to design a new student admission system at the american indonesian education institute, cijantung branch as a solution to the problem of less than optimal student registration. procedure the method used in this research is waterfall. sdlc model waterfall (waterfall) is often also called a linear sequential model (linear sequential) or classical life cycle (classic life cycle) (sidik & rahmawati, 2018). sdlc model waterfall (waterfall) is often also called a linear sequential model (linear sequential) or classical life cycle (classic life cycle) (tabrani muhamad, 2017). with the waterfall model, we use a software approach in sequence starting from the analysis, design, coding, testing, and also the supporting stage. data, instruments, and data collection techniques m the method used in this research is waterfall. sdlc model waterfall (waterfall) is often also called a linear sequential model (linear sequential) or classical life cycle (classic life cycle) (frieyadie, 2016; sidik & rahmawati, 2018). with the waterfall model, we use a software approach in sequence starting from the analysis, design, coding, testing, and also the supporting stage. the following is an explanation of this model: 1. software requirements analysis the method involved with social occasion prerequisites is done seriously to indicate programming necessities so clients can get what sort of programming is required by the client. programming necessities particulars at this stage should be recorded. 2. design programming configuration is a multi-step process that spotlights the plan of a product program including information structures, programming design, interface portrayal, and coding methodology. this stage translates software requirements from the requirements analysis stage 380 to the design representation so that they can be carried out into a program at a later stage. the product configuration created at this stage likewise should be recorded. 3. program code generation the plan should be converted into a product program. the aftereffect of this stage is a pc program by the plans that have been made at the planning stage. 4. testing testing centers around the product as far as the hotel and practical and guarantees that all parts are tried. this is done to limit mistakes (blunders) and guarantee the subsequent result is ideal. 5. support or maintenance try not to preclude the chance of programming changes when it has been shipped off the client. changes can happen because mistakes that show up and are not recognized during testing of the product need to adjust to the new climate. the help or upkeep stage might rehash the advancement interaction going from particular examination to changes to existing programming, however not to make new programming. results and discussion the following is an explanation of the stages of the waterfall method, namely: 1. software requirements analysis a. new students page 1) new students can access the website of the american indonesian education institute cijantung branch including home, latest information, about us, gallery, contact. 2) new students access the login and register menu, log in for those who already have an account, and register for those who do not have an account to register for new students. 3) new students can enter the new student registration form. 4) new students can upload proof of payment. b. customer service page 1) customer service can log in. 2) customer service can manage new student registration data. 3) customer service can manage proof of payment. c. branch manager page 1) the branch manager can view reports. 2. design in this stage there are three stages of design, namely use case diagrams, activity diagrams, and class diagrams. a. system model design 1. use case diagram proposed here's an overview of the use case that has been created. figure 1. use case diagram figure 1 describes the use case diagram which is the main component in a system. use case diagrams to explain the relationship between actors and the system created. 2. activity diagram here's an overview of the activity diagram that has been created. figure 2. activity procedure payment 381 figure 2 describes the payment procedure activity in the system. the function of the activity diagram is to describe what processes occur in the system. 3. class diagram the following is a description of the class diagram of the e-information system that has been created. figure 3. class diagram figure 3 describes the class diagram in the system. the function of the class diagram is to describe various models with various components and what must exist in a system. b. interface design 1. interface home here's a view of the home page that has been created. figure 4. interface home figure 4 describes the home interface. where this is the page for new students when opening the web of the american indonesian education institute. 2. interface customer service login page the following is the display of the customer service login page that has been created. figure 5. interface customer service login figure 5 describes the login page interface which is a page for customer service to enter the system. 3. interface new student data page the following is the view of the new student data page that has been created. figure 6. interface new student data page figure 6 describes the interface for the new student data page. where on this page customer service can process new student data. 4. interface branch manager login page the following is the view of the branch manager login page that has been created. 382 figure 7. interface branch manager login figure 7 depicts the branch manager login page. where on this page only branch managers can only access it. 5. interface report page the following is a view of the report page that has been created. figure 8. interface report page figure 8 describes the report page. here is displayed a report that has been made by customer service. 3. program code generation the web for new student admissions at the american indonesian education institute was designed and built using programming languages, html, css, and sublime applications. where this stage is done by transferring the results of the system design into a predetermined programming language. the following is a little program code that has been made in this research: <?php defined('basepath') or exit('no direct script access allowed'); class web extends ci_controller { function __construct() { parent::__construct(); } public function index() { $data['judul'] = "halaman depan"; $this->load->view('v_header', $data); $this->load->view('v_index', $data); $this->load->view('v_footer', $data); } public function about() { $data['judul'] = "halaman about"; $this->load->view('v_header', $data); $this->load->view('v_about', $data); $this->load->view('v_footer', $data); } } 4. testing at this stage, the author conducts testing of the system that has been created. testing is done by evaluating the system that has been made whether it can run well and as expected. if there are still errors and omissions, they can be corrected immediately. according to pangistu et al in research (novianti et al., 2021). uat is a trying cycle by respondents which is expected to deliver reports that are utilized as proof that the framework being created is ok or not by the respondent, assuming the outcomes are considered to address the issues of the respondent, then, at that point, the application can be applied. testing utilizing uat centers around the course of program info and result. the test was conducted by asking several questions to 15 respondents. then the test results from the respondents were assessed based on 5 categories, namely, a (very good), b (good), c (fair), d (not good), and e (very bad). the following are the results of the tests that have been carried out which can be seen in table 1. table 1. test uat no pertanyaan jawaban a b c d e 1 is the web view of admissions of these new students already interesting? 10 3 2 0 0 2 is the menu on the new student admissions web understandable? 9 5 1 0 0 3 can the menu on the new student admissions web make the admissions process easier? 9 4 2 0 0 4 is the new admissions web easy to use? 10 4 1 0 0 5 is the data from the new student admissions web easy to create a report? 9 3 3 0 0 total 47 19 9 0 0 the results of the recapitulation of answers from all respondents can be seen in table 1. the majority of respondents gave positive responses about the web created, this can be seen from the number of very good answers with a value of 47, while respondents who think quite well are 9. therefore it can be concluded that the web that has been created is well received by users. 383 conclusions and suggestions conclusion so far, data management at the american indonesian education institute, cijantung branch is still using a manual system so that it is still ineffective and inefficient. it is hoped that creating a web design for the new student admissions system, can be problem solving and facilitate the registration process and acceptance of new students at the american indonesian education institute. suggestion in a web, periodic maintenance is needed to reduce errors in the 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(2016). perancangan sistem informasi penerimaan siswa baru berbasis web (studi kasus : smp plus babussalam bandung). jurnal infotronik, 2(1), 54–63. 384 http://183.91.79.105/index.php/infotronik/ article/view/9 371 developing blended learning application utilizing articulate story line 3.0 intregrated with android based system efrizal siregar, nurianti sitorus, juwariah teknik grafika politeknik negeri media kreatif https://polimedia.ac.id/ efrizal_siregar@polimedia.ac.id, nurianti_torus@polimedia.ac.id, juwariah@polimedia.ac.id (*) corresponding author abstrak perkerkembangan ilmu pengetahuan dan teknologi di era digital four point zero memiliki dampak yang besar bagi dunia pendidikan. salah satunya adalah dengan memanfaatkan aplikasi e-learning dalam proses pembelajaran. akan tetapi, teknologi yang digunakan dalam pembelajaran ini terlalu bergantung dengan jaringan internet. oleh karena itu penelitian bertujuan untuk mengembangkan sebuah media berupa aplikasi e-learning berbasis andorid yang dapat digunakan secara online dan offline. penelitian ini menggunakan rancangan penelitian dan pengembangan menggunakan model addie. untuk membuat dan menguji produk tersebut dalam hal ini aplikasi elearning berbsasis android. prosedur penelitian ini dilakukan melalui 6 tahapan yaitu analisis kebutuhan, tahap desaian aplikasi, pengembangan, analasisis data, evaluasi, dan pengembangan produk akhir yang siap untuk digunakan. dari hasil uji validasi materi dan kelayakan media aplikasi yang dikembangkan sangat layak untuk digunakan. kata kunci: aplikasi, blended e-learning, android, offline-online abstract the development of science and technology in the digital era of four point zero has a great impact on the world of education. one of them is by utilizing e-learning applications in the learning process. however, the technology used in this learning is too dependent on the internet network. therefore, the research aims to develop a media in the form of an android-based e-learning application that can be used online and offline. this study uses a research and development design using the addie model. to create and test these products, in this case an android-based e-learning application. procedure this research was carried out through 6 stages, namely needs analysis, application design stage, development, data analysis, evaluation, and development of a final product that is ready to be used. from the results of the material validation test and the feasibility of the developed application media, it is very feasible to use. keywords: aplication, blended e-learning, android, offline-online introduction we are currently entering the digital four point zero era where the use of technology has entered various aspects of life. almost every activity or activity or work has utilized digital technology in its implementation. digital technology in the form of the use of computing connected to the internet makes everything unlimited. many things can be controlled from a distance with digital technology. in addition, the impact of the covid-19 pandemic has also contributed to changes in the pattern of human life (ardhana januar mahardhani, 2020; pantan & benyamin, 2020) to find solutions from the implementation of social distancing by utilizing space on digital technology. this condition also has an impact on the use of information and communication technology which results in more and more new technologies appearing in various aspects of life. rachmad and fragastia mentions that the system used in android is linux, thus providing an open platform for anyone to create their own applications (rahmad & fragastia, 2017). in the world of education, one application that is widely developed on android is the elearning application. the e-learning application is an application used in teaching and learning activities that utilize digital technology in its implementation (harahap, 2015). students can access learning materials in the form of pictures, 372 learning videos, powerpoints, assignments that have been prepared in the e-learning. some of the popular e-learning used are edmodo (ekayati, 2018), moodle (polhun, kramarenko, maloivan, & tomilina, 2021), google classroom (murtikusuma et al., 2019), and quipper (el iq bali et al., 2021). however, the application can only be used if the android device is connected to the internet. if the internet connection is not connected then the application cannot be used. this is one of the obstacles to using e-learning applications. where the internet network has not reached all parts of indonesia and the price of internet quota is relatively expensive and the application used also consumes a lot of internet quota. siregar and manurung in 2020 stated that research shows that there are significant differences in the creativity of prospective teacher students who apply blended learning. thus, it can be said that blended learning has an effect on creativity (siregar & manurung, 2020). khoiroh in 2017 stated that student learning outcomes using the blended learning model were higher than student learning outcomes using the direct learning model. students' motivation in participating in learning using the blended learning learning model is higher than the direct learning learning model. there is an interaction between blended learning and learning motivation on student learning outcomes (khoiroh & anifah, 2017). the results of the research conducted by darmawan and nashoih, showed that the mobile learning media was declared valid and worthy of being used as a learning medium, based on the validation of media and material experts with an average score of 3.46, so it was declared feasible. while the results of the trial showed the effectiveness of the media through the pretest and posttest scores which increased by 26.71, with an average value of 52.39 for pretest and 79.10 for posttest. this figure shows that the application of mobile learning media is able to significantly improve student learning outcomes, as well as support the blended learning model (darmawan & nashoih, 2019). based on the background described above, the researcher wants to develop a blended elearning application, which means it can be used offline and online. online is used when students download the application on the play store available on the android system. while offline is used to access all the material in the application in the form of documents, power points, learning videos and others that are run without using the internet. research methods this research uses quantitative research, with the addie research model (arifin, septanto, & wignyowiyoto, 2018). this research model consists of 5 stages, namely analysis, planning, development, implementation, and evaluation. in this study, researchers only carried out stages 1 to stage 3. the analysis stage is a needs assessment process, identifying problems and conducting analysis using interview instruments to participants (15 people) graphic engineering students who use e-learning applications in the learning process. the design stage (blue print), namely the stage of developing an e-learning application that answers problems at the analysis stage to create an e-learning application that can be used online or offline so that when learning takes place it does not only depend on internet connections (quota). the development stage is the stage for compiling and engineering e-learning applications that have been designed so that android-based e-learning applications can be used, design research model can be seen in figure 1.. analysis design development data analysis data interpretation problem: an e-learning application solution determination of the e-learning data interpretation data analysis data analysis data analysis figure 1. research model types of research this research uses a quantitative approach . his research uses quantitative research, with the addie research model. this research model consists of 5 stages, namely analysis, planning, development, implementation, and evaluation. the development of learning media contains 3 main components, namely (hikmah, saridewi, & agung, 2017). 373 a. development style this is the basis for improving the product to be produced. model development model can be in the form of procedural models, conceptual models and theoretical models. b. development procedure the development procedure is different from the development model because the development procedure shows the properties of the components in each development session, explains analytically the use of components in each development session and explains the bonds between components in the system. c. product trial. the product trials tested were expert testing (expert validation), limited-scale trials and broadscale trials. time and place of research this research will be carried out at the state polytechnic of creative media psdku medan in the graphic engineering study program. held in may-november 2021. research target/subject the target of this research is a product in the form of artyculate story line 3.0-based learning media that can be used online and offline specifically in the graphic material knowledge course. the population of this study were graphic engineering students totaling 15 people. while the expert validators are graphic design lecturers and lecturers who teach graphic material knowledge courses. procedure the steps in designing articulate storybased learning media are as follows: the plan, in the process of making this media begins with the planning process. at this stage, first determine the purpose of the learning media created, namely learning media that can be used on-line and off-line. design, from the information and data collected at the planning stage, the next stage you design the front page display and the articulated media page starting from the log-in view. design is necessary to give the site beauty. this can be a combination of unique colors, layouts, fonts used to make the web attractive and not boring for users. figure 2 here's the media background design. figure 2. designing articulate story-based learning media data, instruments, and data collection techniques data collection techniques using primary data and secondary data (janah, sukarelawati, & agustini, 2019). primary data in the form of data on the feasibility assessment of android-based elearning application products by it experts and the substance of the material obtained by questionnaires in the form of an assessment of the resulting e-learning application and data on the responses of lecturers and students to the material contained in the android-based e-learning application. which is generated. data analysis technique primary data and secondary data were then analyzed. the data analysis used is quantitative and qualitative. qualitative data in the form of a questionnaire validation sheet from an expert validator containing assessments, responses, suggestions and input. the eligibility criteria for android-based e-learning applications are expressed in percentages calculated using the formula in equation 1. while quantitative data are obtained from student perceptions through questionnaires which are then analyzed. 𝑃𝑟𝑒𝑠𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑒𝑙𝑖𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦 𝑣𝑎𝑙𝑢𝑒 = 𝑠𝑐𝑜𝑟𝑒 𝑜𝑏𝑡𝑎𝑖𝑛𝑒𝑑 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑠𝑐𝑜𝑟𝑒 × 100% .............. (1) table 1. eligibility of blended e-learning application products no presentage qualification 1 81-100 very supportive 2 61-80 supportive 3 41-60 enough supportive 4 21-40 less supportive 5 0-20 not supportive eligibility of blended e-learning application products. data obtained from expert validation of learning materials has weaknesses: the type of spiritual labor needed described and given examples; full working characteristics are 374 described again; there are no examples of the types of unemployment. less explanationcomplete; efforts to improve the quality of trained workersgrouped for clarity. the steps that recommended is to fix things that are not appropriate suggested material experts with additional comments is to add types of technological unemployment. the conclusion is the validation results are material declared eligible for field trials with revisions according to suggestions. description of expert validation of learning outcome test instrument materials results and discussion the design of learning media based on articulate story line 3.0 was validated by 3 experts from the design department and graphic department at the state polytechnic of creative media who acted as expert validators. the expert validators of this study are material validators and two lecturers as media validators. this validator assesses every component in the articulate story line 3.0-based learning media in accordance with the guidelines for assessing the feasibility of learning media from wahono (2006). the results of the media feasibility assessment by the validator can be observed in table 1. table 2. recapitulation of media feasibility assessment results material aspect no from matery score learning disain 1 clarity of learning objectives 4 2 relevance of learning objectives 4 3 the suitability of the material with the learning objectives 4 4 appropriate use of learning strategies 4 5 interaktive 3 6 motivation study 4 7 kontekstuality 4 8 materi dept 4 9 easy to understand 4 10 sistematic 4 11 clarity of descriptions, discussions, examples, simulations and exercises 4 12 evaluation 4 table 3. recapitulation of media feasibility assessment results media aspect no from media score soft ware aspek 1 effective and efficient in the development and use of learning media. 4 2 maintenace (can be maintained/managed easily). 3 3 usability (easy to use and simple to operate) with or without an internet connection. 4 4 compatibility (learning media can be installed/run on various existing hardware and software) 4 5 reusable (part/all of the learning media program can be reused to develop other learning media) 4 6 communicative (on target and acceptable to the target's wishes) 7 creative in ideas and pouring ideas 4 8 clarity of descriptions, discussions, examples, simulations and exercises 4 9 the suitability of the material with the learning objectives 4 10 audio (narration, sound effects, background, music) 3 11 clarity of descriptions, discussions, examples, simulations and exercises 4 12 layout interactive 3 based on table 1 and table 2 above, the calculation of each criterion is based on the media feasibility assessment guidelines. highest score = 5, and lowest score = 1 there are 4 desired classes, namely very good (sb), good (b), less good (kb) and not good (tb). range = largest data – smallest data = 5 – 1 = 4 interval length (p) = 0.5 based on the calculation above, it can be determined the range of scores and the criteria for the value as follows. criteria score range. a. 4.0 < score 5.0 very good b. 3.0 < score 4.0 good c. 2.0 < score 3.0 not good d. < score 2.0 not good the design of the articulate story learning media which has been assessed by experts and revised, is then tested on graphic engineering students with a sample of 15 students on a limited scale trial. student response data stated the acceptance of the media as an articulate story linebased learning media in the form of webhtml5 which can be used in a blended manner, namely online and offline. when it reaches a score of 2.51. student responses were taken using a questionnaire accompanied by criticism and suggestions as a revision guide given after learning 375 to use the media articulate stroy line 3.0. the results of student responses are shown in table 3. table 4. recapitulation of student responses on trials limited scale no response criteria score range students give feedback percentage (%) 1 very supportive 3,25 < score  4.00 10 66,66% 2 supportive 2,50 < score  3.25 5 33,33% 3 less supportive 1,75 < score  2.50 0 0 4 no supportive 1 < score  1.75 0 0 the number of students with very supportive and supportive criteria 15 100% based on table 2, students gave 100% "supportive" and "supportive" responses to the learning media using the aticulate story line 3.0. the number of respondents with a "strongly supportive" response is greater than the number of respondents with a "supportive" response. the description above explains that the learning media using aticulate story line 3.0 is well received because it reaches a score of 3.81. furthermore, to find out the average score of student responses, it can be seen in table 5. table 5. recapitulation of student responses on trials limited scale no response score mean criteria 1 interest in using media 3,27 very supportive 2 media according to learning objectives 3,47 very supportive 3 easy-to-operate media 3,07 supportive 4 media helps understanding the material 3,00 supportive 5 media can use online or offline 3,20 supportive 6 media with practice questions and new information 3,47 very supportive 7 media increases learning motivation 3,00 supportive 8 media is used indenpendently 3,47 very supportive 9 more effective and efficient media 3,27 very supportive 10 intersest in using media in other materials 3,33 very supportive the score for the ss answer (strongly agree) is 4 the score for the answer s (agree) is 3 the score for the ks answer (disagree) is 2 the score for the ts answer (disagree) is 1 material aspect total score = 34 average score = 2.88 criteria = very good aspects of expert media 1 total score = 34 average score = 2.88 criteria = very good expert media aspect 2 total score = 32 average score = 2.66 criteria = very good based on the ten response items that received the "very supportive" criteria, there were seven statements and 3 items with the "supporting" criteria. so that the learning media product using the aticulate story line 3.0 developed can be accepted by the respondents. conclusions and suggestions conclusion the first, that the development of blended e-learning applications using soft ware articulate story line 3.0 is very helpful in the process of implementing distance learning which can be done online or non-online and based on media tests and material tests concludes that blended e-learning application development with using articulate story line 3.0 software is feasible to use. the second, based on student responses, 90% strongly supports and 10% supports the application of blended e-learning application development using articulate story line 3.0 soft ware. suggestion it is hoped that this research will continue to develop and be used during the distance learning process that will be implemented. it is hoped that researchers can always upgrade any information about distance learning based on articulate story line 3.0. hopefully this research can be an opening in the implementation and development of articulate story-based learning media. references ardhana januar mahardhani, m. k. 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(2021). shift from blended learning to distance one during the lockdown period using moodle: test control of students’ academic achievement and analysis of its results. journal of physics: conference series, 1840(1), 012053. kryvyi rih: iop publishing. https://doi.org/10.1088/17426596/1840/1/012053 rahmad, i. f., & fragastia, v. a. (2017). perancangan navigasi robot berbasis suara menggunakan android. seminar nasional informatika (snif), 1(1), 320–324. retrieved from http://ejournal.potensiutama.ac.id/ojs/index.php/snif/article/view /193/140 siregar, n., & manurung, s. l. (2020). pengaruh blended learning terhadap kreativitas mahasiswa calon guru di universitas negeri medan. edumatika : jurnal riset pendidikan matematika, 3(1), 44–51. https://doi.org/10.32939/ejrpm.v3i1.485 jurnal riset informatika vol. 3, no. 2 march 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 137 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the black box testing and loc method approach in testing and streamlining the patient registration program joosten information system stmik mikroskil https://www. mikroskil.ac.id joosten.ng@mikroskil.ac.id abstrak software yang baik dapat digunakan jika ada pengujian yang tepat. tahap pengujian cukup penting karena software perlu diuji sebelum digunakan oleh pengguna akhir. pada pembuatan software untuk rumah sakit hewan belum adanya validasi dan verifikasi sehingga diperlukan pengujian. penelitian ini menggunakan informasi bagian pendaftaran pasien rumah sakit hewan dan diuji dengan tiga metode black box testing, yaitu equivalence class partitioning (ecp), boundary value analysis (bva), dan decision table ditambah pendekatan loc. hasil pengujian dari ketiga metode tersebut adalah persentase ecp yang tidak valid lebih besar dari yang valid, sehingga perlu diubah lagi batas nilai inputnya. kemudian untuk pengujian bva, persentase yang valid lebih tinggi daripada tidak valid. dalam tabel keputusan dibuat aturan pemendekan antara layanan operasi dan layanan lainnya sehingga menghasilkan status rawat inap dan besaran uang muka secara otomatis tanpa harus memilih lagi dan diuji kembali oleh decision table dengan cara mencocokkan hasil estimasi dari kedua layanan tersebut. kata kunci: bva, decision table, ecp, pengujian, validasi, verifikasi abstract good software can be used if there is proper testing. the testing phase is quite important because the software needs to be tested before it is used by end users. in making software for animal hospitals there is no validation and verification so testing is needed. this study used information on the registration section of veterinary hospital patients and was tested by three black box testing methods, namely equivalence class partitioning (ecp), boundary value analysis (bva), and decision table plus the loc approach. the test results of the three methods are that the percentage of invalid ecps is greater than the valid ones, so the input value limit needs to be changed again. then for bva testing, the percentage of valid is higher than invalid. in the decision table, a shortening rule is made between operating services and other services so that it produces inpatient status and down payment automatically without choose it again and is tested again by the decision table by matching the estimation results of the two services. keywords: bva, decision table, ecp, testing, validation, verification introduction today technological developments are increasingly developing in this modern world. one of them is software development or what is called software. the widespread software applications of the internet and mobile computing have significantly increased the dependence on enabled software systems (xu et al., 2015). one of the stages in software development is software testing. the role of testing activities is very important because testing is one of the activities that must be carried out on software developed before the software is applied by users or end-users. there are three main reasons for the importance of software testing, namely errors or deficiencies in software can occur, the application must be the best, and end-user satisfaction is everything (solution, 2018). testing is the process of checking software, both internally and externally. from the internal side, testing is carried out to see the statements that have been tested. while on the outer side, testing is directed to find errors that arise from the software and ensure that the limited inputs can produce the desired actual results according to the expected results. the increasing demand for software makes the testing aspect always neglected. testing is an important part of the software development process (hierons & member, 2015). the complexity of the software programming logic flow that developers make is one of the problems when entering the testing phase. developers often find it difficult to get http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 jurnal riset informatika vol. 3, no. 2 march 2021 138 the work is distributed under the creative commons attribution-noncommercial 4.0 international license errors in the software they make due to algorithms that are too complex. users sometimes don't care if the software has an error or not. ignoring testing activities in the software results in the results (output) that is displayed sometimes not as expected. ordinary users will tell the developer to change the program they make in order to produce maximum results and satisfactory program performance. joel spolsky (spolsky, 2020), a software developer in new york city, wrote an article discussing the top 5 reasons (wrong) that a tester is not needed. joel is applying for a job at a company led by mr. gleick. joel asked mr. gleick about the pipeline that gleick created. joel sees that gleick's creation has problems such as not using an error correction protocol so that it can damage or crash. but gleick denies that pipeline has no bugs and is just as bad as ms. word from microsoft. both reasons from gleick kept joel from applying to gleick's company. from joel spolsky's case, it can be concluded that the average person ignores the validation, verification, and testing of the software that is made. neglect of software testing makes the software unable to work optimally and many errors occur. this study discusses several methods that can be used to test software so that the software can work optimally. the use of excessive costs in software testing causes the quality of the software to be poor. every year, poor software quality losses exceed $ 500 billion (shahbazi & miller, 2016). the national institute of standards of technology (nist) (rep, 2002) conducted a study in 2012 that explained the united states economy calculates a loss of $ 59.5 billion every year due to bugs in software created. nist explains about one-third of these losses can be avoided if the developers do software testing better. more than half the cost of fixing bugs given to software users is given to developers and vendors (wong et al., 2016). besides the use of large costs, another thing that assumes that software testing is not needed is the time spent. in the 1980s, one of the most notorious cases of software development failure was the ariane rocket (lynch, 2017). the rocket exploded due to software failure. as a result of the explosion of the rocket, the researchers over the years focusing their efforts on looking for problems or bugs in the software on the rocket. rashad khalid (khalid, 2017) conducted a study that focused on the efficiency side of software testing by combining two methods, namely the black box testing method with the white box testing method. khalid developed an automated analysis and testing technique consisting of two main steps: 1) the first step was to take the software under test (sut) and identify all files, input and output variables, and functions. 2) in the next step, the user selects the desired module part or function to test and selects the required tests such as dead-code, assertion based tests and exception tests. the programming language khalid tested was the c ++ programming language. khalid claims the tools he recommends can perform various tests such as static analysis, unit testing, dead-code testing, exception testing and assertion based testing. but the program code that khalid tested was only a simple program and did not tell from the efficiency side whether the recommended tool could handle the limited testing time or not so that the tool did not guarantee the success of the test when the program code was quite complex. christopher dimas satrio et al (satrio et al., 2018), conducted a study comparing a test case generation with two genetic algorithm approaches, namely mutual analysis and sampling. the two approaches will be compared from the reduction in test cases that occur. apart from reducing the test case, the number of iterations, the total number of individuals, the number of fitness evaluations, and the size of the test suites will also be a comparison between the two approaches. from the two approaches, it was found that the genetic algorithm mutual analysis approach was better than the genetic algorithm sampling in terms of the number of test case reductions and all the comparative variables applied. however, in terms of execution time, it is still assumed that the genetic algorithm mutual analysis is faster, so there has been no decision that the genetic algorithm mutual analysis approach is faster when executing larger sample data. researchers also concluded that to increase the effectiveness and efficiency in software testing by shortening the time that must be allocated for the testing carried out. but the researchers did not explain how long the time should be allocated, so they had to see how complex the software to be tested was. marcel bohme and soumya paul (bohme & paul, 2016), conducted a study that analyzed the efficiency between random testing (r) and systematic testing (s0). they built a general model for software testing by specifying a sampling strategy on random testing and systematic testing associated with cost and sampling time. they considered two such strategies whereby random testing was unaware of partition based errors and systematic testing that sampled each partition exactly once. they perform calculations on these two strategies to calculate the relative efficiency value. in the end they conducted 24000 simulation experiments with various parameters. however, in http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 2 march 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 139 the work is distributed under the creative commons attribution-noncommercial 4.0 international license this study there are recommendations from researchers to compare systematic testing techniques to random techniques with a given time limit practically because they assume random techniques must have the same "power" compared to systematic techniques so that there may be other testing techniques that can overcome the time required. limited compared to using random techniques. research methods this study uses methods to verify, validate, and test the hospital information system program. the methods used are equivalence partitioning (ep), boundary value analysis (bva), and decision table added with the line of code (loc) approach to count the number of rows before using the decision table and after using the decision table. these three methods are part of the black box testing method. black box testing is a software testing technique that focuses on the functional specifications of the software being developed (jaya, 2018). the way black box testing works is only to check the output value based on the software input value without knowing what program code is used. equivalence partitioning (ep) or what is often called equivalence class partitioning (ecp) is a technique or method that produces test data from several system requirements by grouping or dividing input data and testing the data in order to get several understandable and feasible categories (jahanbin & zamani, 2018). from these methods, there are several combinations that can occur in equivalence partitioning, including: a. valid and invalid input values b. a numeric value that is negative, positive, or zero c. an empty, non-empty string boundary value analysis (bva) (ardana, 2019) is a technique of the black box testing method that focuses on the input process by testing the values at the upper and lower limits. there are three principles that underlie the boundary value analysis (bva) method are: a. many errors occur with input errors b. select a test case that tests the limits of input values c. bva is a part of equivalence partitioning that selects elements in the equivalence class at the value limit. decision table is one of the techniques of the black box testing method that uses tables to perform testing and can also be used to shorten the logic flow of software programming. decision table testing (joosten et al., 2020) is a software testing technique used to test software on different input combinations by combining different input and output values and summarizing them into a table. decision table is also often referred to as a cause and effect table because of the several causes and effects used to create a decision table. the reason the decision table is quite important is as follows (joosten et al., 2020): 1. very helpful in test design techniques. 2. help testers to look for the effect of the combination of various inputs and the status of other software that must implement business rules properly. 3. provide a regular way to express complex business rules, which is beneficial for both developers and testers. 4. assist in the development process with a developer to do a better job. testing with all combinations might not be practical. 5. this method is basically a technique used in testing and managing requirements. 6. this method is a structured exercise to prepare the requirements when dealing with complex business rules. 7. it can also be used in complex logic models. line of code or what is often referred to as the source line of code (sloc) is a software metric that is often used to measure the size and complexity of a software project. there are two main types of sloc calculations, namely physical sloc (p-sloc) and logical sloc (l-sloc). the definition of p-sloc is the line count in the source code text of the program as well as comment lines and blank lines. meanwhile, l-sloc is responsible for measuring the number of expressions that can be executed. the intended expressions are operators, functions, etc. so if there is one or more statements which are followed by the end-of-line comment is a line of code and is counted into l-sloc. meanwhile, comment lines and blank lines will not be counted into l-sloc. in addition to physical sloc, logical sloc, comment line of code (cloc), blank line of code (bloc), there are also code & comment source line of code (c & sloc) and comment words (cword). to find out the calculation of the number of lines in a source code such as p-sloc, l-sloc, and others, below is an example of source code: #include <iostream> #include <conio.h> #include <windows.h> using namespace std; int main () { char lagi; int name; int option; int number; int paid; http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 jurnal riset informatika vol. 3, no. 2 march 2021 140 the work is distributed under the creative commons attribution-noncommercial 4.0 international license int price; int total; int code; // create menu list // cout << endl ends list; early: system("cls"); cout<<"======================"<<endl; cout<<"+++++++ keday kopi balog +++++++"<<endl; cout<<"====menu========harga=="<<endl; cout<<"1. black coffee rp.10000"<<endl; cout<<"2. coffee mocca rp.15000"<<endl; cout<<"3. coffee milk rp.9000"<<endl; cout<<"4. love coffee rp.20000"<<endl; cout<<"5. pineapple juicerp.10000"<<endl; cout<<"6. avocado juice rp.12000"<<endl; cout<<"7. orange juice rp.10000"<<endl; cout<<endl; //end cout<<"enter your selection (1-7) ="; cin>>code; from the sample source code above, the number of p-sloc is 29, the number of l-sloc is 24, the number of bloc is 1, the number of cloc is 2. in the source code above, there are c & sloc and cword. the part which is c & sloc is ‘cout<<endl;//selesai’. whereas for cword it is ‘create’, ‘list’, ‘menu’, ‘cout<<endl’, ‘ends’, ‘list;’, and ‘end’ research material this study uses html and php files from a medical information system. the medical record program will be tested first by checking the patient number on the medical record. after being tested properly, the medical record program can be used in the hospital. the contents of the html and php files are part of the patient registration form which is part of the medical information system. the initial display of the registration form on the medical information system is shown in figure 1. figure. 1. patient registration section in this method, the equivalence class partitioning will test the input value of the registration form to determine whether the form input is valid or not. then the boundary value analysis method tests the input value limits on the registration form. then, in the detail section, services and inpatient services are processed using the decision table and loc approaches to calculate the estimated number of rows before using the decision table and after using the decision table. the data used in the decision table is shown in table 1: table 1 dataset of operating services conditio n stub operatin g services scalling castrasi oh sectio caecaria ortopedica services public lab grooming etc action stub no need for hospitalization infectious space hospitalization non-infectious space hospitalization hospitalized healthy room advance payment rp. 3.000.000 advance payment rp. 3.500.000 advance payment rp. 2.500.000 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 2 march 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 141 the work is distributed under the creative commons attribution-noncommercial 4.0 international license results and discussion this study uses three methods of black box testing, namely equivalence class partitioning, boundary value analysis, and decision table coupled with the loc approach to estimate the calculation of the number of program lines. to find out the percentage of valid and invalid test cases, this study used the standard testing metrics formula (akshatha & illango, 2018). 𝑇𝑒𝑠𝑡 𝑐𝑎𝑠𝑒 𝑃𝑎𝑠𝑠 % = ( 𝑁𝑜.𝑜𝑓 𝑡𝑒𝑠𝑡 𝑐𝑎𝑠𝑒 𝑝𝑎𝑠𝑠𝑒𝑑 𝑇𝑜𝑡𝑎𝑙 𝑁𝑜 𝑜𝑓 𝐶𝑎𝑠𝑒𝑠 ) 𝑥 100 ....... (1) from the equivalence class partitioning method, the test results are obtained as in the table 2 below: table 2 testing the equivalence class partitioning no menu elements rule data used e. result result statue 1 nama pemilik min input of 1 char a data is not saved stored data fail 2 nama pemilik input 3-20 char adi stored data stored data pass 3 nama pemilik input 3-20 char, strings & numbers ad1n$e data is not saved stored data fail 4 nomor telepon min input of 1 number 1 data is not saved stored data fail 5 nomor telepon input 10 – 13 number 0812345679 stored data stored data pass 6 nomor telepon input other than numbers asds$@ data is not saved stored data fail 7 alamat min input 1 char a data is not saved stored data fail 8 alamat input at least 10 char & numbers jl. kaliurang no 70 stored data stored data pass 9 alamat input char, numbers, & strings jl. k@l1urang no 118 data is not saved stored data fail 10 nama hewan min input 1 char a data is not saved stored data fail 11 nama hewan input 3-25 char leo stored data stored data pass 12 nama hewan input 3 – 20 char, string, & num @sing data is not saved stored data fail 13 ras min input 1 char a data is not saved stored data fail 14 ras input 3-25 char kampung stored data stored data pass 15 ras input 3 – 20 char, string, & numbers @sing data is not saved stored data fail 16 jenis kelamin please select a gender jantan stored data stored data pass 17 jenis kelamin not choose asked to choose must choose a gender pass 18 tanggal lahir input number 03-04-1990 stored data stored data pass 19 tanggal lahir input other than numbers sadef@#$ cannot be typed must be number pass from the test results in table 2, there were 19 test cases consisting of 9 valid or pass test cases and 10 invalid or failed test cases. so that the percentage of valid test cases is 47.4% and the percentage of invalid test cases is 52.6%. in the bva method, test results are obtained as in table 3: http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 jurnal riset informatika vol. 3, no. 2 march 2021 142 the work is distributed under the creative commons attribution-noncommercial 4.0 international license table 3 testing the boundary value analysis n o menu element rule data used e. result result statu s 1 nama pemilik empty it data is not saved the message "please fill in" appears pass 2 nama pemilik min input of 1 char a data is not saved stored data fail 3 nama pemilik min input of 3 char adi stored data stored data pass 4 nomor telepon empty it data is not saved the message "please fill in" appears pass 5 nomor telepon min input of 1 number 1 data is not saved stored data fail 6 nomor telepon input 10 – 13 number 0812345679 stored data stored data pass 7 nomor telepon input more than 13 numbers 0912345678919 2 data is not saved stored data fail 8 alamat empty it data is not saved the message "please fill in" appears pass 9 alamat min input of 1 char a data is not saved stored data fail 1 0 alamat input at least 10 char & numbers jl. kaliurang no 70 stored data stored data pass 1 1 nama hewan empty it data is not saved the message "please fill in" appears pass 1 2 nama hewan min input of 1 char a data is not saved stored data fail 1 3 nama hewan input 3-25 char leo stored data stored data pass 1 4 nama hewan input above 25 char sadhadsgyqwew wqegwuqegwrw qrwqrwqr data is not saved stored data fail 1 5 ras empty it data is not saved the message "please fill in" appears pass 1 6 ras min input of 1 char a data is not saved stored data fail 1 7 ras input 3-25 char kampung stored data stored data pass 1 8 ras input above 25 char @sing data is not saved stored data fail 1 9 jenis kelamin empty it data is not saved the message "please fill in" appears pass 2 0 jenis kelamin please select a gender jantan stored data stored data pass 2 1 tanggal lahir input number 03-04-1990 stored data stored data pass 2 2 tanggal lahir input other than numbers sadef@#$ cannot be typed must be number pass from the test results in table 3, there are 22 test cases consisting of 14 valid or pass test cases and 8 invalid or failed test cases. so that the percentage of valid test cases is 63.6% and the percentage of invalid test cases is 36.4%. for the decision table method, the part that is processed and tested is the operation service section and the selection of inpatients. in both parts, a rule shortening process is carried out so that when choosing an operating service with another service, it will be notified whether it needs to be switched in or not. the result of the shortening rule from the decision table is shown in figure 2. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 2 march 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i2.188 143 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure. 2. the result of the decision table from the results of the shortened rule, when implemented into the algorithm, the number of algorithm lines is reduced due to the algorithm being changed and the result of the number of lines of code is shown in table 4. table 4 comparison of loc before and after the decision table before use decision table after use decision table (estimation) loc 466 425 p-sloc 456 415 l-sloc 111 111 bloc 6 6 c&sloc 1 1 cloc 4 4 cword 10 10 after getting the final amount of loc, the next step is to calculate the loc efficiency. the formula for finding the loc efficiency is: 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐿𝑂𝐶 𝑎𝑚𝑜𝑢𝑛𝑡−𝐹𝑖𝑛𝑎𝑙 𝐿𝑂𝐶 𝑎𝑚𝑜𝑢𝑛𝑡 𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝐿𝑂𝐶 𝑎𝑚𝑜𝑢𝑛𝑡 𝑥100% ................. (2) in the loc calculation, the results are as follows: efficiency of loc = 466−425 466 𝑥100% = 8,8% .............. (3) in the p-sloc calculation, the results are as follows: efficiency of p-sloc = 456−415 456 𝑥100% = 9% ........... (4) in testing using the decision table method, there are 32 test cases from the scaling operation service with other services. when the input of scaling operating services is true and public services is true, the output expectation is infectious space hospitalization and advance payment of rp. 3,000,000. when the input of scaling operating services is true and grooming services is true, then the output expectation is no need for hospitalization. when the input of scaling operating services is true and lab or etc services is true, then the expected output is non-infectious space hospitalization and advance payment of rp. 3,500,000. the test result becomes an error if other input is outside of the four test cases above. likewise with testing castrasi operation services with other services. conclusions and suggestions conclusion this study is a continuation of previous research that only shortened the rule by using a decision table (joosten et al., 2020). from the results of the research above, the percentage of valid ecps is lower than invalid ones, so the software needs to be fixed. for the bva test results, the percentage of valid bva is higher than the invalid one and can be increased again which is the valid part so that the bva test is even better. for testing using the decision table and loc approach, after changes to the code part of the program were made, the number of locs was reduced and the loc efficiency was 8.8% and the p-sloc was 9% so that it became efficient. in addition to reducing rules, testing is also carried out on the operation service section with other services. suggestion from this research it is hoped that there will be other methods that can be combined with the three methods so that it is more efficient and the testing is getting better so that it does not take a long time and is expensive references akshatha, v., & illango, v. 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(2015). an automated test generation technique for software quality assurance. ieee transactions on reliability, 64(1), 247– 268. https://doi.org/10.1109/tr.2014.2354172 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 57 the work is distributed under the creative commons attribution-noncommercial 4.0 international license accounting information system for purchasing, sales, and service ita natalia1, lisnawanty2*), ardiyansyah3 program studi sistem informasi akuntansi kampus kota pontianak, fakultas teknik dan informatika 1,2,3 universitas bina sarana informatika 1,2,3 www.bsi.ac.id itanatalia27@gmail.com1 , lisnawanty.lsy@bsi.ac.id2 , ardiyansyah.arq@bsi.ac.id3 (*) corresponding author abstrak pt. gm motor pontianak merupakan perusahaan yang bergerak di bidang penjualan dan service motor. pengolahan data transaksi service dan penjualan motor masih dikelola secara sederhana, yaitu mencatat transaksi ke dalam buku besar dan disalin ke microsoft excel untuk rekapitulasi atau penyajian laporan. permasalahan utama yang menjadi kendala bagi perusahaan adalah admin sebagai pelaku pengolah data transaksi sering melakukan kesalahan dan keterlambatan dalam pembuatan rekapitulasi transaksi penjualan bulanan, serta kesalahan dalam pembuatan laporan keuangan. maka dari itu, penelitian ini membahas mengenai rancangan sistem informasi akuntansi pembelian, penjualan, dan service yang diimplementasikan pada pt. gm motor pontianak. metode yang digunakan dalam pengembangan sistem ini adalah model waterfall, karena desain sistem dilakukan secara sistematis dengan melalui tahapan analisis, desain, implementasi, pengujian, dan dukungan. sistem informasi akuntansi yang dihasilkan ini menyediakan fasilitas kepada dua pengguna, yaitu admin dan direktur. admin yang dapat mengelola data akun, merk spare part, data spare part, data mekanik, data tarif, data akun, transaksi pembelian spare part, transaksi penjualan spare part, transaksi jasa service, penjualan, serta transaksi pengeluaran. direktur dapat mengolah data pengguna, mengakses laporan stok spare part, laporan pembelian, laporan penjualan, laporan jasa service, laporan insentif mekanik, laporan jurnal umum, laporan buku besar, laporan neraca saldo dan laporan laba rugi. sistem yang dibangun ini diharapkan membantu pt. gm motor pontianak dalam pengelolaan transaksi pembelian, penjualan dan jasa servis dan menghasilkan laporan keuangan sesuai dengan standar akuntansi keuangan. kata kunci: sistem informasi akuntansi, model waterfall, jasa service, penjualan, pembelian abstract pt. gm motor pontianak is a company engaged in the sale and service of motorbikes. service transaction data processing and motorbike sales are still managed in a simple way, namely recording transactions into a ledger and copied to microsoft excel for recapitulation or report presentation. the main problem that becomes an obstacle for the company is that the admin as a transaction data processor often makes mistakes and delays in making a recapitulation of monthly sales transactions, as well as errors in making financial reports. therefore, this study discusses the design of the accounting information system for purchases, sales, and services implemented at pt. gm motor pontianak. the method used in the development of this system is the waterfall model, because the system design is carried out systematically through the stages of analysis, design, implementation, testing and support. the resulting accounting information system provides facilities to two users, namely admin and director. admin who can manage account data, spare part brands, spare part data, mechanical data, tariff data, account data, spare part purchase transactions, spare part sales transactions, service transactions, sales and expense transactions. the director can process user data, access spare part stock reports, purchase reports, sales reports, service service reports, mechanical incentive reports, general journal reports, general ledger reports, trial balance reports and income statements. the system built is expected to help pt. gm motor pontianak in managing purchases, sales and service transactions and producing financial reports in accordance with financial accounting standards. keywords: accounting information systems, waterfall model, service, sales, purchases http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 jurnal riset informatika vol. 3, no. 1 december 2020 58 the work is distributed under the creative commons attribution-noncommercial 4.0 international license introduction the development of information & communication technology demands the need to make changes, both individuals and companies (otinur, pangemanan, & warongan, 2017). computers provide an important role as an efficient tool, and can summarize the process of an activity so that it can simplify all existing work. pt. gm motor pontianak is a company engaged in the sale & service of motorbikes. the data processing of motorbike sales & service transactions is still managed in a simple way, namely recording transactions to a ledger and copied to microsoft excel as material for data recapitulation or report presentation (frieyadie, 2015). the main problem for the company is that the admin, as a transaction data processor, often makes mistakes and delays in making a recapitulation of monthly sales transactions, and mistakes in making financial reports (uddin, suryadi, & maesaroh, 2020). this problem is similar to a research conducted by (damayanti & hernandez, 2018), the finance department managing financial transactions using microsoft excel. the main obstacle occurred when conducting data searches, the delivery of information required by the bookkeeping division and management often experienced delays. therefore, this study produces an accounting information system designed using the waterfall model. the accounting information system can assist the finance department in processing financial transactions, as well as simplify and accelerate the presentation of reports so that leaders can immediately make decisions based on the reports received. similar issues are also discussed in other research sourced from (putra & megawati, 2018). financial transactions that have used the accounting cycle are still managed manually (handwritten), and microsoft excel is used in the preparation of financial reports. the main problem that arises is that there are often errors in posting daily journals to the ledger so that it has an impact on unbalanced balance reports. therefore, this study produces an accounting information system with a waterfall model to make it easier for admins to input all financial transactions and automate financial statements. the results of this research test show the percentage of the success of this system is 100%. therefore, this study aims to provide solutions for companies in processing purchase, sale, and service transactions in the form of an accounting information system. accounting information systems can be a tool in managing physical resources and other resources to convert economic data into accounting information needed by various parties (mahatmyo, 2016), as well as assist in making decisions related to various transactions in a company (mulyani, 2016). with this accounting information system, so that it can make it easier for the admin to process data and the director in monitoring the transactions that have occurred. research methods this research focuses on designing an accounting information system that is implemented at pt. gm motor pontianak as a solution in processing purchase, sales, and service transaction data. the software development method used is the waterfall model (achyani & velayati, 2020). according to sommerville in (fridayanthie & mahdiati, 2016), the waterfall model is a software development method that is systematic and can be used as a reference in developing innovative and complex projects. the reason for using the waterfall model in this research is because system development is carried out systematically through the stages of analysis, design, implementation, testing and support (frieyadie, 2015). types of research this study used qualitative research methods (wangi, udjulawa, & parlindungan, 2015). in this qualitative research, the activities carried out by the object are directly observed. so that the technique of observation and in-depth interviews are needed to obtain the data needed in the study (rukajat, 2018). time and place of research this research was conducted at pt. gm motor pontianak, located at jalan ismail marzuki no. 12 pontianak. this research was conducted in november 2020. research target / subject the target or subject in this research is pt. gm motor pontianak. procedure the following describes the procedure for the system running at pt. gm motor pontianak: 1. procurement of goods the admin section manages the goods at pt. gm motor is then processed and recorded into goods data. this item data is used as the basis for the admin section to create a procurement form. goods data and goods procurement forms are submitted to the director for approval. admin procures goods by contacting the supplier, then submitting the procurement http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 59 the work is distributed under the creative commons attribution-noncommercial 4.0 international license form to the supplier. the supplier prepares the requested items, then the admin pays the transaction and gets a purchase note. 2. service and sales consumers come to the company and meet the admin to service or buy goods. admin serves consumers. when buying goods, the admin immediately creates a sales note according to consumer orders, the consumer pays for and gets the goods and sales notes. if the customer performs service, the mechanic will do the repair or service. the mechanic reports the service description to the admin. the customer pays a service fee to the admin, the admin makes a service note to be handed over to the consumer, after the car is serviced. 3. report at the end of the month, the admin collects the goods data archive, purchase notes, service notes and sales to be processed into reports and submitted to the director. data, instruments, and data collection techniques according to saefudin and ardhani in (lisnawanty & bambang kurniawan, 2019), data collection techniques are a method used to collect data needed in a study. the data used in this study are data related to purchase, sale, and service transactions. the data collection techniques used in this study include: 1. observation observations were made by observing the activities carried out at the gm motor workshop so that they could find out every process carried out by the workers, as well as observing the running system business processes related to purchase, sale and service transactions. 2. interview interviews were conducted by asking questions to mr. sugiato ali, as the head of pt. gm pontianak, to find out business processes, problems, and needs in purchasing, sales and service transactions. 3. literature study the literature study technique is carried out by collecting references from books, journals, and other references related to the discussion in this research (sari & trisna, 2019). data analysis technique data analysis is a step used to organize data, determine problems in a data processing process, and draw conclusions. in this study, data were analyzed based on input documents and output documents used by pt. gm motor pontianak in processing purchases, sales and service transactions. the techniques used in collecting this document are observation and interviews (n & nugroho, 2018). after the data is obtained, it is known the problems and system requirements, then the database design and system design using the unified modeling language will be created (frieyadie, 2015). results and discussion the design of accounting information systems for purchases, sales and services at pt. gm motor pontianak consists of two accesses, namely admin and director, as described below: 1. software requirements analysis the following is a description of the system requirements analysis for the admin and director. a. admin a1. login a2. manage spare part brand data a3. manage spare part data a4. manage mechanical data a5. manage service tariff data a6. manage account data a7. manage spare part purchase transactions a8. manage spare part sales transactions a9. manage service and sales service transactions a10. manage expense transactions a11. logout b. director b1. login b2. manage user data b3. access spare part stock reports b4. access purchase reports b5. access sales reports b6. access service reports b7. access mechanical incentive reports b8. access general journal reports b9. access general ledger reports b10. access the trial balance report b11. access the income statement b12. logout 2. design a. system design description of this system design using uml modeling. admin and director facilities in using the system are described as follows: http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 jurnal riset informatika vol. 3, no. 1 december 2020 60 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 1. use case diagram table 1 below is a description of the use case diagram based on figure 1 (fatayat & frieyadie, 2019). table 1. use case diagram description use case narative goal the admin and directors section can process and access data in the accounting information system for purchases, sales, and services. description this designed system provides facilities for the admin to manage spare part brand data, spare part data, mechanical data, service tariff data, account data, spare part purchase transactions, spare part sales transactions, service and sales transactions and expenditure transactions. the director can process user data, access spare part stock reports, purchase reports, sales reports, service reports for mechanical incentives, general journal reports, general ledger reports, trial balance reports and income statements. main scenario actor admin and director pre-condition admin and director must log in by filling in the username and password. actor action system reaction 1. admin selects the spare part brand data. 2. admin selects the spare part data. 3. admin selects the mechanical data. 4. admin selects the service tariff data. 5. admin selects the account data. 6. admin selects the spare part purchase transaction. 7. admin selects the spare part sales transaction. 8. admin selects transaction services and sales services. 9. admin selects the expenditure transaction. the system displays the spare part brand data page. the system displays spare part data page. the system displays a mechanical data page. the system displays the service rates page. the system displays the account page. the system displays the spare part purchase transaction page. the system displays the spare part sales transaction page. the system displays the transaction page for service and sales services. the system displays the expense transaction page. 1. the director selects the user data. 2. the director selects the spare part stock report. 3. the director selects the purchase report. 4. the director selects the sales report. 5. the director selects the service report. 6. the director selects the mechanical incentive report. 7. the director selects the general journal report. 8. the director selects the general ledger report. 9. the director selects the trial balance report. 10. the director selects the income statement. the system displays the user data page. the system displays the spare part stock report page. the system displays the purchase report page. the system displays a sales report page. the system displays the service report page. the system displays a mechanical incentive report page. the system displays general journal report pages. the system displays the ledger report page. the system displays a trial balance report page. the system displays the income statement page. post-condition if according to the command, the accounting information system will display the menu selected by the user. table 1 above shows the description that the admin and the director have different data access. admin focuses on input and processing of master data and transactions. meanwhile, the director focuses on user management and transaction reports based on uc usecasediagram sistem informasi akuntansi pembelian, penjualan, dan service admin direktur login kedalam sistem mengelola data merk spare part mengelola data spare part mengelola data mekanik mengelola data tarif serv ice mengelola data akun mengelola transaksi pembelian spare partmengelola transaksi penj ualan spare part mengelola transaksi j asa serv ice mengelola transaksi pengeluaran mengelola data pengguna mengakses laporan stok spare part mengakses laporan pembelian mengakses laporan penj ualan mengakses laporan j asa serv ice mengakses laporan insentif mekanik mengakses laporan j urnal umum mengakses laporan buku besar mengakses laporan neraca saldo mengakses laporan laba rugi http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 61 the work is distributed under the creative commons attribution-noncommercial 4.0 international license the output of transactions that have been filled in by the admin. both actors must log in to be able to access the data. the work flow of login processes in the accounting information system for purchases, sales and services is described in the form of an activity diagram as follows (figure 2). figure 2. activity diagram b. database design in designing this accounting information system, the tools used to model the database design are entity relationship diagram (erd) and logical record structure (lrs). erd is one of the most frequently used conceptual models in the process of developing relational type data (lubis, 2016). meanwhile, the logical record structure (lrs) is formed from the transformation of the entity relationship diagram (erd) into a structure of records in tables (fridayanthie & mahdiati, 2016). figure 3. entity relationship diagram (erd) http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 jurnal riset informatika vol. 3, no. 1 december 2020 62 the work is distributed under the creative commons attribution-noncommercial 4.0 international license @kdakun namaakun akun @@[@kdpengeluaranumum] @@[@kdakun] jumlahpengeluaran pengeluaranumumdetail @kdpengeluaranumum tglpengeluaranumum keterangan @@[@idpegawai] tglinsert tglupdate pengeluaranumum @@[@kdpenerimaan] @@[@kdbarang] hargabeli hargajual qtypenerimaan diskon diskon2 penerimaanbarangdetail @kdpenerimaan tglpenerimaan nofaktur keterangan @@[@idpegawai] tglinsert tglupdate penerimaanbarang @idpegawai nmpegawai nik alamat notelp level foto jk username password pegawai @@[@kdpengeluaran] @@[@kdbarang] hargabeli hargajual qtypenerimaan pengeluaranbarangdetail @kdpengeluaran tglpengeluaran keterangan @@[@idpegawai] tglinsert tglupdate pengeluaranbarang@kdbarang nmbarang @@[@idmerk] stok satuan hargabeli hargajual hargacustom fotobarang barang @idmerk nmmerk merk @@[@kdservicejual] @@[@kdbarang] hargabeli hargajual qtyservicejual servicejualdetail @kdservicejual tglservicejual keterangan @@[@idpegawai] @@[@kdservice] @@[@kdmekanik] biayatambahan tglinsert tglupdate servicejual @kdservice nmservice tarifservice statusaktif service @kdmekanik nik nmmekanik jk notelp alamat email statusaktif mekanik @kdhistori kdtransaksi tgltransaksi jenistransaksi @@[@kdbarang] stokawal masuk keluar stokakhir keterangan @@[@idpegawai] tglinput historybarang figure 4. logical record structure (lrs) 3. implementation based on the above design, the following is the result of the interface design or user interface design of the accounting information system for purchases, sales and services at pt. gm motor pontianak. user interface for admin level the admin must log in by filling in the username and password (figure 5) to be able to access the application according to the access level. figure 5. log in admin can access the dashboard or homepage page (figure 6) if they have passed the login stage. figure 6. admin dashboard in figure 7, figure 8 and figure 9, to process data on purchase transactions, sales transactions, and service transactions the admin can add, change, delete, and search for data as needed.. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 63 the work is distributed under the creative commons attribution-noncommercial 4.0 international license figure 7. add purchase transaction figure 8. add sales transaction figure 9. service and sales service transactions the data that has been inputted will be displayed on each page in figure 10 and figure 11. figure 10. purchase transaction data figure 11. sales transaction data user interface for director level to be able to access the director page, the director must also log in by filling in the username and password to be able to access the application according to his access level, as shown in figure 5. then the director will be able to access the dashboard page (figure 12). figure 12. director dashboard the director can manage user data, access inventory reports, purchase reports, sales reports, service reports, mechanical incentive reports, general ledger, general ledger reports, trial balance reports, and general income reports. purchase reports, sales reports, service reports, general ledger, trial balance reports, and general income reports, can be viewed by selecting the reporting period (start date and end date). meanwhile, to be able to view general ledger reports, the director must select the reporting period and one of the accounts. this accounting information system provides adequate control to guarantee all transactions has been recorded, is valid, accurate, and can protect company activities effectively and efficiently (nuryanti & suprantiningrum, 2016). 4. testing the system that has been created must be tested first to find out the errors that exist in the system. testing is carried out aiming to find out errors and correct these errors. the testing technique used is the black box testing method. the following are the results of testing the accounting information system for purchases, sales and services at pt. gm motor pontianak. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 | e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 jurnal riset informatika vol. 3, no. 1 december 2020 64 the work is distributed under the creative commons attribution-noncommercial 4.0 international license a. browser testing table 2 below is a blackbox testing in browser use. this test uses two browsers, namely mozilla firefox and google chrome. table 2. browser testing test scenar io test case expected results test results conclusi on 1 open with google chrom e googl e chro me (open ) applicatio ns can be accessed and displayed according ly according to expectatio ns valid 2 open with mozill a firefox mozill a firefo x (open ) applicatio ns can be accessed and displayed according ly according to expectatio ns valid table 2 above shows that the system can be accessed and displayed properly on both browsers. b. login testing table 3 below is a blackbox testing to check the system login functionality can run well and as expected. table 3. login testing test scenario test case expected results test results conclusio n 1 if any column is left blank userna me: (empt y) passw ord: (empt y) applicati on refuses access and displays a message "login failed ... usernam e and passwor d cannot be empty" according to expectatio ns valid 2 filled and appropri ate fields (appropr iate usernam e and passwor d) userna me: (admi n) passw ord: (admi n) the applicati on receives access and opens a dashboa rd page. according to expectatio ns valid table 3 above shows that users can log in smoothly, by entering a username and password. when the entered username and password do not match, the system will refuse so that unauthorized users cannot enter the system. c. add user testing table 4 below is a blackbox test to check functionality in the process of adding users to run well and as expected. table 4. add user testing test scenario test case expected results test results conclusion 1 add data, if the column is blank all column s: (empty) the applicatio n denies access and displays a message in each column "(column name) may not be empty" according to expectations valid 2 add data, if the column is filled all column s: (filled) the applicatio n receives access and saves the data and displays the message "data successful ly saved" according to expectations valid 3 edit data, if the column is filled one of the column s: (modifi ed) the applicatio n receives access and changes the data and displays the message "data was successful ly updated" according to expectations valid 4 clear data pressin g the delete button on the row of data you want to delete the applicatio n receives access and deletes the data and displays the message "data was successful ly deleted" according to expectations valid based on table 4 above, it can be seen that the system can process the addition of users so that it can run with the specified scenarios, such as: http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 3, no. 1 december 2020 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v3i1.190 65 the work is distributed under the creative commons attribution-noncommercial 4.0 international license 1) if all fields are empty, the application will reject the process of adding user data and display a message "columns cannot be empty". 2) if all fields are filled in correctly, the system will successfully save data and is indicated by displaying the message "data saved successfully". 3) the process of changing the data has also been successfully carried out on the condition that it meets the conditions of the data being changed correctly and completely. 4) the process of deleting data has also been successfully carried out by selecting the deleted data and clicking the delete button. d. support the specifications that a computer must have to be able to run this accounting information system include: a. operating system: windows 10 32/64-bit. b. webserver: 1) apache: apache 2.4.43 2) mysql: mariadb 10.4.11 3) php: php 7.4.6 (vc15 x86 64bit thread safe) + pear c. web editor: sublime text 3. d. web browser: 1) mozzila firefox: version 77.01 2) google chrome: version 83.0.4103.97 conclusions and suggestions conclusion accounting information system for purchases, sales and services at pt. gm motor pontianak provides facilities to the admin who can manage account data, spare part brands, spare part data, mechanical data, service tariff data, account data, spare part purchase transactions, spare part sales transactions, service and sales transactions and expense transactions. this system provides facilities to the director to process user data, access spare part stock reports, purchase reports, sales reports, service service reports, mechanical incentive reports, general journal reports, general ledger reports, trial balance reports and income statements. this system that has been created helps pt. gm motor pontianak in improving company performance such as processing and processing data on purchases, sales and services as well as producing information or reports that are fast and efficient. suggestion features for consumers or general users so that they can make reservations or booking services, as well as an android-based system design can also be added in the next research. references achyani, y. e., & velayati, a. 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(2018). pendekatan penelitian kualitatif. yogyakarta: deepublish. sari, s. y., & trisna, n. (2019). perancangan sistem informasi pengolahan data transaksi penerimaan dan pengeluaran kas untuk perencanaan pengendalian keuangan. jurnal teknologi informasi, 3(1), 25. https://doi.org/10.36294/jurti.v3i1.684 uddin, b., suryadi, d., & maesaroh, s. (2020). perancangan dan implementasi sistem informasi penjualan pada cv. cihanjuang inti teknik. jurnal teknologi terpadu, 6(1), 25– 30. wangi, p. r., udjulawa, d., & parlindungan, r. (2015). analisis dan perancangan sistem informasi akuntansi ( studi kasus pt arraudhah wisata imani palembang ). jurnal skripsi stie mdp, (x), 1–18. http://creativecommons.org/licenses/by-nc/4.0/ 29 analysis and design of mobile web-based menu e-order systems using the pieces method (case study: café 50/50 coffee) nabilah ananda pratiwi1), agung triayudi2*), endah tri esti handayani3) sistem informasi, fakultas teknologi komunikasi dan informatika universitas nasional 2018nabilah@student.unas.ac.id1), agungtriayudi@civitas.unas.ac.id2*), endahtriesti@civitas.unas.ac.id3) (*) corresponding author abstrak kafe sebagai tempat untuk melepas penat maupun bercengkerama dan pengunjung dapat melakukan pemesanan menu yang tersedia. pada umumnya sebuah kafe sering mengalami kesulitan dalam melayani pelanggan, terutama untuk fasilitas pemesanan menu. hal ini juga dialami oleh kafe 50/50 coffee yang masih melakukan pemesanan menu secara manual. berdasarkan permasalahan tersebut, dirancang sebuah sistem e-order menu aplikasi mobile berbasis web. penelitian ini bertujuan untuk menghasilkan sistem pemesanan mobile web yang kemudian dianalisis dengan indikator pieces untuk mendapatkan nilai kepuasan dari pengguna. perancangan sistem menggunakan metode pengembangan system development life cycle (sdlc) model waterfall kemudian dilakukan analisis tingkat kepuasan pengguna dengan metode pieces. pengujian sistem menggunakan usability testing dengan metode use questionnaire. implementasi sistem dibuat dengan bantuan framework codeigniter dan menggunakan bahasa pemrograman php. hasil penelitian berupa sistem e-order menu pada kafe 50/50 coffee dengan kesimpulan dari hasil analisis bahwa pengguna sistem e-order sudah merasa “puas”. kata kunci: sistem e-order, mobile web, metode waterfall, pieces framework, use questionnaire abstract café as a place to relax or chatter where visitors can order the menu available. in general, a café often has difficulty in serving customers, especially for menu ordering facilities. this is also experienced by café 50/50 coffee which still makes menu reservations manually. based on these problems, a system of e-order menus of web-based mobile applications is designed. the study aims to produce a mobile web ordering system that is then analyzed with the pieces indicator to determine the level of user satisfaction. design of this system using the waterfall model system development life cycle (sdlc) development method and then analyzed the level of user satisfaction with the pieces method. system testing uses usability testing with the use questionnaire method. system implementations are created with the help of the codeigniter framework and use the php programming language. the results of the study in the form of a menu e-order system at the 50/50 coffee café with the conclusion of the analysis that the users of the e-order system were “satisfied”. keywords: e-order system, mobile web, waterfall method, pieces framework, use questionnaire introduction information systems and technology are increasingly evolving so as to change the way and lifestyle also facilitate people in daily activities. technology is growing rapidly, so most people prefer to use technology in their activities. one of the example is internet and websites. website can help in terms of promoting a business, making it easier for customers to order menus and in order to win the competition (marudut & siregar, 2018). café as a place to relieve exhaustion or chatter where visitors can order the menu available (bastian, 2020). in addition to the menu offered, restaurant and café services in providing satisfaction to customers are also very influential on competition in the culinary business (triayudi & rodhi, 2018). café 50/50 coffee is one of the businesses in the culinary sector. this café is located at jalan nusantara no. 50, depok city, west java. during this time the booking facility is still done manually, i.e. visitors must do it directly mailto:2018nabilah@student.unas.ac.id mailto:agungtriayudi@civitas.unas.ac.id 30 through the cashier so that it can cause queues. this is less effective, especially during the covid-19 pandemic that is currently ongoing almost all over the world, including in indonesia. therefore, there is a rule that requires people to do social distancing to limit social activities in order to avoid physical contact and crowds (suherman et al., 2021). apart from that, there are other problems i.e. to find out information related to the menu in more detail or information about the availability of tables, visitors must directly come to the café. the menu e-order system at the café 50/50 coffee can make it easier for visitors to make table reservations and menu reservations more effectively and efficiently. then an analysis of user satisfaction levels with the pieces method is carried out in order to get an evaluation of a system that has been created before (julian, triayudi, & benrahman, 2021). as for the analysis conducted so that the e-order system can meet the qualifications in the pieces framework (tjiptabudi, 2017). this study aims to produce a mobile web ordering system which is then analyzed with the pieces method to find out the level of user satisfaction. with the food and beverage menu e-order system, it is expected to be useful to help visitors in ordering online. some previous research related to the development of menu e-order systems, in the study (fonggo, beng, & arisandi, 2020) the ordering system using the sdlc method with the waterfall model. program creation uses html and php also mysql as database storage. the results of the study in the form of a web-based payment and booking system implemented in the canteen. the research (manikam & ardiyansah, 2019) conducted a system design at bebek goreng haji yogi restaurant that utilizes the pieces method for problem analysis. system design uses balsamiq software with php programming language. the result of the study is in the form of the design of an e-order system. in research (dhiman, 2021) online food ordering management system used in the culinary field. website implementations are performed using php, html, css and datasets stored in the phpmyadmin sql database. the result is a website-based online food ordering system that will be applied in small restaurants and locations such as college canteens, etc. research (riswanda & priandika, 2021) about ordering system on the donnys store uses php programming languages and mysql databases also designed using uml (unified modeling language). system development utilizes waterfall methods and analyzes system needs with the pieces method. the results of the study are in the form of items order management applications. in the study (dzulfiqar, 2019) apply pieces analysis to the current system. by using uml as a system design. the results of this study are in the form of an application that makes it easier for customers to order food on bu sri’s food stalls. based on these studies, the authors built a food and beverage menu e-order system to be applied to café 50/50 coffee. system design applies the system development life cycle (sdlc) waterfall model method as well as the pieces framework method to determine the level of user satisfaction. as for testing using usability testing with the use questionnaire method. research methods types of research the type of research used by the authors is qualitative descriptive using data collection methods namely observation, interviews, and literature studies. time and place of research the study was conducted in october 2021 at the café 50/50 coffee. research target / subject the study focused on issues related to the ordering system at café 50/50 coffee. data collection methods the data was collected with observations related to the problems that occurred in the café 50/50 coffee and the collection of references obtained from previous studies to get an idea of the needs of the system to be made. questionnaires were also distributed to system users and interviews with the owners/employees of the café in order to produce accurate data. system development methods sdlc is a life cycle in software development. sdlc helps developers improve the quality of the software produced. sdlc emphasizes the needs of users and with a structured approach to building or developing new systems (rahayu, susanto, & suwarjono, 2020). waterfall method is included in the system development model by focusing on sequenced and systematic stages. waterfall method stages (muhammad robith adani, 2020): 1. requirement the first stage is to analyze the needs of the system to be built. information is obtained from observations, analysis with pieces framework 31 indicators, and interviews with the related café, as well as conducting literature studies. 2. design the next stage is the creation of a system design which is the process by which functional analysis of the system is applied to the unified modelling language (uml). 3. implementation the implementation stages of program code use visual studio code tools as text editors with the help of the codeigniter framework, also use the php programming language. in addition, the system is designed with some supporting software such as xampp. 4. integration & testing this stage aims to know the suitability of the system with the design, functionality of the system, and to prevent bugs or errors in the system. testing is done by applying usability testing method use questionnaire. 5. operation & maintenance after testing the system, then proceed to the system usage stage by the user (user). at this stage, the developer can make improvements if an error is found in the system that has been used by the user. methods of analysis the analysis method used is pieces framework which is a method to identify in order to solve problems that occur in the system against indicators performance, information, economy, control, efficiency, and service (salwa husna, fadli, & hajar, 2018). table 1. analysis of problem identification with the pieces method pieces indicators old system new system performanc e menu ordering is still done directly through the cashier. ordering takes a long time in choosing a menu, so it can cause queues. menu reservations can be made via smartphone which can be easily accessed by users before coming to the café. information customers need to come directly to get more information related to detailed order and menu and information related to order data and menu details also table availability is faster and easier to get pieces indicators old system new system also information about table availability. just by accessing the web. economy errors may occur regarding orders and total payments generated. menu ordering activities require more manpower. the order process, order details and total payment are processed automatically through the system. control data can be accessed by anyone, so security is less maintained. the process of managing data that is still manual takes a long time. existing data can only be accessed by the related admin. the data management process takes place quickly. efficiency the process of recapting data can takes a long time because recording is still done manually. the time required for the recap process is only a little because admins can directly access it on the system. service the exchange of information that occurs is still done directly. the exchange of information is more detail and fast because it is easily accessible online. data analysis was conducted from the questionnaires distribution to find out the level of user satisfaction with the e-order system involving 6 pieces indicators. respondents were involved as many as 20 people according to the range of café 50/50 coffee visitors every day. the study used the satisfaction level likert scale listed in table 2. table 2. satisfaction level likert scale answer options score strongly agree 5 agree 4 neutral 3 32 disagree 2 strongly disagree 1 table 3 is the assessment characteristic for the pieces framework method (ramadhani & kusuma, 2018). table 3. level of satisfaction categories value range very dissatisfied 1-1,79 not satisfied 1,8-2,59 quite satisfied 2,6-3,39 satisfied 3,4-4,91 very satisfied 4,92-5 testing methods the test used is the usability testing method use questionnaire, which consists of 4 aspects, namely usefulness, easy of use, easy of learning, and satisfaction (retnoningsih & fauziah, 2019). the test on this study involved 20 respondents from a population that is the range of visitors at a café 50/50 coffee every day. this is based on nielsen's theory that results with a large number of usability testing and with only 20 respondents will not be much different, so it will save more costs and time if the number of respondents is less (setiyani, syamsudin, gintings, & arifin, 2020). the test was conducted by distributing questionnaires by providing 5 alternative assessments using the likert scale as in table 2. then an average search is performed for each respondent's answer and performs interval calculations as in equation 1. interval (i) = 100 jumlah skor (likert) ................................. (1) interval (i) = 100 5 = 20 from the results of the interval can be categorized in the eligibility table contained in table 4. table 4. eligibility category eligibility category score (%) very useless <21 not useful 21-40 quite useful 41-60 useful 61-80 very useful 81-100 results and discussion requirements the café menu e-order system has 2 levels of users, namely visitors who want to order at the café and admin. 1. café visitors visitors can view the available menus, order menus, manage orders, receive confirmations and order details. 2. admin admins who also act as cashiers can login, manage master data, monitor desks, manage orders, confirm arrivals and payments of visitors, print proof of payment, and perform logout. system design a. system menu scheme design figure 1. system main menu scheme in figure 1, it can be seen that the café 50/50 coffee menu e-order system has many menu features that are divided into two parts of the interface, namely the user side as a visitor and the admin side. both of these sections have interfaces, as well as different roles and menu features. the difference is that visitors can only access parts of the web interface. while admins can access, change, delete available data and set up a web interface. b. use case diagram figure 2. use case diagram 33 in figure 2 there is a use case diagram proposed as a depiction of the functionality of eorder system. there are two actors who interact with the system, namely admin and user. such interactions are like viewing, ordering, inputting, confirming order data for user and processing data, order, and user for admin. c. activity diagram in figure 3 there is an activity diagram that contains the ordering activity in ordering through the e-order menu system. starting from the booker who sees and selects the menu you want to order until the payment process. figure 3. activity diagram system interface implementation 1. admin page – login page view figure 4. login page in figure 4 displays the login page, where an admin needs to enter his username and password to be able to enter the application. admins who have successfully logged in and entered the application, will go to the dashboard page. 2. admin page – dashboard page view 34 figure 5. dashboard page in figure 5 there is a dashboard page where admins can monitor the number of menus that are currently available, revenue per month and total overall revenue. 3. user page – menu details view figure 6. menu details page figure 6 displays the menu details page listed photos, descriptions, length of creation and price as well as the number of menus that visitors want to order. 4. user page – order menu view figure 7. order menu page in figure 7 displays the order basket from the menu that the visitor has chosen to order. on this page visitors can still change the number and delete the order that has been selected. system analysis a. results of calculation and analysis of user satisfaction levels the assessment of user satisfaction levels of the e-order menu system uses a likert scale such as table 2 based on questionnaires distributed to 20 respondents. the method used is pieces with the formula of the average level of satisfaction (rk) of the system as in equation 2. rk = number of questionnaire scores number of questionnaires (2) here are the results of the questionnaire calculations of each pieces indkator as in table 5. table 5. results of satisfaction level of pieces indicator pieces indicators average satisfaction (rk) score performance 4,35 satisfied information 4,43 satisfied economics 4,27 satisfied control 4,33 satisfied efficiency 4,48 satisfied service 4,5 satisfied total 4,39 satisfied 35 the results of the calculation of average satisfaction (rk) users on the performance indicator get a value of 4.35 then based on the level of satisfaction in table 3 can be categorized as satisfied. in the information indicator gets a value of 4.43 then it can be categorized as satisfied. in the economics indicator gets a value of 4.27 then the economic value indicator can be categorized as satisfied. in the control indicator gets a value of 4.33 then it can be categorized as satisfied. in the efficiency indicator gets 4.48 then it can be categorized as satisfied. on the service indicator gets 4.5 then it can be categorized as satisfied. based on these results, it can be concluded that users of the menu e-order system at café 50/50 coffee are satisfied. system testing a. measurement of usability with use questionnaire method testing was conducted by spreading questionnaires that adopted questions related to 4 aspects of the use questionnaire. questionnaires were distributed to prospective users of the café 50/50 coffee e-order system as many as 20 respondents. respondents accessed the e-order system and used it, then filled out a questionnaire by giving an assessment according to the likert scale in table 2. the measurement of usability is obtained by calculating the percentage of answers from all respondents as in equation 3. the number of observed scores was obtained by calculating the results of all respondents's answers to each aspect which was then multiplied by each score using the likert scale. the number of expected scores obtained from the highest value of the likert scale is multiplied by the number of respondents and then multiplied by the number of questions each aspect of usability. pk (%) = observed score expected score × 100 ..................................... (3) the results of usability measurements can be seen in table 6, then obtained calculations and percentage eligibility (pk). table 6. usability aspect measurement results aspects of usability respondent score max score (%) usefulness 694 800 86,7% easy of use 945 1.100 85,9% easy of learning 345 400 86,2% satisfaction 602 700 86% total 2.586 3.000 86,2% the results of measurements on the usefullness aspect obtained a percentage of 86.7% then the conclusion obtained that the e-order system of café 50/50 coffee menu is very useful. the results of measuring easy of use aspects obtained a percentage of 85.9% then the conclusion obtained that the e-order system of café 50/50 coffee menu is very easy to use. the results of the measurement of easy of learning aspects obtained a percentage of 86.2% then the conclusion obtained that the e-order system of café 50/50 coffee menu is very easy to learn. the results of measurements in the satisfaction aspect obtained a percentage of 86% then the conclusion obtained that the e-order system of café 50/50 coffee menu is very satisfying for users. the score observed from the total score of answers came from 20 respondents, which was 2,568, while the expected score was 3,000. based on these results, it was obtained a measurement of 86.2% which in table 3 is at intervals of 81-100% so that the results of measuring the usability of the café 50/50 coffee menu e-order system are classified "very useful". conclusions and suggestions conclusion café 50/50 coffee menu e-order system designed with waterfall method and uses pieces method for user satisfaction level analysis. based on the results of the analysis of the calculation of the average amount of satisfaction level of 4.39, it can be concluded that users of the e-order menu system at café 50/50 coffee are satisfied. while the results of usability measurement of 86.2% which shows that the e-order system of café 50/50 coffee menu is classified as very useful. suggestion based on the conclusions obtained, there are several suggestions that can later be done for future research that researchers can add other features in the e-order menu system, such as installing payment gateways for payment processes. further research can also add and develop analytical and testing methods with other methods. references bastian, r. a. 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(2018). waterfall modelling pada sistem e-restorant. jurnal protekinfo, 5, 2406–7741. 233 employee turnover classification using pso-based naïve bayes and naïve bayes algorithm in pt. mastersystem infotama endang sri palupi sistem informasi universitas bina sarana informatika www.bsi.ac.id endang.epl@bsi.ac.id abstrak turnover yang terjadi karena banyak karyawan yang keluar dan karyawan yang baru masuk, sehingga perputaran keluar masuk karyawan cukup tinggi, karena itu turnover dapat dikendalikan dengan strategi peningkatan engagement karyawan. pt. mastersystem infotama adalah sebuah perusahaan system integrator atau lebih dikenal dengan perusahaan it yang cukup besar dengan jumlah karyawan keseluruhan kurang lebih 600 karyawan. turnover yang cukup tinggi membuat beberapa divisi kekurangan sumber daya manusia, dan divisi human capital management cukup kesulitan melakukan perekrutan karyawan untuk mencari kandidat dengan berbagai kriteria yang harus tersedia dalam waktu singkat. persaingan dunia it cukup ketat baik dalam perusahaan ataupun karyawan dengan pengalaman dan kemampuan yang sudah bagus. terutama bagian sales yang memegang database para customer yang potensial, dan bagian engineer yang sudah mempunyai sertifikat keahlian yang banyak digunakan dalam dunia bisnis it. oleh karena itu perlu diklasifikasi faktor apa saja yang membuat tingginya turnover karyawan dengan menggunakan algoritma naïve bayes dan naïve bayes berbasis particle swarm optimization, sehingga bisa menjadi bahan evaluasi internal untuk meningkatkan engagement karyawan. hasil penelitian ini klasifikasi menggunakan algoritma naïve bayes akurasinya sebesar 79.17% sedangkan klasifikasi menggunakan algoritma naïve bayes berbasis particle swarm optimization sebesar 94.17%. kata kunci: algoritma naïve bayes, particle swarm optimization, turnover abstract turnover occurs because many employees leave and new employees enter, so the turnover in and out of employees is quite high, therefore turnover can be controlled with a strategy to increase employee engagement. pt. mastersystem infotama is a system integrator company or better known as a fairly large it company with a total of approximately 600 employees. turnover is high enough to make some divisions lack human resources, and the human capital management division is quite difficult to recruit employees to find candidates with various criteria that must be available in a short time. competition in the it world is quite tight both within companies and employees with good experience and abilities. especially the sales department that holds a database of potential customers, and the engineer section that already has a certificate of expertise that is widely used in the it business world. therefore, it is necessary to classify what factors make employee turnover high by using the naïve bayes and naïve bayes algorithms based on particle swarm optimization, so that they can be used as material for internal evaluation to increase employee engagement. the results of this study, classification using the naïve bayes algorithm, has an accuracy of 79.17%, while the classification using the naïve bayes algorithm based on particle swarm optimization is 94.17%. keywords: algoritma naïve bayes, particle swarm optimization, turnover introduction the employee turnover rate in a company which is measured based on the number of workers in a certain period or known as high turnover is a serious problem at pt mastersystem infotama. with the increase in turnover, many projects are delayed or the team dismantles so that the project is not http://creativecommons.org/licenses/by-nc/4.0/ 234 completed on time. recruiting new employees is timeconsuming and costly, has to be by the specifications of the requirements, skills according to the positions required, not to mention the adaptation of employees to the current work system. for this reason, the company wants to minimize the turnover that occurs by improving several matters related to career development, company regulations, bonuses and incentives, the applicable system, and other policies. it is expected that after that the turnover rate at the company will decrease so that it can achieve the company's goals and targets well, and be able to compete with competing companies. (noviyanto, 2018) in previous research written by stefani arika noviyanti in a thesis entitled employee turnover prediction using the naïve bayes classification method in 2018, to predict employees who will resign in 2019. the results of this study are used for the development of employee planning so that they can assist in achieving the target company. meanwhile, the authors classify the turnover factors that occur, so that they can make better human resource development plans and increase employee engagement to reduce turnover. (noviyanto, 2018) in 2018 there was also research and was stated in a journal entitled classification of employee status determination using the naïve bayes method written by fattya ariani and friends. this study uses five attributes, namely: attendance, attitude, psychological test, interview, and appointment. the attributes used as labels are lifted. the result is that manual calculation and the use of rapidminer yield the same values. the value of c1 (lifted) is 0.567, while the value of c2 (not lifted) is 0.433. and the highest value is elevated. the difference with this study is that the authors classify the turnover factor, although many employees are appointed as permanent employees if the high turnover rate is futile and they have to recruit new employees again so that it takes time and operational costs. (ariani et al., 2018) another research was conducted by taufik asra in 2019 with the title comparison of c4.5, k-nn, and naïve bayes algorithms in employee admission using pso at pt. xyz. this study calculates employee acceptance in the company by comparing 3 algorithms, namely c4., k-nn, and naïve bayes. the result is the highest level of accuracy in the calculation with the naïve bayes algorithm optimized with pso with an accuracy rate of 91.25%. the difference with the author's research is that the author uses the naïve bayes algorithm and the pso-based naïve bayes algorithm and the authors classify the turnover factors that occur in the company. (asra, 2019) another journal that discusses classification using the naïve bayes algorithm in 2015 by alfa saleh with the title implementation of the naïve bayes classification method in predicting the amount of household electricity usage. this study uses 60 data on electricity users and the results are accurate with a percentage of 78.333% of the 60 data on electricity users, 47 electricity users have been classified correctly. the author uses more employee data, namely 400 permanent employee data so that more data can be classified. (saleh, 2015) the journal entitled decision support system for determining employee monthly incentives using the naïve bayes method was written by victor marudut mulia siregar in 2018. this study aims to determine the number of employee incentives and implemented them in a microsoft visual studio 2013 application. the results of the calculation use the naïve algorithm method. bayes is accurate and can be used to calculate appropriate incentives for each employee. the difference with this study is that the authors classify employee turnover using the naïve bayes algorithm method. (marudut et al., 2018) then in the journal written by taufik asra in 2019 entitled comparison of c4.5, k-nn and naïve bayes algorithms in employee admission using pso at pt. xyz, the result has the highest accuracy using the naïve bayes algorithm, optimized with pso, which is 91.25%. the difference with this journal is that the authors classify employee turnover while in this study the comparison of employee acceptance and the author only uses the naïve bayes algorithm and psobased naïve bayes algorithm. (asra, 2019) the journal with the title decision making for non-permanent employees to become permanent employees with a decision tree was written by febryantahauji and friends in 2018. this journal uses the c4.5 decision tree algorithm for the selection of permanent employee admissions. this study only uses the decision tree c4.5 algorithm method, there is no comparison. the purpose of this research is to avoid naughty people in the appointment of permanent employees so that the company gets qualified employees, while this study aims to classify employee turnover so that the company can create programs to increase employee engagement. (sembiring, 2018) the journal entitled prediction of employee promotion with the c4.5 algorithm (case study: senayan jakarta apartments) was written by sunarti in 2019. the results of the study were 78% accurate using the c4.5 algorithm. this journal only uses the c4.5 algorithm method and its purpose is to predict employee promotions. meanwhile, the authors compared the naïve bayes algorithm with the psobased naïve bayes algorithm to classify employee turnover. (sunarti, 2019) http://creativecommons.org/licenses/by-nc/4.0/ 235 furthermore, the journal entitled prediction of loyalty in employee engagement with the company using the c4.5 algorithm (case study of pt. xyz) was written by bayu ferdiansyah and leonard goeirmanto in 2020. the study conducted a loyalty survey using ees (employee engagement survey) with the google form application, for the question using excel. using the c4.5 algorithm to predict employee engagement loyalty using 1002 sample data. the result of accuracy is 90.11% with the statement that employees are not loyal to the company. this research uses only one algorithm, c4.5, with no comparison with other algorithms and does not use pso optimization. (bayu ferdiansyah, 2020) the next journal entitled application of employee status classification using the naïve bayes algorithm method at h. syaiful anwar hospital was written by rino guphita in 2018. this research is to classify the status of new employees using variables of education level, work experience, and basic salary. this study only uses one naïve bayes algorithm method and does not use pso optimization. this research is also implemented using php and mysql platforms. (rino gupitha, 2018) the research entitled factors affecting turnover intention of employees at pt. mandiri tunas finance bengkulu was written by andriyani prawitasari in 2005. the results of this study the factors that influence turnover are: age, length of work, education, attachment to the company, job satisfaction with the company, corporate culture. the author uses these factors as data attributes. the difference is that the authors calculate the accuracy of these factors. (prawitasari, 2016) the next journal reference is the journal entitled comparison of employee acceptance analysis using the c4.5 algorithm, k-nn, and psobased naïve bayes. this research was written by ayuni asistyasari and yosep nuryaman in 2019 by comparing the 3 algorithms used according to the journal title. the result of the lowest order accuracy is the classification using the c4.5 algorithm based on pso 86.25%, then the second classification uses the pso-based k-nn algorithm the accuracy value is 82.50%, and the last one with the highest accuracy is the classification using the naïve bayes algorithm. based on pso with an accuracy value of 91.25%. classification using the pso-based naïve bayes algorithm produces high scores as well as this study. (asistyasari, 2019) the journal entitled determination of work schedule based on employee data classification using the c4.5 decision tree method (case study of the university of muhammadiyah surabaya) was written in 2016 by triuli novianti and iwan santosa. this journal discusses determining employee work schedules based on classification using the c4.5 decision tree algorithm. the final result of all data using cross-validation 5 fold the accuracy is 70%. the journal only uses one algorithm, namely the decision tree algorithm c4.5 without using pso optimization so that there is no comparison of the results with other algorithms. (triuli novianti, 2016) the next journal entitled determination of overtime schedule with classification of employee data using the c4.5 algorithm was written by ikhsan romli and ahmad turmudi zy pda in 2020. this research was made to determine employee overtime schedules and the results were 91% accuracy with 86.05% precision and 92 recall. 5%. this study only uses one c4.5 algorithm without using pso optimization, while the authors use pso optimization for comparisons. (ikhsan romli, 2020) the next reference is the journal entitled naïve bayes algorithm analysis for classification of exemplary employees at pt. toyoseal indonesia, written by chandra naya and muhlisin in 2019. this study determines exemplary and non-exemplary employees (performance) in a company with 5 attributes, 160 data, and with only one algorithm method, namely naïve bayes with an accuracy of 81.25%, precision 77.78% and recall 87.50%. this research does not use optimization so there is no comparison to compare the results of its accuracy. (chandra naya, 2019) in 2019 deni anugrah sahputra and friends wrote a journal entitled determining the eligibility factors for employee admission using the decision tree algorithm at pt. outsourcing personnel. with the amount of data as many as 19163 candidates who applied in 2019, using the decision tree algorithm resulted in an accuracy of 73.27% and an auc value of 0.789. the most influential recruitment eligibility factors are references from friends, age from 26-30 years old, female gender, for office services positions, domiciled in jabodetabek, and undergraduate education level. this study only uses one algorithm, namely the decision tree, so there is no comparison for comparisons and does not use pso optimization. (sahputra & saelan, 2020) the purpose of this study is to classify the turnover factor at pt. mastersystem infotama so that the human capital management section can change the system or work program so that it can reduce the turnover rate at the company. with the decrease in the turnover rate, the employee's commitment to the company increases, saves time and costs for the recruitment of new employees, and employees last a long time to work at the pt mastersystem infotama company, so that all employees can more easily work together with the company to achieve targets and goals. http://creativecommons.org/licenses/by-nc/4.0/ 236 research methods this study uses the crisp-dm (crossindustry standard process for data mining) model. the cross-industry standard process for data mining (crisp-dm) was developed in 1996 by analysts from several industries. crisp-dm provides a standard data mining process for solving general problems of business or research units. (daniel t. larose, 2005) crisp-dm stands for cross-industry standard process model for data mining, which describes the data mining process in 6 stages. figure 1. stages of crisp-dm (larose, 2014) 1. business understanding base on figure 1. stages of crips-dm, the purpose of this study is to classify the turnover factors that occur in the company. it is hoped that the company can make a better work program and a more conducive office atmosphere, increase employee engagement with the company, and make employees feel more comfortable so that the turnover rate decreases. reduced turnover can reduce the budget for recruiting employees, running projects are not interrupted due to changes or lack of resources, sales last a long time so that good relationship with customers are well maintained because they are maintained with the same people. 2. data understanding at this stage, the authors take the employee data from the hcm section plus the results of the employee satisfaction survey at the company via a google form. data of 400 employees and only taken from employee data with permanent employee status, employees with contract status, and data from outsourcing is not taken. 3. data preparation after all, data has been collected there are several useless attributes that must be removed using remove useless attributes, after the remove useless attributes process in the rapid miner studio framework, the attributes used in the data are: name, age, years of service, education, the value of satisfaction with the company, loyalty, kpi and survive or not. 4. modelling in this phase, the authors conducted modeling using the rapid miner studio framework with the naïve bayes algorithm and the pso-based naïve bayes algorithm. first, the author uses the validation function and the naïve bayes algorithm to get the accuracy and auc values. then the two authors used the validation function and the pso-based naïve bayes algorithm to get the accuracy value and auc as a comparison. 5. evaluation at this stage, the authors evaluate the results of the modeling phase and the results using the psobased validation function and naïve bayes algorithm to get the accuracy value and the auc results are better using pso optimization. 6. deployment the best result of modeling is to use the naïve bayes algorithm based on particle swarm optimization with an accuracy value of 81.32% and an auc value of 0.837. the use of pso increases the classification accuracy value for weighting the naïve bayes algorithm. types of research this research uses qualitative methods with case studies at pt. mastersystem infotama is a system integrator company in jakarta. against the background of the high turnover rate at the company, which could hinder the company from achieving its target. time and place of research this research was conducted in february 2019 at pt mastersystem infotama in central jakarta. the author is an employee of the company, making it easier for permission and access to conduct observations and research to obtain data. research target / subject the subjects of this study were employees at pt. mastersystem infotama, to classify what factors cause high turnover rates. researchers conducted http://creativecommons.org/licenses/by-nc/4.0/ 237 observations, research, and interviews between office hours and during breaks. procedure this framework represents the steps and procedures that will be carried out in this research process. the research framework can be described in the following figure: figure 2. research framework (ryfa, 2020) 1. identification of problems by figure 2. the research framework in the identification of the problem, the problem is determining the turnover factor classification pattern so that the turnover rate decreases to achieve the company's target well. 2. data collection at the data collection stage in figure 2. research framework, the data used are the results of observations, research, interviews, and from the company what is the turnover rate that occurred in 2018. 3. manual data processing using the naïve bayes method the next step is shown in figure 2 of the research framework, which is processing data using the naïve bayes method. this step performs manual calculations using the naïve bayes formula from the data that has been obtained. 4. data processing using rapid miner then next is the use of the rapid miner application using the naïve bayes algorithm operation according to the steps in figure 2 of the research framework. 5. conclusion the last stage in the research framework stage is a conclusion. the conclusion is obtained after getting the results from both manual data processing and also using rapid miner. (ariani et al., 2018) data, instruments, and data collection techniques the author obtained data from the company's human capital management division in the form of 400 employee data. then the authors also conducted literature studies, observations, and interviews. 1. literature study collect literature, data, good information from scientific journals, books, websites, and magazines. 2. observation read and observe directly at the company where you research how the work system is happening every day so far. 3. interview interview with employee correspondents who work at the research company, what obstacles are often faced, what factors cause employees to want to resign, as well as confirm the results of the data obtained from observations. (ryfa, 2020) data analysis technique quantitative research, in general, can be described as scientific research that is used to test a theory, using a theory consisting of variables and models, analyzed and measured by statistical numbers. in this study, the authors used quantitative data analysis techniques, namely observation and interviews with employees to find out why the turnover rate was high, what factors caused turnover. the author also obtained employee data as many as 400 permanent employee data. it is hoped that with this research the human capital management division can implement and implement policies that can reduce employee turnover so that all employees and the company can work together to achieve goals and targets. results and discussion model testing with the naïve bayes algorithm using employee data with permanent employee status in 2018 as many as 400 employee data. http://creativecommons.org/licenses/by-nc/4.0/ 238 information gain feature selection process figure 3. remove useless attributes after the dataset is available, the next step is to remove unused attributes using the remove useless attributes operator in the rapidminer studio framework. as in figure 3 using the useless attributes function in the rapidminer studio framework. figure 4. information gain furthermore, in figure 4. the information gain, the authors weighting using the information gain operator on the framework rapidminer studio. table 1. employee data name age years of services (year) education value of satisfaction to the company loyalty kpi stay edi bakhtiar 57 14 diploma good good good yes endang sri palupi 38 7 magister good good good yes yanto susanto 55 14 high school good average average no alex yusuf efendy 40 14 magister average good not good no shanti cecilia 42 14 diploma good average good yes nadia rahmawati 35 14 diploma not good good average no cecilia fang 39 14 bachelor average good not good no kevin aryanti 32 14 diploma good good good yes ari baharudin 35 14 bachelor good good average yes aryo nugrohon 30 13 bachelor not good average not good no budi susanto 29 14 bachelor not good good average no paulus alexander 26 13 bachelor good average average no anita nurhasanah 23 14 high school good average good yes siti azarah 22 13 high school good good good yes muhammad dzikra 27 14 bachelor not good good average no ryan nurwahid 24 13 diploma average not good average no putra muhammad hadi 24 13 bachelor average not good average no rachmat setiawan 29 13 high school good good good yes wawan cahyadi 32 13 magister good good good yes fika taulani sari 24 13 bachelor good average good yes alfin dwi iswinantyo 23 12 bachelor good not good average no susi yulianti 22 12 high school good average not good no felicia cecilia que 27 12 bachelor not good good not good no after the process of removing unused attributes with the operator, removing useless attributes, and weighting them with the information gain operator, this is the employee data that is ready for use in table 1. employee data, naïve bayes classification in figure 5. design of the naïve bayes algorithm model, creating a model design using the naïve bayes algorithm using the rapid miner studio framework. figure 5. design of the naïve bayes algorithm model figure 6. validation of the naïve bayes algorithm figure 6. validation of the naïve bayes algorithm is a validation modeling using the naïve bayes algorithm using the rapid miner studio framework. figure 7. accuracy of the naïve bayes algorithm method http://creativecommons.org/licenses/by-nc/4.0/ 239 figure 7. accuracy of the naïve bayes algorithm method is modeling using the naïve bayes algorithm using the rapid miner studio framework figure 8. area under roc curve base on the figure 8 area under the roc curve, the auc value of 0.917 is a good classification category. naïve bayes classification then we will create a model design using the pso-based naïve bayes algorithm using the rapid miner studio framework. figure 9. design of the naïve bayes algorithm model based on particle swarm optimization figure 9 is the naïve bayes algorithm modeling design that is optimized using particle swarm optimization in the rapid miner studio framework. figure 10. particle swarm optimization process figure 10 is the process of using particle swarm optimization in the rapidminer studio framework. figure 11. validation of the naïve bayes algorithm based on particle swarm optimization figure 11 is the validation process of the naïve bayes algorithm based on particle swarm optimization using the rapid miner studio framework. figure 12. accuracy of the naïve bayes algorithm method based on particle swarm optimization base on figure 12 the results of the accuracy using the naïve bayes algorithm method based on particle swarm optimization amounted to 94.17%. figure 13. area under roc curve the auc value of 0.960 is a good classification category. http://creativecommons.org/licenses/by-nc/4.0/ 240 conclusions and suggestions conclusion the results of the classification of the naïve bayes algorithm have an accuracy of 79.17%, 66.67% precision, 91.30% recall, and auc 0.917. while the classification uses the naïve bayes algorithm method based on particle swarm optimization with an accuracy of 94.17.32%, 89.80% precision, 95.65% recall, and 0.960 auc. from the results, it can be seen that the pso-based naïve bayes algorithm method has better accuracy than using the naïve bayes algorithm method alone. suggestion for further research, you can use bigger attributes and more data. can use other algorithms that are optimized with particle swarm optimization so that the accuracy value is more perfect. references ariani, f., amir, alam, n., & rizal, k. (2018). klasifikasi penetapan status karyawan dengan menggunakan metode naive bayes. jurnal paradigma, xx(2), 33–38. https://doi.org/10.31294/p.v asistyasari, a. n. y. (2019). komparasi analisis penerimaan karyawan menggunakan. november, 519–525. https://doi.org/10.30998/simponi.v0i0.519 asra, t. (2019). komparasi algoritma c4.5, k-nn dan naïve bayes dalam penerimaan karyawan menggunakan pso pada pt. xyz. indonesian journal on software engineering (ijse), 5(2), 71– 79. https://doi.org/10.31294/ijse.v5i2.6959 bayu ferdiansyah, l. goeirmanto. (2020). prediksi loyalitas dalam keterikatan karyawan terhadap perusahaan menggunakan algoritma. jurnal sistem dan teknologi informasi, 8(1), 87– 97. chandra naya, m. (2019). analisa algoritma naïve bayes untuk klasifikasi karyawan teladan pada pt. toyoseal indonesia. sigma jurnal teknologi pelita bangsa, 10(2407–3903), 39– 45. herry derajad, w (2020). implementasi data mining dengan algoritma naïve bayes pada penjualan obat. jurnal informatika, vol 7 no 1 april, 1-7. ikhsan romli, a. t. z. (2020). penentuan jadwal overtime dengan klasifikasi data karyawan menggunakan algoritma c4.5. jurnal sains komputer & informatika (j-sakti), 4(september), 694–702. larose, d. t. (2014). discovering knowledge in data : an introduction to data mining. (i. new jersey: john wiley & sons (ed.)). marudut, v., siregar, m., komputer, t., indonesia, p. b., utara, s., cafe, e. s., cafe, s., & cafe, e. s. (2018). sistem pendukung keputusan penentuan insentif bulanan pegawai dengan menggunakan metode naïve bayes. 7, 87–94. noviyanto, a. s. (2018). prekdiksi turnover karyawan menggunakan metode klasifikasi naive bayes. v. prawitasari, a. (2016). faktor-faktor yang mempengaruhi turnover intention karyawan pada pt. mandiri tunas finance bengkulu. ekombis review: jurnal ilmiah ekonomi dan bisnis, 4(2), 177–186. https://doi.org/10.37676/ekombis.v4i2.283 rino gupitha. (2018). penerapan klasifikasi status pegawai menggunakan metode naïve bayes kajian teori penelitian terkait. jurnal global, v(1), 28–38. ryfa. (2020). teknik pengumpulan data kualitatif dan kuantitatif dalam metode penelitian. https://kumparan.com/berita-update/teknikpengumpulan-data-kualitatif-dan-kuantitatifdalam-metode-penelitian-1ukfvn1fwgs/full sahputra, d. a., & saelan, m. r. r. (2020). penentuan faktor kelayakan penerimaan karyawan menggunakan algoritma decission tree pada perusahaan pt . personel alih daya. jurnal sains dan informatika, 6(november), 148–156. https://doi.org/10.34128/jsi.v6i2.225 saleh, a. (2015). implementasi metode klasifikasi naïve bayes dalam memprediksi besarnya penggunaan listrik rumah tangga. creative information technology journal, 2(3), 207–217. sembiring, i. (2018). pengambilan keputusan pegawai tidak tetap menjadi pegawai tetap dengan decission tree. journal of informatics education, 1, 26–37. sugiyono. (2013). metode penelitian kuantitatif, kualitatif dan r&d. sunarti. (2019). prediksi promosi jabatan karyawan dengan algoritma c4 . 5 ( studi kasus : apartemen senayan jakarta ). techno.com, 18(4), 288–298. triuli novianti, i. s. (2016). penentuan jadwal kerja berdasarkan klasifikasi data karyawan menggunakan metode decision tree c4 . 5 ( studi kasus universitas muhammadiyah surabaya). jurnal komunikasi, media dan informatika, 5(1). http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 4, no. 1 december 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.279 23 prediction of android handphone sales during pandemic using naïve bayes and k-nn methods based on particle swarm optimization endang sri palupi sistem informasi universitas bina sarana informatika endang.epl@bsi.ac.id abstrak pada masa pandemic sebagian besar sekolah, kampus, dan tempat pendidikan melakukan kegiatan belajar mengajar secara online. kegiatan belajar mengajar banyak dilakukan dengan menggunakan aplikasi zoom, google, webex, atau microsoft teams. semua itu bisa dilakukan melalui laptop, bisa juga menggunakan handphone (hp) sehingga kebutuhan akan laptop dan hp meningkat baik barang baru maupun barang yang bekas. walaupun dimasa pandemic keadaan ekonomi menurun, banyak perusahaan yang mengalami kerugian, sehingga terjadi pengurangan karyawan dan menimbulkan angka pengangguran yang tinggi, kebutuhan akan hp android tetap tinggi. selain untuk sarana pembelajaran jarak jauh secara online, hp android juga bisa digunakan untuk penjualan online melalui e-commerce, market place, media sosial, dan platform digital lainnya. saat ini hp android mempunyai banyak pilihan dan sesuai dana yang kita miliki, dengan berbagai brand dan spesifikasi. banyak merk mengeluarkan produk hp android dengan spesifikasi yang cukup bagus dan harga yang terjangkau, sehingga walaupun daya beli menurun akibat pandemi, penjualan hp android tetap banyak. dalam penelitian ini penulis memprediksi penjualan hp android yang terbanyak menggunakan metode naïve bayes dan metode k-nearest neighbor berbasis particle swarm optimization.hasil dari penelitian ini menggunakan algoritma naïve bayes yaitu sebesar 74.92%, sedangkan menggunakan algoritma k-nearest neighbor berbasis particle swarm optimization nilai akurasi sebesar 81.33%. kata kunci: android, k-nearest neighbor, naïve bayes abstract during the pandemic, most schools, campuses, and places of education conducted online teaching and learning activities. many teaching and learning activities are carried out using the zoom, google, webex, or microsoft teams applications. all of that can be done through a laptop, you can also use a cellphone (hp) so that the need for laptops and cellphones increases, both new and used goods. even though during the pandemic the economic situation was declining, many companies suffered losses, resulting in a reduction in employees and causing a high unemployment rate, the need for android phones remains high. in addition to online distance learning facilities, android phones can also be used for online sales through e-commerce, market places, social media, and other digital ceilings. currently, android phones have many choices and according to the funds we have, with various brands and specifications. many brands issue android cellphone products with pretty good specifications and affordable prices, so that even though purchasing power has decreased due to the pandemic, sales of android cellphones are still high. in this study, the author predicts the highest sales of android cellphones using the naïve bayes method and the k-nearest neighbor method based on particle swarm optimization. accuracy of 81.33%. keywords: android, k-nearest neighbor, naïve bayes introduction when the covid-19 pandemic hit indonesia, sales of android cellphones actually increased, both new and used cellphones. the need for an android cellphone is related to online teaching and learning activities from kindergarten to university level students, online business that many people do during a pandemic, for content creators and various activities can be done using an android cellphone during this pandemic. the author conducted a study to predict the most sales 24 of android cellphones during the covid19 pandemic to find out the biggest target market for indonesian people is the lower middle class. android cellphones at affordable prices but with good specifications and performance are the most widely purchased flagship by the public. various brands and types of android phones with competitive quality and prices are currently very busy on the market, so people have many choices depending on their needs and available funds. no matter how good the sophistication of a smartphone, it will be redundant and useless if it doesn't fit your needs. (solihin, 2017) prediction is an attempt to predict future conditions through testing past conditions. forecasting sales means determining the estimated amount of sales volume, even determining to make decisions or policies in accordance with the results of the sales predictions, before this scientific research is carried out the potential sales and market area controlled in the future. (wibowo, 2018). with this research, it is possible to predict sales of hp that are selling well and not selling well according to the interests of buyers, so that sellers can prepare inventory of goods in the future according to the interests of buyers and are expected to further increase sales and reduce the risk of loss due to stock items that are not selling well. in a previous study, eka pandu cyntia and edi ismanto in 2018 conducted a study entitled "c4.5 decision tree algorithm method in classifying sales data for fast food outlets" (cynthia & ismanto, 2018). the purpose of this study is to classify fast food outlets that are popular (selling) and less popular (less selling) using algortima c4.5. the results of this study indicate price amount sold food menu (rice bento = less selling, dada = selling) with the weight of each attribute: price (0.738), menu type (0.067), amount sold (0.156), status sales (0.040). in this study, eka pandu and colleagues only used one c4.5 algorithm and displayed only the decision tree, not calculating its accuracy. the results of this study are the value of each attribute used and what foods are selling well and not selling well. while in this study the authors predict sales of android phones using 2 methods of the decision tree algorithm and the k-nn algorithm as a comparison and calculate the accuracy of each algorithm. another study was written by juna eska in 2016 from stmik royal ksiaran entitled "application of data mining for wallpaper sales prediction using the c4.5 algorithm" (eska, 2016). the result of this research is that the highest factor influencing sales is the number of wallpaper motifs. the factors of price, size, quality of materials, and colors do not affect purchases because wallpapers with high prices, small sizes, good quality materials, and few colors are still in demand by customers. this study only uses the c4.5 algorithm method, previously calculated the gain and entropy values manually using excel, then for the decision tree using rapidminer, the results of the study are that the highest factor affecting wallpaper sales is motifs. while the writer uses the c4.5 algorithm, the k-nn algorithm as a comparison, the results of this study are the accuracy values of each algorithm and the conclusion is that the pso-based k-nn algorithm has a greater accuracy value than the c4.5 algorithm. the research entitled prediction of honda's best selling products with classification method using the c4.5 algorithm (case study: sales data of pt prospect motor, cikarang) was written by aswan s sunge and heri fidiawan in 2019.(sunge & fidiawan, 2019) from the research the accuracy obtained is 67.5%, while in this study the author uses 2 algorithms as comparison material, namely the c4.5 algorithm and the pso-based k-nn algorithm with greater accuracy using the psobased k-nn algorithm with an accuracy value of 81.33%. in 2020 alfian faiz i and sulastri conducted a research entitled "classification of android application sales using the c4.5 algorithm". this study classifies sales of applications that can be used on android. three classification experiments resulted in different accuracy values. the highest accuracy value is obtained from experiments with 210 training data and 90 testing data, which shows an accuracy rate of 73.3%. rating is the attribute that most influences the application that is classified as selling or not selling. the difference is that the research classifies sales of android applications, while in this study the authors predict sales of cellphones using android applications. this research only uses the c4.5 algorithm method, there is no comparison with other algorithms (alfian faiz izzulhaq1, 2020) in 2020 ahmad zakir and colleagues conducted a study entitled "application of data mining for classification of best selling food sales data with algorithm with c4.5 algorithm". the research was conducted at a burger shop, and implemented using the c4.5 algorithm with php programming language and mysql database. the result of this research is to produce a system that can determine which foods are selling well and not selling well using the c4.5 algorithm. the difference with this study is that the author uses two algorithms as a comparison with the results of the greatest accuracy value using the pso-based k-nn algorithm of 81.33%, and the author uses the jurnal riset informatika vol. 4, no. 1 december 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.279 25 rapidminer framework in the implementation of the algorithm. (zakir, ndruru, & hadinata, 2020) in 2020 ismasari nawangsih conducted a study entitled "application of the naïve bayes algorithm to determine the classification of the best selling products in credit sales" in this study the authors performed calculations manually using the naïve bayes formula then calculated using rapid miner. the result of this research is that the name of the telkomsel pulsa product is the bestselling product and the accuracy value is 97.50% so that naïve bayes is a fairly good method in classification. while the authors in this study directly calculate using rapidminer to calculate accuracy using 2 algorithm methods, namely the naïve bayes algorithm and the pso-based k-nn algorithm, the results using the pso-based k-nn algorithm have a greater accuracy value of 81.33%. (ismasari nawangsih1) , 2020) this study aims to predict the most sales of android phones during the pandemic. in this pandemic period, the need for android cellphones is increasing, and with this research, it can be used as a reference for buying cellphones or buying and selling cellphones, what android cellphones are the most popular and currently selling the most. in addition, from the hp manufacturer's point of view, this research can also see what needs are most needed by the community in using hp in terms of features, price, specifications, and quality. predicting market needs is very difficult which is a problem faced by distribution companies. to find market needs, it is necessary to identify customer characteristics. (faradillah, 2013) research methods this research uses quantitative techniques. according to sugiyono, quantitative research methods can be interpreted as research methods based on the philosophy of positivism, used to examine certain populations or samples. (sugiyono, 2016). quantitative research can be defined as a process of finding knowledge by using data in the form of numbers as a tool to analyze information about what you want to know. this research method translates data into numbers to analyze the findings. the variables used in classifying bestselling products are types of goods, brands and prices with the target class being in demand and not selling well. these three variables are the benchmarks used in researching best-selling products in trading companies. (asmaul husnah nasrullah, 2021). types of research this type of research is quantitative, by taking data on sales of android cellphones during the period of the pandemic in indonesia. where during the pandemic the need for android cellphones actually increased for the teaching and learning process, business processes such as creating content or selling online through social media, marketplaces, and e-commerce. time and place of research this research was conducted in june 2020 when the pandemic occurred in indonesia, using hp sales data for the period june 2020 to may 2021 at itc cempaka mas, itc roxy mas, and several ecommerce sites in indonesia. research target / subject the target of this research is the productive age of android hp users from 12 years to 50 years. researchers conducted direct interviews, observations, and distributed google forms on whatsapp groups of students, junior high and high school students regarding the needs of hp and cellphones they currently have or want/have purchased. procedure this framework represents the steps and procedures that will be carried out in this research process. figure 1. cross-industry standard process for data mining (crisp-dm) 1. business understanding this study aims to predict the sales of android cellphones during the pandemic, which cellphones are the best and the least sold. it is hoped that with this research distributors can understand the needs of the community so that 26 in the future they can stock goods and sell goods according to the needs and desires of the community, thereby increasing sales and avoiding losses due to selling cellphones that are not selling well. 2. data understanding at this stage, the authors take hp sales data at hp stores in the itc cempaka mas and itc roxy areas. conduct observations and interviews with hp sellers and buyers. as well as making observations on e-commerce sales of android cellphones, what android cellphones sell best. sales data was taken when the pandemic occurred, which is between june 2020 to may 2021. 3. data preparation at this stage, all the data that has been collected there are some useless attributes that must be removed using delete useless attributes. the attributes used after this process are brand, type, price, ram, memory. 4. modeling the author does the modeling using rapid miner studio using the naïve bayes algorithm and the pso-based k-nn algorithm to get the accuracy value. the author uses 2 algorithms as a comparison to get the best accuracy value. 5. evaluation from the modeling results, it can be seen that using the pso-based k-nn algorithm modeling to get the best accuracy value compared to using the naïve bayes algorithm. 6. deployment the best results from this modeling use the pso-based k-nn algorithm with an accuracy value of 81.33%. data, instruments, and data collection techniques data collection using the following techniques: 1. interview interviews are used as a data collection technique to find problems that must be investigated and also if researchers want to know things from respondents more deeply. about behavior, and the meaning of that behavior (sugiyono, 2016). the author visited several hp shops at itc roxy mas and itc cempaka mas, to get data on any hp sales in the period june 2020 to may 2021. where in june 2020 and may 2021 there was an eid moment so that sales increased. the author also interviewed shoppers to find out what cellphones they wanted to buy or which had been purchased. 2. observation observation is a data collection method that uses direct or indirect observation (yatim riyanto, 2010). the author observes the sale of android cellphones in e-commerce such as shopee, tokopedia, jd.id, bhinneka.com, and others. by checking the number of items sold and the rating given by the buyer, as well as checking the best seller type of hp at that time in e-commerce. 3. questionnaire a questionnaire is a written list of questions given to the subject under study to collect the information needed by the researcher (kusumah, 2011). the author shares a google form link that contains questions related to the needs of hp and hp currently owned or that want / have been purchased on the student whatsapp group, high school and junior high school student whatsapp group. the result is that 110 people filled out the google form. data analysis technique put forward quantitative research, namely a research approach that uses a lot of numbers, starting from collecting data, interpreting the data obtained, and presenting the results. (arikunto, 2006). in this study, the author uses quantitative data analysis techniques, namely interviews with cellphone sellers and cellphone shop visitors, observing cellphone sales to various e-commerce, and distributing google form questionnaires to students, middle and high school students regarding the needs of cellphones that they already have or want to buy. a total of 110 people filled out the google form and the total data obtained was 500 datasets. with this research, the author wants to classify the types of android cellphones that are sold the most during the pandemic, where during the pandemic the community's economy is also experiencing a decline, but the need for android cellphones is also increasing with the online teaching and learning process, and activities that many people do during the pandemic. i.e. like content creators or selling online. results and discussion the following sales dataset used in this research is 500 data: table 1. sales data jurnal riset informatika vol. 4, no. 1 december 2021 p-issn: 2656-1743 |e-issn: 2656-1735 doi: https://doi.org/10.34288/jri.v4i1.279 27 data penjualan hp android juni 2020 mei 2021 n o merk type harga (idr) ram (gb) memor y bel i 1 xiaomi redmi 8a 1,400,000.00 4 256 no 2 samsun g galaxy a11 1,900,000.0 0 3 512 yes 3 samsun g galaxy a21s 2,900,000.0 0 3 128 no 4 xiaomi redmi note 8 pro 10,200,000. 00 4 256 yes 5 xiaomi redmi note 8 11,000,000. 00 4 256 yes 6 samsun g a51 4,700,000.00 3 256 no 7 xiaomi redmi 9a 1,100,000.0 0 3 256 no 8 samsun g galaxy m11 1,400,000.0 0 3 256 no 9 xiaomi poco x3 nfc 2,800,000.0 0 4 512 yes 10 realme c20 1,200,000.0 0 2 128 yes 11 infinix hot 10s 1,700,000.0 0 4 128 yes 12 samsun g galaxy a12 2,000,000.0 0 4 512 yes 13 tecno spark 7 pro 1,700,000.0 0 6 512 yes 14 xiaomi redmi note 10 2,300,000.0 0 4 128 yes 15 xiaomi poco m3 pro 5g 2,500,000.0 0 4 128 yes 16 infinix note 10 pro 2,500,000.0 0 8 128 yes 17 samsun g galaxy a325g 3,800,000.0 0 8 512 no 18 xiaomi poco x3 pro 3,500,000.0 0 8 256 yes 19 oppo a54 2,700,000.0 0 4 256 yes 20 oppo a15 2,500,000.0 0 4 256 yes 21 xiaomi redmi 9c 1,700,000.0 0 4 128 yes 22 samsun g galaxy a02 1,500,000.0 0 2 512 no 23 vivo y12s 1,800,000.0 0 4 256 yes naïve bayes classification figure 1. naïve bayes algorithm figure 1 is modeling using the naïve bayes algorithm using 500 datasets. figure 2. nave bayes algorithm validation process in figure 2 is the validation process using the naïve bayes algorithm modeling to get the accuracy value. figure 3. the results of the accuracy of the naïve bayes algorithm figure 3 shows the accuracy results using the naïve bayes algorithm of 74.92%, with true no 77.85% and true yes 74.18%. knn algorithm based on particle swarm optimization figure 4. design knn algorithm based on particle swarm optimization figure 4 is a design for weighting the knn algorithm based on particle swarm optimization using 500 datasets. figure 5. validation of the particle swarm optimization-based knn algorithm in figure 5 is the validation process of the psobased k-nn algorithm to increase the weight so that the accuracy value is better. figure 6. design knn algorithm based on particle swarm optimization 28 after the weighting process using pso, in figure 6 the modeling using the pso-based k-nn algorithm is ready to run to get a better accuracy value. figure 7. accuracy results of the particle swarm optimization-based knn algorithm the accuracy results in figure 7 using the psobased k-nn algorithm are better at 81.33% with true no 17.03% and true yes 99,12%. conclusions and suggestions conclusion the result of this study is the classification using the naïve bayes algorithm is an accuracy value of 74.92% with a true no recall class of 77.85% and true yes 74.18%. while the classification results using the particle swarm optimization-based knn algorithm have an accuracy of 81.33% with a true no 27.03% recall class and 99.12% true yes. classification using the particle swarm optimization-based knn algorithm has higher results than using the naïve bayes algorithm. dengan demikian algoritma k-nn berbasis pso bisa digunakan sebagai metode prediksi penjualan dengan hasil akurasi yang tinggi, sehingga dapat membantu meningkatkan penjualan. suggestion future research can use more varied data on questions about the applications needed, battery strength, camera needs and so on. correspondence segments interviewed can be from business people who use android cellphones, not just students and students. references alfian faiz izzulhaq1, s. 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(2017). 10 tips panduan memilih & membeli hp android berkualitas. retrieved from septian website: https://www.septian.web.id/10-tipspanduan-memilih-membeli-hp-androidbagus-berkualitas-html/ sugiyono. (2016). metode penelitian kuantitatif, kualitatif dan r&d. bandung: alfabeta. sunge, a., & fidiawan, h. (2019). prediksi produk laris mobil honda dengan metode klasifikasi menggunakan algoritma c4. 5 (studi kasus: data penjualan sales pt prospect motor, cikarang). jurnal sigma, 9(4), 97–103. retrieved from https://jurnal.pelitabangsa.ac.id/index.php/s igma/article/view/461 wibowo, d. a. (2018). prediksi penjualan obat herbal hp pro menggunakan algoritma neural network. technologia: jurnal ilmiah, 9(1), 33–41. https://doi.org/10.31602/tji.v9i1.1100 yatim riyanto. (2010). metodologi penelitian pendidikan. surabaya: sic. zakir, a., ndruru, y., & hadinata, e. (2020). penerapan data mining untuk klasifikasi data penjualan makanan terlaris dengan algoritma c45. jurnal ilmiah teknologi informasi dan robotika, 2(2), 7–12. retrieved from http://jifti.upnjatim.ac.id/index.php/jifti/arti cle/view/33 87 image enhancement on object detection using l0 gradient prior sunario megawan1*), hernawati gohzali2, apriyanto halim3 program studi teknik informatika universitas mikroskil https://www.mikroskil.ac.id/ 1*)sunario@mikroskil.ac.id, 2hernawati.gohzali@mikroskil.ac.id, 3apriyanto.halim@mikroskil.ac.id (*) corresponding author abstrak pendeteksian objek merupakan teknik yang digunakan untuk mengambil bagian-bagain tertentu pada citra. bagian tersebut dapat berupa pemandangan, manusia atau benda-benda lainnya. pada saat pendeteksian objek, citra yang didapatkan dapat mengalami penurunan kualitas citra yang dapat diakibatkan dari faktor cuaca, yaitu kabut, asap, debu, hujan dan lainnya. penurunan kualitas pada citra, dapat mengakibatkan kesalahan pada klasifikasi dan tidak mampuan dalam mengenali objek pada citra. oleh karena itu, proses perbaikan kualitas citra menjadi sangat penting untuk dilakukan pada saat tahap pre-processing dalam pendeteksian objek citra. fokus masalah yang akan diselesaikan pada penelitian ini adalah pengembali an citra kabur dengan menggunakan l0 gradient prior. hasil penelitian menunjukan penerapan l0 gradient prior dalam mengembalikan citra yang kabur dapat meningkat jumlah objek yang dapat diditeksi oleh sistem penditeksian objek. kata kunci: peningkatan kualitas citra, deteksi objek abstract object detection is a technique used to retrieve certain parts of the image. the part can be in the form of scenery, people, or other objects. at the time of object detection, the image obtained can experience a decrease in image quality which can be caused by weather factors, namely fog, smoke, dust, rain, and others. a decrease in the quality of the image can result in errors in classification and the inability to recognize objects in the image. therefore, the process of improving image quality becomes very important to do at the pre-processing stage in detecting image objects. the focus of the problem to be solved in this study is the return of a blurred image using l0 gradient prior. the results showed that the application of l0 gradient prior in rest oring a blurred image can increase the number of objects that can be detected by the object detection system. keywords: image enhancement, object detection introduction object detection is a technique used to retrieve certain parts of the image. the part can be in the form of scenery, humans or other objects (vidal, banerjee, grm, struc, & scheirer, 2018). when performing object detection, the image obtained can experience a decrease in image quality which can be caused by weather factors, like fog, smoke, dust, rain, and others (roy & bhowmik, 2021). decrease in image quality can also occur due to the process of increasing the size of the object in the image, thus making the image blurry or unclear. as for the consequences that can occur due to a decrease in image quality, namely errors in image classifiers or object suitability (borel-donohue & young, 2019). image quality degradation can also result in changes to information. changes in information that are too large of course result in a lot of information being lost in the image. image quality degradation may cause the system to not detect objects properly (hasirlioglu, reway, klingenberg, riener, & huber, 2019). this is of course very necessary for further processes in the form of object detection, angle detection and others. measurement of the level of change in the image before and after improving image quality can be seen from the success of object detection in the image. the method used in this research for object detection in the image is the faster r-cnn algorithm (ren, he, girshick, & sun, 2017). faster rcnn is the latest region-based generic object detection method that shows excellent results in various object detections (wu, yin, wang, & xu, 88 2019). several studies have shown that faster rcnn can detect objects well with an accuracy value of 7299% (gavrilescu, zet, fosalau, skoczylas, & cotovanu, 2018) (cai, li, xie, zhao, & lu, 2018) (chandan, jain, jain, & mohana, 2018)(zhang et al., 2020). several research methods have been carried out to restore blurred images, including the dark channel method (dark layer) which gives better results compared to other methods with a success rate of around 27.94 db (decibels) compared to other methods (pan, sun, pfister, & yang, 2018)(zhou, zhuang, xiong, zhao, & du, 2020). in another study conducted by (anger, facciolo, & delbracio, 2019) who used l0 gradient before restoring a blurred image. the research carried out gave the results obtained in the form of good performance which can be seen from several tests carried out in the form of various images and the addition of noise. based on the research reference, the method used in this research for restoring a blurred image is l0 gradient prior. the purpose of this research is to increase the accuracy of object detection in the image by adding a pre-processing stage in the form of improving image quality by returning a blurred image so that the object detection results are expected to have better accuracy and be able to recognize more objects in the image. research methods the research method used is l0 gradient prior to restore blurred images and faster r-cnn for object detection in video images. l0 gradient prior method consists of 3 simple stages, namely multiscale kernel estimation, sharp prediction, and kernel prediction. in figure 1, shows the flowchart of l0 gradient prior. faster r-cnn algorithm is divided into 2 important parts, namely the regional proposal network (rpn) and the classifier. rpn is used to find the input results in the image that allows the location of the object quickly. the results of the rpn process will later be made in the form of an roi (region of interest). the classifier is a process that classifies roi from the previous step into corresponding classes (abbas & singh, 2018). in figure 2, shows the flowchart of faster r-cnn. figure 1 l0 gradient prior flowchart figure 2 faster rcnn flowchart 89 types of research this research is related to computer visualization and image processing that deals with detecting examples of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. time and place of research in this study, the dataset is collected from seven intersections in the danish cities of aalborg and viborg. the resolution of cameras are 640x480 pixels and the frame rate is fixed at 20 frames/second. cctv video data on datasets taken at night were not used because the lighting conditions at night made object detection difficult. research target / subject the purpose of this research is to increase the accuracy of object detection in the image by adding a pre-processing stage in the form of improving image quality to eliminate blurred images, so that object detection results are expected to have better accuracy and be able to recognize more objects. procedure this type of research focuses on improving the quality of the image which will then proceed to the object detection process stage. improving the quality of the image is done through the process of returning the blurred image using l0 gradient prior. the results of the blurred image return process are then processed to the object detection stage with the faster r-cnn algorithm. after all, processes are successfully carried out, the stages of testing the results obtained using the confusion matrix are carried out. the next step is to compare the results of object detection accuracy before and after improving image quality. it aims to see the level of accuracy in this study. data, instruments, and data collection techniques the dataset used in this study is the aau rainsnow traffic surveillance dataset (bahnsen & moeslund, 2018). this dataset contains cctv video data that monitors road conditions in rainy and snowy conditions. data analysis technique in this study, we will use a cctv capture dataset in which there is a blurred image object. open cv library is used to convert a video into a series of images. furthermore, the series of images is processed to restore the blurred image using l0 gradient prior. furthermore, the data will be separated into two parts, namely data for training and data for testing. the test data are then analyzed to determine the accuracy of the research results using the confusion matrix. results and discussion result of l0 gradient prior the results of the implementation of blurred image improvement can be seen in figures 3 and 4. figure 3 shows a sample image on cctv video before the implementation of blurred image correction. in figure 4, after the process of correcting the blurred image using the l0 gradient prior algorithm, it looks sharper than figure 3. figure 3. sample image of cctv video before the implementation of blurred image correction figure 4. sample image of cctv video after the implementation of blurred image correction result of faster rcnn in this study, the object detection process is divided into two processes, namely the training process and the testing process. in the training process, objects trained on the system are limited to a minimum size of 50 x 50 pixels to reduce object 90 detection errors caused by the size of the trained object being too small. there were 2 (two) cctv video conditions observed, namely the condition of the cctv video that had not been processed to restore the blurred image and the condition of the cctv video that had gone through the process of returning the blurred image. examples of cctv video images that have been processed with faster rcnn can be seen in figure 5 and figure 6. figure 5 shows a sample image on cctv video before processing to restore the blurred image. figure 6 shows a sample image on cctv video after processing to restore the blurred image. the results of object detection on 2 (two) cctv video conditions can be seen in table 1. in table 1, tp represents the system successfully classifying objects moving vehicles on video correctly, fp represents the system incorrectly recognizing objects that are not vehicles but are recognized as vehicles, tn represents the system successfully correctly detects non-vehicle objects, and fn represents the system failed to detect moving vehicles as non-vehicle objects. figure 7 shows a sample image on cctv video after processing to restore the blurred image, the results of object detection with the faster rcnn algorithm show an error in object detection. figure 5. sample object detection image on cctv video before processing to restore the blurred image figure 6. sample object detection image on cctv video afterprocessing to restore the blurred image figure 7. sample image on cctv video after processing to restore the blurred image that has an error in object detection the results of table 1 then calculate the detection accuracy using the confusion matrix to measure the accuracy of the object detection results. the calculation results can be seen in table 2. based on the data in tables 1 and 2, it can be concluded that the accuracy of object detection decreased slightly when cctv video was given the process of returning a blurred image on video testing 1 but the number of objects detected by the system increased in test videos 1 and 2. tabel 1. object detection result in 2 cctv video condition video condition number of object detected test video 1 test video 2 tp fp tn fn tp fp tn fn video without deblur process 394 0 0 0 212 0 0 0 video with deblur 439 5 0 0 233 0 0 0 91 tabel 2. accuracy calculation result using the confusion matrix video condition confusion matrix result for accuracy value (%) test video 1 test video 2 video without deblur process 100 100 video with deblur process 98,87 100 conclusions and suggestions conclusion the accuracy of object detection decreased slightly when cctv video was given the process of returning a blurred image on video testing 1 but the number of objects detected by the system increased in test videos 1 and 2. this means that by applying the blur image return algorithm, the number of objects that can be recognized is more than without the application of the blur image return algorithm suggestion in this study, it is still not able to recognize objects with a size of less than 50 x 50 pixels so that in the future it is recommended to apply an algorithm to increase the size of the image so that small objects can still be detected. acknowledgement the researcher would like to thank the ministry of research and technology / national research and innovation agency (ristekbrin) who funded this research from start to finish to the publication stage. references abbas, s. m., & singh, s. n. (2018). region-based object detection and classification using faster r-cnn. 2018 4th international conference on computational intelligence & communication technology (cict), 1–6. ghaziabad: ieee. https://doi.org/10.1109/ciact.2018.84804 13 anger, j., facciolo, g., & delbracio, m. (2019). blind image deblurring using the l0 gradient prior. ipol journal · image processing on line, 9, 124–142. https://doi.org/doi.org/10.5201/ipol.2019.2 43 bahnsen, c. h., & moeslund, t. b. (2018, september 21). aau rainsnow traffic surveillance dataset. retrieved january 11, 2022, from kaggle website: https://www.kaggle.com/aalborguniversity/ aau-rainsnow/metadata borel-donohue, c. c., & young, s. s. (2019). image quality and super resolution effects on object recognition using deep neural networks. https://doi.org/10.1117/12.2518524, 11006, 596–604. baltimore: spie. https://doi.org/10.1117/12.2518524 cai, w., li, j., xie, z., zhao, t., & lu, k. 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(2020). blind image deblurring with joint extreme channels and l0-regularized intensity and gradient priors. proceedings international conference on image processing, icip, 2020-october, 873–877. https://doi.org/10.1109/icip40778.2020.91 91010 jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 71 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penerapan algoritma naïve bayes pada penilaian kinerja pemerintah desa dalam pengelolaan dana desa muliadi 1, syafruddin syarif 2, agus salim3 1,2,3 pascasarjana sistem komputer stmik handayani makassar http://pps.handayani.ac.id/ 1 muliadiscdtolis@gmail.com abstrak tujuan dari penelitian ini untuk mengklasifikasikan pemerintah desa mana yang baik dan buruk dalam pengelolaan dana desa di kab tolitoli untuk menjadi acuan untuk pemerintah daerah untuk mengontrol kinerja pemerintah desa dalam mengelolaan dana desa.metode yang digunakan dalam penelitian ini ialah naïve bayes klasifikasi berbasis pemrograman php dengan komputerisasi diharapkan dapat membantu penilaian kinerja pemerintah desa dalam pengelolaan dana desa agar lebih cepat dan tepat dalam mengklasifikasikan kinerja pemerintah desa.berdasarkan hasil analisis perhitungan yang sudah dilakukan, penerapan metode naïve bayes klasifikasi dalam penilaian kinerja pemerintah desa yang suda h di lakukan terhadap 2(dua) desa dengan kreteria yang digunakan : jumlah anggaran, anggaran bidang pemerintahan, anggaran bidang pembangunan, anggaran bidang pemberdayaan, anggaran bidang pembinaan, anggaran tak terduga dan sisa anggaran sehingga hasil akhir penilaian kinerja pemerintah desa dalam pengelolaan dana desa yakni desa dengan nama desa teluk jaya dinyatakan baik karena memiliki nilai likelihood baik 3.57921𝐸−58 > nilai likelihood buruk 3.39635𝐸−60 sedangkan desa dengan nama desa salumpaga dinyatakan buruk karena memiliki nilai likelihood baik 2.421𝐸−139 < nilai likelihood buruk 3.20355𝐸−83 kata kunci: algoritma naïve bayes klasifikasi , penilaian kinerja pemerintah desa, pengelolaan dana desa abstract the purpose of this study is to classify which village government is good and bad in managing village funds in tolitoli regency to be a reference for local government to control the performance of village government in managing village funds. the method used in this research is naïve bayes classification based on php programming computerization is expected to help assess village government performance in village fund management so that it is faster and more accurate in classifying village government performance. based on the analysis of calculations that have been done, the application of the naïve bayes method of classification in village government performance appraisal has been done against 2 (two) village with criteria used: budget amount, government budget, development budget, empowerment budget, guidance field budget, unexpected budget and remaining budget so that the final result of village government performance assessment in management village funds namely villages with the name teluk jaya village are declared good because they have a good likelihood value of 3.57921e ^ (58)> bad likelihood value of 3.39635e ^ (60) while the village with the name salumpaga village is declared bad because it has a good likelihood value of 2.421e ^ ( -139) <bad likelihood value 3.20355e ^ (83) keywords: naïve bayes classification algorithm, village government performance assessment, village fund management pendahuluan pemerintah desa diyakini lebih mampu melihat prioritas kebutuhan masyarakat dibandingkan pemerintah kabupaten yang secara nyata memiliki ruang lingkup permasalahan lebih luas dan rumit (romantis & ainurrohma, 2015), (aminah & sari, 2018). untuk itu, pembangunan pedesaan yang dilaksanakan harus sesuai dengan masalah yang dihadapi, potensi yang dimiliki, http://creativecommons.org/licenses/by-nc/4.0/ mailto:muliadiscdtolis@gmail.com p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 72 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional aspirasi masyarakat dan prioritas pembangunan pedesaan yang telah ditetapkan. pemerintah kemudian mengeluarkan peraturan pemerintah no. 72 tahun 2005 tentang desa dan peraturan menteri dalam negeri nomor 37 tahun 2007 tentang pedoman pengelolaan keuangan desa. pemerintah daerah mempunyai kewenangan yang lebih luas dalam pengelolaan daerahnya. salah satu bentuk kepedulian pemerintah terhadap pengembangan wilayah pedesaaan adalah adanya anggaran pembangunan secara khusus yang dicantumkan dalam anggaran pendapatan dan belanja daerah (apbd) untuk pembangunan wilayah pedesaan, yakni dalam bentuk anggaran dana desa (add). dengan adanya (add) pemerintah desa mempunyai peranan penting dalam pengelolaan dana desa tetapi belum adanya sistem kontrol, yang baik sebagai standar untuk menentukan pemerintah desa mana kinerjanya baik dan buruk dalam mengelolah dana desa sehingga daerah tidak mengetahui kinerja pemerintah desa dalam pengelolaan dana desa, untuk itu pemerintah daerah harus mengklasifikasikan kinerja pemerintah desa mana yang kinerja nya baik dan buruk. pengklasifikasian tersebut dapat menggunakan beberapa algoritma misalnya kmeans, c45, knn dan naïve bayes. tapi pada kasus ini penulis menggunakan algoritma naïve bayes untuk mengklasifikasikan kinerja (iskandar & suprapto, 2016) pemerintah dalam mengelola dana desa karena melihat keunggulan algoritma naïve bayes dengan algoritma lain contohnya mampu menangani data kuantitatif dan diskrit, cepat dan efesien ruangan, cukup akurat, kokoh terhadap attibut yang relevan dan tentunya popular [menurut ashraf udin , south asian university](wati & hadi, 2017). dengan data yang sudah diklasifikasikan menggunakan naïve bayes diharapkan pemerintah daerah dapat mengontrol kinerja pemerintah desa mana yang kinerja nya baik, dan buruk. 1. rumusan masalah a. bagaimana membuat sistem penilaian kinerja pemerintah desa dalam pengelolaan dana desa di kab tolitoli dengan algoritma naïve bayes classifier b. bagaimana menentukan pemerintah desa mana yang kinerjanya baik, dan buruk dalam mengelola dana desa dengan algoritma .naive bayes classifier. 2. tujuan penelitian berdasarkan permasalahan diatas maka tujuan penelitian yaitu : a. dapat mengklasifikasikan pemerintah desa mana yang kinerjanya baik, dan buruk dalam pengelolaan dana desa di kab tolitoli. b. dapat menjadi acuan untuk pemerintah daerah untuk mengontrol kinerja pemerintah desa dalam mengelola dana desa. metode penelitian 1. desa menurut peraturan pemerintah nomor 72 tahun 2005 tentang desa, disebut bahwa desa adalah kesatuan masyarakat hukum yang memiliki batas-batas wilayah yang berwenang untuk mengatur dan mengurus kepentingan masyarakat setempat, berdasarkan asal usul dan adat istiadat setempat yang diakui dan dihormati dalam sistem pemerintahan negara kesatuan republik indonesia. sedangkan menurut undang-undang nomor 6 tahun 2014 tentang desa, ditentukan bahwa desa adalah desa dan desa adat atau yang disebut dengan nama lain, selanjutnya disebut desa, adalah kesatuan masyarakat hukum yang memiliki batas wilayah yang berwenang untuk mengatur dan mengurus urusan pemerintahan, kepentingan masyarakat setempat berdasarkan prakarsa masyarakat, hak asal usul, dan/atau hak tradisional yang diakui dan dihormati dalam sistem pemerintahan negara kesatuan republik indonesia (presiden republik indonesia, 2014) 2. alokasi dana desa alokasi dana desa (add) adalah dana yang bersumber dari anggaran pendapatan dan belanja daerah (apbd) kabupaten yang dialokasikan dengan tujuan pemerataan kemampuan keuangan antar desa untuk mendanai kebutuhan desa dalam rangka penyelenggaraan pemerintahan dan pelaksanaan pembangunan serta pelayanan masyarakat. alokasi dana desa diperoleh dari dana perimbangan apbn yang diterima oleh kabupaten/kota dalam anggaran pendapatan dan belanja daerah (apbd) setelah dikurangi dana alokasi khusus (dak) sebesar 10% (presiden republik indonesia, 2014) 3. belanja desa belanja desa merupakan semua pengeluaran dari rekening desa yang merupakan kewajiban desa dalam 1 (satu) tahun anggaran yang tidak akandiperoleh pembayarannya kembali oleh desa. belanja desa dipergunakan dalam rangka mendanai penyelenggaraan kewenangan desa.belanja desa yang ditetapkan dalam apb http://creativecommons.org/licenses/by-nc/4.0/ https://id.wikisource.org/wiki/peraturan_pemerintah_republik_indonesia_nomor_72_tahun_2005 https://id.wikisource.org/wiki/peraturan_pemerintah_republik_indonesia_nomor_72_tahun_2005 jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 73 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional desa sesuai pasal 100 pp nomor 43 tahun 2014 digunakan dengan ketentuan:  paling sedikit 70% (≥ 70%) dari jumlah anggaran belanja desa digunakan untuk mendanai penyelenggaraan pemerintahan desa, pelaksanaan pembangunan desa, pembinaan kemasyarakatan desa, dan pemberdayaan masyarakat desa.  paling banyak 30% (≤ 30%) dari jumlah anggaran belanja desa digunakan untuk: 1. penghasilan tetap dan tunjangan kepala desa dan perangkat desa; 2. operasional pemerintah desa; 3. tunjangan dan operasional badan permusyawaratan desa; 4. insentif rukun tetangga dan rukun warga yaitu bantuan kelembagaan yang digunakan untuk operasional rt dan rw.\ penghasilan tetap, operasional pemerintah desa, dan tunjangan dan operasional bpd serta insentif rt dan rw dibiayai dengan menggunakan sumber dana dari alokasi dana desa. sedangkan penggunaan dana desa diprioritaskan untuk membiayai pembangunan dan pemberdayaan masyarakat. kebutuhan pembangunan meliputi tetapi tidak terbatas pada kebutuhan primer, pelayanan dasar, lingkungan, dan kegiatan pemberdayaan masyarakat desa. pengertian tidak terbatas adalah kebutuhan pembangunan di luar pelayanan dasar yang dibutuhkan masyarakat desa. kebutuhan primer adalah kebutuhan pangan, sandang, dan papan. pelayanan dasar antara lain pendidikan, kesehatan, dan infrastruktur dasar. 4. data mining data mining merupakan proses pengekstraksian informasi dari sekumpulan data yang sangat besar melalui penggunaan algoritma dan teknik penarikan dalam bidang statistik, pembelajaran mesin dan sistem manajemen basis data. data mining adalah proses menganalisa data dari perspektif yang berbeda dan menyimpulkannya menjadi informasi-informasi penting yang dapat dipakai untuk meningkatkan keuntungan, memperkecil biaya pengeluaran, atau bahkan keduanya (mustafa, ramadhan, & thenata, 2017) definisi lain mengatakan data mining adalah kegiatan yang meliputi pengumpulan, pemakaian data historis untuk menemukan keteraturan, pola atau hubungan dalam data berukuran besar. dari beberapa definisi di atas dapat ditarik kesimpulan bahwa data mining merupakan proses ataupun kegiatan untuk mengumpulkan data yang berukuran besar kemudian mengekstraksi data tersebut menjadi informasi – informasi yang nantinya dapat digunakan (olson & delen, 2008) 5. data training data training digunakan untuk membentuk sebuah model classifier. model ini merupakan representasi pengetahuan yang akan digunakan untuk prediksi kelas data baru yang belum pernah ada. data ini akan digunakan untuk proses mining berupa 40 sampel penggunaan anggaran dana desa tahun 2015-2016 dengan pengklasifikasiaan 12 desa yang dianggap kinerja baik dan 28 desa kinerja dianggap buruk data ini memiliki attribut tahun ,nama desa, jumlah anggaran, bidang pemerintahan, bidang pembangunan, bidang pembinaan, bidang pemberdayaan, bidang tak terduga , sisa anggaran ,dan hasil kinerja. beberapa data training dapat dilihat pada tabel 1. 6. data testing data testing digunakan sejauh mana algoritma naïve bayes classifier berhasil melakukan klasifikasi dengan benar karna itu data yang ada pada testing set seharusnya tidak boleh ada pada training set sehingga dapat diketahui apakah model classifier sudah berhasil dalam melakukan klasifikasi data ini akan digunakan untuk mengujian, berupa data penggunaan anggaran dana desa pada tahun 2017 ,data ini memiliki attribut tahun ,nama desa, jumlah anggaran, bidang pemerintahan, bidang pembangunan, bidang pembinaan, bidang pemberdayaan, bidang tak terduga dan sisa anggaran. beberapa data testing dapat dilihat pada tabel 2. 7. metode klasifikasi klasifikasi adalah proses untuk menemukan model atau fungsi yang menjelaskan atau membedakan konsep atau kelas data, dengan tujuan untuk dapat memperkirakan kelas dari suatu objek yang labelnya tidak diketahui. dalam mencapai tujuan tersebut, proses klasifikasi membentuk suatu model yang mampu membedakan data kedalam kelas-kelas yang berbeda berdasarkan aturan atau fungsi tertentu. model itu sendiri bisa berupa aturan “jika-maka”, berupa pohon keputusan, atau formula matematis (mustafa et al., 2017). 8. naïve bayes clasifier naive bayes classifier adalah sebuah metoda klasifikasi yang berdasar pada teorema bayes. metode pengklasifikasian ini menggunakan metode probabilitas dan statistik yang pertama kali dikemukakan oleh ilmuwan inggris bernama http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 74 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional thomas bayes, yaitu suatu metode untuk memprediksi peluang di masa depan berdasarkan pengalaman di masa sebelumnya, sehingga metode ini dikenal sebagaiteorema bayes. ciri utama dari naive bayes classifier ini adalah asumsi yang sangat kuat akan independensi dari masingmasing kondisi atau kejadian (ridwan, suyono, & sarosa, 2013). olson dan delen dalam bukunya, menjelaskan bahwa naive bayes untuk setiap kelas keputusan, menghitung probabilitas dengan syarat bahwa kelas keputusan adalah benar,. algoritma ini berdasar pada mengasumsikan bahwa atribut obyek adalah sesuatu yang independen. probabilitas yang terlibat dalam membat perkiraan akhir dihitung sebagai jumlah frekuensi dari "master" tabel keputusan. naive bayes classifier memiliki akurasi lebih baik dibanding dengan model classifier lainnya. penelitian yang dilakukan oleh xhemali, hinde dan stone dalam jurnalnya “naive bayes vs. decision trees vs. neural networks in the classification of training web pages” mengatakan bahwa “naïve bayes classifier memiliki tingkat akurasi yang lebih baik dibanding model classifier lainnya” (ridwan et al., 2013). keuntungan penggunaan naive bayes classifier adalah bahwa metode ini hanya membutuhkan jumlah data pelatihan (training data) yang kecil untuk menentukan estimasi paremeter yang diperlukan dalam proses pengklasifikasian (deputi bidang pengawasan penyelenggaraan keuangan daerah, 2015). naive bayes sering bekerja jauh lebih baik dalam kebanyakan situasi dunia nyata yang kompleks dari pada yang diharapkan. persamaan teorema bayes p(h/x)= 𝐏(𝐇 𝐗).(𝐇) 𝐏(𝐗) …………………………………… (1) keterangan : data dengan class yang belum diketahui hipotesis data 𝑋 merupakan suatu class spesifik (𝐻|𝑋) : probabilitas hipotesis 𝐻 berdasar kondisi𝑋 (posteriori probability) (𝐻) : probabilitas hipotesis 𝐻 (prior probability) (𝑋|𝐻) : probabilitas 𝑋 berdasarkan kondisi pada hipotesis 𝐻 (𝑋) : probabilitas untuk menjelaskan teorema naive bayes classifier, perlu diketahui bahwa proses klasifikasi memerlukan sejumlah petunjuk untuk menentukan kelas apa yang cocok bagi sampel yang dianalisis tersebut (setyaji & pujiono, 2015) .karena itu, teorema bayes di atas disesuaikan sebagai berikut : 𝐶|𝐹1)= p(c)p(f1 … .fn |c) p(f1.… fn ) ………………………………. (2) dimana variabel c merepresentasikan kelas, sementara variabel f1 ... fnmerepresentasikan karakteristik petunjuk yang dibutuhkan untuk melakukan klasifikasi. maka rumus tersebut menjelaskan bahwa peluang masuknya sampel karakteristik tertentu dalam kelas c (posterior) adalah peluang munculnya kelas c (sebelum masuknya sampel tersebut, seringkali disebut prior), dikali dengan peluang kemunculan karakteristik – karakteristik sampel pada kelas c (disebut juga likelihood), dibagi dengan peluang kemunculan karakteristik – karakteristik sampel secara global ( disebut juga evidence). adapun alur dari metode naive bayes classifier adalah sebagai berikut : 1. baca data training 2. hitung jumlah dan probabilitas, namun apabila data numerik maka: a. cari nilai mean dan standar deviasi dari masing – masing parameter yang merupakan data numerik. b. cari nilai probabilistik dengan cara menghitung jumlah data yang sesuai dari kategori yang sama dibagi dengan jumlah data pada kategori tersebut. 3. mendapatkan nilai dalam tabel mean, standart deviasi dan probabilitas formula perhitungan naive bayes classifier berdasarkan probabilitas ditunjukkan sebagai berikut: 𝑝(𝐴|𝐵). 𝑝(𝐵) = 𝑝(𝐵|𝐴). 𝑝(𝐴) …………………….… (3) 𝑝(𝐴𝑖|𝐵) = 𝑝(𝐴𝑖).𝑝(𝐵|𝐴𝑖) ∑ =1𝑝(𝐴𝑗).𝑝(𝐵|𝐴𝑗)𝑐𝑗 …………………………... (4) dengan mengubah nilai 𝐴𝑖 dan 𝐴𝑗 kedalam vector “x” maka didapatkan bentuk formula sebagai berikut : 𝑝(𝑥|𝑖) = 𝑝(𝐴𝑖).𝑝(𝑥) ∑ =1𝑝(𝑗).𝑝(𝑥)𝑐𝑗 …………………………………….. (5) adapun perhitungan naïve bayes classifier untuk data kontinu menggunakan distribusi gauss sebagai berikut : 𝑔(𝑥, 𝜇, 𝜎) = 1 √2𝜋𝜎 𝑒 (𝑥−𝜇)2 2𝜎2 ………………….……….. (6) keterangan : (𝑥|𝑖) = probabilitas hipotesis x jika diberikan fakta atau record i (posterior probability). (𝑖|𝑥) = mencari nilai parameter yang memberi kemungkinan yang paling besar (likelihood) (𝑥) = prior probability dari i (prior http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 75 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional probability) (𝑖) = jumlah probability tuple yang muncul g = distribusi gauss 𝜇 = rata-rata 𝜎 = standar deviasi 9. skema naïve bayes gambar 1 skema naive bayes 10. rancangan system a. flowchart flowchart userdesa admin userdaerah ph as e login masukkan username & password konfirmasi tampilkan halaman utama ya tdk simpan input data pengelolaan dana desa da ta data pengelolaan dana desa proses pengklasifikasian naïve bayes hasil klasifikasi laporan laporan selesai gambar 2 flowchart sistem b. use case login input user input data training laporan operator / pemerintah daerah user / desa input data pengelolaan dana desa melihat hasil penilaian kinerja melakukan klasifikasi gambar 3 use case sistem c. diagram activity login admin sistem ph as e login masukkan user name password validasi tampilkan halaman utama selesai ya tdk gambar 4 diagram activity login a. diagram activity user title user sistem ph as e login laporan selesai halaman utama input data traning pengelolaan dana desa proses klasifikasi naïve bayes gambar 5 diagram activity user http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 76 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional d. penentuan spesifikasi. a. hadware 1.1. satu unit komputer atau notbook 1.2. memory ram minimal 1 giga 1.3. hardisk 250 giga b. software 1.1. sublime text 1.2. sql server 1.3. php c. desain 1.1. flowchart 1.2. uml hasil penelitian dan pembahasan sistem penilaian kinerja pemerintah desa dalam pengelolaan dana desa ini akan mengelompokan data desa yang sesuai dengan kreteria mana yang telah ditetap kan dalam pengelolaan dana desa , dalam kasus ini hasil klasifikasi dibagi menjadi dua(2), yaitu klasifikasi desa yang baik dan buruk. pengolahan data setelah semua data pengelolaan dana desa diinput kan maka data akan diproses dengan menggunakan algoritma naïve bayes classifier untuk menentukan hasil klasifikasi dengan langkah sebagai berikut : 1. inputkan jumlah dana desa dan anggaran yang telah dikeluarkan dalam setiap bidang yang telah ditentukan. 2. dalam penelitian ini titik awal ditentukan dari data sebelum nya dapat dilihat dari tabel 1 training tabel 1 data testing tahu n nama desa jml anggaran bidang pemerintah an bidang pembangun an bidang pembinaan bidang pemberday aan bidang tak terduga sisa anggaran 2017 teluk jaya rp1,545,000, 000 rp440,000, 000 rp650,000,0 00 rp152,000, 000 rp290,000,0 00 rp6,000,00 0 rp7,000,00 0 2017 salumpa ga rp2,100,000, 000 rp560,000, 000 rp900,000,0 00 rp200,000, 000 rp250,000,0 00 rp100,000, 000 rp90,000,0 00 algoritma naïve bayes classifier dalam penelitian menggunakan data testing yang diberikan dapat dilihat pada tabel 1. langkah-langkah algoritma naïve bayes classifikasi dapat dilihat pada tabel 4.2 1. menentukan prior probality(p) tabel 2 menentukan prior probality(p) kinerja jumlah baik 12 buruk 28 jumlah 40 p(baik) = 12 40 = 0,3 p(buruk) = 28 40 = 0,7 2. menentukan mean dan standar deviasi untuk attribut jumlah anggaran (x1), bidang pemerintahan (x2), bidang pembangunan (x3), bidang pembinaan (x4), bidang pemberdayaan (x5), bidang tak terduga (x6), dan sisa anggaran (x7).karna nilai atrribut berupa data diskrit yang nantinya akan dimasukkan kedalam rumus distribusi gauss. tabel 3 data mean dan stdv x1 baik buruk rp1,708,614,700 rp2,604,839,000 rp2,540,007,900 rp1,271,191,948 rp1,850,005,600 rp1,606,981,900 rp1,930,000,700 rp2,205,600,000 rp1,606,981,900 rp1,589,000,400 rp1,850,005,600 ………………………… rp1,405,600,000 rp2,540,007,900 rp1,740,000,300 rp1,850,005,600 rp1,708,614,700 rp1,405,600,000 rp1,271,191,948 rp1,589,000,400 rp1,708,614,700 rp1,850,005,600 rp1,740,000,300 rp1,405,600,000 rp21,059,638,348 rp52,543,519,456 rp1,754,969,862 rp1,876,554,266 308939459.3 422878731.1 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 77 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional x2 baik buruk rp498,821,900 rp600,039,000 rp630,000,000 rp365,924,100 rp500,006,600 rp320,800,000 rp520,000,000 rp515,000,500 rp430,000,000 rp320,400,000 rp513,000,500 ……………………. rp390,400,000 rp513,000,500 rp490,000,000 rp390,400,000 rp498,821,900 rp310,000,000 rp320,800,000 rp490,000,000 rp430,000,000 rp390,400,000 rp513,000,500 rp310,000,000 rp5,734,851,400 rp12,486,652,300 rp477,904,283 rp445,951,868 77346301.28 118768388.5 x3 baik buruk rp763,698,550 rp1,250,000,600 rp1,250,088,800 rp610,000,000 rp800,050,000 rp750,056,900 rp900,400,000 rp1,110,000,000 rp720,056,900 rp790,000,000 rp780,050,000 ……………………. rp650,000,000 rp763,698,550 rp789,500,000 rp750,056,900 rp763,698,550 rp690,000,000 rp590,500,000 rp800,500,000 rp850,400,000 rp750,056,900 rp852,400,000 rp750,050,000 rp9,710,842,800 rp22,787,936,300 rp809,236,900 rp813,854,868 163163932.8 203685445.8 x4 baik buruk rp148,760,000 rp221,000,500 rp300,002,320 rp218,230,500 rp250,004,020 rp120,060,700 rp210,000,000 rp230,000,000 rp190,002,320 rp148,400,230 rp230,000,000 ……………………. rp148,400,230 rp170,400,000 rp170,400,000 rp250,004,020 rp230,000,000 rp230,000,000 rp130,000,000 rp148,400,230 rp148,400,230 rp270,400,000 rp123,004,000 rp230,000,000 rp2,278,973,120 rp5,339,316,190 rp189,914,427 rp190,689,890 54718576.12 43099385.78 x5 baik buruk rp297,334,250 rp353,004,000 rp350,400,890 rp71,507,800 rp270,451,000 rp400,500,000 rp289,000,000 rp318,000,000 rp260,400,890 rp300,000,450 rp310,000,000 ……………….. rp216,000,450 rp300,500,000 rp289,000,000 rp260,400,890 rp210,000,000 rp160,451,000 rp228,000,000 rp150,091,800 rp271,507,800 rp310,500,000 rp250,334,250 rp110,500,000 rp3,242,429,530 rp7,427,274,530 rp270,202,461 rp265,259,805 40781329.67 83122598.17 x6 baik buruk rp20,000,000 rp2,000,000 rp20,000,000 rp1,000,000 rp21,000,000 rp22,000,000 rp10,500,000 rp11,000000 13435028.84 9000000 x7 baik buruk rp9,515,890 rp180,794,900 rp9,493,980 rp5,529,548 rp10,600,700 rp15,564,300 rp6,521,790 rp32,599,500 rp16,955,100 ……………….. rp799,320 rp792,408,850 rp1,100,300 rp199,143,790 rp6,094,250 rp15,149,000 rp1,891,948 rp8,370 rp8,306,670 rp128,648,700 rp261,550 rp5,050,000 rp71,541,498 rp4,480,339,416 rp6,503,773 rp160,012,122 5190571.136 238014836.1 3. menghitung probabilitas dari setiap attribute. a. pengujian data testing pertama desa teluk jaya x1 : rp1,545,000,000 x2 : rp440,000,000 x3 : rp650,000,000 x4 : rp152,000,000 x5 : rp290,000,000 x6 : rp6,000,000 x7 : rp7,000,000 p(x1 = rp1,545,000,000 / baik) = 1 √2𝜋(295787013) 𝑒 −(𝟏,𝟓𝟒𝟓,𝟎𝟎𝟎,𝟎𝟎𝟎 −1,754,969,862 )2 2(295787013)2 http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 78 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional = 1.04835𝑥10−9 p(x1 = rp1,545,000,000 / buruk) = 1 √2𝜋(422878731.1) 𝑒 −(𝟏,𝟓𝟒𝟓,𝟎𝟎𝟎,𝟎𝟎𝟎 −1876554266)2 2(422878731.1)2 = 6.9376𝑥10−10 p(x2 = rp440,000,000 / baik) = 1 √2𝜋(74053445.53) 𝑒 −(𝟒𝟒𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−477904283)2 2(74053445.53)2 = 4.72579𝑥10−9 p(x2 = rp440,000,000 / buruk) = 1 √2𝜋(118768388.5) 𝑒 −(𝟒𝟒𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−445951868)2 2(118768388.5)2 = 3.35478𝑥10−9 p(x3 = rp650,000,000/ baik) = 1 √2𝜋(156217572.3) 𝑒 −(𝟔𝟓𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−809236900)2 2(156217572.3)2 = 1.519𝑥10−9 p(x3 = rp650,000,000/ buruk) = 1 √2𝜋(203685445.8) 𝑒 −(𝟔𝟓𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−813854868)2 2(203685445.8)2 = 1,4500614𝑥10−9 p(x4 = rp152,000,000/ baik) = 1 √2𝜋(52389048.07) 𝑒 −(152,000,000−189914427)2 2(52389048.07)2 = 5.86054𝑥10−9 p(x4 = rp152,000,000/ buruk) = 1 √2𝜋(43099385.78) 𝑒 −(152,000,000−190689890)2 2(43099385.78)2 = 6.18659𝑥10−9 p(x5 = rp290,000,000/ baik) = 1 √2𝜋(39045150.52) 𝑒 −(290,000,000−270202469)2 2(39045150.52)2 = 8.98496𝑥10−9 p(x5 = rp290,000,000/ buruk) = 1 √2𝜋(83122598.17) 𝑒 −(290,000,000−265259805)2 2(83122598.17)2 = 4.5915𝑥10−9 p(x6 = rp6,000,000/ baik) = 1 √2𝜋(9500000) 𝑒 −(6,000,000−10500000)2 2(9500000)2 = 3.75374𝑥10−8 p(x6 = rp6,000,000/ buruk) = 1 √2𝜋(9000000) 𝑒 −(6,000,000−11000000)2 2(9000000)2 = 3.7988𝑥10−8 p(x7 = rp7,000,000/ baik) = 1 √2𝜋(4949015.395) 𝑒 −(7,000,000−6503773)2 2(4949015.395)2 = 8.02062𝑥10−8 p(x7 = rp7,000,000/ buruk) = 1 √2𝜋(238014836) 𝑒 −(7,000,000−160012122)2 2(238014836)2 = 1.36321𝑥10−9 sehingga likelihood baik  1.04835x10−9 x 4.72579x10−9 x 1.519x10−9 x 5.86054x10−9 x 8.98496x10−9 x 3.75374x10−8 x 8.02062x10−8 x 0.3 = 3.57921x10−58 likelihood buruk  6.9376x10−10 x 3.35478x10−9 x 1.41718x10−9 x 6.18659x10−9 x 4.5915x10−9 x 3.7988x10−8 x 1.36321x10−9 x 0.7 = 3.39635x10−60 kesimpulan kinerja pemerintah = baik karena nilai likelihood baik lebih besar dari likelihood buruk. gambar 2 hasil klasifikasi baik b. pengujian data testing kedua desa salumpaga x1 : 2,100,000,000 x2 : 560,000,000 x3 : 900,000,000 x4 : 200,000,000 x5 : 250,000,000 x6 : 100000000 x7 : 90,000,000 p(x1 = rp2,100,000,000 / baik) = 1 √2𝜋(295787013) 𝑒 −(𝟐,𝟏𝟎𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎 −1,754,969,862 )2 2(295787013)2 = 6.83067𝑥10−10 p(x1 = rp2,100,000,000 / buruk) = 1 √2𝜋(422878731.1) 𝑒 −(𝟐,𝟏𝟎𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−1876554266)2 2(422878731.1)2 http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 79 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional = 8.20478𝑥10−10 p(x2 = rp560,000,000 / baik) = 1 √2𝜋(74053445.53) 𝑒 −(𝟓𝟔𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−477904283)2 2(74053445.53)2 = 2.91401𝑥10−9 p(x2 = rp560,000,000 / buruk) = 1 √2𝜋(118768388.5) 𝑒 −(𝟓𝟔𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−445951868)2 2(118768388.5)2 = 2.11826𝑥10−9 p(x3 = rp900,000,000/ baik) = 1 √2𝜋(156217572.3) 𝑒 −(𝟗𝟎𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−809236900)2 2(156217572.3)2 = 2.15714𝑥10−9 p(x3 = rp900,000,000/ buruk) = 1 √2𝜋(203685445.8) 𝑒 −(𝟗𝟎𝟎,𝟎𝟎𝟎,𝟎𝟎𝟎−813854868)2 2(203685445.8)2 = 1.79105𝑥10−9 p(x4 = rp 200,000,000/ baik) = 1 √2𝜋(52389048.07) 𝑒 −(200,000,000−189914427)2 2(52389048.07)2 = 7.47518𝑥10−9 p(x4 = rp200,000,000/ buruk) = 1 √2𝜋(43099385.78) 𝑒 −(200,000,000−190689890)2 2(43099385.78)2 = 9.04287𝑥10−9 p(x5 = rp250,000,000/ baik) = 1 √2𝜋(39045150.52) 𝑒 −(250,000,000−270202469)2 2(39045150.52)2 = 8.93736𝑥10−9 p(x5 = rp2500,000,000/ buruk) = 1 √2𝜋(83122598.17) 𝑒 −(250,000,000−265259805)2 2(83122598.17)2 = 4.71925𝑥10−9 p(x6 = rp100,000,000/ baik) = 1 √2𝜋(9500000) 𝑒 −(100,000,000−10500000)2 2(9500000)2 = 2.2388𝑥10−27 p(x6 = rp100,000,000/ buruk) = 1 √2𝜋(9000000) 𝑒 −(100,000,000−11000000)2 2(9000000)2 = 2.58114𝑥10−29 p(x7 = rp90,000,000/ baik) = 1 √2𝜋(4949015.395) 𝑒 −(90,000,000−6503773)2 2(4949015.395)2 = 1.25192𝑥10−69 p(x7 = rp90,000,000/ buruk) = 1 √2𝜋(238014836) 𝑒 −(90,000,000−160012122)2 2(238014836)2 = 1.33471𝑥10−11 sehingga likelihood baik  6.83067x10−10 x 2.91401x10−9 x 2.15714x10−9 x 7.47518x10−9 x 8.93736x10−9 x 2.2388x10−27 x 1.25192x10−69 x 0.3 = 2.421x10−139 likelihood buruk  8.20478x10−10 x 2.11826x10−9 x 1.79105x10−9 x 9.04287x10−9 x 4.71925x10−9 x 2.58114x10−29 x 1.33471x10−11 x 0.7 = 3.20355x10−83 gambar 3 hasil klasifikasi buruk kesimpulan dan saran kesimpulan berdasarkan hasil analisis perhitungan yang sudah dilakukan, penerapan metode naïve bayes klasifikasi dalam penilaian kinerja pemerintah desa yang sudah di lakukan terhadap 2(dua) desa dengan kreteria yang digunakan adalah jumlah anggaran, anggaran bidang pemerintahan, anggaran bidang pembangunan, anggaran bidang pemberdayaan, anggaaran bidang pembinaan , anggaran tak terduga dan sisa anggaran sehingga hasil akhir penilaian kinerja pemerintah desa dalam pengelolaan dana desa yakni desa dengan nama desa teluk jaya dinyatakan baik karena memiliki nilai likelihood baik 3.57921𝐸−58 > nilai likelihood buruk 3.39635𝐸−60 sedangkan desa dengan nama desa salumpaga dinyatakan buruk karena memiliki nilai likelihood baik 2.421𝐸−139 < nilai likelihood buruk 3.20355𝐸−83 . http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 80 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional saran pengembangan penelitian selanjutnya yang perlu dilakukan sebagai berikut: penelitian selanjutnya dapat dikembangkan dengan menggunakan data training yang lebih banyak dan kreteria-kreteria yang digunakan lebih detail. pada penerapan algoritma naïve bayes classifier dalam penilaian kinerja pemerintah desa dapat di padukan dengan algoritma pengklasifikasian lainnya. daftar pustaka aminah, a., & sari, n. (2018). pengelolaan dan pemanfaatan dana desa untuk pemberdayaan masyarakat (studi kasus di gampong gunong meulinteung kecamatan panga kabupaten aceh jaya). jurnal public policy, 4(1), 22–32. retrieved from http://jurnal.utu.ac.id/jppolicy/article/vie w/238 deputi bidang pengawasan penyelenggaraan keuangan daerah. (2015). petunjuk pelaksanaan bimbingan dan konsultasi pengelolaan keuangan desa. jakarta. iskandar, d., & suprapto, y. k. (2016). perbandingan akurasi klasifikasi tingkat kemiskinan antara algoritma c 4.5 dan naïve bayes. network engineering research operation, 2(1). https://doi.org/10.28961/nero.v2i1.42 mustafa, m. s., ramadhan, m. r., & thenata, a. p. (2017). implementasi data mining untuk evaluasi kinerja akademik mahasiswa menggunakan algoritma naive bayes classifier. creative information technology journal (citec journal), 4(2). retrieved from http://citec.amikom.ac.id/main/index.php/ citec/article/view/106 olson, d. l., & delen, d. (2008). advanced data mining techniques (1st ed.). verlag berlin heidelberg: springer. https://doi.org/10.1007/978-3-54076917-0 presiden republik indonesia. undang-undang republik indonesia nomor 6 tahun 2014 tentang desa, pub. l. no. 6, 38 (2014). indonesia. retrieved from http://www.dpr.go.id/dokjdih/document/ uu/uu_2014_6.pdf ridwan, m., suyono, h., & sarosa, m. (2013). penerapan data mining untuk evaluasi kinerja akademik mahasiswa menggunakan algoritma naive bayes classifier. jurnal eeccis (electrics, electronics, communications, controls, informatics, systems)., 7(1), 59–64. retrieved from https://jurnaleeccis.ub.ac.id/index.php/eec cis/article/view/204 romantis, p. a., & ainurrohma, p. (2015). akuntabilitas pengelolaan alokasi dana desa di kecamatan panarukan kabupaten situbondo tahun 2014 (no. 65226). kabupaten jember. retrieved from http://repository.unej.ac.id/handle/12345 6789/65226 setyaji, b., & pujiono, p. (2015). sistem pendukung keputusan penentuan kelayakan tenaga kerja menggunakan metode naive bayes classification (no. 16562). semarang. retrieved from http://eprints.dinus.ac.id/16562/ wati, m., & hadi, a. (2017). implementasi algoritma naive bayesian dalam penentuan penerima program bantuan pemerintah. stmik kharisma makassar, 3(1), 22–26. http://creativecommons.org/licenses/by-nc/4.0/ 343 selection of the best prewedding photo location using the ahp method muhammad noval alfarizi1, mariah nur azizah2, wikara dwi saputra3, siti ernawati4*) program studi sistem informasi universitas nusa mandiri muhammadnoval616258@gmail.com1, mariahazizah@gmail.com2, wikaradwi.saputra14@gmail.com3, siti.ste@nusamandiri.ac.id4 (*) corresponding author abstrak lokasi foto prewedding merupakan sebuah tempat yang dapat menggambarkan kebahagiaan calon pengantin. dalam kondisi tersebut terkadang masyarakat umum khususnya calon pengantin dan fotografer mengalami kesulitan dalam memilih lokasi foto prewedding terbaik. maka mengakibatkan pengambil keputusan akan mendapatkan lokasi foto prewedding yang tidak sesuai dengan apa yang diharapkan. dalam penelitian ini memanfaatkan metode analytical hierarchy process (ahp) untuk membantu calon pengantin maupun fotografer dalam memilih lokasi foto prewedding terbaik khususnya di daerah jakarta. penelitian ini menggunakan enam kriteria diantaranya jarak lokasi, jumlah spot, transportasi, waktu, biaya lokasi, dan tema. dari hasil analisa dan pengolahan data diperoleh bahwa ancol unggul dengan nilai 0,224047721 (22%) berbandingan dengan cafe batavia dengan nilai 0,195494507 (20%), pelabuhan sunda kelapa dengan nilai 0,187335550 (19%), taman wisata mangrove angke kapuk dengan nilai 0,171584976 (17%), kota tua dengan nilai 0,162696386 (16%), dan glodok dengan nilai 0,058840858 (6%). kata kunci: lokasi foto prewedding, analytical hierarchy process, sistem penunjang keputusan abstract the location of the photo prewedding is a place that can describe the happiness of the bride and groom. in these conditions sometimes the general public especially prospective bride and groom and the photographer have difficulty in choosing the location of the photo prewedding best. then the resulting decision-makers will get the location of the photo prewedding which is not in accordance with what is expected. in this study utilizes the method of analytical hierarchy process (ahp) to help the bride and groom and the photographer in choosing the location of the photo prewedding best, especially in the area of jakarta. this study using six criteria such as distance, number of spots, transportation, time, cost, location, and theme. from the results of the analysis and processing of the data obtained that the ancol with superior value 0,224047721 (22%) berbandingan to cafe batavia with a value of 0,195494507 (20%), sunda kelapa harbor to the value 0,187335550 (19%), taman wisata mangrove angke kapuk with a value of 0,171584976 (17%), old city, with the value of 0,162696386 (16%), and glodok, with a value of 0,058840858 (6%). keywords: pre-wedding photo locations, analytical hierarchy process, decision support system introduction the location of the photo prewedding is a place that can describe the happiness of both pairs of the bride and groom. in reality, photo prewedding like this just as a complement to a wedding procession, but for most people a photo prewedding is very important. no wonder if many people are willing to pay money to hundreds of millions for a photo prewedding (kusuma, 2010). the problem is either that the general public especially prospective bride and groom and the photographer have difficult in choosing the location of the photo prewedding best in jakarta. as a result, decisionmakers will get the location of the photo prewedding which is not in accordance with what is expected. the bride and groom or the photographer should have some options or special consideration in choosing the location of the photo prewedding best. consideration or the option can be any number of spot, a distance of location, transportation, fees and other (syahputra, winanjaya, & okprana, 2019). when talking about a location or a place of interest to be used as the location of the photo prewedding, general public or a bride looking for information through friends, friends, family, and the internet. such information can also they get better it’s through social media, or search sites like google. 344 information from various sources will they compare to get the right location. even so, the amount of information obtained to make the general public especially prospective bride and groom and the photographer difficulties in screening. this makes the general public who will do a photo prewedding feel hesitate in determining the location. the presence of such doubt strengthen the desire of researchers to create a decision support system the selection of the location of the photo prewedding. decision support system is a system specific information that is used to help management to take decisions related to semistructured problems. this system has the usefulness to get some alternatives that are interactive can be used by the user (latif, jamil, & abbas, 2018). decision support system is one of the computer-based system including systems of knowledge-based or knowledge management that can be used to support decision-making in a company or organization (safitri & tinus waruwu, 2017). dss can create quick decision-making, either in a group or individual to be able to determine an objective decisions. the reason dss very necessary, because of the amount of information and data that allows to use dss (utama, 2017). there are some studies related to the use of the method of analytical hierarchy process to get the best options, including : the research discussed about the selection of subjects the practice of using ahp method which has 6 criteria and 6 alternative. from to 6 alternate owned, an alternative to a4 has a priority value and the percentage of the highest priority that is 0,235 and 23,5. based on these results demonstrate that the alternative to a4 be selected according to the criteria of the existing (azhar, 2019). research regarding the selection of laptops for the needs of the operational directorate of course and training kemdikbud by using the ahp method, which has 5 criteria and 3 alternatives. from the 3rd alternative, an alternative to a-1 (laptop a) to have the highest value, i.e. to 39,9%. based on these results, a laptop a is superior to other alternatives in the selection of the laptop to operational needs (sanyoto, handayani, & widanengsih, 2017). based on the research related to the above, the obtained gap analysis or the analysis of the problem which the researcher has made the following: figure 1. gap analysis from fig.1. gap analysis above, there are several problems that exist in the 2-related research. to it it can be a material consideration to be able to include in this study. in this research, the researcher uses the method of analytical hierarchy process (ahp) which is a method to be able to decipher a problem with many complex criteria into a hierarchy (nurhidayah, fauzan, & rahayu, 2020). ahp is a theory of measurement that can be used to find the scale ratio with how to perform a pairwise comparison between elements (muhaimin hasanudin, yansen marli, 2018). the advantages of the use of ahp is that it can easily determine the choice of location prewedding by using the calculation centralized on the weights of the criteria, respectively. another reason researchers use the method of analytical hierarchy process (ahp) because basically ahp included in the methods section with the primary inputs are the human perception (sanyoto et al., 2017). the purpose of this research is to facilitate the general public especially prospective bride and groom and the photographer to select the location of photo prewedding best in jakarta. 345 research methods the quantitative approach used in this research, where data collection was done using questionnaires. this research using the method of ahp (analytical hierarchy process) consider the six criteria, namely the distance of the location, the number of spot, transportation, time, cost, location, and theme. figure 2 shows the stages of the research conducted. figure 2. stages of research population and study sample after the questionnaire was distributed to the community at large, obtained the 53 respondents who have answered the question or questionnaire that has been given. table 1 is the data of the respondents that displays the position of the 53 respondents have been obtained. table 1. the data of respondents no. position amount 1 school students 1 2 teachers 1 3 photographer 1 4 marketing 1 5 entrepreneurs 1 6 telemarketing 1 7 administration 1 8 graphica 1 9 wood artisans 1 10 housewife 2 11 entrepreneurs 2 12 employees 3 13 female employees 3 14 private employees 6 15 college students 28 amount 53 data analysis techniques analysis is a critical stage in the methodology of scientific research, for the reason when doing the analysis of such data can give the sense and meaning which is very useful in resolving the problem. the stage of data analysis has the aim to provide a clear understanding of the working of dss, so that the strengths and weaknessess of dss can be seen with the implementation of the method of analytical hierarchy process (ahp). the method of analytical hierarchy process is a method to be able to take a comprehensive decision (frieyadie, 2018), to apply the ahp method with the wellneeded goal (goal), the criteria and the alternatives. then the calculation can be done to get the best alternative (syahputra et al., 2019). to determine the location of photo prewedding best in jakarta with the use of ahp, it takes the criteria provided in table 2 to determine the location of which will be selected as the location of the photo prewedding. table 2. criteria data code criteria k1 location distance k2 number of places k3 transportation k4 time k5 location fee k6 theme the alternative is a main object in decisionmaking. the data used is the data location to determine the location of photo prewedding best in jakarta. alternative data can be seen in table 3. table 3. alternative data code alternative a1 glodok a2 kota tua a3 taman wisata mangrove angke kapuk a4 ancol a5 cafe batavia a6 pelabuhan sunda kelapa results and discussion data processing using analytical hierarchy process here are the stages of completion of the calculation using the method of ahp. creating a hierarchical structure figure 3 is a hierarchical structure for the research conducted. 346 figure 3. the structure of the hierarchy of the selection of the location prewedding best in jakarta assessment criteria and alternatives after the preparation of the hierarchy, can be done in the collection of value from the results of the questionnaire has been answered by 53 respondents. because of having more than one respondent, then the next stage is to perform the calculation of the geometric mean (pratiwi, mz, & aprilyanti, 2018). weight ratings of some of the respondents in a group are averaged by using the assessment of the geometric (geometric mean). the goal is to get a single value that represent the number of respondents. the formula used to find the geometric average is the following (handayani & darmianti, 2017): g= √𝑋1 ∗ 𝑋2 ∗ … 𝑋𝑛 𝑛 ........................................................ (1) description : g : geometric mean xn : assessment 1,2,3 ... n n : number of ratings after getting the results of the geometric average of each respondent. then, each value of the geometric average is inserted into the table of paired comparisons (pairwise comparison). below shown table 4 shows the pairwise comparison matrix between criteria, which is the results of searching for the average value of the ratio of the geometric numerous criteria. table 4. pairwise comparison matrix between criteria criteria k1 k2 k3 k4 k5 k6 k1 1,00 1,58 1,06 0,40 0,39 0,58 k2 0,63 1,00 1,18 0,64 0,45 0,38 k3 0,94 0,85 1,00 0,52 0,38 0,79 k4 2,50 1,56 1,92 1,00 0,49 0,99 k5 2,56 2,22 2,63 2,04 1,00 0,83 k6 1,72 2,63 1,27 1,01 1,20 1,00 amount 9,36 9,84 9,06 5,61 3,91 4,57 table 5 shows the pairwise comparison matrix between alternatives based on the criteria of distance, which is the resultsof searching for the average value of the geometric comparison of alternatives based on the criteria of the distance of the location. table 5. pairwise comparison matrix between alternatives based on the criteria of location distance k1 a1 a2 a3 a4 a5 a6 a1 1,00 0,19 0,22 0,26 0,16 0,30 a2 5,26 1,00 0,80 0,69 0,68 1,07 a3 4,53 1,26 1,00 0,92 1,05 0,80 a4 3,91 1,44 1,09 1,00 0,92 1,47 a5 6,34 1,48 0,96 1,09 1,00 0,96 a6 3,37 0,94 1,24 0,68 1,04 1,00 amount 24,40 6,31 5,30 4,64 4,84 5,60 pairwise comparison matrix between alternatives based on the criteria of the number of spots can be seen in table 6, which is the result of searching for the average value of the geometric comparison of alternatives based on the criteria of number of spot. table 6. pairwise comparison matrix between alternatives based on the criteria of number of spot k2 a1 a2 a3 a4 a5 a6 a1 1,00 0,23 0,21 0,23 0,22 0,27 a2 4,32 1,00 0,47 0,73 1,01 0,92 a3 4,84 2,11 1,00 0,90 1,18 0,65 a4 4,35 1,38 1,11 1,00 1,23 1,22 a5 4,51 0,99 0,85 0,81 1,00 1,10 a6 3,70 1,09 1,53 0,82 0,91 1,00 amount 22,72 6,80 5,17 4,49 5,55 5,17 pairwise comparison matrix between alternatives based on the criteria of transport can be seen in table 7, which is the results of searching for the average value of the geometric comparison of alternatives based on the criteria transport. table 7. pairwise comparison matrix between alternatives based on the criteria of transport k3 a1 a2 a3 a4 a5 a6 a1 1,00 0,22 0,46 0,26 0,28 0,43 a2 4,56 1,00 0,87 0,56 0,71 1,17 a3 2,20 1,15 1,00 0,43 0,83 0,90 a4 3,78 1,80 2,35 1,00 0,80 1,00 a5 3,57 1,40 1,21 1,25 1,00 0,71 a6 2,35 0,85 1,11 1,00 1,41 1,00 amount 17,45 6,42 6,99 4,50 5,03 5,21 table 8 shows the pairwise comparison matrix between alternatives based on the criteria of time, which is the result of searching for the 347 average value of the geometric comparison of alternatives based on the criteria of time. table 8. pairwise comparison matrix between alternatives based on the criteria of time k4 a1 a2 a3 a4 a5 a6 a1 1,00 0,19 0,30 0,25 0,38 0,55 a2 5,24 1,00 0,59 0,60 0,84 1,03 a3 3,30 1,69 1,00 0,67 0,90 0,77 a4 4,03 1,66 1,49 1,00 0,84 1,15 a5 2,62 1,20 1,11 1,20 1,00 0,84 a6 1,82 0,97 1,30 0,87 1,19 1,00 amount 18,01 6,71 5,79 4,58 5,14 5,35 table 9 is the pairwise comparison matrix between alternatives based on the criteria of the cost of the location, which is the result of searching for the average value of the geometric comparison of alternatives based on the criteria of cost locations. table 9. pairwise comparison matrix between alternatives based on the criteria of cost locations k5 a1 a2 a3 a4 a5 a6 a1 1,00 0,31 0,62 0,33 0,45 0,66 a2 3,21 1,00 0,78 0,54 1,11 1,01 a3 1,62 1,28 1,00 0,53 0,69 0,70 a4 3,03 1,86 1,89 1,00 0,73 0,96 a5 2,20 0,90 1,46 1,38 1,00 0,85 a6 1,52 0,99 1,43 1,04 1,18 1,00 amount 12,58 6,34 7,17 4,81 5,16 5,18 table 10 is a pairwise comparison matrix between alternatives based on the criteria of the theme, which is the result of searching for the average value of the geometric comparison of alternatives based on the criteria of the theme. table 10. pairwise comparison matrix between alternatives based on the criteria of the theme k6 a1 a2 a3 a4 a5 a6 a1 1,00 0,21 0,23 0,18 0,24 0,31 a2 4,88 1,00 0,45 0,37 0,77 0,84 a3 4,40 2,23 1,00 0,63 0,87 0,90 a4 5,71 2,70 1,58 1,00 0,70 1,02 a5 4,20 1,30 1,15 1,42 1,00 0,67 a6 3,27 1,19 1,12 0,98 1,49 1,00 amount 23,45 8,62 5,52 4,58 5,07 4,73 perbandingan comparison of criteria and alternatives is filled with a single value that represents the number of respondents in accordance with what has been established when searching for a geometric average. then, prepared in accordance with the scale of assessment of pairwise comparison. the last process that is the sum of the value of a column on each table paired comparison. specify the priority (synthesis of priority) and measuring consistency thingking of the pairwise comparison synthesized to get all of the priorities. how that is done in this phase is to add up the values of each column in the matrix, divide each value of the column with the total of the column in question to obtain the normalization of the matrix, summing the values of each row and divide it by the number of elements to get the average value (handayani & darmianti, 2017). whereas, to measure consistency in decision-making, it is important to be aware of how well the consistency of the obtained because the researcher would not expect to get a decision based on considerations with low consistency (azhar, 2019). the determination of the weights between criteria figure 4. the normalization of the matrix between criteria figure 4 shows the normalization of the matrix between criteria, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between the criteria. based on the results obtained, the highest priority is the cost of the location (k5) with the value of the weight 0,265 (27%) of the total criteria, the priority of rank 2 is a theme (k6) with the value of the weight 0,216 (22%), the priority rank 3 is the time (k4) with the value of the weight 0,193 (19%), priority in rank to 4 are the distance (k1) with the value of the weight 0,114 (11%), priority in rank to 5 is transportation (k3) with the value of the weight 0,110 (11%), and priority in the ranking of the last is the number of spot (k2) with the value of the weight 0,102 (10%). after obtaining the weights of criteria (vector priority). the will check the consistency of 348 the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,17. find the value of consistency index (ci), is produced by 0,035. calculate the consistency ratio (cr), the results obtained 0,028 (the results obtained is less than 0,1 then it could be said to be consistent). the determination of the weights between alternatives based on the criteria of distance (k1) figure 5. the normalization of the matrix between alternatives based on the criteria of location distance the normalization of the matrix between alternatives based on the criteria of the distance of the location can be seen in figure 5, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of the distance of the location. based on the results obtained, the highest priority is the cafe batavia (a5) with the value of the weight 0,215 (21%) of the total alternative, the priority rank 2 is ancol (a4) with the value of the weight 0,210 (21%), priority in rank 3 is to tourist park mangrove angke kapuk (a3) with the value of the weight 0,189 (19%), the priority at rank 4 is the port of sunda kelapa (a6) with the value of the weight 0,177 (18%), priority in rank to 5 is the old town (a2) with the value of the weight 0,167 (17%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,042 (4%). after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,09. find the value of consistency index (ci), resulting 0,018. calculate the consistency ratio (cr), the results obtained 0,015 (the result obtained is less than 0.1 then it could be said to be consistent). the determination of the weights between alternatives based on the criteria of number of spot (k2) figure 6. the normalization of the matrix between alternatives based on the criteria of number of spot figure 6. is the normalization of the matrix between alternatives based on the criteria of number of spot, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of number of spot. based on the results obtained, the highest priority is ancol (a4) with the value of the weight 0,215 (22%) of the total alternative, the priority rank 2 is tourist park mangrove angke kapuk (a3) with the value of the weight 0,209 (21%), priority in rank to 3 is the port of sunda kelapa (a6) with the value of the weight 0,193 (19%), priority in rank to 4 is a cafe batavia (a5) with a weight value of 0.180 (18%), priority in rank to 5 is the old town (a2) with the value of the weight to 0.158 (16%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,044 (4%). after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,11. find the value of consistency index (ci), is produced by 0,023. calculate the consistency ratio (cr), the results obtained 0,018 (the result obtained is less than 0.1 then it could be said to be consistent). 349 the determination of the weights between alternatives based on the criteria of transportation (k3) figure 7 is a normalization matrix between alternatives based on the criteria of transportation, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of transport. based on the results obtained, the highest priority is ancol (a4) with the value of the weight 0,234 (23%) of the total alternative, the priority of rank 2 is a cafe batavia (a5) with the value of the weight 0,201 (20%), the priority rank 3 is the port of sunda kelapa (a6) with the value of the weight 0,187 (19%), the priority at rank 4 is the old town (a2) with the value of the weight 0,172 (17%), priority in rank to 5 is tourist park mangrove angke kapuk (a3) with a weight value of 0.147 (15%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,059 (6%). figure 7. the normalization of the matrix between alternatives based on the criteria of transport after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,20. find the value of consistency index (ci), is produced by 0.039 to. calculate the consistency ratio (cr), the results obtained to 0.032 (the result obtained is less than 0.1 then it could be said to be consistent). the determination of the weights between alternatives based on the criteria of time (k4) the normalization of the matrix between alternatives based on the criteria of time can be seen in figure 8, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of time. based on the results obtained, the highest priority is ancol (a4) with the value of the weight 0,221 (22%) of the total alternative, the priority of rank 2 is a cafe batavia (a5) with the value of the weight 0,188 (19%), the priority rank 3 is the port of sunda kelapa (a6) with the value of the weight 0,1794 (18%), priority in rank to 4 is tourist park mangrove angke kapuk (a3) with the value of the weight 0,1789 (18%), priority in rank to 5 is the old town (a2) with the value of the weight 0,171 (17%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,061 (6%). figure 8. the normalization of the matrix between alternatives based on the criteria of time after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,23. find the value of consistency index (ci), is produced by 0,046. calculate the consistency ratio (cr), the results obtained 0,037 (the result obtained is less than 0.1 then it could be said to be consistent). the determination of the weights between alternatives based on the criteria of cost locations (k5) figure 9. the normalization of the matrix between alternatives based on the criteria of cost locations 350 figure 9 shows the normalization of the matrix between alternatives based on the criteria of cost locations, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of cost locations. based on the results obtained, the highest priority is ancol (a4) with the value of the weight 0,222 (22%) of the total alternative, the priority of rank 2 is a cafe batavia (a5) with the value of the weight 0,194 (19%), the priority rank 3 is the port of sunda kelapa (a6) with the value of the weight 0,186 (19%), the priority at rank 4 is the old town (a2) with the value of the weight 0,174 (17%), priority in rank to 5 is tourist park mangrove angke kapuk (a3) with the value of the weight content 0,141 (14%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,083 (8%). after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,23. find the value of consistency index (ci), is produced by 0,046. calculate the consistency ratio (cr), the results obtained 0,037 (the result obtained is less than 0.1 then it could be said to be consistent). the determination of the weights between alternatives based on the criteria of the theme (k6) figure 10. normalisai matrix between alternatives based on the criteria of the theme figure 10 shows the normalization of the matrix between alternatives based on the criteria of the theme, is the result obtained after performing the calculation in finding the average or the priority value in the normalization between alternatives based on the criteria of the theme. based on the results obtained, the highest priority is ancol (a4) with the value of the weight 0,236 (24%) of the total alternative, the priority rank 2 is the port of sunda kelapa (a6) with the value of the weight 0,200 (20%), the priority rank 3 is a cafe batavia (a5) with the value of the weight 0,198 (20%), priority in rank to 4 is tourist park mangrove angke kapuk (a3) with the value of the weight 0,188 (19%), the priority at rank 5 is the old town (a2) with the value of the weight 0,136 (14%), and priority in the ranking latter is glodok (a1) with the value of the weight 0,043 (4%). after obtaining the weights of criteria (vector priority). then will check the consistency of the data to calculate the consistency ratio (cr), it takes the λ max and the consistency index (ci). find the value of λ max, the results obtained λ max = 6,24. find the value of consistency index (ci), is produced by 0,049. calculate the consistency ratio (cr), the results obtained 0.039 to (the result obtained is less than 0.1 then it could be said to be consistent). ranking after doing some calculations, and then create a table ranking. ranking was conducted to determine the location of the photo prewedding best in jakarta. table 11 shows the results of ranking. the appearance of the ranking in the form of diagrams can be seen in figure 11 which is the result of the calculation of the value in preferences on each alternative. table 11. table of results ranking kode lokasi preferensi peringkat a1 glodok 0,058840858 6 a2 the old town 0,162696386 5 a3 tourist park mangrove angke kapuk 0,171584976 4 a4 ancol 0,224047721 1 a5 café batavia 0,195494507 2 a6 the port of sunda kelapa 0,187335550 3 on the determination of the ranking of the site consists of the multiplication between the priority or the average in the table normalization between the criteria with a priority of each alternative based on 6 criteria used. after that, the result of the multiplication directly summed. the results show that the ancol with the value of the preference 0,224047721 be the place of location of photo prewedding best in jakarta. 351 figure 11. the appearance of the ranking in the form of a diagram conclusions and suggestions conclusions in the results of calculations using the method of analytical hierarchy process, earned the priority of the most important criteria in determining the best location in the photo prewedding in jakarta, where the distance of the location, number of spots, transportation, time, cost, location, and theme to be a priority in determining the location of photo prewedding best in jakarta. the results of the analysis stated that the alternative ancol with the value of the preference 0,224047721 be the location of photo prewedding best in jakarta. excess use of ahp according to the experience of researchers in its use, particularly when you have the problem of multi respondents or problems that have a lot of opinions. by forming the structure of the hierarchy, the problem of it being visible can be overcome in such a way. suggestions expected to continue the research with the theme of the tools using the method of decision support system to another, such as the use of the method of topsis, moora, etc. moreover, it can add a criteria other than the criteria used in this study. references azhar, z. (2019). analisis pemilihan mata kuliah praktek menggunakan metode ahp. prosiding seminar nasional riset information science (senaris), 1(september), 1131. https://doi.org/10.30645/senaris.v1i0.126 frieyadie. (2018). metode ahp sebagai penunjang keputusan untuk penilaian kinerja kerja karyawan spbu. jurnal techno nusa mandiri, 15(1), 63–68. handayani, r. irma, & darmianti, y. (2017). pemilihan supplier bahan baku bangunan dengan metode analytical hierarchy process (ahp) pada pt. cipta nuansa prima tangerang. jurnal techno nusa mandiri, 14(1), 1–8. https://doi.org/10.33480/techno.v14i1.176 kusuma, y. (2010). trik foto pre-wedding kreatif. grasindo. latif, l. a., jamil, m., & abbas, s. h. (2018). buku ajar: sistem pendukung keputusan teori dan implementasi (1st ed.). yogyakarta: deepublish. muhaimin hasanudin, yansen marli, b. h. 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(2017). sistem pendukung keputusan pemilihan laptop untuk kebutuhan operasional dengan metode ahp (studi kasus: direktorat pembinaan kursus dan pelatihan kemdikbud). jurnal pilar nusa mandiri, 13(2), 167–174. syahputra, m. r., winanjaya, r., & okprana, h. (2019). sistem pendukung keputusan pemilihan lokasi prewedding menggunakan metode weight product. komik (konferensi nasional teknologi informasi dan komputer), 3(1), 695–701. https://doi.org/10.30865/komik.v3i1.1680 352 utama, d. n. (2017). sistem penunjang keputusan: filosofi teori dan implementasi. in garudhawaca. yogyakarta: garudhawaca. 71 village grouping based on the number of health facilities in west java using k-means clustering algorithm frieyadie1*), anggie andriansyah2, tyas setiyorini3 sistem informasi1,2, teknik informatika3 universitas nusa mandiri www.nusamandiri.ac.id frieyadie@nusamandiri.ac.id1*), anggie.andriansyah@gmail.com2, tyas.setiyorini@gmail.com3, (*) corresponding author abstract health is very important for the welfare and development of the indonesian nation because as a capital for the implementation of national development, it is essentially the development of all indonesian people and the development of all indonesian people. due to the outbreak of the covid-19 virus, many health facilities must be provided for patients. of course, the government must pay attention to the health facilities that can be used in every district/city in west java in the future. therefore, to determine the level of availability of sanitation facilities in each district/city in west java, we need a technology that can classify data correctly. one method of data processing in data mining is clustering. the application of clustering to this problem can use the k-means algorithm method to group the most frequently used data. the purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities. keywords: health facilities; covid-19, clustering, k-means algorithm abstrak kesehatan sangat penting artinya bagi kesejahteraan dan pembangunan bangsa indonesia, karena sebagai modal terselenggaranya pembangunan nasional pada hakekatnya pembangunan seluruh rakyat indonesia dan pembangunan seluruh masyarakat indonesia. dikarenakan merebaknya virus covid-19, maka banyak fasilitas kesehatan yang harus disediakan untuk pasien. tentunya pemerintah harus memperhatikan fasilitas kesehatan yang bisa digunakan di setiap kabupaten/kota di jawa barat ke depan. oleh karena itu, untuk mengetahui tingkat ketersediaan sarana sanitasi di setiap kabupaten/kota di jawa barat, diperlukan suatu teknologi yang dapat mengelompokkan data dengan benar. salah satu metode pengolahan data dalam data mining adalah clustering. penerapan clustering pada permasalahan ini dapat menggunakan metode algoritma k-means untuk mengelompokkan data yang paling sering digunakan. tujuan dari penelitian ini adalah untuk mengelompokkan data sanitasi fasilitas sanitasi tertinggi, fasilitas sanitasi sedang, dan fasilitas sanitasi rendah, sehingga daerah/kota yang termasuk dalam cluster rendah akan mendapat perhatian lebih dari pemerintah untuk meningkatkan/menyediakan fasilitas sanitasi. kata kunci: sarana kesehatan; covid-19, klastering, algoritma k-means introduction the need for public health facilities is one of the most important things for the community. with the current state of the covid-19 virus outbreak. health facilities are urgently needed because many patients must be treated quickly. we can get health services or facilities from where we live, but can we use health facilities if the current conditions are the spread of the covid-19 virus outbreak? moreover, if the health facilities owned by the district/city where we live have low health facilities, it will be difficult to use/obtain these health facilities. with the boom in the use of health facilities for patients due to the covid-19 virus outbreak, of course, the government must have thought about the health facilities available in every regency/city of west java in the future. therefore, to determine the level of availability of health facilities owned by each district/city in west java, a technique that can group data correctly is needed. one method of data processing in data mining is clustering. the application of clustering to this problem can use the k-means algorithm method to group the most frequently used data. the purpose of this algorithm is to divide the data into several groups. 72 wellbeing administrations for the local area is a common liberty that should be executed by the state. the public authority should have the option to give equivalent treatment to its residents in wellbeing administrations and other public administrations (fadillah rijal, h. muhammad siridangnga, usman, & niar novita sari, 2019). the right to a satisfactory way of life for the wellbeing and prosperity of himself and his family is a common freedom and is perceived by all countries on the planet, including indonesia (usman & kara, 2016). this acknowledgment is contained in the 1948 united nations declaration on human rights. article 25 section (1) of the declaration states, "everyone has the right to a standard of living adequate for the health and well-being of himself and his family, including the right to food, clothing, housing, and medical care as well as necessary social services and the right to security in the event of unemployment, suffering illness, disability, being a widow/widower, reaching old age or other conditions that result in a lack of livelihood, which is beyond his control.” (yasira & jamhir, 2019). wellbeing offices are places used to do wellbeing endeavors (pajow, mandagi, rumayar, masyarakat, & ratulangi, 2017). health is very meaningful for the development of the welfare of the indonesian people and as a capital for the implementation of national development which is essentially the development of the whole indonesian people and the development of the entire indonesian society. (nahar, 2017). puskesmas is one of the health service facilities that organizes public health efforts and first-level individual health efforts, by prioritizing promotive and preventive efforts to achieve the highest degree of public health in its working area. (susanti & widodo, 2017). world health organization (who) states that indonesia is included in the group of countries with the most serious shortage of health workers, both in terms of number and distribution (susanti & widodo, 2017). health facilities are the most important infrastructure in an area, especially in urban areas where the population is larger than in rural areas (naibaho, 2016). puskesmas, clinics, hospitals, and medical centers are places for treatment and health care services that are commonly used by the community (naibaho, 2016) (semendawai & wahyono, 2013). s.defiyanti, m.jajuli (defiyanti & jajuli, 2015) suggested that in the codification dataset the overall purity value of the k-means algorithm cluster results was 0.806 or 80.56%. for the original data dataset, the purity value of the k-means algorithm cluster results is 0.750 or 75%. the comparison criteria for data mining algorithms, especially for the clustering method, which was carried out in this study were only on the measurement of clustering results. u.marifatin (marifatin, 2020) said that based on the description of the problem above, it can be concluded that the initialization of the number of clusters is 2 by the definition of the value of k with the number of acute clusters being 376 items, non-acute clusters having 624 items with a total number of data being 1000. using a variety of other methods for grouping diseases of patients in puskesmas warujayeng u.rosiani, c.rahmad, m.rahmawati et al. (rosiani, rahmad, rahmawati, & tupamahu, n.d.) stated that the results of the image detection test for corn leaf disease, namely leaf blight and leaf spot that had been carried out, obtained a percentage of system testing of 90%. conduct research on datasets with the same provisions as distance, leaf position, and light. cluster analysis is a multivariate technique with the main objective of grouping objects based on their characteristics. currently, cluster analysis has been widely applied in various fields written in various studies and journals (windarto, 2017). with the data that has been grouped using the k-means algorithm method, it is hoped that it will make it easier for the government, especially in west java, to get the results needed in the management of health facilities. based on the description above, this research will utilize data on health facilities obtained from the website of the central statistics agency. the purpose of this study is to cluster data on health facilities from the highest health facilities, medium health facilities, and low health facilities so that districts/cities that fall into the low cluster get more attention from the government to improve/provide health facilities. materials and methods case study method in the case of studies, there is no single definition included in the social sciences, there are broad definitions and are divided into four categories. teaching cases do not need to accurately describe certain individuals, events or processes, because the main goal is to improve learning. teaching cases can be in the form of illustrations and although they come from observations of case studies, they are not always by a particular research methodology (prihatsanti, suryanto, & hendriani, 2018). research considers what type of case study is carried out, it depends on the study as a whole. contextual investigations are logical exercises in a program, occasions, and exercises completed seriously and exhaustively (hidayat, 2019). contextual analysis research plans to uncover the 73 uniqueness or uniqueness of the attributes contained for the situation under study. the actual case is the reason for leading contextual analysis research, hence the primary reason and focal point of contextual investigation research are looking into it that is the object of examination. cases can exist and are found in practically all fields, hence everything connected with the case like the idea of the case, exercises, capacities, history, ecological conditions, and different issues relating and impacting the case should be explored to clarify and comprehend the presence of the case. the case completely and exhaustively (hidayat, 2019). data collection in classifying the number of villages/subdistricts that have health facilities according to districts/cities in west java in utilizing the k-means clustering algorithm, related data are needed. the source of research data is obtained from data collected based on health information documents produced by the directorate general through the website https://www.bps.go.id. the data used is data on the number of villages/sub-districts that have health facilities according to districts/cities in west java in 2018-2019 which consists of 27 districts/cities. this study uses 6 variables, namely: hospital, maternity hospital, polyclinic, public health center (puskesmas), auxiliary health center (puskesmas pembantu), and pharmacy. the data will be processed by dividing into 3 clusters, namely the high health facility cluster, the medium health facility cluster, and the low health facility cluster. the data that has been processed will be processed first to be clustered. in the previous stage, the data for each province will be summed for each aspect so that at this stage a value calculation has been obtained which will be processed at the clustering stage. k-means method the method used in k-means. this k-means method is for grouping non-hierarchical or partitioned data (wanto et al., 2020). the k-means algorithm attempts to divide the existing data into several groups, where the data in one group has the same characteristics as each other, but the data in other groups have different characteristics. (wanto et al., 2020). the data that has been accumulated will be processed by data mining to find the 3 desired clusters in the data using the k-means algorithm for grouping, namely: 𝑑(𝑥, 𝑦) = √∑ (𝑥𝑖 − 𝑦𝑖)2𝑛𝑖=1 ......................................... (1) where d is the distance from the result of subtraction between xi (data value) and y (central value) derived from the calculation of euclidean distance. after manual calculations have found results until each value in a cluster does not change, the authors use tools in the form of rapidminer software to get a comparison of the results of data calculations by data mining calculations with the kmeans algorithm method manually with the data generated by the k-means algorithm calculations. means computerized. results and discussion the k-means calculation endeavors to partition the current information into a few gatherings, where the information in one gathering has similar attributes as one another, however the information in different gatherings have various qualities (wanto et al., 2020). 1. data transformation transforming the data, the data is then accumulated and the average value of each criterion (6) is taken, namely hospitals, bersalin hospitals, polyclinics, community health centers, supporting health centers, pharmacies. to facilitate data processing as shown in table 1 below: table 1. data transformation no county/city hospital maternity hospital polyclinic puskesmas puskesmas pembantu pharmacy 1 bogor 23,5 11 166,5 102 124 136,5 2 sukabumi 8 5 53,5 61 176 56 3 cianjur 6,5 0,5 42,5 48,5 112 71,5 4 bandung 9,5 11 129,5 64,5 102 131,5 5 garut 8 0 90 72,5 144,5 77,5 6 tasikmalaya 2,5 0,5 54,5 44 156,5 66,5 7 ciamis 4 0,5 43 36,5 96,5 53 8 kuningan 9 0,5 37 39,5 62,5 56 9 cirebon 11,5 2,5 70,5 61 90,5 129,5 10 majalengka 3 1,5 29 34,5 68,5 65 11 sumedang 3,5 2 54,5 38,5 62 59,5 12 indramayu 10 4 50,5 50,5 71,5 85 13 subang 7 2,5 71,5 44,5 49,5 75,5 14 purwakarta 8,5 4 55,5 20,5 50 38 https://www.bps.go.id/ 74 no county/city hospital maternity hospital polyclinic puskesmas puskesmas pembantu pharmacy 15 karawang 18,5 3 150,5 49 66 123 16 bekasi 30,5 21 137,5 50,5 61 77,5 17 bandung barat 7,5 4 52,5 32,5 51,5 55 18 pangandaran 0 0 16,5 19 34 23 19 kota bogor 18 6,5 41,5 25,5 32,5 48,5 20 kota sukabumi 6 2 9 16 19 15 21 kota bandung 27,5 20 111,5 70,5 11,5 129,5 22 kota cirebon 9 3 11,5 21 9 19,5 23 kota bekasi 26 11 53 39,5 11,5 54,5 24 kota depok 18 7 51,5 35 7,5 54 25 kota cimahi 5 0 14 12,5 4 15 26 kota tasikmalaya 7 7 33,5 22 26 41 27 kota banjar 3 0 7 9,5 8,5 8,5 after being accumulated and looking for the average value, the value of each variable will be obtained. then the data will enter the clustering stage by applying the k-means algorithm to cluster the data into three clusters. in the application of the k-means algorithm, the midpoint or centroid value is obtained from the data obtained with the provision that the desired clusterization is 3. the cluster determination is divided into three parts, namely low population level clusters (c1), medium population-level clusters (c2), and high-level clusters. high population (c3). then the value of the midpoint or centroid also has 3 points. 2. initialize determination of the initial cluster point or centroid is done by taking the smallest (minimum) value for the low population level cluster (c1), the average value for the medium population-level cluster (c2), and the largest (maximum) value for the high population level cluster ( c3). here is the initial centroid table 2: table 2 initial centroid value a b c d e f description c1 0 0 7 9,5 4 8,5 (lowest cluster) c2 10,75925926 4,814814815 60,64815 41,5 63,25925926 65,37 (medium cluster) c3 30,5 21 166,5 102 176 136,5 (highest cluster) 3. k-means application process a. iteration 1 the iteration process 1 calculates the distance between each data to the nearest centroid to determine the new k-means member according to the minimum or closest distance from the centroid. after getting the results of the iteration centroid distance 1, then determine the shortest distance. the shortest distance is the smallest value between c1, c2, and c3 in each data. after getting the centroid results from each data, then calculate the number of each new k-means based on the data that is incorporated in each k-means. the calculation of the k-means algorithm will continue until the umpteenth iteration until each cluster within the centroid distance has not changed. the average results obtained for each result of the cluster 1 iteration calculation process are taken as the new centroid as shown in table 3 below: table 3 new centroid iteration 1 centroid iteration 1 a b c d e f c1 5 2 15,25 16,66666667 16,75 20,33333333 c2 11,97368421 5,052631579 65,78947368 44,94736842 72,71052632 72,36842105 c3 16,5 11 148 83,25 113 134 b. iteration 2 the process of recalculating the distance between each data to the nearest centroid to determine new cluster members according to the minimum/closest distance from the centroid. after getting the results of iteration 2 centroid distance, then determine the shortest distance. the shortest distance is the smallest value between c1, c2, and c3 in each data. after getting the centroid results from each data, then calculate the number of each new k-means based on the data that is incorporated in each k-means. the average results obtained for 75 each result of the cluster 2 iteration calculation process are taken as the new centroid as shown in table 4. table 4 new centroid iteration 2 centroid iteration 2 a b c d e f c1 10,22222222 4,055555556 26,38888889 22,22222222 16,88888889 31 c2 9,8 4,566666667 63,56666667 47 84,26666667 73 c3 17,16666667 8,333333333 148,8333333 71,83333333 97,33333333 130,3333333 c. iteration 3 the process of recalculating the distance between each data to the nearest centroid to determine new cluster members according to the minimum/closest distance from the centroid. after getting the centroid results from each data, then calculate the number of each new k-means based on the data that is incorporated in each k-means. the average results obtained in each of the results of the iteration 3 cluster calculation process above are taken as the new centroid as shown in table 5. table 5 new centroid iteration 3 centroid iteration 3 a b c d e f c1 10,05 4,05 29,3 22,05 20,2 31,7 c2 6,708333 1,958333 54,08333 46,95833 95,125 70,83333 c3 21,9 13,2 139,1 67,3 72,9 119,6 d. iteration 4 the resulting process recalculates the distance between each data to the nearest centroid to determine new cluster members according to the minimum/closest distance from the centroid. after getting the results of the iteration centroid distance 4, then determine the shortest distance. the shortest distance is the smallest value between c1, c2, and c3 in each data. after getting the results of the centroid from each data, then calculate the number of each new k-means based on the data that is incorporated in each k-means. the average results obtained for each result of the cluster 4 iteration process are taken as the new centroid as shown in table 6. table 6 new centroid iteration 4 centroid iteration 4 a b c d e f c1 9,818181818 4,045454545 31,40909091 23 23,04545455 33,81818182 c2 6,636363636 1,772727273 54,22727273 48,27272727 99,09090909 72,27272727 c3 21,9 13,2 139,1 67,3 72,9 119,6 e. iteration 5 the resulting process recalculates the distance between each data to the nearest centroid to determine new cluster members according to the minimum/closest distance from the centroid. after getting the results of the iteration centroid distance 5, then determine the shortest distance. the shortest distance is the smallest value between c1, c2, and c3 in each data. after getting the centroid results from each data, then calculate the number of each new k-means based on the data that is incorporated in each k-means. based on the calculation of iteration 4 and iteration 5 in each cluster within the centroid distance there is no change, then the calculation is stopped at iteration 5. with the results of the final centroid of iteration 5 cluster 1 has 11 data, cluster 2 has 11 data, and cluster 3 has 5 data in table 7. 76 table 7 new centroid iteration 5 centroid iteration 5 a b c d e f c1 9,818181818 4,045454545 31,40909091 23 23,0454545 33,81818182 c2 6,636363636 1,772727273 54,22727273 48,27273 99,0909091 72,27272727 c3 21,9 13,2 139,1 67,3 72,9 119,6 k-means results figure 1. clustering model in figure 1 it can be seen that after processing, the clustering model (clustering) menu will display the results of the sum of each data in 3 clusters in the description display. the results of these calculations, the results of low clusters (cluster 0) consisting of regencies/cities, which include kuningan, majalengka, sumedang, subang, purwakarta, west bandung, pengandaran, bogor city, sukabumi city, cirebon city, bekasi city, city depok, cimahi city, tasikmalaya city and banjar city. the results of the medium cluster (cluster 1) consisting of regencies/cities, which include sukabumi, cianjur, garut, tasikmalaya, ciamis, cirebon and indramayu. the results of the highest cluster (cluster 2) consisting of regencies/cities, which include bogor, bandung, karawang, bekasi and bandung city to assess the results of villages with sanitation facilities according to regency/city, the k-means clustering method can be used. the data is processed to get the value of the village with sanitation facilities by regency/city. the results of the data processing determine the centroid value in 3 clusters, namely (1) low-level clusters, (2) medium-level clusters, and (3) high-level clusters. assessment based on the index of villages that have health facilities as many as 15 low-level regencies/cities, 17 medium-level regencies/cities, and 5 high-level regencies/cities. in figure 2, it can be seen that there are still health facilities that are not owned by the regions in the cluster 0 area, including hospitals, maternity hospitals, polyclinics, health centers, auxiliary health centers, and pharmacies. and in cluster 1 there are still health facilities that are still lacking. hospitals, maternity hospitals. figure 2. cluster results using k-means conclusions and suggestions conclusion the results of clustering using the k-means method obtained 3 clusters, namely high, medium and low. bekasi and bandung are included in the high cluster. sukabumi, cianjur, garut, tasikmalaya, ciamis, cirebon and indramayu are included in the medium cluster. meanwhile, bogor, bandung, karawang,. kuningan, majalengka, sumedang, subang, purwakarta, west bandung, pengandaran, bogor city, sukabumi city, cirebon city, bekasi city, depok city, cimahi city, tasikmalaya city and banjar city are included in the low cluster. the results obtained from the study show that there are still many cities/districts that are still included in the low cluster. this can be an input for the government to continue to improve and carry out an even distribution of good health facilities in all cities/districts. this is very important considering that during the covid-19 pandemic, health facilities are very much needed by the community. the local government must fulfill the development of health facilities, especially in the low cluster, namely in the province of west java. suggestion the results obtained from the study show that there are still many cities/districts that are still included in the low cluster. this can be an input for the government to continue to improve and carry out an even distribution 77 of good health facilities in all cities/districts. this is very important considering that during the covid-19 pandemic, health facilities are very much needed by the community. the local government must fulfill the development of health facilities, especially in the low cluster, namely in the province of west java. reference defiyanti, s., & jajuli, m. 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(2019). pelayanan badan penyelenggara jaminan sosial. jurnal justisia : jurnal ilmu hukum, perundang-undangan dan pranata sosial, 3(2), 276. https://doi.org/10.22373/justisia.v3i2.5933 78 149 implementation of electronic customer relationship management in the donation service information system ali ibrahim1*), beriadi agung nur rezqeb2 *1,2department of information systems faculty of computer science, universitas sriwijaya, indonesia *1,2management of information systems and business (misab) research group, faculty of computer universitas sriwijaya, indonesia. *1,2multimedia and game programming laboratory faculty of computer universitas sriwijaya, indonesia https://unsri.ac.id/ aliibrahim@unsri.ac.id*1, beriadiberlix@gmail.com2 (*)corresponding author abstrak laznas lmi kanwil sumsel merupakan lembaga yang bergerak di bidang agama dan kemanusiaan. salah satu layanan yang diberikan oleh kanwil lmi sumsel adalah layanan donasi. namun, pelayanan tersebut masih belum efektif dan efisien. berdasarkan hasil kuisioner pelayanan donasi pada lmi kanwil sumsel didapatkan hasil perhitungan. diperoleh net promoter score1 sebesar -61,6% yang menunjukkan nilai negatif. hal ini menunjukkan perlunya peningkatan pelayanan donasi agar tercipta loyalitas. konsep e-crm bertujuan untuk memberikan kepuasan dan meningkatkan loyalitas. oleh karena itu diperlukan suatu sistem informasi pelayanan donasi yang dapat mempermudah baik lmi maupun donatur. serta pengukuran yang mengukur loyalitas donatur dengan menggunakan metode perhitungan net promoter score dan metode pengembangan sistem fast. dari hasil pengukuran net promoter score2 didapatkan hasil perhitungan sebesar 77 % hal ini menunjukkan bahwa loyalitas donatur mengalami peningkatan terhadap peningkatan pelayanan donasi di lmi kanwil sumsel. keywords: dona, e-crm, fast, lmi, net promoter score abstract laznas lmi regional office of south sumatra is an institution engaged in the field of religion and humanity. one of the services provided by the lmi regional office of south sumatra is a donation service. however, the service is still not effective and efficient. based on the results of the questionnaire on donation services at the lmi regional office of south sumatra, the calculation results. obtained net promoter score1 of -61.6% which indicates a negative value. this shows the need to improve donation services in order to create loyalty. the ecrm concept aims to provide satisfaction and increase loyalty. therefore we need a donation service information system that can make it easier for both lmi and donors. as well as a measurement that measures the loyalty of donors using the net promoter score calculation method and the fast system development method. from the measurement results of net promoter score2, the calculation results are 77%, this shows that the loyalty of donors has increased towards the improvement of donation services at lmi regional office of south sumatra. keywords: donation, e-crm, fast, lmi, net promoter score introduction donation or donation or charity is a gift that is generally physical in nature by individuals or legal entities. this giving has a voluntary nature without any benefit in nature, although the donation can be in the form of food, goods, clothing, toys or vehicles but this is not always the case, in the event of a disaster emergency or in certain other circumstances (dewantry, budiwati, & sanjaya, 2015). donation activities are generally found around roads and places where natural disasters are occurring. the results of the donation are given to someone or to an agency that needs the donation. someone who donates is also called a donor. in a national zakat institution, donation is one of the programs contained in the national zakat institution. like in lmi, baznas, dompet dhuafa and others. almost in every city in indonesia there are branches or representative offices respectively. where each branch or representative office of zakat institutions has a donation program that must be carried out and achieved every year. like one of them in south sumatra, namely the infaq management institute (lmi), lmi has one of the mailto:aliibrahim@unsri.ac.id*1 150 programs, namely donation, in which the donation focuses on the south sumatra region. trust and ease of donation are the most important things that must be considered. the trust of donors has made the donation program continue to this day. donor loyalty is a very important aspect as a place to manage these donations. besides being able to make a donor a regular donor. donor loyalty can strengthen the relationship between lmi and the donors themselves. for now, every donor who donates will be given a small magazine as a form of gratitude for donating at lmi. the continuation of the donation program depends on the perception of donors who trust the institution. therefore, lmi must be able to create an effective and efficient donation service, so that donors will have a good perception of the institution that manages the donation, and later will be able to become loyal donors. in the donation service process at lmi, there are currently three ways to donate. the first way donors must come directly to lmi's place, the second way donors must first fill in their bio in the google forms listed on the lmi website then donors can donate. the third way is that lmi will pick up donations. this process is less effective and efficient. therefore, lmi must be able to provide effective and efficient services to donors in order to create a good relationship between donors and lmi. for that we need the right way so that lmi becomes a place to donate that knows its donors very well, so that donors become loyal to donate at lmi. the best way to build relationships with donors is to build customer relationship management (crm). customer relationship management (crm) is a customer-oriented business strategy in which a company tries to increase customer satisfaction and loyalty by offering services through several specific programs to customers (tanjungsewu paribhasagita & lisnawati, 2016). electronic customer relationship management (e-crm) is a form of it application in the crm field of a company by utilizing internet technology. by using e-crm, lmi can provide the best service to donors and can build better relationships with donors. the method used to measure customer loyalty is the net promoter score method. the net promoter score method was developed by fred reichheld of harvard. the net promoter score (nps) is a model the measurement of loyalty is very simple. based on the results of the questionnaire on donation services at the lmi regional office of south sumatra, the calculation results are net promoter score1 of -61.6% which shows a negative value. this shows that there is a need for innovation and service improvement so as to create loyalty from donors in a study conducted by rahayu and irawan, in this study researchers designed an ecrm system to improve service and loyalty in kumon educational institutions. this research produced an e-crm prototype which has main features, namely promotion, registration, payment, score lists, learning evaluations, announcements, and complaint handling thereby increasing loyalty and customer service to kumon educational institutions (rahayu & irawan, 2019). other research nikou (nikou, harihodin bin selamat, yusoff, & khiabani, 2016) how the role of e-crm in increasing customer loyalty provides useful insights for service industry managers to implement e-crm in its best form and adapt it to their organizational or industry culture to increase customer loyalty levels and get more profit and revenue for their own business. other research by huseynov and amazhanova (aldaihani & ali, 2018) the impact of e-crm features on customer satisfaction and perceived utility as a mediating variable in the turkish e-commerce sector is investigated in this study. quantitative research methodologies were used in this study. self-managed likert-type online surveys were used to obtain primary data from 210 respondents. customer satisfaction is impacted directly and indirectly (through perceived usefulness characteristics) by the e-crm elements studied in this study (complaint management, communication, information content, security, and privacy). (aldaihani & ali, 2018). in another study by bezhovski (bezovski & hussain, 2016), which identifies and describes the benefits of using this advanced technology in the banking sector and helps banks make the right decisions regarding the implementation and / or further improvement of existing e-crm. the results of the study found that e-crm has reduced workload at branches, lowered administrative costs, increased cross selling, bank revenue and enabled bankers to analyze customer needs by having access to all past transactions. echannels have improved information dissemination and allowed management to introduce new products and schemes more quickly (bezovski & hussain, 2016). other research conducted by ali ibrahim et al, (ibrahim et al., 2019) the problems faced by the fasilkom canteen related to customers can be overcome with the customer relationship management (crm) model. the method used is the crm scorecard. the number of respondents to the questionnaire was 59 people consisting of fasilkom students from the 2013-2016 class. these findings will provide future evaluations for the canteen facilitators in managing customers (ibrahim et al., 2019). in the benefits of implementing crm, the 151 data validation process in social media is of concern. because one of the important things in the implementation of social crm is the data the objective of this study is to show the results of research on current social crm together see the problem this time and provide a solution (ibrahim, ermatita, saparudin, & adetya, 2018). and other research conducted by asmara and ratnasari (asmara, 2016), this study measures visitor satisfaction and visitor loyalty in cave tourism by using the net promoter score calculation. from the measurement results, the level of visitor loyalty is very low, which is -13%. that is, it is likely that visitors will move to other tourist attractions. other research which is conducted korneta (korneta, 2018) researchers verified the effect of nps on growth and profitability of transport companies in poland. this goal is achieved by using the spearman rank correlation and linear regression. other research conducted by rajasekaran and dinesh (rajasekaran & dinesh, 2018) in this research, researchers research on how the net promoter score and how the customers rate your company using the net promoter score. other research conducted by dash (dash, 2018) this study examines nps in measuring customer loyalty. in another study by stander (stander, 2016), in order to assess the empirical accuracy of this indicator, nps was assessed in the context of professional football customer behavior in south africa. measurement and structural models are evaluated with a defined direct path between nps and consumer spending, as well as an indirect pathway with nps postulated as a mediator to activate purchasing behavior in nomological networks that include consumption motives traditional sports, using the theoretical context of loyalty-based business models and relationship marketing as points of departure. the survey included 2465 adult fans from one of the most popular and well-known professional football clubs in the country. nps is a direct predictor of consumer expenditure, according to the results of this study. it also shows that nps can act as a buffer between sports consumption motivations and spending. the findings of the study are examined, and future suggestions are suggested. (stander, 2016). according to buttle (buttle, 2007) customer relationship management or customer relationship management (crm) is a deep core strategy integrating internal processes and functions with the network external to create and deliver value for consumers profitable advice. meanwhile, according to brown and rigby, reincheld, dawson in vanessa (gaffar, 2007) reveals crm is the process of obtaining, sustaining, and developing profitable services, and it necessitates a laser-like concentration on the characteristics of a service that will generate client loyalty. (gaffar, 2007). e-crm is an electronic crm that uses a web browser, the internet, and other electronic media such as email, call centers, and personalization to deploy. e-crm is often referred to as e-service. (turban, chung, lee, & chung, 1999). the sustainability of the company depends on the company's ability to maintain its customers so that their customers can be loyal to the company and do not move with other companies. having loyal customers is an important asset that must be maintained by the company so that the company can continue to compete with other competitors. according to kotler and keller (kotler & keller, 2007) the following is a definition of client loyalty: despite situational variables and marketing efforts having the ability to trigger switching behavior, a strong desire to repurchase or repatronize a preferred product or service in the future. . an information system is a collection of people, data, procedures, and information technology (it) that work together to collect, process, store, and provide the information needed to run a business. (whitten, bentley, & dittman, 2004). the information system receives data input and instructions, processes the data with instructions and produces results. the basic system model of input, processing and output is suitable for simple processing systems (b.davis, 1992). research methods a. systems development method this research system development method is the fast (framework for the application of systems techniques) system development method because fast has a good standard and a planned process. in addition, the fast method is suitable for implementation in the scope of small and large projects (whitten et al., 2004). 152 fig 1. fast system development method (whitten et al., 2004) in fig 1. fast defines the stages to identify and observe problems, opportunities, obstacles, and hopefully what is expected to happen repair repair. this development is lifecycle because after completion implementation of implementation and maintenance then the system will provide feedback to the system analysis that has been designed. so that the stages the development above is continuously carried out for the improvement of the system. b. net promoter score in this research method there is also a measurement of donor loyalty with the number of respondents who will be measured 10-15 respondents. which of these respondents will be measured loyalty using a simple loyalty measurement net promoter score. the net promoter score (nps) method is a very simple but easy to understand and effective method for measuring the level of loyalty, so this method is widely used. using a scale of 0-10 and then dividing the customers who took the survey into three groups based on their answers. nps is the percentage of promoters minus the percentage of detractors presented in an easy to understand way, as well as the most effective short summary of how a company is doing (f. f. reichheld & covey, 2006). in nps, the types of customers are divided into: 1. promoters of customers who are enthusiastic about your product and will continue to buy. they will be happy to refer your product to others. promoters are also defined as those who give a score of 9 or 10. 2. passives customers who are satisfied with your product but not enthusiastic and may at any time move to another product if they find a more attractive deal. their passives that give them a 7 or 8. 3. customer detractors who have had a bad experience with our products and if the opportunity arises they will spread negative news about our products (negative word of mouth). customers usually give a value of only 0 to 6. fig. 2. classification of respondents net promoter score (f. reichheld, 2011) figure 2. explains the classification of respondents and how to calculate the net promoter score. c. crm phase according to robinson and kalakota (2001) there are three stages in the crm phase, namely getting new customers (acquire), improving customer relationships (enhance), and retaining customers (rertain). table 1 describes the three crm phases that will be applied to the donation service system at laznas lmi regional office of south sumatra (kalakota & robinson, 2001). table 1. crm phase no crm phase solution offered 1 acquire phase (get new customers) donation service information interesting and accurate that later will attract interest in donating. ease of service process donations for 153 no crm phase solution offered donors user built systems friendly 2 enhance phase (increase relationship with customers) there is login access for each donor. there is customer service make it easy for donors to communicate with lmi. there is a means of conveying criticism and suggestions 3 retain phase (maintain customer) there is a response to criticism and suggestions which has been given. there is documentation of evidence donation distribution of donations there is a page for donors to see donations that have been made paid, unpaid donations table. 1. describes the crm phases in the donation service system that will be implemented into the system. d. old system process analysis the old system process is currently running in the donation service process. fig 3. dfd level 0 old system figure 3. explains the old system process for donation fig. 3. explains services at laznas lmi regional office of south. e. dfd level 0 new system fig 4. dfd level 0 new system figure 4. describes the flow of the proposed system. there are 3 entities that use the system, namely admin, donors, and leader. admin can login to enter the system and manage user data, donation data, data donation transactions, faq data, criticism and suggestions data, documentation data donations and chat online. donors can view donation data and faq as well as processing donation transactions, donation confirmation, criticism and suggestions, as well as online chat. leaders can come inside system and view donor data, donation data, and donation transaction data and print reports. f. dfd level 1 as for dfd level 1 in the new system, there are 5 processes of registration, login, donation, customer service and also print reports. 154 fig. 5 dfd level 1 figure 5. describes the process for the donation service. in the donation service, there are 5 processes, namely the list process, the login process, the donation process, customer service and print reports. results and discussion a. results the final stage of this research is to produce a donation service information system by implementing e-crm and measuring the loyalty of donors using the net promoter score calculation method. in this system there are 3 system users, namely admin, leadership and also donors. b. discussion this discussion section will discuss the results of the system software that has been developed. the discussion will be divided based on the use of a system where there are admins, leaders and donors. here's the discussion system software in detail. admin page is a page for admins who are logged in and have entered the system. pages displayed according to rights admin access that is able to manage donations, donors, donation transactions, donation confirmation, donation documentation, faq, criticism and suggestions, and users. fig. 6 admin page figure 6. describes the admin page contained in the donation service system. this leadership page is a page for leaders who are logged in and have entered the system. the page displayed corresponds to management access rights, namely being able to view donation data and print it, view donor data and print it, view donation transaction data and print it out. fig. 7 describes the leader's page contained in the donation service system 155 this donor page is a page for donors to interact with the donation service system. the pages on the donor are dashboard, list of donors, homepage, about, faq, donation, donation details, continue donating, continue payment and confirm donations. fig 8. donors page figure 8. describes the donor page contained in the donation service system. c. calculation of the net promoter score researchers have distributed questionnaires that were filled in by 13 donors. from the results of this questionnaire, the score for the nps1 questions was obtained. from the results of the nps questions on the first questionnaire, the promoters, passives and detractors were grouped below. nps1 = %promoters — %detractors = 15.4% 77% = 61.6% after the new system was implemented, the second questionnaire was distributed. from the results of the nps questions on the second questionnaire, the promoters, passives, and detractors were grouped below. nps2 = %promoters — %detractors = 77% 0% = 77% from the results of the calculation of nps2 there is an increase from the first negative and the second result is positive. from the results of nps2 it can be concluded that the donors are loyal to the improvement of the donation service system for laznas kanwil south sumatra. in terms of arrangement and content, the results section and the subsequent discussion part provide the most flexibility. in general, the unadulterated, uninterpreted results should be presented first. the raw data or the findings after applying the strategies indicated in the methods section should be presented in these results. the outcomes are just that: outcomes; they don't draw any conclusions. the major goal of the results section is to offer the study's data so that other researchers can form their own conclusions and fully comprehend the reasoning behind them. a popular approach for the results section is to offer a series of figures followed by a detailed description of the figures in the text. a well-written results section includes clear figures and concise language. the numbers should substantiate the claims made in the paper or provide new insights. results should be presented in terms of non-dimensional variables whenever possible. conclusions based on the results of the research conducted and the results of the discussion previously described, the following conclusions can be drawn: with an increase in donation services at laznas lmi regional office of south sumatra, it can make it easier for lmi parties and also donors donate. by implementing e-crm in this donation service information system, donors will be more loyal to lmi. from the results of the distribution of the first questionnaire, it was found that nps1 showed a calculation result of -61.6%, this indicates that donors were not loyal to the donation services provided. after developing new systems and innovations, the second questionnaire was distributed, it was found that nps2 showed a 77% calculation. it can be concluded that the donors were very loyal with the increase in donation services at laznas lmi regional office of south sumatra. references aldaihani, f. m. f., & ali, n. a. bin. 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(2004). metode desain & analisis sistem (6th ed.). yogyakarta: penerbit andi. jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 91 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penerapan metode profile matching sebagai pendukung keputusan pemilihan jurusan pada smk al hidayah fintri indriyani sistem informasi akuntansi universitas bina sarana informatika jakarta fintri.fni@bsi.ac.id abstrak pemilihan jurusan pada smk al hidayah dilakukan pada saat siswa/siswi mendaftar di sekolah tersebut, dengan acuan nilai minimal tiga mata pelajaran memenuhi standard minimal nilai yang telah ditetapkan pihak sekolah untuk tiap jurusan. penempatan jurusan dilakukan secara manual oleh panitia penerimaan siswa baru dengan melihat data nilai siswa dan minat siswa. hal ini memiliki kelemahan dari sisi waktu tidak efisien dan juga memungkinkan kesalahan input oleh panitia penerimaan siswa baru. sehingga diperlukan sebuah sistem berbasis komputer untuk membantu penentuan jurusan yang sesuai dengan kompetensi calon siswa. metode profile matching dalam penelitian ini digunakan untuk membantu mempercepat proses analisa kriteria nilai dan minat siswa disesuaikan dengan standard dari pihak sekolah. kriteria nilai meliputi nilai matematika, bahasa inggris dan bahasa indonesia, sedangkan pilihan jurusan ada akuntansi, administrasi perkantoran dan pemasaran. hasil penelitian adalah rekomendasi jurusan sehingga dapat mempercepat proses penjurusan sesuai dengan profile masing-masing siswa. kata kunci: evaluasi potensi, gap, profile matching, pemilihan jurusan, sistem pendukung keputusan. abstract the selection of majors at al hidayah vocational school is done when students enroll in the school, with a reference to a minimum of three subjects meeting the minimum standards of value set by the school for each department. placement of majors is done manually by the new student admission committee by looking at student grades and student interests. this has weaknesses in terms of inefficient time and also allows input errors by the new student admissions committee. so we need a computer-based system to help determine majors that are in accordance with the competencies of prospective students. the profile matching method in this study was used to help speed up the process of analyzing the value criteria and student interest according to the standards of the school. criteria for grades include math, english and indonesian languages, while the choice of majors is accounting, office administration and marketing. the results of the study are department recommendations so that they can accelerate the process of majors in accordance with the profile of each student. keywords: potential evaluation, gap, profile matching, selection of departments, decision support systems. pendahuluan sesuai dengan peraturan pemerintah no.29 tahun 1990 pasal 2 menyebutkan tujuan dari pendidikan menegah adalah meningkatkan pengetahuan siswa untuk melanjutkan pendidikan pada jenjang yang lebih tinggi dan untuk mengembangkan diri sejalan dengan perkembangan ilmu pengetahuan, teknologi dan kesenian dan meningkatkan kemampuan siswa sebagai anggota masyarakat dalam mengadakan hubungan timbalbalik dengan lingkungan sosial, budaya dan alam sekitarnya(kebudayaan, n.d.). pendidikan tingkat menengah di indonesia secara global terbagi menjadi dua yaitu sma dan smk dimana untuk siswa yang ingin segera terserap di dunia kerja kebanyakan memilih untuk masuk ke smk dikarenakan di smk siswa diberi pembekalan materi yang lebih aplikatif dan memfokuskan pada keterampilan siswa didik. smk al-hidayah yang berlokasi di cinere depok merupakan salah satu sekolah kejuruan yang memiliki tiga jurusan yaitu akuntansi, administrasi perkantoran dan pemasaran, dimana para calon siswa yang ingin masuk ke sekolah ini dapat memilih jurusan sesuai keinginan mereka. akan tetapi pihak sekolah juga memiliki standar minimum nilai yang digunakan untuk penempatan jurusan. selama ini panitia penerimaan siswa baru smk al-hidayah melakukan pendataan nilai dari tiga matapelajaran yaitu matematika, bahasa inggris dan bahasa indonesia dan http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 92 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional minat dari calon siswa, proses tersebut dilakukan secara manual (pratama, 2016). hal ini mengakibatkan proses penempatan jurusan menjadi lebih lama (prabowo, kusrini, & sunyoto, 2015) dan bisa jadi tidak sesuai dengan kompetensi calon siswa (susilawati, 2017) sehingga proses yang ada menjadi tidak efektif dan efisien (farida & firliana, 2016). berdasarkan permasalahan tersebut maka penelitian ini dilakukan dengan tujuan untuk membantu pihak sekolah dalam mempercepat proses penempatan jurusan sesuai dengan kompetensi siswa. pada penelitian terdahulu tentang pemilihan jurusan di perguruan tinggi stmik royal digunakan system pendukung keputusan menggunakan metode profile matching untuk memudahkan siswa dalam menentukam pilihan jurusan (yuma & rizaldi, 2018). ada pula pemilihan jurusan di man gresik menggunakan metode profile matching dan metode smarter untuk pembobotan tiap kriterianya lalu diuji cobakan dengan 200 data siswa di bandingkan dengan perhitungan manual dari pihak sekolah sehingga disimpulkan bahwa metode profile matching dapat dijadikan sebagai bahan pertimbangan dalam penentuan jurusan dengan hasil 67,41% keakuratan data (afifah, cahyani, & yunitarini, 2015). dalam implementasinya metode ini juga bisa di terapkan di berbagai kasus diantaranya sebagai sistem penunjang keputusan untuk kenaikan jabatan dengan penggunaan metode ini bias meminimalisir penilaian sepihak atau subyektifitas dan mengurangi kesalahan dalam meloloskan calon pegawai yang sesuai dengan standar perusahaan (frieyadie, 2016) dapat pula diterapkan pada pemilihan ketua jurusan dengan melihat factor-faktor kemampuan yang dimiliki calon ketua jurusan (anto & susilo, 2017). metode penelitian metode penelitian pada penelitian ini digunakan metode kuantitatif deskriptif untuk menentukan hubungan antara variabel dengan populasi dengan sekali pengukuran. metode pengumpulan data data diambil dari panitia penerimaan siswa baru pada smk al-hidayah yang merupakan data primer dengan metode wawancara dan dokumentasi. data yang disajikan merupakan data tahun ajaran 2018/2019. profile matching secara umum profile matching merupakan metode yang digunakan untuk membandingkan kompetensi objek penelitian dengan kompetensi yang diharapkan. dari selisih hasil perbandingan didapatlah gap, semakin kecil nilai gap maka terdapat peluang besar untuk nilai prioritas karena nilai bobot semakin besar. metode profile matching dapat digunakan untuk menyelesaikan masalah semi terstruktur. profile matching telah banyak digunakan dalam berbagai bidang untuk system pendukung keputusan diantaranya untuk penentuan kenaikan jabatan karyawan, pemilihan beasiswa, pemilihan mitra kerja termasuk juga pemilihan jurusan. tahapan penelitian penelitian ini dilakukan dengan tahapantahapan yang mengacu pada tahapan metode profile matching (kusrini, 2017) yang disesuaikan dengan objek penelitian yang ada, berikut adalah tahapannya: sumber: (indriyani, 2019) gambar 1. tahapan penelitian tahap pertama, penentuan kriteria profile ideal. pada tahap ini diambil nilai sebagai acuan di smk alhidayah untuk mata pelajaran matematika, bahasa indonesia dan bahasa inggris yang menjadi syarat minimal untuk tiap jurusan. tahap kedua, penentuan bobot per aspek. pada tahap ini nilai per aspek dari setiap calon siswa di data sesuai dengan acuan input nilai. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 93 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tahap ketiga, pemetaan gap. menghitung gap dengan cara nilai mahasiswa per aspek dikurangi nilai profile ideal. kemudian ditentukan nilai bobot sesuai dengan table gap. tahap keempat, menghitung ncf dan nsf. ncf didapat dari rata-rata bobot nilai per aspek per calon siswa. nilai nsf didapat dari bobot nilai secondary factor. adapun rumus mencari ncf adalah: 𝑁𝐹𝐶 = ∑ 𝑁𝐶 (𝑎,𝑛) ∑ 𝐼𝐶 ……………..………………………. (1) dimana ncf = nilai rata-rata core factor nc(a,n)= jumlah total nilai core factor (matematika, bahasa indonesia dan bahasa inggris) ic= jumlah item core factor setelah nilai ncf dan nsf didapat maka dihitunglah nilai total dengan rumus 90% ncf ditambah 10%nsf dengan rumus sebagai berikut: 𝑁(𝑎, 𝑛) = (𝑥)% 𝑁𝐶𝐹(𝑎, 𝑛) + (𝑥)%𝑁𝑆𝐹(𝑎, 𝑛) ……. (2) dimana n(a,n) = total nilai aspek (x)% = nilai persen yang diinput ncf(a,n) = nilai rata-rata core factor nsf(a,n) = nilai rata-rata secondary factor tahap kelima. buat rekomendasi. pada tahap ini di data setiap calon siswa dibandingkan dan dipilih nilai terbesar sebagai rekomendasi. objek penelitian objek penelitian adalah smk al-hidayah yang didirikan pada tanggal 02 mei 1998 dibawah yayasan pendidikan islam al-hidayah cinere yang diketuai oleh h. abdullah h.m. pada awal berdirinya smk al-hidayah cinere dipimpin oleh bapak h.ardani m. nur sh, (alm). adapun 3 jurusan yang disediakan yaitu akuntansi, sekretaris , dan penjualan. akuntansi merupakan jurusan dengan minat terbanyak dan dengan nilai standar tertinggi dibandingkan dua jurusan yang lain. kemudian setelah berganti kurikulum jurusan tersebut berubah menjadi akuntansi, administrasi perkantoran, dan penjualan atau pemasaran. data diambil dari panitia penerimaan siswa baru sebanyak 67 calon siswa dari populasi 200 orang yang mendaftar di tahun ajaran 2018-2019 dengan menggunakan rumus slovin dengan tingkat error 10% (sugiyono, 2017) dengan penjabaran sebagai berikut: n = n / (1 + (n x e²)) n=200/(1+(200 x 0.12)) n=200/(1+(200 x 0,01)) n=200/3 n=66,6 atau dibulatkan menjadi 67 hasil penelitian dan pembahasan kriteria profile ideal untuk core factor sesuai dengan kondisi real di smk al-hidayah penjurusan dengan menimbang tiga nilai mata pelajaran yaitu nilai matematika, nilai bahasa inggris dan nilai bahasa indonesia, sedang untuk secondary factor digunakan minat calon siswa. pertama kita tentukan dulu nilai profile ideal, seperti dijabarkan di table 1 dan 2. tabel1. tabel acuan input nilai nilai sub kriteria range besar nilai < 64 1 65-74 2 75-84 3 85-94 4 95-100 5 sumber: (indriyani, 2019) tabel 2. tabel kriteria nilai profile ideal jurusan/mapel mtk bhs ing bhs ind akuntansi 2 2 2 administrasi perkantoran 1 2 2 pemasaran 1 1 2 sumber: (indriyani, 2019) penentuan nilai bobot per aspek nilai setiap aspek dari masing-masing jurusan dihitung dengan standard yang telah ditetapkan diawal oleh panitia penerimaan siswa baru untuk setiap calon siswa baru, ditabel 3 disajikan nilai dengan perbandingan profile ideal untuk jurusan akuntansi. langkah yang sama juga dilakukan untuk jurusan administrasi perkantoran dan pemasaran. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 94 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel 3. data per aspek di jurusan akuntansi no nama siswa cf sf mtk bhs ing bhs ind minat siswa 1 nur amelia hasanah 3 3 3 1 2 diela ellandini 1 2 2 1 3 nabillah frana putri 3 1 2 1 4 fitriani apitah nurjanah 2 1 3 1 5 junaedi 2 2 2 1 6 ari munfako 2 1 1 2 7 raditya ludfianto 2 1 1 1 8 al-fath filsafah 3 3 2 3 9 rifaldy ahmad 2 1 1 2 10 ridwan hadi putra 1 3 1 3 profile ideal 2 2 2 1 sumber: (indriyani, 2019) pemetaan gap pada tahapan ini dibuat acuan gap dan bobot nilai dengan sesuai dengan pedoman tabel 4. tabel 4. tabel bobot nilai gap gap bobot nilai keterangan 0 3 kompetensi sesuai dengan profile ideal 1 3.5 kompetensi individu kelebihan 1 level -1 2.5 kompetensi individu kekurangan 1 level 2 4 kompetensi individu kelebihan 2 level -2 2 kompetensi individu kekurangan 2 level 3 4.5 kompetensi individu kelebihan 3 level -3 1.5 kompetensi individu kekurangan 3 level 4 5 kompetensi individu kelebihan 4 level -4 1 kompetensi individu kekurangan 4 level sumber: (indriyani, 2019) sehingga didapat nilai sebagai berikut untuk jurusan akuntansi. tabel 5. pemetaan gap no nama siswa gap bobot gap bobot gap bobot gap bobot 1 nur amelia hasanah 1 4,5 1 4,5 1 4,5 0 5 2 diela ellandini -1 4 0 5 0 5 0 5 3 nabillah frana putri 1 4,5 -1 4 0 5 0 5 4 fitriani apitah nurjanah 0 5 -1 4 1 4,5 0 5 5 junaedi 0 5 0 5 0 5 0 5 6 ari munfako 0 5 -1 4 -1 4 1 4,5 7 raditya ludfianto 0 5 -1 4 -1 4 0 5 8 al-fath filsafah 1 4,5 1 4,5 0 5 2 3,5 9 rifaldy ahmad 0 5 -1 4 -1 4 1 4,5 10 ridwan hadi putra -1 4 1 4,5 -1 4 2 3,5 sumber: (indriyani, 2019) perhitungan ncf dan nsf langkah selanjutnya adalah menghitung total core factor dengan menghitung rata-rata dari bobot nilai gap dari core factor dan membandingkan dengan secondary factor dengan perbandingan 90% untuk core factor dan 10% untuk secondary factor. seperti terlihat pada table 6. http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 95 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel 6 nilai ncf dan nsf untuk jurusan akuntansi no nama siswa cf sf nsk 1 nur amelia hasanah 3,6 3 3,6 2 diela ellandini 3,5 3 3,5 3 nabillah frana putri 3,1 3 3,1 4 fitriani apitah nurjanah 3,1 3 3,1 5 junaedi 3,8 3 3,7 6 ari munfako 3 4 3,1 7 raditya ludfianto 3 3 3 8 al-fath filsafah 3,8 5 3,9 9 rifaldy ahmad 3 4 3,1 10 ridwan hadi putra 3,4 5 3,5 sumber: (indriyani, 2019) rekomendasi langkah terakhir adalah membuat rekomendasi dengan cara membandingkan nilai akhir dari setiap jurusan dan hasil rekomendasi diambil dari nilai tertinggi. seperti terlihat di tabel 7. tabel 7. rekomendasi jurusan dengan metode profile matching no nama nsk ak nsk ap nsk pm rekomendasi 1 nur amelia hasanah 3,5625 3,3375 3,3375 1 2 diela ellandini 3,45 3,675 3,7875 3 3 nabillah frana putri 3,1125 2,8875 3,3375 3 4 fitriani apitah nurjanah 3,1125 3 3,45 3 5 junaedi 3,675 3,5625 3,675 1 6 ari munfako 3,1 2,9875 3,4375 3 7 raditya ludfianto 3 2,8875 3,3375 3 8 al-fath filsafah 3,875 3,65 3,65 1 9 rifaldy ahmad 3,1 2,9875 3,4375 3 10 ridwan hadi putra 3,5375 3,7625 3,7625 3 sumber: (indriyani, 2019) simpulan dan saran simpulan penerapan metode profile matching dalam menentukan jurusan pada smk al-hidayah dapat menghasilkan rekomendasi jurusan sesuai profile standar yang ditentukan pihak sekolah, dengan menghitung nilai gap kompetensi siswa dan standard ideal. sehingga dalam menentukan jurusan tidak hanya berdasarkan pilihan dari siswa seperti yang berlangsung saat ini, sehingga didapat hasil yang lebih baik dan efektif. dengan adanya penelitian ini panitia penerimaan siswa baru pada smk al-hidayah diberikan kemudahan dalam menentukan jurusan untuk calon siswanya sehingga proses pendataan menjadi lebih cepat. saran untuk pengembangan lebih lanjut dan agar pihak sekolah mudah dalam penggunaan metode ini maka perlu dibuat sebuah aplikasi dengan acuan metode profile matching. apabila dirasa perlu dapat dikembangkan menjadi expert system. daftar referensi afifah, n., cahyani, a. d., & yunitarini, r. (2015). sistem pendukung keputusan penentuan jurusan untuk siswa man gresik dengan metode profile matching. jurnal sistem informasi indonesia, 1(1). retrieved from http://www.publications.aisindo.org/index.ph p/jsii/article/view/4 anto, a., & susilo, t. (2017). penerapan metode profile matching pada sistem pendukung keputusan pemilihan ketua program studi ( studi kasus : program studi teknik informatika stmik musi rawas ), v(november), 87–93. farida, i. n., & firliana, r. (2016). implementasi metode profile matching untuk evaluasi potensi akademik penjurusan siswa man 2 kota kediri. jurnal infotel informatika telekomunikasi elektronika, 8(2), 156. https://doi.org/10.20895/infotel.v8i2.121 frieyadie, f. (2016). penggunaan metode profile matching untuk sistem penunjang keputusan kenaikan jabatan pada instansi pemerintah. paradigma jurnal komputer dan informatika, 18(2), 75–80. https://doi.org/10.31294/p.v18i2.1228 indriyani, f. (2019). laporan akhir penelitian: sistem pendukung keputusan pemilihan jurusan pada smk al hidayah. jakarta. kebudayaan, k. p. dan. (n.d.). peraturan pemerintah http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 96 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional no.29 tahun 1990 tentang pendidikan menengah. kusrini. (2017). konsep dan aplikasi sistem pendukung keputusan. yogyakarta: andi + amikom. prabowo, y. s., kusrini, k., & sunyoto, a. (2015). sistem pendukung keputusan pemilihan jurusan snmptn bagi siswa sman 7 purworejo. in proceedings konferensi nasional sistem dan informatika (kns&i) (vol. 0, pp. 180–185). bali: stmik stikom. retrieved from http://www.ejournal.stikombali.ac.id/index.php/knsi/article/view/438 pratama, r. p. (2016). sistem pendukung keputusan penjurusan siswa pada sman 5 kediri dengan menggunakan metode profil matching. universitas nusantara pgri kediri. retrieved from http://simki.unpkediri.ac.id/mahasiswa/file_a rtikel/2016/11.1.03.03.0230.pdf sugiyono. (2017). metode penelitian kuantitatif, kualitatif, dan r&d. bandung: alfabeta. susilawati, s. (2017). perangkat lunak berbasis website untuk membantu pemilihan jurusan di smk negeri 10 pandeglang. in prosiding seminar nasional riset terapan | senasset (pp. 26–32). serang: universitas serang raya. retrieved from http://ejurnal.lppmunsera.org/index.php/senasset/ar ticle/view/420 yuma, f. m., & rizaldi, r. (2018). sistem pendukung keputusan dalam pemilihan jurusan di perguruan tinggi dengan metode profile matching. seminar nasional royal (senar), 1(1), 181–184. retrieved from https://jurnal.stmikroyal.ac.id/index.php/sen ar/article/view/164 http://creativecommons.org/licenses/by-nc/4.0/ 134 application of profile matching algorithm in selection of the best employees in property company laeli nurchasanah1, annisa cintakami firdaus2, desti fitriati3 informatics engineering study program1,2,3 universitas pancasila1,2,3 laelifebry90@gmail.com1, cintakamiannisa@gmail.com2, desti.fitriati@univpancasila.ac.id3 (*) corresponding author abstrak pemberian pengharagaan bagi karyawan yang memiliki kelebihan dan kinerja kerja yang baik merupakan salah satu cara untuk meningkatkan daya saing yang positif antar karyawan di suatu perusahaan. penelitian ini bertujuan untuk mencari keunggulan yang dimiliki setiap karyawan agar dapat mengetahui karyawan yang berprestasi. melalui prestasi dalam dunia kerja dapat menjadi tolak ukur untuk mencari karyawan terbaik yang layak mendapatkan penghargaan. analisa data yang digunakan dalam penelitian ini bersumber dari data penjualan perusahaan properti selama tiga bulan terakhir. penelitian ini menggunakan metode profile matching untuk menentukan karyawan terbaik di perusahaan properti. penelitian dengan ini dilakukan dengan cara membandingkan karyawan satu dengan kandidat karyawan yang lainnya berdasarkan kriteria – kriteria yang telah ditentukan. hasil dari penelitian ini berupa perangkingan yang menunjukan urutan karyawan terbaik yang berhak mendapat penghargaan dari perusahaan. kata kunci: profile matching, sistem pendukung keputusan, gap abstract giving awards to employees who have advantages and good work performance is one way to increase positive competitiveness among employees in a company. this study aims to find the advantages of each employee to find out which employees excel. through achievements in the world of work, it can be a benchmark for finding the best employees who deserve awards. analysis of the data used in this study is sourced from data on sales of property companies for the last three months. this study uses the profile matching method to determine the best employees in property companies. this research was conducted by comparing one employee with another employee candidate based on predetermined criteria. the results of this study are in the form of rankings that show the order of the best employees who are entitled to an award from the company. keywords: profile matching, decision support system, gap introduction human resources are one of the main factors to create an effective and efficient performance in a company (sholihaningtias, 2018). the placement of employees according to their knowledge and skills will improve the performance of the employee (sugiartawan, ardriani, & kusuma, 2021). finding the right person worthy of appreciation is not easy. a person's performance is influenced by many factors such as personality and behavioral factors (herlambang, dewanto, harjanta, & setyawati, 2018) the selection of the best employees aims to increase the competence of employees (idam, junaidi, & handayani, 2019). computerization or the use of computers on a large scale in terms of processing data, one of which is the decision-making process for selecting the best employees can minimize the occurrence of inaccuracies in decision making (fitriana, ripanti, & tursina, 2018). to make a decision, of course, it is necessary to do careful analysis and calculations by predetermined criteria. decision-making is generally done using more than one criterion or even many criteria. therefore, an assessment decision model is needed to make it easier for the assessment team to make decisions about who deserves to be the best employee at property company. the assessment factors that will be carried out include several criteria, including based on discipline, number of absences, accumulation of surveyed consumers, the number of bookings, the number of consumers of credit contracts, and length of work. mailto:laelifebry90@gmail.com1 mailto:cintakamiannisa@gmail.com2 135 the profile matching method is one of the approach methods from several studies in decision support systems (fitriana et al., 2018)). the final result of this method aims to produce data in the final form of ranking (ermawita & fauzi, 2020) with a decision support system, it is hoped that it can support decisions that will be taken by the selection team in a company (wahyudi & utama, 2019). currently, at this property company's marketing performance assessment is still not available. to overcome this problem, manual calculations and systems are made using the profile matching algorithm. this method was chosen in the hope of being able to produce the best data and is also expected to help determine who is the best marketing actor who deserves a reward from the property company. the purpose of the study was to produce a decision support system for selecting the best employees according to predefined criteria. research methods types of research the data obtained in this study is sourced from primary data provided directly by the marketing manager of one of the property companies in indonesia. the data provided is sales data in the last three months (august, september, and october). time and place of research the research was conducted over the last three months from august 2021 to early november 2021. the research was conducted at the marketing office of a property company in cibinong. sales data from the last three months was obtained after the interview ended. decision support system decision support systems (dss) is a part of a computer-based (including knowledge-based) information system that is used to support decisionmaking in an organization or company (wahyudi & utama, 2019). dss by utilizing data and models to identify, solve problems, and make decisions aims to help make better decisions (sutinah, 2017). in general, a decision support system is designed as a support for decision-making in a problem that involves many criteria (fauzi, 2019) the stages of the decision support system are problem definition, collecting data or relevant information elements, processing data into information in the form of graphic and written reports, and determining alternative solutions (can be in percentages). the objectives of dss include helping to solve semi-structured problems, supporting managers in making decisions about a problem, and increasing the effectiveness not the efficiency of decision making. profile matching profile matching is a component that expects an ideal indicator variable level with a base level that should be met or passed by workers (kurnia, 2021). in profile matching, the minimum value for each variable is called the gap value. the smaller the gap value, the greater the weight (herlambang et al., 2018). the profile matching method is commonly used in solving semistructured problems in solving problems of determining the best employees, selecting scholarships, and selecting collaboration partners (indriyani, 2019). procedure the profile matching process includes several stages as follows: 1. investigation of a problem 2. determine criteria, sub-criteria, and values 3. determining the value of each alternative 4. determination of target values 5. determine the gap the gap is the difference in the value obtained from the selection team for employees who are nominated for the best employee (saputra, regasari, & sutrisno, 2017). gap collection for each attribute has a different calculation for each problem. the gap value is obtained from the following formula. gap = value – standard value ..................................... (1) 6. convert gap to the weighted value 7. calculation of core factor and secondary factor in the calculation of the profile matching method, the core factor and secondary factor have different assessment weights (sudarmadi, santoso, & sutrisno, 2017). the core factor is the attribute that takes precedence or is the most prominent in a position (pungkasanti & nurma’arif, 2019). the core factor aspect is estimated to produce optimal performance. the calculation of the core factor is formulated as follows. 𝑁𝐶𝐹 = ƹ𝑁𝐶 ƹ𝐼𝐶 ......................................................................... (2) description : ncf = average core factor nc = total number of core factor values ic = number of aspects core factor (malau, 2020) . 136 the secondary factor is supporting aspects other than those in the core factor (pungkasanti & nurma’arif, 2019). the secondary factor calculation can be seen in the following formula. 𝑁𝑆𝐹 = ƹ𝑁𝑆 ƹ𝐼𝑆 .......................................................................... (3) description : nsf = average value of secondary factor ns = total number of secondary factor scores is = number of aspects of a secondary factor (kusumantara, pamuji, & putri, 2019). 8. sum of core factor and secondary factory value 9. calculation of criteria value 10. determine the total value the calculation of the total value is obtained from the following formula. np = (x) % ncf + (x) % nsf......................................... (4) description: np = total score of criteria nfs = average score secondary factor average nfc = core average factor (x) % = the percent value that input. 11. rank after going through the calculation process above, researchers will get conclusions about the value of several alternatives that have been inputted. the highest scoring alternative will be a priority to choose the best employee and will be entitled to a gift from the property company. data, instruments, and data collection techniques this study uses primary data. the technique of collecting criteria and alternative data that will be used in system testing is obtained by conducting direct interviews with the marketing manager of a property company. for information on sales data for the last three months, it was obtained directly in hardcopy form by the marketing manager of the property company after the interview. data analysis technique the data in this study consisted of quantitative and qualitative data. for qualitative data, researchers convert it into numbers (quantitative data) to make it easier to calculate both manual and system calculations. results and discussion the first step for profile matching is to determine the alternatives and criteria used. alternatives can be seen in table 1. table 1. alternatives alternate code alternative name a1 kahfi a2 hendri a3 surya a4 winda after determining the alternatives, the criteria and values are determined as in table 2. table 2. criteria table 2 contains the type of each criterion specified by the company. the score for the criteria obtained from the property company can be seen in table 3. table 3. criteria value of each alternative criteria alternative kahfi hendri surya winda discipline good excellent enough enough number of absences 1 0 4 3 accumulated consumer survey 15 12 10 20 number of bookings 5 5 5 12 jumlah credit akad consumer 5 4 4 10 long work 7years 7 years 5 years 3 years after knowing the information about the value of each criterion for 4 predefined alternatives. the weight value of the criteria expected by the company can be seen in table 4. table 4. criteria weight value from the company 137 the next step is to determine the value of the core factor and secondary factor. the value can be seen in table 5. table 5. weight of core factor and secondary factor core factor 60% secondary factor 40% the next step is to determine the gap mapping. the results can be seen in table 6. table 6. gap mapping criteria assessment factor weight discipline excellent 5 good 4 enough 3 less 2 ineligible 1 number of absences 0 5 1 3 4 4 6 3 7 9 2 >9 1 accumulated consumer survey >15 5 11 15 4 6 10 3 1 5 2 0 1 number of bookings > 12 5 9 12 4 5 8 3 1 4 2 0 1 number of consumer credit agreements > 9 5 7 9 4 4 6 3 1 3 2 0 1 working period > 6 years 5 5 6 years 4 3 4 years 3 1 – 2 years 2 < 1 year 1 the results of the gap calculation can be seen in table 7. table 7. gap calculation the next step is determining the weight of the gap value based on table 8. table 8. weighted gap value the results can be seen in table 9. table 9. weighted results the next step is to calculate the value of the core factor and secondary factor which is then multiplied directly by the weight. the results can be seen in table 10. table 10. calculation results of core factor and secondary factor 138 employees ncf nsf core factor 60% secondary factor 40% a1 3,6 4,5 2,16 1,8 a2 4 4,5 2,4 1,8 a3 3 5 1,8 2 a4 4,2 4 2,52 1,6 the final assessment calculation is determined using formula 4. the final calculation results can be seen in table 11. table 11. ranking values employees np ranking hendri 4,2 1 winda 4,12 2 kahfi 3,96 3 surya 3,8 4 based on table 11, it is known that the order of employees who deserve to be appreciated are hendri, winda, kahfi, and surya. system result implementation to determine the best employees as shown in figure 1. input the username and password to enter the system. input alternatives, criteria, core factor value, and secondary factor value. figure 1. system main page after successfully inputting the value, click the "continue to process" button. to find out the rankings, click the "save and view ratings" button. figure 2. ranking results figure 2 is the result of the calculation according to the criteria and alternatives that have been determined at the beginning. evaluation result accuracy testing aims to find out how much accuracy the level of the system has been made. for accuracy testing, the results of the decision support system with the profile matching method will be compared between manual calculations and the system. accuracy is determined by the following formula. 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 𝑥 100% ........................... (5) description : tp = true positive tn = true negative fn = false positive fn = false negative the research was conducted using 30 sample data. by using the formula above obtained an accuracy value of 100%. conclusions and suggestions conclusions decision support systems using profile matching algorithms can help the role of property management in objective decision making with more effective time. the implementation of the profile matching method for the selection of the best employee selection at property company obtained excellent results by its accuracy rate of 100%. suggestions in the next research, decision support systems using profile matching algorithms could be created not only to determine the best employees in property companies but also to be applied to other fields to increase the variety of use of profile matching algorithms. reference ermawita, o. :, & fauzi, r. 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(2019). sistem pendukung keputusan penerimaan dosen baru menggunakan metode profile matching (studi kasus: universitas islam raden rahmat malang). jurnal teknologi terapan: g-tech, 3(1), 168–174. https://doi.org/10.33379/gtech.v3i1.338 119 pakpak language translator application into indonesian using algorithmboyer moore based on android khairani purba1, charles jhony mantho sianturi2*), mikha dayan sinaga3, nita sari sembiring4, erwing ginting5, muhammad fauzi6 1,2,3,4,5,6jurusan teknik informatika universitas potensi utama https://potensi-utama.ac.id/ khairani0610@gmail.com1,79sianturi@gmail.com2, mikhadayan88@gmail.com3, nita.sembiring86@gmail.com4,erwinginting82@gmail.com 5, mfauzixx@gmail.com6 abstrak minimnya pelestarian juga pengetahuan mengenai bahasa pakpak di indonesia yang menyebabkan bahasa pakpak kelestariannya kurang terjaga terutama bagi para kalangan muda yang terus mengikuti perkembangan zaman. hal ini menyebabkan proses globalisasi dan urbanisasi yang menyebabkan terjadinya asimilasi dan akulturasi budaya, khususnya daerah perkotaan. keadaan ini memicu bahasa baru yang sangat disukai terutama bagi kalangan muda yang tanpa disadari hal tersebut membuat kita kehilangan identitas sebagai masyarakat yang memiliki suku dan adat istiadat masing – masing dan munculnya sekolah internasional dan nasional yang mengharuskan siswanya berbahasa asing. sebab itu, dibutuhkannya pembelajaran untuk menjaga kelestarian bahasa pakpak. dengan cara membuat aplikasi penerjemah bahasa pakpak ke bahasa indonesia yang memakai algoritma boyer moore berbasis android. aplikasi penerjemah ini dibuat untuk pengenalan bahasa pakpak untuk masyarakat luas sehingga membuat bahasa pakpak kelestariannya tetap terjaga dan aplikasi penerjemah ini bias menerjemahkan bahasa pakpak ke bahasa indonesia atau sebaliknya yang berdasarkan kamus pakpak – indonesia yang berlaku. kata kunci: bahasa pakpak, bahasa indonesia, algoritma boyer moore, android abstract the lack of preservation is also the knowledge of the pakpak language in indonesia, which causes the pakpak language to be less preserved, especially for young people who continue to keep abreast of the times. this causes the process of globalization and urbanization which causes assimilation and acculturation, especially in urban areas. this situation triggers a new language that is very popular, especially for young people who unconsciously, it makes us lose our identity as a society that has tribes and customs each – and the emergence of international and national schools that require students to speak foreign languages. therefore, learning is needed to preserve pakpak language. by making a pakpak to indonesian translator application that uses the android based boyer moore algorithm. this translator application was made for the introduction of the pakpaklangugae to the wider community so that the sustainability of the pakpak language is maintained and this translator application can translate the pakpak language into indonesian or vice versa based on the prevailing pakpak – indonesian dictionary. keywords: pakpak language, indonesian, boyer moore's algorithm, android introduction regional languages are a means of communication for every ethnic/tribe in indonesia. regional languages are rarely used as a means of communication when they are overseas, especially the pakpak language. given that the pakpak language has begun to be abandoned, where most of the younger generation are less aware of the importance of regional languages. the pakpak language translator application into indonesian is designed to translate words and sentences where this application has 2 features, namely pakpak indonesian and indonesian pakpak by applying the boyer moore algorithm. with this application, it can help in the communication process between anyone who does not understand the pakpak language and introduces regional languages, especially the pakpak language. as a type of art, literature has existed in the course of human civilization. the process of education, introduction, and understanding of 120 literature will be able to enrich humans as individuals in continuous dialogue with the human world and humanity. in this context, literature has the potential as a transmitter of various values and can be a source of inspiration about virtue and wisdom (sukirman, 2021). regional languages also have an important role in shaping human character. regional languages are part of a culture that lives and develops according to the needs of the community. however, in this era of globalization, there is concern from language observers that many regional languages are threatened with extinction. this anxiety deserves attention because the loss of a regional language is an indication of the loss of world culture and civilization (nikmatuzaroh, 2018). the phenomenon of the extinction of regional languages in indonesia seems to have become a problem that has attracted the attention of many scientists, especially linguists. various efforts have been and are being made to save regional languages which tend to lead to the process of extinction. of course, it is quite reasonable considering that indonesia is a country that has the second most regional languages in the world after papua new guinea(tondo, 2009). the division (read: division) of dairi regency into two regencies is a political decision (action) involving the pakpak ethnic group or the pakpak people. the political action that gave birth to west pakpak regency as a new division, and dairi regency as the parent regency, was confirmed by law of the republic of indonesia number 9 of 2003 concerning the establishment of south nias regency, pakpak bharat regency, and humbang hasundutan regency in north sumatra province. the expansion of dairi regency is inseparable from the official implementation in 2001 of law no. 22 of 1999 concerning regional government(zuska, 2013). in ida basaria's research, i want to examine the pragmatic functions of the pakpak dairi language (hereinafter referred to as bpd), so that it will be known whether bpd belongs to a language group that emphasizes a subject (subject prominent language) or a language group that highlights a topic (topic prominent language). the study is based on the theoretical framework of language typology about the difference between language that emphasizes the subject and language that emphasizes(basaria, 2014). regional languages are a valuable asset of a nation. however, the paradigm of 21st-century society views that foreign languages have higher prestige than national and regional languages. in other words, regional languages are the third priority in their use after national and foreign languages. the preservation of regional languages is one of the phenomena as well as steps that arise in the mindset of the polemic of a shift in regional languages(widianto, 2018). the government cannot maintain the regional language without the awareness of the speakers to maintain the regional language itself. the role and contribution of local language users itself greatly affect the success of local language defense(alimin & fajri, 2020). the translation is the process of transferring language, word by word from one language to another. another theory states that translation is an activity of replacing text/speech/speech material in the source language to equivalent text/speech/speech material into the target language. (priyanto & ulinnuha, 2017). research from muhammad zulham, helmi kurniawan, and iwan fitrianto rahmad entitled design of email data security applications using android-based rc6 encryption algorithm. the purpose of this study is to secure messages that will be encoded on email in data confidentiality and can send messages and receive messages through email intermediaries using the rc6 android application. (zulham et al., 2014). based on the research entitled "implementation of the salsa20 stream cipher cryptographic technique for database security". this study concludes that the salsa20 stream cipher cryptographic technique can be used to provide increased data security through the encryptiondecryption process before the data is stored in a database. cryptographic techniques with the stream cipher algorithm method can be applied to ensure the security of electronic document files. one that can be used is the encryption and decryption of text data or in other words, encoding the data so that only the intended person can know the contents of the data. (munandar et al., 2020). research conducted by asbon hendra and ratih adinda destari entitled expert system to identify ariston water heater damage using webbased backward chaining method. from the research conducted, it is stated that the application is built using the backward chaining method and several programming languages such as php, mysql and others. so it doesn't take long and gives a good impact(hendra & destari, 2015). research conducted by khairani puspita and m. rhifky wayahdi entitled analysis of the combination of ceaser cipher, vernam cipher and hill cipher methods in the cryptographic process. in this study caesar cipher, vernam cipher, hill cipher are classical cryptography. this method is very strong in security and has security that is very 121 difficult to break and can be modified and combined(puspita & wayahdi, 2015). the boyer-moore algorithm is a string search algorithm, published by robert s. boyer, and j. strother moore in 1977. this algorithm is considered the most efficient algorithm in general applications (argakusumah & hansun, 2014). unlike the string search algorithm found previously, the boyer-moore algorithm starts matching characters from the right of the pattern (the pattern you are looking for). (ramadhansyah, 2013). the idea behind this algorithm is that by starting character matching from the right, and not from the left, more information will be obtained (darmawan et al., 2018). boyer-moore algorithm is famous because it is widely applied to the matching algorithm for many strings (multiple patterns). the basic idea of the boyer-moore algorithm is as simple as the brute force algorithm by using the help of a table containing the steps for moving the pattern to the right when it encounters a character mismatch. (ardi et al., 2017). android is a subset of software for mobile devices that includes an operating system, middleware¸, and core applications released by google. android is a mobile operating system that adopts the linux operating system but has been modified(lengkong et al., 2015). the purpose of this research is to introduce and provide knowledge about the pakpak language, assist in communicating with people who use the pakpak tribal language, apply the boyer moore algorithm in translating pakpak language into indonesian, build an application that can translate pakpak language into indonesian using the boyer moore method, and building an application that can translate pakpak language into indonesian using the android-based boyer moore method. research methods data collection at this stage the author also collects data through: 1. field study this research is a direct study to see or review the location. data collection techniques are as follows: a) interviews, namely holding questions and answers to related sources. mr. drs. amhar kudadiri, m.hum who knows about pakpak culture and language and is the secretary of the literature study program, faculty of science and culture at the university of north sumatra. b) observations, namely by observing the pakpak people in north sumatra, especially in the medan city area. 2. literature study this stage is carried out to collect data on writing this thesis through articles, books, journals and others. design procedure this analysis is carried out to determine the stages of research completion, as shown in figure 1: pakpak language translator application into indonesian requirements analysis tools (hardware & software) data boyer moore algorithm computer unity vuforia android application uml design tool coding application testing testing method figure 1. fishbone diagram research research results and discussion 1. discussion a. boyer moore algorithm method boyer moore algorithm can be seen in the figure 2 flowchart as follows. figure 2 it can be seen the boyer moore process contained in the system to be built. first, enter the 122 pattern in the form of characters to be able to do a string search. then read the pattern on the text in sqlite, whether the searched string is a match in the text. performs string matching from right to left according to the input pattern desired by the user. start input pattern pattern identification in text database test string similarity from right to left no finding pattern in text yes finding meaning end figure 2. flowchart diagram of boyer moore algorithm this is done until the pattern you are looking for is matched. after doing the matching, the pattern will get a match on the available text. where there has been grouping of data to directly know the meaning. finally, the system issues an output, where the system automatically issues all options based on the inputted pattern and displays the translation results based on the search process using the boyer moore algorithm. b. system modeling 1) use case diagram use case diagrams are used to see a sequence of systems built and can see the related relationships. the use case diagram of the research can be seen in figure 3 below. pakpak language translator application into indonesian user insert word search for word using boyer moore algorithm result * * * * ** figure 3. use case diagram of the pakpak to indonesian translator application 2) activity diagram activity diagram is a process that explains the various related flows in the system that will be designed from the beginning to the end, it can be seen in figure 4: sistempengguna masukkan kata proses pencarian kata dengan algoritma boyer moore terjemahanhasil pencarian dan terjemahan ya tidak figure 4. activity diagram of the pakpak language translator application to indonesian sequence diagram sequence diagram displays the interaction between objects in the system built, can be seen in figure 5: translation page database insert word searching result _ _ _ _ _ _ figure 5. sequence diagram of the pakpak to indonesian translator application 2. results a. user interface these results are used to show the appearance of the pakpak language translator application into indonesian using the android-based boyer moore algorithm. the following will explain the display of the results of the pakpak language translator application into indonesian. 1) splash screen splash screen is the display that will appear the first time when we run the pakpak language translator application into indonesian based on android, it can be seen in figure 6: 123 figure 6. splash screen display 2) main page the main page of the application has several menu options such as the homepage, pakpak indonesian translator, pakpak indonesian translator, about and exit, can be seen in figure 7. figure 7. main page display 3) home when the home menu is clicked by the user, it will display information at a glance about a brief explanation of the translator application, which can be seen in figure 8: figure 8. home view 4) pakpak – indonesia this page shows that testing the boyer moore algorithm for translating pakpak language into indonesian can display perfect translation results as in the sqlite database, can be seen in figure 9: figure 9. pakpak display – indonesia 5) indonesia – pakpak this display shows that testing the boyer moore algorithm for translating indonesian into pakpak language can display perfect translation results such as those in the sqlite database, can be seen in figure 10: figure 10. indonesian display – pakpak 6) about this page contains the author's information briefly such as name, number, study program, address and email, you can see figure 11: 124 figure 11. display about c. testing this trial is carried out to find out the system designed is in good condition or condition or not and if it is in accordance with what was built it can be used, this application test is carried out using blackbox testing: table 1. application test plan tested results test items splash carry out the loading process to enter the main menu view home showing the meaning of translator pakpak – indonesia displays the pakpak – indonesian language translation process where words can be input and displays the translation results indonesia – pakpak displays the indonesian pakpak translation process where words can be input and display the translation results about showing about the author exit the process of exiting the application conclusions and suggestions conclusion from the discussion and trials that have been made, it can be concluded that the boyer moore algorithm can be used to complete the translation of words or sentences and this application system performs the process of translating words or sentences based on the vocabulary in the sqlite database in the application. this language translator application has been successfully built and can be used as a learning medium to make it easier for people to know the local language, namely pakpak language. suggestion in order to improve the designed application, suggestions are given, such as further development of the addition of copy, cut, text to speech, and other additional features, and in the translator application. pakpak language into indonesian so that it can be further developed in terms of adding information or information to the word you are looking for. to be able to use language more effectively on vocabulary. acknowledgments the author would like to thank the university of potential utama for giving me the opportunity to complete this research. reference alimin, r., & fajri, r. 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(2013). politik etnisitas dalam pemekaran daerah. antropologi indonesia, 33(3). https://doi.org/10.7454/ai.v33i3.2464 126 141 classification of burned peatland using probabilistic neural network algorithm based on high temporal data neneng rachmalia feta information systems and technology bank rakyat indonesia institute of technology and business http://bri-institute.ac.id/ nenengrachmaliafeta@gmail.com abstrak kebakaran lahan di indonesia terjadi di lahan kering dan juga di lahan gambut. kebakaran di lahan gambut lebih berbahaya dan lebih menantang untuk diatasi daripada kebakaran di lahan non-gambut, dan akibat kebakaran lahan gambut yang terjadi sangat merugikan masyarakat. salah satu solusi yang ditawarkan dalam menilai kebakaran hutan dan lahan gambut adalah teknologi penginderaan jarak jauh. citra satelit yang diperoleh dari teknologi penginderaan jarak jauh biasanya diklasifikasikan untuk analisis lebih lanjut. tujuan utama dari penelitian ini adalah untuk mengembangkan model klasifikasi menggunakan probabilistic neural network (pnn) untuk mengklasifikasikan area di lahan gambut sebelum, selama, dan setelah terbakar pada citra satelit landsat 7 etm+. selanjutnya model tersebut digunakan untuk mendapatkan pola trajectory area yang terbakar menggunakan algoritma dbscan. daerah penelitian adalah kabupaten ogan komering ilir provinsi sumatera selatan, citra landsat 7 etm+ diambil dari januari 2015 – desember 2015. kata kunci: lahan gambut, klasifikasi, pnn, dbscan, landsat 7 etm + citra abstract land fires in indonesia occur on dry land as well as on peatlands. fires on peatlands are more dangerous and more challenging to tackle than fires on non-peatlands, and the consequences of peatland fires that occur are very detrimental to communities. one of the solutions offered in assessing forest and peatland fires is remote sensing technology. satellite images obtained from remote sensing technology are usually classified for further analysis. the main objective of this study is to develop a classification model using the probabilistic neural network (pnn) to classify areas in peatlands before, during, and after burning on landsat 7 etm+ satellite imagery. furthermore, the model is used to obtain the trajectory pattern of the burned area using the dbscan algorithm. the research area is ogan komering ilir regency; south sumatra province landsat 7 etm+ images were taken from january 2015 – december 2015. keywords: peatland, classification, pnn, dbscan, landsat 7 etm + imagery introduction peatlands in indonesia are spread over several islands such as sumatra, kalimantan, and papua. peatland consists of many organic materials derived from the dead and decaying plant remains (w. w. international, 2015). the ability of peat to absorb water is relatively high; therefore, natural peatlands are not flammable. however, ecological balance can be disrupted by land conversion or canal making. peatland conditions will dry to a certain depth in the dry season and cause flammable land. peatland fires are much more challenging to handle than fires in the highlands as the flames spread below the surface (adinugroho et al., 2005). global forest watch analysis shows that 75% of hot spot warnings occur in areas of peatlands that are mainly composed of decomposing organic materials (sizer et al., 2014). meanwhile, in this decade, peatland fires have averaged 32.1% in sumatra and 25.1% in kalimantan (wwf, 2015). in september 2015, the agency for disaster management (bpbd) of dry ogan regency detected 234 hot spots spread over ten sub-districts (r. d, 2015). according to greenpeace analysis (greenpeace, 2014), the frequency of hotspots is five times as much on peatland compared to mineral soil (dryland). detecting peatland fires can be done by taking advantage of remote sensing technology. the processing of satellite imagery generated from remote sensing is beneficial for relevant 142 stakeholders in providing information on the spatial distribution of areas experiencing forest and land fires (burning areas), especially information on the area of burned land (suwarsono et al., 2013). from the satellite, image data can be used for to classification process. classification is one method in data mining used to create models that describe data classes and predict data classes in new data. antropov et al. (antropov et al., 2014) evaluate the performance of fully polarimetric sar (polsar) data in several land cover mapping studies in boreal forest environments, taking advantage of the high canopy penetration capability in the lband. the research included mapping multiclass land cover, forest-non-forest delineation, and classification of soil types under vegetation using the probabilistic neural network (pnn). the study results obtained an accuracy of up to 82.6% on land cover mapping of five classes. more than 90% of forest-non-forest mapping in wall-to-wall validation shows the suitability of polsar data for mapping large areas of land and forest cover. boulila et al. (boulila et al., 2010) researched a high-level approach for modeling spatio-temporal knowledge from satellite images and proposed multi-approach segmentation involving several segmentation methods that help improve image modeling interpretation. the experiments made on landsat scenes show that the approach outperforms classical methods in image segmentation and can predict spatio-temporal changes of satellite images. this research will classify burned peatland in ogan komering ilir district using the pnn algorithm. landsat 7 image satellite is used as data set in this research. extracted image features as input for the classifier are image bands. moreover, the result of the pnn classification model is used to get the trajectory pattern of a burned area using the dbscan algorithm. research methods this research consists of four phases: image pra-processing, building an image classification model, trajectory pattern, and evaluation. image pre-processing is done using quantum gis, and this phase eliminates all areas except peatland in ogan komering ilir and determines class labels. the image classification model is built using pnn, trajectory pattern using dbscan algorithm, and the last phase is evaluation. every phase in this research is shown in figure 1. figure 1. research methodology study area the study area in this research is peatland in ogan komering ilir district, province south sumatera, which has 19,023.47 km2. this district is located at 1040.20’ to 1060.00’ east longitude and 20.30’ to 40.15’ south latitude and geographically bordered by ogan ilir district, banyuasin district and palembang city (north), lampung province (south), ogan ilir district, and ogan komering ulu timur (west), bangka strait and java sea (east). data data sets used in this research are image data from satellite, peatland map, and hotspot data. image data ogan komering ilir district taken from http://earthexplorer.usgs.gov, this image data is landsat 7 etm+ with some path/row is 124/62 and has resolution 30x30, it is mean 1 pixel on the image has 30 meters in real. this image data was taken from satelite in 2015. peatland map data was taken from http://data.globalforestwatch.org on 27 may 2017, and this map consists of indonesian peatland. this data is an shp file representing peatland area in polygon and is used to clip ogan komering ilir district landsat image. hotspot data is used from range date 1 – 6 september 2015, and it is taken from firms modis fire/hotspot, nasa/university of maryland. hotspot data determines class labels in the building classification model process. image pre-processing landsat 7 etm+ image data is preprocessed with the following processes. the first process is filling the gap in the image. the raw data satellite image consists of 8 bands, and all image has a hole. this gap is caused by landsat 7 satellite has failure at scan line corrector (sln) sensor. this process is done by filling the image with a mask included in the downloaded data set file. 143 the second process is the compositing band. compositing is a method for combining multiple rounds to 1 layer, and bands are used for compositing are band7 (red), band4 (green), and band2 (blue). the output of the compositing band is an image with rgb color, and each pixel has three digital numbers: red, green, and blue in range 0-255 for each. the third process is clipping images with polygon peatland boundary. area study focuses on peatland, area out of peatland eliminated with clipping process. peatland indonesian and ogan komering ilir district map are two map polygon data for clipping. the fourth process is determining class labels. class label is defined to the area in peatland manually, seen in display monitor and helped with additional data set: hotspot data. hotspot data is used to determine which peatland location is burning, burned, and not burned. at the end of preprocessing, we collected a sample area with has 4 class labels: burning, burned, not burned, and cloud k-fold k-fold cross validation is a method for partitioning data. data sets with a class label result in pre-processing data divided to be 2 data types, learning data, and test data. the number of k in this research is 10; input for the classification process is one partition as test data and nine partitions as learning data. index of partition test data is incremental from 1 to 10, and every iteration of the others nine partitions learning information is all data except test data partition. this process is done using the caret package in r. pnn pnn is artificial neural network (ann) that uses classical probability theorem (bayes classification); this classifier is supervised training (specht, 1990). pnn is composed of 4 layers: the input layer, the pattern layer, the summation layers, and the output layer (sa’adah & pratiwi, 2020). this layer is shown in figure 2. the input layer is 𝓍 object which consists of some 𝑘 lengths features vector and classified to 𝑛 class (guan et al., 2021). processes after input layer executed are: pattern layer the pattern layer uses one node for every learning data, and this node has number 𝑡 data for each class. every pattern node is dot-matrix multiplication from 𝓍𝓍ij, then divide with bias σ, all included in radial basis function 𝑟𝑎𝑑𝑏𝑎𝑠 = 𝑒𝓍𝑝(‒𝑛). the combined formula for the pattern layer is: 𝑓(𝓍) = ℯ𝓍p(‒ (𝓍− 𝓍𝑖𝑗) 𝑇 − (𝓍− 𝓍𝑖𝑗) 2𝜎2 ) .................................(1) summation layer this layer receives input from pattern layer nodes, and output is input for the output layer. the summation layer is summing 𝑡 nodes for each class in the pattern layer. figure 2. pnn layers 𝑝(𝓍) = 1 2(𝜋) 𝑘 2 𝜎𝑘𝑡 1 𝑁𝑖 ∑ ℯ𝓍p(‒ (𝓍− 𝓍𝑖𝑗) 𝑇 − (𝓍− 𝓍𝑖𝑗) 2𝜎2 ) 𝑡 𝑖=1 . .................................................................................................. (2) output layer this layer determines class from a given input. input 𝓍 classified as class y, if 𝑝𝑦(𝓍) is more significant than other classes. evaluation classification model the evaluation classification model is done by calculating the classification accuracy, and the classification model is tested to whole image data sets. 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = σ 𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑑𝑎𝑡𝑎 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 σ 𝑡𝑒𝑠𝑡𝑖𝑛𝑔 𝑑𝑎𝑡𝑎 𝑥 100% dbscan density-based spatial clustering of applications with noise (dbscan) is a clustering method that groups dots based on data density in a region (deng, 2020). the dbscan algorithm is designed to search for clusters and outliers in spatial data (ester m, kriegel h p, 1996). the dbscan algorithm looks for a central object that is 144 an object that has a dense neighborhood. the dbscan algorithm connects the object center with its neighborhood to form 1 region as a cluster (chen et al., 2021). the dbscan algorithm requires two input parameters: epsilon distance (eps) and a minimum number of points (minpts) (ohadi et al., 2020). epsilon is the distance between points indicating the object's density, while the minpts is the minimum number of points in the center of a cluster object (mustakim et al., 2021). the neighboring neighborhood that meets the epsilon distance is called the e-neighborhood. the following dbscan algorithm (han et al., 2012): 1) select a starting point (p) at random. 2) get the e-neighborhood point from point p. 3) if the number of points from step 2 meets the minpts value, the point p is the core point, forming a cluster. 4) if the point number of step 2 does not meet the minpts value, then point p is the border point and select the next point. 5) perform steps 2 through 4 until all the points have been processed and no dots can be added to the cluster. results and discussion image pre-processing slc failure happened on 31 mei 2003 and caused a gap in the image captured by landsat 7. a gap in the image makes the data lose some information and can make low accuracy. filling the gap is done using qgis; every image band is masked with file masks. nasa provides mask files. figure 3 is shown an example image gap filling. (a) (b) figure 3. pre-process (a) satelite image band7 with a gap (b) satelite image band7 after gap filled before compositing, all images are in brown; every pixel in the image has a one-digit number. in this process, image band7, band4, and band two are combined to become one layer. image band7 is represented red color intensity, image band4 is represented green color intensity, and image band2 is represented blue color intensity. every brand has a different digital number. afterimage is composited, every pixel in the image consists of 3 digital numbers representing value for band7, band4, and band2. visually after the composite is done, the image in brown color becomes an image rgb color. compositing image band is done using qgis, below is shown in figure 4 process compositing image. (a) (b) image from the satellite is captured not only peatland but also other land use and other districts. (c) (d) figure 4. process compositing band (a) satelite image band7 before compositing (b) satelite image band4 before compositing (c) satelite image band2 before compositing (d) composited image overlay and clipping are used to extract peatland from other land use. the overlay shows peatland polygon on top image satellite, and clipping is used to take and cut peatland area only in the imaging satellite. this process is done using qgis and is shown in figure 5. (a) (b) figure 5. extracting peatland (a) overlaying image satellite with peatland polygon (b) peatland image satellite extracted 145 image satellite still has no class label in this process, and supervised learning in the classification phase needs a class label. therefore, the following process is determining class labels. to choose a class label, hotspot data is used to make a class label. hotspot data represented in points and shp format file. hotspot data can be overlayed to a peatland image satellite. the class label used in this research is 4: burning, burned, not burned, and cloud. a sample is taken from image satellite data to represent each class; the example of each class is shown in figure 6. (a) (b) (c) (d) figure 6. example landuse (a) burning class (b) burned class (c) not burned class (d) cloud class build classification model a digital number is extracted from the sample pixel. using package caret in r, these digital numbers are divided into learning and testing data using the k-fold method. data sets are divided into 10 group experiments with a proportion of 9/10 used as training data and 1/10 used as testing data. every experiment iteration uses different learning and testing data; therefore, every iteration has different accuracy. the classification was conducted using pnn with given data sets in each iteration. the smoothing parameters are used in this research are in the range 0.8 1. in a study conducted by (liu, wang, & cheng, 2011), smoothing parameters can be adjusted based on learning data. figure 7 below shows the result of each fold classification accuracies using pnn. figure 7. result pnn classification accuracies figure 7 above shows the accuracy results in each fold. in some folds such as 2, 5, 7, 8, and 9, the accuracy reaches 100%, while the lowest is on fold one, which only reaches 0.996%. the overall accuracy of classification using the pnn algorithm results very well. pnn classification model is then used to classify 12 other landsat images taken per month for one year in 2015. one of the classification results using the model made can be seen in figure 8. the pixels included in the burned area are colored with a burning red, then the burnt area is dark red, the green color is applied to the unburned area, and the cloud area is given a white color. trajectory pattern after obtaining the classification model with pnn, the next step was to classify all landsat images in 2015. the entire image is pre-processed before classification. one by one, imagery is classified at the pixel level. once everything is successfully classified, the next step is to search the trajectory pattern of the burning land area. the algorithm used is dbscan. in 2015 the landsat image of the ogan komering ilir area had a lot of fog. figure 8. result of classification landsat etm + image the classification process on the whole image has been done, but the image data required dbscan algorithm is not sufficient to make a trajectory pattern. in figure 9, we can see the result of the image preview. 146 figure 9. all landsat etm + image in 2015 at ogan komering ilir conclusions and suggestions conclusion this research succeeded in implementing the pnn algorithm to classify the burning peatland area in ogan komering ilir district, south sumatra. the average accuracy of algorithms is 99%. the result of the image classification of the pnn algorithm shows a similarity of pixel between land after burning with the burning field. because the burnt land has a brownish-red color, and the red color is identical to the burning class. the model generated by the pnn algorithm was successfully used to classify peatland areas by 2015. but the result is that many clouds block peat so that the cloud class dominates the area. this causes trajectory pattern on this data can not be done. suggestion suggestions that can later be made for further research include implementing another comparison algorithm besides the pnn and dbscan algorithms. then use updated data and a more extended period, for example, more than one year, maybe two years or more, so that the research results are more representative. references adinugroho, wahyu catur, suryadiputra, i. n. n., saharjo, b. h., & siboro, l. 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(1990). probabilistic neural networks. neural networks. https://doi.org/10.1016/08936080(90)90049-q suwarsono, rokhmatuloh, & waryono, t. (2013). pengembangan model identifikasi daerah bekas kebakaran hutan dan lahan ( burned area ) menggunakan citra modis di kalimantan. jurnal penginderaan jauh. w. w. international. (2015). "peatland,”. internet, tech. rep. http://www.wetlands.org/whatarewetlands /peatlands/tabid/2737/default.aspx wwf. (2015). world wide fund for nature 2015 could the decline in hotspots is reached? internet, tech. rep. http://www.wwf.or.id/tentangwwf/upayaka mi/iklimdan_nergi/solusikami/ 148 jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 81 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sistem pendukung keputusan pemilihan perguruan tinggi swasta menggunakan topsis tati mardiana1; siska selvia tanjung2 program studi sistem informasi universitas bina sarana informatika tati.mardiana@gmail.com; siskaselvia677@gmail.com abstrak memilih perguruan tinggi yang tepat merupakan langkah penting bagi siswa dalam mempersiapkan karir dan masa depannya. dengan pendidikan di perguruan tinggi, siswa meningkatkan kesempatannya untuk mendapatkan pekerjaan yang lebih baik. tetapi keterbatasan daya tampung perguruan tinggi negeri membuat siswa dan orang tua harus memilih perguruan tinggi swasta yang sesuai dengan keinginan dan kemampuan. kesalahan dalam memilih perguruan tinggi menyebabkan siswa mengalami kegagalan dalam menjalankan pendidikan di perguruan tinggi tersebut. oleh karena itu, siswa dan orang tua perlu mempertimbangkan beberapa faktor seperti status akreditasi, biaya, jumlah mahasiswa, jumlah dosen, fasilitas, program studi dan lain-lain untuk memilih perguruan tinggi swasta. kendati demikian, banyak siswa dan orang tua yang mengalami kebingungan dalam memilih perguruan tinggi swasta. hal ini disebabkan karena banyaknya perguruan tinggi swasta dan minimnya informasi tentang perguruan tinggi swasta tersebut. tujuan penelitian ini adalah membangun sistem pendukung keputusan pemilihan perguruan tinggi swasta yang sesuai keinginan dan kemampuan siswa dan orang tua. penelitian ini menggunakan logika fuzzy multiple attribute decision making (fmadm) dengan metode technique for order preference by similarity to ideal solution (topsis) untuk melakukan perangkingan dari setiap alternatif perguruan tinggi swasta. hasil pengujian menunjukkan bahwa kinerja sistem memenuhi persyaratan fungsional dan kinerja sistem mencapai akurasi sebesar 83,33%. sistem pendukung keputusan ini membantu siswa dan orang tua membuat keputusan untuk memilih perguruan tinggi swasta yang sesuai dengan keinginan dan kemampuan mereka secara akurat. kata kunci: pemilihan, pendidikan, perguruan tinggi swasta, topsis, sistem pendukung keputusan abstract choosing the right college is a crucial step for students in preparing for their careers and future. with education at the college, the student increases their chances of getting better jobs. but because of the limited capacity of public college, students and parents must select a private college that agrees with desires and abilities. errors in choosing a college result in students experiencing failure in carrying out education in the college. therefore, students and parents need to consider several factors such as accreditation status, costs, the number of students, lecturers, facilities, study programs, and others to select a private college. nevertheless, students and parents experience confusion in choosing a private college. this is due to many private colleges and the lack of information about these private colleges. the aim of this study is to build a decision support system to select a private college that matches the desires and abilities of students and parents. this study uses fuzzy multiple attribute decision making (fmadm) logic with the technique for order preference by similarity to ideal solution (topsis) method to rank each alternative private college. the test results showed that the features of the supporting system are functioning properly and the system performance reaches an accuracy of 83.33%. this decision support system helps students and parents make decisions to select a private college that according to their desires and abilities accurately. keywords: selection, education, private college, topsis, decision support system pendahuluan kesadaran masyarakat indonesia akan pentingnya melanjutkan pendidikan ke jenjang perguruan tinggi dalam beberapa tahun terakhir mengalami peningkatan yang cukup signifikan. pada tahun 2017, angka partisipasi kasar (apk) perguruan tinggi menunjukkan persentase jumlah http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 82 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional penduduk yang sedang kuliah di perguruan tinggi terhadap jumlah penduduk usia kuliah (19-23 tahun) sebesar 33,37% lebih tinggi 7,61% dibandingkan tahun 2014 (badan pusat statistik, 2018). jumlah lulusan sekolah menengah ke atas (sma) dan sekolah menengah kejuruan (smk) pada tahun 2017 sebesar 2,707,954 (kementerian pendidikan dan kebudayaan, 2017). dari angka tersebut, dengan apk perguruan tinggi sebesar 33,37% maka diperkirakan lulusan sma dan smk yang melanjutkan ke perguruan tinggi sekitar 90,364,425 orang. berdasarkan data dari pangkalan data pendidikan tinggi (pdpt) pada tahun 2017 terdapat 122 perguruan tinggi negeri (ptn) dan 3154 perguruan tinggi swasta (pts) dengan komposisi berdasarkan berbagai bentuk pendidikan yang terdiri atas akademi, politeknik, sekolah tinggi, universitas, institut dan akademi komunitas (kemeristekdikti, 2017). setiap tahun daya tampung ptn sebesar 250.000 sehingga sekitar 90,114,425 lulusan sma dan smk akan melanjutkan kuliah di perguruan tinggi swasta. setiap orang tua menginginkan pendidikan yang terbaik untuk putra-putrinya. memilih perguruan tinggi swasta yang tepat sangat penting bagi calon mahasiswa untuk mempersiapkan karir dan masa depannya. namun, banyak orang tua dan calon mahasiswa yang mengalami kebingungan dalam memilih perguruan tinggi swasta karena banyaknya pilihan dan minimnya informasi tentang perguruan tinggi swasta tersebut. kesalahan dalam memilih perguruan tinggi menyebabkan calon mahasiswa mengalami kegagalan dalam menjalankan pendidikan di perguruan tinggi tersebut (surya, 2017). pengambilan keputusan yang cepat dan baik membutuhkan sebuah sistem pendukung keputusan yang akan memberikan informasi sebagai dasar pengambilan keputusan berdasarkan data–data mengenai setiap alternatif yang diolah dengan suatu mekanisme tertentu untuk selanjutnya menjadi dasar pengambilan keputusan. proses pengambilan keputusan yang memiliki beberapa kriteria menggunakan logika fuzzy multiple attribute decision making (fmadm) untuk mencari alternatif optimal dari sejumlah alternatif dengan kriteria tertentu (mardiana, 2018). metode yang dapat digunakan untuk menyelesaikan permasalahan menggunakan logika fmadm adalah simple additive weighted (saw), weighted product (wp), electre, technique for order preference by similarity to ideal solution (topsis), dan analytic hierarchy process (ahp). beberapa peneliti telah melakukan penelitian terkait permasalahan pemilihan jurusan perguruan tinggi. (budilaksono, suwarno, & herwanto, 2016) mengusulkan model pengambilan keputusan untuk pemilihan perguruan tinggi swasta program studi teknik informatika strata satu dengan metode ahp dan preference ranking organization method for enrichment evaluation (promethee). proses pengambilan keputusan menggunakan 7 kriteria, 26 sub kriteria dan 4 alternatif. sedangkan (triono & aminudin, 2017) mengusulkan model pengambilan keputusan untuk pemilihan perguruan tinggi menggunakan metode saw. metode saw mampu memberikan rekomendasi perguruan tinggi berdasarkan bobot kriteria penilaian yang telah ditentukan kepada calon mahasiswa dan orang tua. penelitian berikutnya (danang & nisar, 2017) mengusulkan sistem pendukung keputusan pemilihan perguruan tinggi jurusan komputer di semarang dengan metode ahp berdasarkan delapan kriteria, yaitu akreditasi institusi, jurusan, akreditasi jurusan, staf pengajar, peminat, biaya, prospek kerja, fasilitas, dan waktu kuliah. selain itu, selain itu (walidain & ardian, 2017) juga mengusulkan sistem pendukung keputusan pemilihan perguruan tinggi dan jurusan menggunakan topsis untuk menghasilkan ranking perguruan tinggi dan jurusan sehingga diharapkan dalam pemilihan perguruan tinggi dan jurusan yang direkomendasikan benar-benar sesuai dengan keinginan, dan kemampuan siswa. berdasarkan penelitian sebelumnya maka tujuan penelitian adalah menghasilkan sistem pendukung keputusan memilih perguruan tinggi swasta menggunakan topsis untuk memberikan rekomendasi perguruan tinggi yang perguruan tinggi swasta yang sesuai dengan keinginan dan kemampuan siswa dan orang tua. metode penelitian disain penelitian sesuai dengan permasalahan yang telah dikemukakan sebelumnya, maka jenis penelitian ini adalah penelitian eksperimen. objek penelitian ini penelitian dilakukan pada bulan maret sampai dengan bulan september 2017 di jakarta. penelitian ini berawal dari pengamatan tentang pemiihan perguruan tinggi swasta. berdasarkan hasil pengamatan, calon siswa dan orang tua mengalami kesulitan dalam menentukan perguruan tinggi yang sesuai keinginan dan kemampuannya.. solusi dari permasalahan ini adalah menerapkan topsis dalam sistem pendukung keputusan untuk memilih perguruan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 83 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tinggi swasta. sistem pendukung keputusan ini berbasis web yang dapat diakses oleh calon siswa dan orang tua kapan saja dan dimana saja untuk memperoleh informasi dan rekomendasi perguruan tinggi yang sesuai keinginan dan kemampuan secara cepat dan akurat. berdasarkan uraian di atas, kerangka penelitian pada tugas akhir ini disajikan pada gambar 1. sumber : (mardiana & tanjung, 2017) gambar 1. kerangka pemikiran data, instrumen, dan teknik pengumpulan data penelitian ini mengunakan data primer dan skunder. data primer diperoleh secara langsung dari sumber, misalnya data-data hasil wawancara dan diskusi langsung dengan orang tua dan siswa sma/smk kelas ix. sedangkan data sekunder berupa data perguruan tinggi swasta di indonesia yang diperoleh dari situs pangkalan data pendidikan tinggi, literatur-literatur, jurnaljurnal penelitian, maupun data dokumen yang diperlukan untuk menyusun penelitian ini. instrumen penelitian merupakan alat bantu bagi peneliti untuk mengumpulkan data sehingga kegiatan tersebut menjadi sistematis dan efisien. instrumen yang digunakan pada penelitian ini adalah pedoman wawancara dan panduan studi dokumentasi. teknik pengumpulan data yang dilakukan dalam tugas akhir ini, sebagai berikut : a. observasi observasi dilakukan dengan mengumpulkan data dengan melakukan pengamatan secara langung terhadap proses pendaftaran pada perguruan tinggi swasta di jakarta. b. wawancara wawancara dilakukan kepada pelaku usaha waralaba makanan untuk memperoleh informasi lebih mendalam terkait proses pemilihan perguruan tinggi swasta yang tepat dan sesuai keinginan. c. studi pustaka studi pustaka dilakukan dengan mengumpulkan dan mempelajari literatur yang berkaitan dengan pemecahan masalah pemilihan perguruan tinggi swasta. sumber literatur berupa buku teks, karya ilmiah, dan situs-situs penunjang. kegunaan metode ini diharapkan dapat mempertegas teori serta keperluan analisis dan mendapatkan data yang sesungguhnya. teknik analisis data berdasarkan data yang sudah terkumpul, dilakukan analisis mengenai kriteria dan bagaimana penerapan metode topsis dalam studi kasus pemilihan perguruan tinggi swasta. kriteriakriteria dalam pemilihan perguruan tinggi swasta didapatkan dari pangkalan data perguruan tinggi (pdpt). ada enam kriteria dalam pemilihan perguruan tinggi swasta, antara lain akreditasi (a), jumlah mahasiswa(m), jumlah dosen (d), biaya (b), jumlah fasilitas (f) dan jumlah jurusan (j). contoh kasus yang digunakan dalam analisis proses pengambilan keputusan sebanyak tiga perguruan tinggi swasta yang disusun pada tabel 1 dan bobot dari masing-masing kriteria seperti terlihat pada tabel 2. tabel 1. alternatif perguruan tinggi swasta alternatif a m d b f j unas b 6,495 313 4.6 7 32 binus b 28,437 1,001 14.7 11 34 trisakti b 23,149 895 7.3 27 47 sumber : (mardiana & tanjung, 2017) http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 84 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional tabel 2. bobot kriteria perguruan tinggi swasta kriteria keterangan bobot c1 akreditasi 5 c2 jumlah mahasiswa 2 c3 jumlah dosen 4 c4 biaya 5 c5 fasilitas 5 c6 jumlah jurusan 3 sumber : (mardiana & tanjung, 2017) untuk memperoleh ranking kecocokan dari alternatif pemilihan perguruan tinggi swasta maka harus menentukan nilai kecocokan antara alternatif dan kriteria dengan interval dari 1 sampai 5, dengan ketentuan 1 = tidak baik; 2 = kurang baik; 3 = cukup baik; 4 = baik; 5 = sangat baik. nilai kecocokan pada setiap kriteria dapat dilihat pada tabel 3 dan hasil rangking kecocokan dari setiap alternatif pada setiap kriterianya dapat dilihat pada tabel 4. tabel 3. nilai kecocokan pada setiap kriteria no atribut nilai nilai kecocokan c1 akreditasi tidak terakreditasi 2 c 3 b 4 a 5 c2 jumlah mahasiswa < 1000 1 1000 – 7000 2 7001 – 14.000 3 14.001 – 30.000 4 > 30.000 5 c3 jumlah dosen < 100 1 101 – 300 2 301 – 700 3 701 – 1000 4 >1000 5 c4 biaya >20 jt 1 16 jt – 20 jt 2 11 jt -15 jt 3 6 jt – 10 jt 4 1 jt – 5 jt 5 c5 fasilitas <5 1 5 – 8 2 9 – 12 3 13 – 16 4 > 16 5 c6 jumlah jurusan <15 1 15 – 30 2 31 – 45 3 46 – 60 4 >60 5 sumber : (mardiana & tanjung, 2017) tabel 4. nilai rangking kecocokan alternatif pada setiap kriteria alternatif c1 c2 c3 c4 c5 c6 unas 4 2 3 5 2 3 binus 4 4 5 3 3 3 trisakti 4 4 4 4 5 4 sumber : (mardiana & tanjung, 2017) langkah-langkah yang dilakukan dalam menyelesaikan suatu permasalahan menggunakan topsis sebagai berikut : 1. menggambarkan alternatif (m) dan kriteria (n) ke dalam sebuah matriks, dimana xij adalah pengukuran pilihan dari alternatif k e-i dan kriteria ke-j. matriks ini dapat dilihat pada persamaan satu. 𝐷 = 𝑋11 𝑋12 𝑋13 𝑋21 𝑋22 𝑋23 𝑋𝑖1 𝑋𝑖1 𝑋𝑖1 (1) hasil matriks xij sebagai berikut : 𝐷 = 4 2 3 5 2 3 4 4 5 3 3 3 4 4 4 4 5 4 2. membuat matriks r yaitu matriks keputusan ternormalisasi setiap normalisasi dari nilai rij dapat dilakukan dengan perhitungan menggunakan persamaan dua. 𝑟𝑖𝑗 = 𝑋𝑖𝑗 √∑ 𝑋𝑖𝑗 2𝑚 𝑖=1 (2) hasil matriks ternormalisasi r sebagai berikut : r = [ 0,5774 0,3333 0,4243 0,5774 0,6667 0,7071 0,5774 0,6667 0,5657 0,7071 0,3244 0,5145 0,4242 0,4667 0,5145 0,5657 0,8111 0,6860 ] 3. membuat pembobotan pada matriks yang telah dinormalisasi setelah dinormalisasi, setiap kolom pada matriks r dikalikan dengan bobot (wj) untuk menghasilkan matriks pada persamaan tiga. 𝐷 = 𝑊1𝑟11 𝑊1𝑟12 𝑊𝑛𝑟𝑛 𝑊2𝑟21 … … . … … 𝑊𝑗 𝑟𝑚1 𝑊𝑗 𝑟𝑚2 𝑊1𝑟𝑚𝑚 (3) hasil perkalian matriks w * r sebagai berikut : d = [ 2,8870 0,6666 1,6972 2,8870 1,3334 2,8284 2,8870 1,3334 2,2628 3,5355 1,6220 1,5435 2,1215 2,4335 1,5435 2,8285 4,0555 2,0580 ] 4. menentukan nilai solusi ideal positif dan solusi ideal negatif. solusi ideal dinotasikan a+, sedangkan solusi ideal negatif dinotasikan http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 85 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional a-. persamaan untuk menentukan solusi ideal dapat dilihat pada persamaan empat. a+ = {(max vij | j € j’), = 1,2,3,…,m} = v1 + v2 + …… + vn} a = {(max vij | j € j’), (min vij | j € j’), = 1,2,3,…,m} = v1 v2 ……vn } (4) j = {j=1,2,3, …., n dan j merupakan benefit criteria} j’ = {j=1,2,3, …., n dan j merupakan benefit criteria} hasil matrik solusi ideal positif (a+) dan matrik solusi ideal negatif (a-). 𝐴+ = [2,8870 1,3334 2,8284 3,5355 4,0555 2,0580] 𝐴− = [2,8865 0,6666 1,6972 2,1215 1,6220 1,5435] 5. menghitung separation measure. separation measure ini merupakan pengukuran jarak dari suatu alternatif ke solusi ideal positif dan solusi ideal negatif. perhitungan solusi ideal positif dapat dilihat pada persamaan lima : si+ = √∑ (𝑌𝑖 + − 𝑌𝑖𝑗 +) 2𝑛 𝑖=1 (5) dengan i = 1, 2, 3, …, n perhitungan solusi ideal negatif dapat dilihat pada persamaan enam : si+ = √∑ (𝑉𝑖𝑗 − 𝑉𝑗 −) 2𝑛 𝑖=1 (6) dengan i = 1, 2, 3, …, n hasil pengukuran jarak dari suatu alternatif ke solusi ideal positif dan solusi ideal negatif sebagai berikut: s1+ = 2.8126 s2+ = 2,2125 s3+ = 0,9054 s1= 1,4140 s2= 1,5436 s3= 2,7297 6. menghitung nilai preferensi untuk setiap al ternatif. untuk menentukan ranking tiap-tiap alternatif yang ada maka perlu dihitung terlebih dahulu nilai preferensi dari tiap alternatif. perhitungan nilai preferensi dapat dilihat melalui persamaan tujuh. 𝑉𝑖 + = 𝑆𝑖 − 𝑆𝑖 ++ 𝑆𝑖 − (7) dimana 0< ci + < 1 dan i=1,2,3,…, m setelah didapat nilai si+, maka alternatif dapat diranking berdasarkan urutan si+. dari hasil perankingan ini dapat dilihat alternatif terbaik yaitu alternatif yang memiliki jarak terpendek dari solusi ideal dan berjarak terjauh dari solusi ideal negatif. nilai preferensi untuk setiap alternatif sebagai berikut : v1 = 0,3345 v2 =0,4110 v3 =0,7509 dari perhitungan diatas dapat diperoleh nilai v3 memiliki nilai terbesar, sehingga rekomendasi perguruan tinggi swasta yang dipilih untuk melanjutkan pendidikan adalah universitas trisakti. hasil penelitian dan pembahasan desain sistem setelah menentukan pemodelan yang akan digunakan dalam sistem pendukung keputusan, maka langkah selanjutnya adalah membuat desain diagram alur data (dad). pemakai yang akan berinteraksi dengan sistem pendukung keputusan ini adalah administrator dan pengguna (siswa atau orang tua). administrator pengguna (siswa/orang tua) 0.0 sistem pendukung keputusan pemilihan perguruan tinggi swasta data pts rekomendasi pts data administrator username dan password username dan password administrator alternatif pts data pengguna data pengguna data pengguna sumber : (mardiana & tanjung, 2017) gambar 2. desain diagram konteks sistem pendukung keputusan pemilihan perguruan tingi swasta http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 86 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional administrator pengguna (siswa/orang tua) 1.0 mengelola data master data pts alternatif pts rekomendasi pts 2.0 pemilihan pts administratordata administrator pts data pts data administrator data pts pemilihan detail pemilihan data pemilihan data detail pemilihan username dan password administrator username dan password pengguna data administrator data pemilihan data detail pemilihan pengguna data pengguna data pengguna data pengguna pengguna data pengguna data pengguna data pengguna data pengguna sumber : (mardiana & tanjung, 2017) gambar 3. desain diagram nol sistem pendukung keputusan pemilihan perguruan tingi swasta administrator 2.1 login data pengguna administrator data pengguna data pengguna 2.2 mengelola data administrator username dan password pengguna 2.2 mengelola data pts data pts data pts pts data pts 2.3 mengelola data pts data pts data pts data pengguna data pengguna pts data pengguna data pengguna sumber : (mardiana & tanjung, 2017) gambar 4. desain diagram detail 1.0 sistem pendukung keputusan pemilihan perguruan tingi swasta pengguna (siswa/orang tua) alternatif pts rekomendasi pts 2.1 registrasi pengguna data pts pemilihan detail pemilihan data pemilihan data pengguna pengguna data pengguna data pengguna 2.2 login username dan password pengguna 2.3 pilih alternatif pts 2.4 perangkingan alternatif menggunakan topsis pts alternatif pts alternatif pts data pemilihan sumber : (mardiana & tanjung, 2017) gambar 5. desain diagram detail 2.0 sistem pendukung keputusan pemilihan perguruan tingi swasta basis data yang digunakan dalam membangun sistem pendukung keputusan pemilihan perguruan tinggi swasta terdiri dari 4 tabel, yaitu : administrator, pengguna, perguruan tinggi swasta, pemilihan, dan detail pemilihan. relasi antar tabel ditunjukkan pada gambar 6. sumber : (mardiana & tanjung, 2017) gambar 6. desain basis data konseptual sistem pendukung keputusan pemilihan perguruan tingi swasta http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 87 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional implementasi sistem sistem pendukung keputusan merupakan solusi yang tepat dalam proses pengambilan keputusan pemilihan perguruan tinggi swasta sesuai dengan keinginan dan kemampuan siswa dan orang tua. dalam sistem pendukung keputusan ini terdapat dua hak akses yaitu administrator dan pengguna (siswa atau orang tua). halaman yang terdapat pada sistem pendukung keputusan pemilihan perguruan tinggi swasta berbasis web, sebagai berikut : 1. halaman administrator seorang administrator memiliki tugas dan tanggung jawab dalam mengelola sistem pendukung keputusan ini agar dapat diakses oleh pengguna (siswa atau orang tua). administrator melakukan login untuk dapat masuk ke dalam sistem pendukung keputusan ini. setelah melakukan login administrator dapat mengelola data perguruan tinggi swasta, data pengguna dan hak akses administrator. sumber : (mardiana & tanjung, 2017) gambar 7. halaman login administrator sumber : (mardiana & tanjung, 2017) gambar 8. halaman menu administrator sumber : (mardiana & tanjung, 2017) gambar 9. halaman mengelola data perguruan tinggi swasta sumber : (mardiana & tanjung, 2017) gambar 10. halaman mengelola data pengguna sumber : (mardiana & tanjung, 2017) gambar 11. halaman mengelola data administrator http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 88 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional 2. implementasi halaman pengguna siswa dan orang tua dapat mengakses sistem pendukung keputusan ini dengan melakukan login menggunakan username dan password yang sudah teregistrasi. pengguna memilih beberapa perguruan tinggi swasta untuk proses perangkingan berdasarkan kriteria dan bobot yang telah ditentukan. sistem pendukung keputusan akan memberikan rekomendasi perguruan tinggi swasta yang sesuai keinginan dan kemampuan siswa dan orang tua. sumber : (mardiana & tanjung, 2017) gambar 12. halaman login pengguna sumber : (mardiana & tanjung, 2017) gambar 13. halaman menu pengguna sumber : (mardiana & tanjung, 2017) gambar 14. halaman alternatif perguruan tinggi swasta sumber : (mardiana & tanjung, 2017) gambar 15. halaman proses matriks kriteria alternatif sumber : (mardiana & tanjung, 2017) gambar 16. halaman proses matriks keputusan ternormalisasi sumber : (mardiana & tanjung, 2017) gambar 17. halaman proses matriks w * r http://creativecommons.org/licenses/by-nc/4.0/ jurnal riset informatika vol. 1, no. 2 maret 2019 p-issn: 2656-1743 e-issn: 2656-1735 89 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional sumber : (mardiana & tanjung, 2017) gambar 18. halaman rekomendasi perguruan tinggi swasta pengujian sistem pengujian terhadap sistem pendukung keputusan pemilihan perguruan tinggi swasta dilakukan dua tahap pengujian, sebagai berikut : 1. pengujian fungsional sistem pengujian fungsional sistem dilakukan untuk mengetahui apakah sistem sudah memenuhi persyaratan yang ditentukan pengguna. pengujian ini menggunakan metode pengujian black box dengan cara menjalankan sistem sesuai dengan skenario pengujian yang telah disusun, hasil pengujian menunjukkan fitur-fitur pada sistem pendukung keputusan pemilihan perguruan tinggi swasta berfungsi dengan baik. 2. pengujian akurasi pengujian akurasi dilakukan untuk mengetahui tingkat akurasi hasil keluaran dari sistem pendukung keputusan. untuk pengujian akurasi, hasil perangkingan sistem pendukung pemilihan perguruan tinggi swasta dibandingkan dengan dengan pilihan siswa dan orang tua terhadap alternatif perguruan tinggi swasta. pengujian ini menggunakan sampel sebanyak 30 orang siswa untuk menentukan sebanyak tiga alternatif perguruan tinggi swasta kemudian memilih perguruan tinggi swasta yang sesuai keinginan dan kemampuan dari ketiga perguruan tinggi swasta tersebut. selanjutnya, siswa diminta untuk menggunakan sistem pendukung keputusan dalam memilih perguruan tinggi swasta. hasil pengujian diperoleh 25 orang memiliki pilihan yang sama dengan hasil sistem pendukung keputusan pemilihan perguruan tinggi swasta. dari 25 pilihan yang benar tersebut maka akan diperoleh tingkat akurasi sistem pendukung keputusan pemilihan perguruan tinggi swasta, sebagai berikut : akurasi = 25 30 𝑥 100 % = 83.33% berdasarkan hasil perhitungan diperoleh akurasi sistem pendukung keputusan pemilihan perguruan tinggi swasta sebesar 83.33%. hasil pengujian akurasi tersebut menunjukkan topsis dapat diterapkan pada sistem pendukung keputusan pemilihan perguruan tinggi swasta yang sesuai keinginan dan kemampuan siswa dan orang tua. simpulan dan saran sistem pendukung keputusan pemilihan perguruan tinggi swasta menggunakan topsis diimplementasikan berdasarkan enam kriteria perguruan tinggi swasta. hasil pengujian menunjukkan fitur-fitur pada sistem pendukung keputusan pemilihan perguruan tinggi swasta sudah berfungsi dengan baik dan kinerja sistem mencapai akurasi sebesar 83,33%. dengan adanya sistem pendukung keputusan pemilihan perguruan tinggi swasta ini membantu siswa dan orang tua dalam memilih perguruan tinggi swasta yang sesuai keinginan dan kemampuan. dalam pengembangan selanjutnya dapat dilakukan penambahan kriteria dan mengkombinasikan metode analytical hierarchy process (ahp) dengan topsis dalam pengambilan keputusan pemilihan perguruan tinggi swasta. daftar referensi badan pusat statistik. (2018). buku statistik indonesia 2018. badan pusat statistik indonesia. budilaksono, s., suwarno, & herwanto, a. (2016). sistem pendukung keputusan pemilihan perguruan tinggi swasta program teknik informatika di propinsi dki jakarta dengan metode ahp dan promethee. in seminar nasional teknologi informasi dan multimedia 2016 stmik amikom yogyakarta (vol. 2, pp. 58–70). danang, & nisar, m. f. (2017). sistem pendukung keputusan pemilihan pts jurusan komputer menggunakan metode ahp di kota semarang. simetris, 8(1), 45–52. http://creativecommons.org/licenses/by-nc/4.0/ p-issn: 2656-1743 e-issn: 2656-1735 jurnal riset informatika vol. 1, no. 2 maret 2019 90 ciptaan disebarluaskan di bawah lisensi creative commons atribusi-nonkomersial 4.0 internasional kementerian pendidikan dan kebudayaan, p. (2017). ikhtisar data pendidikan dan kebudayaan tahun 2017/2018. retrieved from http://publikasi.data.kemdikbud.go.id/uploa ddir/isi_4b619f17-97f5-4b93-bb0b7f83b5512b0b_.pdf kemeristekdikti, p. (2017). buku statistik pendidikan tinggi 2017. mardiana, t. (2018). sistem pendukung keputusan pemilihan mobil murah ramah lingkungan menggunakan metode topsis. jurnal techno nusa mandiri, 15(1 maret), 37–42. mardiana, t., tanjung, s. (2017). laporan penelitian sistem pendukung pemilihan perguruan tinggi swasta menggunakan topsis. universitas bina sarana informatika. surya, c. (2017). sistem pendukung keputusan pemilihan perguruan tinggi menggunakan fuzzy multi attribute decision making ( fmadm ) dan simple additive weighting ( saw ). jaringan sistem informasi robotik, 1(01, maret), 18–24. triono, a., & aminudin, n. (2017). sistem pendukung keputusan pemilihan perguruan tinggi dengan simple additive weighting ( saw). konferensi mahasiswa sistem informasi, 5(.1), 51–57. walidain, b., & ardian, y. (2017). sistem pendukung keputusan pemilihan perguruan tinggi dan jurusan berbasis web menggunakan metode topsis. jurnal bimasakti, 1(5), 1-8. http://creativecommons.org/licenses/by-nc/4.0/ 93 implementation analytical hierarchy process and weighted product method for lovebird selection identification applications yolanda nur oktavia1, nurhayati2, iskandar fitri3*) sistem informasi, fakultas teknologi komunikasi dan informatika, universitas nasional yolandanuroktavia2018@student.unas.ac.id1, nurhayati@civitas.unas.ac.id2, iskandar.fitri@civitas.unas.ac.id3*) (*)corresponding author abstrak diantara banyaknya jenis burung di indonesia burung lovebird adalah hewan peliharaan yang paling banyak menarik perhatian dan menjadi favorit di kalangan masyarakat. hal ini dibuktikan dengan sekian banyaknya komunitas pencinta lovebird di seluruh penjuru indonesia. permasalahan yang ada bagi orang awam yang kurang paham tentang dunia hewan dan memiliki pengetahuan yang sedikit mengenai bagaimana kualitas burung lovebird tersebut, tidak jarang kendalanya ialah dalam berbisnis bagi penjual sulit untuk memilih keputusan dalam mengatasi masalah pemilihan burung terbaik. atas permasalahan tersebut penulis melakukan perbandingan kombinasi metode analytical hierarchy process dan weighted product dengan analytical hierarchy process dan topsis dari perbandingan tersebut diperoleh nilai tertinggi sebesar 392.63 pada analytical hierarchy process dan weighted product untuk aplikasi perancangan aplikasi rekomendasi seleksi lovebird berkualitas terbaik berbasis website. dari hasil pengujian aplikasi maupun perhitungan manual dengan 64 data sampel disimpulkan 4 pengguna atau sekitar 6% masuk pada kategori ketepatan rendah dalam hasil perekomendasian lovebird terbaik, 12 pengguna atau sebesar 19% dari seluruh total pengujian dinyatakan pada dengan tingkat kategori ketepatan sedang dalam hasil perekomendasian lovebird terbaik dan 48 pengguna atau sebesar 75% dari seluruh total pengujian dinyatakan pada dengan tingkat kategori ketepatan tinggi dalam hasil perekomendasian lovebird terbaik dengan nilai ketepatan tertinggi sebesar 80.2% pada jenis lovebird albino. kata kunci: sistem pendukung keputusan, lovebird,, berkualitas, kombinasi, ahp, wp abstract among the many types of birds in indonesia, lovebirds are pets that attract the most attention and become a favorite among the public. this is evidenced by the many communities of lovebird lovers throughout indonesia. the problem that exists for ordinary people who do not understand the animal world and have little knowledge about the quality of the lovebirds, it is not uncommon for the problem to be in doing business for sellers it is difficult to make decisions in overcoming the problem of selecting the best birds. based on this problem, the author compares the combination of the analytical hierarchy process and weighted product methods with the analytical hierarchy process and topsis from this comparison, the highest score is 392.63 on the analytical hierarchy process and weighted product for the best quality webbased lovebird selection recommendation application design application. from the results of application testing and manual calculations with 64 sample data, it was concluded that 4 users or about 6% were included in the low accuracy category in the best lovebird recommendation results, 12 users or 19% of the total tests were stated at a moderate level of accuracy in the lovebird recommendation results. the best and 48 users or 75% of the total tests were stated at the high accuracy category level in the best lovebird recommendation results with the highest accuracy value of 80.2% on the albino lovebird type. keywords: decision support system, lovebird, quality,combination, ahp, wp introduction lovebirds are currently kept by many people because they have a melodious chirping sound and beautiful color gradations in the appearance of their feathers. the various types of lovebird names and their criteria are very unique and varied, so that they affect the price on the market every year depending on the season and the physical quality of the lovebird. cases of problems in the system to be built are interrelated. 94 the type of poultry that is in demand on the market in the livestock category at this time is the lovebird. so that the interest from buyers who are looking for and buying the bird is very high. this of course requires the shop owner to understand the quality of good birds to sell to buyers. the problem is from the side of the seller who is still new to the business in the pet sector. the offer of a bird provider from a supplier is often rejected by the seller because it avoids the risk of loss due to not understanding the quality of a good lovebird for sale. the loss that occurred was that they had experienced fraud against the condition of the bird which was in fact unhealthy and imperfect, and in the end it affected income so that turnover decreased. as a result, the seller decided to stop selling lovebirds, to help the seller's decision problem in making it easier to choose a quality lovebird, a decision support system was made that can be used so as to increase buyer satisfaction. the research which in this case discusses the selection of pigeons that has been carried out previously in 2021 by a.ramadhan et al., namely the application produced is very user friendly and the admin or user does not find it difficult to determine the best quality of pigeons with the conclusion of determining the highest ranking alternative of 0.327 in system(ramadhan, suprianto, surmarno, & dijaya, 2021). other research is still on the same topic, namely the recommendation for selecting different types of chirping birds in 2019 by r.rudiantoro et al by testing several different bird species to find out which sings best and the final result shows the highest preference value of 6.34 for the lovebird species.(rudiantoro, cholissodin, & dewi, 2019). in a further study regarding similar research objects in 2019 conducted by s. bahtiar et al, namely lovebirds, which aims to assist the judges in objectively assessing the best bird competition contest. the output generated by computerization and manual calculation is the same with the highest value reaching 1.85(bahtiar, gunawan, safii, & parlina, 2019). the next similar research conducted by e.l amalia et al in 2019 was about determining in choosing the most superior lovebird in the competition. application with a combination of ahp and topsis methods (amalia, rda, & pratama, 2019). the system is made to determine the best quality in determining the assessment in decision making in the competition so that it can help the judges with an accuracy rate of 98% system with manual calculations. then further research is still using the same method but with a different title for the discussion in 2021 conducted by s. defit et al., discussing a system that identifies the quality of a wallet bird's nest with the application of the weighted product method. the results of this system show the ranking data well with an accuracy level reaching 100% according to manual calculations and applications(defit, nurcahyo, studi, & ilmu, 2021). however, research related to the selection of lovebirds and using a combination of analytical hierarchy process and weighted product on the same issue has not been found before. for this reason, the author proposes a combination of analytical hierarchy process and weighted product methods in this study regarding a recommendation system for selecting quality lovebirds that can be used by ordinary people, especially bird sellers to determine good quality for sale to buyers. the selection of the ahp and wp methods was based on the results of the comparison test of the combination of these methods with other methods, namely the analytical hierarchy process and the technique for others reference by similarity to ideal solution (topsis)(amalia et al., 2019) using the mean squared error (mse) forecasting model to find out which method is the best (sugianto, roslina, & situmorang, 2021). the final result shows the highest value in the ahp-wp combination, which is 392.63, it can be concluded that the ahp-wp method is the best and most accurate method of research in determining the selection of quality lovebirds. it is hoped that with this decision support system in the application of recommendations for determining quality lovebirds, it can overcome decisions in choosing which types meet the good quality of lovebirds precisely and accurately. research methods the stages of research are carried out so that the plan can be neatly arranged and get maximum results. figure 1. research framework figure 1 describes the framework of the stages in the preparation of this research, starting with collecting data on criteria and alternative lovebirds, then comparing the combination of 95 methods used with other combination methods, then making a system design after applying the ahp and wp methods into the system and finally doing testing. to determine the level of system accuracy. collecting data this study relies on reference weighting criteria data derived from journals, interviewing interviewees of resource persons observation data from bird breeding places, in addition to strengthening other official sources as well as from the indonesian lovebird breeding website.(dlhk provinsi banten, 2019) then processed manually as needed to design a decision support system(defit et al., 2021). the data criteria are variables used in calculating the best lovebird quality recommendations table.1 list of criteria and sub criteria code criterions sub criteria preference value kc1 beak shape dry short 1 thin pointy 3 thick curved 5 kc2 foot paralyzed 1 limp 3 gripping 5 kc3 head shape oval shape 1 round shape 3 protruding forehead 5 kc4 posture disability 1 no defects (standard) 3 proportional 5 kc5 fur condition loss 1 tidy 3 soft 5 shiny 7 kc6 behaviour silent snuggle 1 very agile 3 sounding voice 5 table 1 shows a list of types of criteria or physical characteristics of lovebirds as many as 6 criteria names and has each sub-criterion consisting of codes c1 to c6 that have been determined and processed preference values by related sources. furthermore, alternative data are the names of lovebird species that belong to 9 species of the genus agapornis(charli, syaputra, akbar, sauda, & panjaitan, 2020). and the assessment will be done is one of these types is the type of agapornis roseicollis. table.2 list of alternatives to lovebird options alternative code alternative name l1 lovebird albino l2 lovebird lutino l3 lovebird golden cherry l4 lovebird pied l5 lovebird cinnamon l6 lovebird biru table 2 contains data on lovebird names displayed as many as 6 tails of the same species, data obtained from the indonesian lovebird farming website (dlhk provinsi banten, 2019) it consists of each of the alternative codes from l1 to l6 applied to the study. comparison of method combinations after collecting the data needed to find out the consistency of precision and accuracy of which combination of methods will be applied to this study, a comparison test is conducted. by comparing 2 other method combinations namely analitycal hierarchy process and weighted product with analitycal hierarchy process and technique for others reference by similarity to ideal solution. with the formula of the combination method ahp and wp (krismadewi, 2021) λmaks = jumlah n ci = ⋏𝑚𝑎𝑘𝑠−𝑛 𝑛 …………………………………….……..(1) cr = 𝐶𝐼 𝐼𝑅 cr = ratio consistency ci = index consistency n = the number of elements ir = random index the preference for ai alternatives starts from looking for vector values s and vector v 𝑆𝑖 = ∏ = 1 𝑋𝑖𝑗 𝑊𝑗 𝑛𝑗 …………………………………………... (2) 𝑉𝑖 = ∏ =1 𝑋𝑖𝑗 𝑊𝑗 𝑛 𝑗 ∏ =1 𝑋𝑖𝑗 𝑊𝑗 𝑛𝑗 ∗𝑊𝑗 …………………………………………... (3) stated, i is the result of alternative preferences to – i and π is the sum of the results of the multiplication of alternative rankings of each attribute. and the results showed the highest and lowest values(perdana, defit, & sumijan, 2020). furthermore, the formula combination of ahp and topsis methods is used to determine the final data results (amalia et al., 2019). for the 96 formula ahp is the same as the equation(1) and continued with the formula topsis. the method comparison process in this study uses the mean squared error (mse)forecasting model to find out which method is best(sugianto et al., 2021). the formula for calculating mse is: mse = ∑𝑒𝑖 2 𝑛 = ∑(𝑋𝑖−𝐹𝑖) 2 𝑛 ...................................................... (4) xi : preliminary data fi : final data n : number of criteria the first step is done by determining the initial amount of data from each criterion and alternative. then calculate the deviation value of each method. table 3 determination of deviation of ahp-wp preliminary data final data (deviation) 2 20 0.185 392.63 20 0.146 394.18 18 0.249 315.09 20 0.152 393.94 16 0.137 251.63 20 0.131 394.77 sum 2355.78 table 3 contains the initial data obtained from the sum of the comparison of criteria of the ahp method and the final data is obtained from the total calculation of the ahp and wp methods. then count using the equation (4). mse = 2355.78/6 = 392.63 table.4 determination of deviation of ahptopsis preliminary data final data (deviation) 2 20 0.494 380.48 20 0.429 383.02 18 0.678 300.05 20 0.442 382.51 16 0.437 242.20 20 0.372 385.25 sum 2073.51 table 4 contains the initial data obtained from the sum of the comparison of criteria of the ahp method and the final data is obtained from the total calculation of the ahp and topsis methods. then calculate using the equation (1). mse = 2073.51/6 = 345.58 table.5 final results of method comparison no combination of methods mse 1 analytical hierarchy process dan weighted product 392.63 2 analytical hierarchy rocess dan topsis 345.58 max deviasi 392.63 figure2. end results of method comparison based on table 5 it can be determined that the visualization of the graph calculation of the bar diagram shows the ahp-wp method gets a deviation value of 2355.78 and a total of mse 392.63 and the ahp-topsis method gets a deviation value of 2073.51 and a total of mse 345.58, so the researcher decided to use the combination of ahp-wp methods as the basic reason for producing the highest value in determining the selection of quality lovebirds (imam, 2020). system creation creation starts from research on criteria data and criterion values. the creation of a flow overview of the implementation of the analitycal hierarchy process and weighted product method can be seen in the flowchart image(novira, mubarok, & shofa, 2020): figure 3. flowchart system figure 3 can be explained that the flow of the system design begins by using the analytical hierarchy process method. the first process is to input the criteria data, then continue by inputting 2355,78 2073,51 392,63 345,58 0 500 1000 1500 2000 2500 ahp-wp ahp-topsis deviasi mean squared error (mse) 97 the weights for each criterion, then the system will calculate the eigenvalues and cr values to find out the logical consistency of the criteria. calculate the value of vector s then vector v and arrive at the ranking of vector values so that it can display alternative recommendations based on the highest rank. figure 4. data flow diagram in figure 4 of the system's data flow diagram for admins and users. the administrator's role is to manage system work starting from login and then inputting each criterion and preference weight value on alternative data and criteria. then the role of the user is to input some alternative data that will later be processed by the system and produce a calculation output in the form of a list of the best quality lovebirds (ramadhan et al., 2021). figure 5. database view the next stage is to create a database containing 12 tables with results in the database view in figure 5 which is used to accommodate table data both input and output in the application of quality lovebird selection decision support system. figure 6. program code view after creating the database, in figure 6 is designed in the form of a coding structure using the php native programming language with visual studio code editor software, which begins to create a framework with bootstrap, to produce a website application support system for choosing the best lovebird decision from the input process to output that can be run. system implementation the application of this system is websitebased using php native, html, and mysql languages as databases used. the interface results of this spk system consist of login pages, home, criteria data, alternative data, analysis, and calculations. figure 7. home view in figure 7 is the main page and there are several menus, namely criteria data, alternative data, analysis, and calculations. figure 8. criteria data view 98 in figure 8 is a menu that contains criteria information consisting of the criteria id, the name of the criteria, the weight of the criteria,and then the option to edit. figure 9. alternative data view figure 9 is an alternative data menu that features add alternative data and options for editing and deleting. in addition, there is a search feature to search for the required data using keywords quickly. figure 10. analysis view in figure 10 is an analysis menu whose function provides a graph of the results of calculations done to choose a quality lovebird results and discussion determining criteria and weighting based on the information that has been collected by direct observation in the field, some quality lovebird criteria that have the following characteristics that will be applied to the ahp method are: table 6. physical characteristics kode kriteria jenis kc1 beak shape benefits kc2 foot benefits kc3 head shape benefits kc4 posture benefits kc5 fur condition benefits kc6 behavior benefits table 6 classifies criteria based on the physical characteristics of a lovebird. where because all criteria have a value weight that the higher the value means the better, then it belongs to the category of benefits. table 7. criteria comparison matrix kc1 kc2 kc3 kc4 kc5 kc6 kc1 1.00 4.00 2.00 3.00 7.00 5.00 kc2 0.25 1.00 0.33 0.50 5.00 2.00 kc3 0.50 3.00 1.00 2.00 4.00 5.00 kc4 0.33 2.00 0.50 1.00 5.00 3.00 kc5 0.14 0.20 0.25 0.20 1.00 0.33 kc6 0.20 0.50 0.20 0.33 3.00 1.00 sum 2.43 10.7 4.28 7.03 25.00 16.33 in table 7 to determine the assessment, a comparison of the criteria comparison matrix (andriyani & yuma, 2020). the description is based on the level of importance of the criteria compared to other criteria. the weighting of each criterion is based on determining the ahp formula that has been determined according to the priority interests obtained from the table(andriyani & yuma, 2020) table 8. saaty table value definition information 1 equally equally important both have the same influence. 3 a little important the ratio of one is slightly higher than the second. 5 more important the ratio of one is higher than the second. 7 very important the ratio of one is very higher than the second. 9 absolutely essential the ratio of one is absolutely very strong from the second. 2, 4, 6, 8 value among them both have an adjacent assessment. table 8 is a saaty table that has a relative importance level value between two criteria based on the decision maker's assessment and will form a paired comparison matrix(nurajizah, ambarwati, & muryani, 2020). table 9. synthesis of criteria comparison number of each element su m averag e 0.4 1 0.3 7 0.4 7 0.4 3 0.2 8 0.3 1 2.2 7 0.38 0.1 0 0.0 9 0.0 8 0.0 7 0.2 0 0.1 2 0.6 7 0.11 0.2 1 0.2 8 0.2 3 0.2 8 0.1 6 0.3 1 1.4 7 0.25 99 0.1 4 0.1 9 0.1 2 0.1 4 0.2 0 0.1 8 0.9 7 0.16 0.0 6 0.0 2 0.0 6 0.0 3 0.0 4 0.0 2 0.2 2 0.04 0.0 8 0.0 5 0.0 5 0.0 5 0.1 2 0.0 6 0.4 0 0.07 table 9 is the result of the calculation of each element and the number and average of the elements form a comprehensive comparison table of standards that will be used as the basis for ranking criteria. the next step is to calculate the consistency ratio (cr). by calculating the first contingency index (ci) using the equation (1). ci = ⋏𝑚𝑎𝑘𝑠−𝑛 𝑛 λ maks = (2.43*0.38)+(10.7*0.12)+(4.28*0.25)+( 7.03*0.16)+(25*0.04)+(16.33*0.07) = 6,533 n = 6 ci = (6,533-6)/(6-1) = 0,106 cr = ci/ir = 0,106/1,24 = 0,085 referring to the above point of 0.085 it can meet the provisions of cr<0.1 so that the process of analyzing quality lovebird selection criteria is said to be consistent. the result of calculating the above average value is the main weight or component of each criterion, and certainly becomes the preferred weight of the wp method. then continued with the calculation of the wp method that determines some alternatives to lovebird and previous criteria to get a quality type ofovebird l. table 10. alternative value criteria alternatif criterions kc1 kc2 kc3 kc4 kc5 kc6 l1 3 5 1 5 3 3 l2 1 3 3 5 7 1 l3 5 1 5 3 1 3 l4 3 5 1 1 5 5 l5 3 1 1 3 7 1 l6 1 5 3 1 5 5 table 10 is a data input by the user that displays the preference value based on the type of lovebird with its sub criteria referring to table 1 and then is the initial data in the determination of the deviation of ahp-wp in table 3. table11. preference weights kc1 kc2 kc3 kc4 kc5 kc6 0.38 0.11 0.25 0.16 0.04 0.07 table 11 is the result of calculating the average value of each criterion in table 12 and will be the power value in the next calculation step. determining vector value s the next step is to determine the vector s, i.e. by adjusting the weight of the criteria and multiplying by the weight of each preference using the equation (2). tabel 12. vector class s vector value s result vs1 2,645 vs2 2,076 vs3 3,548 vs4 2,163 vs5 1,956 vs6 1,875 sum 14,265 table 12 is the result of the weight of the data from each calculation of the value of the vector s of each alternative. determining value v the next determination is the process of playing by calculating vector classes. that is, the result of dividing the value of weight s with the number of alternative rating multiplication results per attribute using the equation (3). table 13. ranking results vector result ranking v1 0.185 2 v2 0.146 4 v3 0.249 1 v4 0.152 3 v5 0.137 5 v6 0.131 6 in table 13 is a settlement in the assessment process in the form of a role. based on the results of preference calculations in table 13. so in order of the best quality lovebird is the first rank of golden cherry lovebird with code l3, second rank albino lovebird with code l1, third rank lovebird pied with code l4, fourth rank lovebird lutino with code l2, fifth rank lovebird cinnamon with code l5, and last rank blue lovebird with code l6. testing results from the results of manual calculations that have been done by entering 6 different criteria obtained the final results of the assessment that has been included in reference to table 13 has recommended the best quality lovebird that 100 ranked first, namely the golden cherry lovebird with a value of 0.249. table 14. alternative sample data input scenario testing results no alternative code & preference value system values manual value highest rank desc 1 l1: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] ; l2: [kc1.3], [kc2.1],[kc3.3], [kc4.1], [kc5.1], [kc6.3] 0.704 0.704 lovebird albino 28 2 l1: [kc1.3], [kc2[5], [kc3.1], [kc4.5], kc5.3], [kc6.3] ; l2: [kc1.1], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.1] ; l3: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.1], [kc6.3] ; l4: [kc1.3], [kc2.5], [kc3.1], [kc4.1], [kc5.5], [kc6.5] ; l5: [kc1.3], [kc2.1], [kc3.1], [kc4.53, [kc5.7], [kc6.1] ; l6 : [kc1.1], [kc2.5], [kc3.3], [kc4.1], [kc5.5], [kc6.5] 0.2487 0.249 lovebird golden cherry 63 3 l4: [kc1.1], [kc2.5], [kc3.1], [kc4.1], [kc5.3], [kc6.5] ; l5: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] 0.786 0.786 lovebird cinnamon 12 4 l4: [kc1.3], [kc2.5], [kc3.1], [kc4.1], [kc5.5], [kc6.5] ; l2: [kc1.3], [kc2.5], [kc3.3], [kc4.3], [kc5.3], [kc6.3] 0.5973 0.597 lovebird lutino 49 5 l1: [kc1.5], [kc2.5], [kc3.5], [kc4.3], [kc5.7], [kc6.5] ; l3: [kc1.1], [kc2.3], [kc3.1], [kc4.1], [kc5.1], [kc6.5] 0.789 0.790 lovebird albino 9 6 l1: [kc1.1], [kc2.5], [kc3.5], [kc4.3], [kc5.1], [kc6.3] ; l4: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.5] ; l5: [kc1.1], [kc2.1], [kc3.1], [kc4.3], [kc5.3], [kc6.1] 0.4316 0.432 lovebird pied 59 7 l2: [kc1.5], [kc2.5], [kc3.3], [kc4.3], [kc5.7], [kc6.5] ; l4: [kc1.1], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc.5] 0.799 0.799 lovebird lutino 4 8 l2: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.3], [kc6.3] ; l5: [kc1.1], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.1] ; l6: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.1] 0.4528 0.453 lovebird lutino 57 9 l3: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.1] ; l4: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.3], [kc6.1] 0.743 0.743 lovebird golden cherry 20 10 l3: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.1] ; l5: [kc1.5], [kc2.3], [kc3.3], [kc4.5], [kc5.5], k[c6.5] 0.6176 0.618 lovebird cinnamon 46 11 l4: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] ; l1: [kc1.1], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.1] 0.837 0.837 lovebird pied 1 12 l2: [kc1.1], [kc2.1], [kc3.5], [kc4.1], [kc5.1], [kc6.3] ; l4: [kc1.5], [kc2.5], [kc3.1], [kc4.5], [kc5.5], [kc6.5] 0.623 0.623 lovebird lutino 44 13 l6: [kc1.5], [kc2.5], [kc3.5], [kc4.3], [kc5.7], [kc6.3] ; l1: [kc1.1], [kc2.1], [kc3.1], [kc4.3], [kc5.1], [kc6.1] 0.793 0.793 lovebird albino 6 14 l5: [kc1.3], [kc2.5], [kc3.5], [kc4.3], [kc5.5], [kc6.5] ; l3: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.1], k[c6.3] 0.701 0.702 lovebird cinnamon 29 15 l5: [kc1.1], [kc2.3], [kc3.3], [kc4.3], [kc5.3], [kc6.1] ; l6: [kc1.5], [kc2.1], [kc3.1], [kc4.3], [kc5.7], [kc6.5] 0.610 0.600 lovebird biru 48 16 l3: [kc1.5], [kc2.3], [kc3.5], [kc4.5], [kc5.7], [kc6.3] ; l2: [kc1.1], [kc2.3], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.759 0.760 lovebird golden cherry 16 17 l3: [kc1.1], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.5] ; l4: [kc1.3], [kc2.3], [kc3.1], [kc4.3], [kc5.7], [kc6.3] ; l5: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.7], [kc6.3] ; l6: [kc1.1], [kc2.5], [kc3.3], [kc4.5], [kc5.7], [kc6.1] 0.353 0.353 lovebird golden cherry 62 18 l5: [kc1.5], [kc2.5], [kc3.5], [kc4.3], [kc5.5], [kc6.5] ; l6: [kc1.3], [kc2.1], [kc3.1], [kc4.3], [kc5.1], [kc6.1] 0.733 0.733 lovebird cinnamon 22 19 l4: [kc1.3], [kc2.5], [kc3.3], [kc4.1], [kc5.3], [kc6.5] ; l2: [kc1.3], [kc2.5], [kc3.3], [kc4.3], [kc5.5], [kc6.5] 0.556 0.556 lovebird lutino 51 20 l1: [kc1.5], [kc2.5], [kc3.5], [kc4.1], [kc5.7], [kc6.5] ; l3: [kc1.5], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.666 0.666 lovebird albino 36 21 l3: [kc1.5], [kc2.5], [kc3.3], [kc4.3], [kc5.3], [kc6.5] ; l5: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.660 0.660 lovebird golden cherry 37 22 l2: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] ; l4: [kc1.1], [kc2.3], [kc3.1], [kc4.3], [kc5.1], [kc6.1] 0.771 0.771 lovebird lutino 14 101 no alternative code & preference value system values manual value highest rank desc 23 l2: [kc1.1], [kc2.3], [kc3.1], [kc4.3], [kc5.1], [kc6.1] ; l5: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.1], [kc6.5] 0.757 0.757 lovebird lutino 17 24 l3: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] ; l4: [kc1.1], [kc2.3], [kc3.1], [kc4.3], [kc5.3], [kc6.1] 0.791 0.792 lovebird golden cherry 7 25 l3: [kc1.3], [kc2.5], [kc3.3], [kc4.1], [kc5.5], [kc6.1] ; l5: [kc1.5], [kc2.3], [kc3.3], [kc4.5], [kc5.5], [kc6.5] 0.624 0.624 lovebird cinnamon 43 26 l4: [kc1.1], [kc2.1], [kc3.5], [kc4.1], [kc5.3], [kc6.1] ; l1: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.1], [kc6.3] 0.721 0.721 lovebird albino 25 27 l2: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.7], [kc6.5] ; l4: [kc1.5], [kc2.1], [kc3.1], [kc4.1], [kc5.5], [kc6.1] 0.697 0.697 lovebird lutino 30 28 l6: [kc1.5], [kc2.3], [kc3.5], [kc4.3], [kc5.7], [kc6.5] ; l5: [kc1.3], [kc2.1], [kc3.5], [kc4.1], [kc5.1], [kc6.3] 0.646 0.647 lovebird pied 40 29 l3: [kc1.5], [kc2.3], [kc3.5], [kc4.7], [kc5.3], [kc6.5] ; l5: [kc1.1], [kc2.3], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.794 0.794 lovebird golden cherry 5 30 l5: [kc1.1], [kc2.1], [kc3.5], [kc4.3], [kc5.7], [kc6.5] ; l6: [kc1.5], [kc2.1], [kc3.1], [kc4.3], [kc5.7], [kc6.3] 0.543 0.543 lovebird biru 52 31 l1: [kc1.5], [kc2.5], [kc3.5], [kc4.3], [kc5.7], [kc6.5] ; l2: [kc1.1], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.800 0.800 lovebird albino 3 32 l1: [kc1.3], [kc2.3], [kc3.1], [kc4.1], [kc5.7], [kc6.3]; l3: [kc1.1], [kc2.5], [kc3.1], [kc4.1], [kc5.1], [kc6.3] ; l4: [kc1.1], [kc2.3], [kc3.5], [kc4.1], [kc5.1], [kc6.5] 0.394 0.394 lovebird albino 61 33 l4: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.3] ; l5: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.724 0.725 lovebird pied 24 34 l4: [kc1.3], [kc2.5], [kc3.1], k[c4.1], [kc5.5], [kc6.5] ; l2: [kc1.3], [kc2.5], [kc3.3], [kc4.3], [kc5.5], [kc6.3] 0.622 0.622 lovebird lutino 45 35 l1: [kc1.1], [kc2.5], [kc3.3], [kc4.1], [kc5.3], [kc6.5] ; l3: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.7], [kc6.5] 0.6839 0.684 lovebird golden cherry 33 36 l1: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.5] ; l4: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.5] 0.802 0.802 lovebird albino 2 37 l2: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.1] ; l4: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.5] 0.730 0.730 lovebird lutino 23 38 l2: [kc1.5], [kc2.5], [kc3.5], [kc4.3], [kc5.3], [kc6.3] ; l5: [kc1.1], [kc2.3], [kc3.3], [kc4.3], [kc5.7], [kc6.1] ; l6: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.1] 0.484 0.484 lovebird lutino 56 39 l3: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.5] ; l4: [kc1.3], [kc2.3], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.707 0.707 lovebird golden cherry 27 40 l3: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.1] ; l5: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.5] 0.650 0.650 lovebird cinnamon 39 41 l4: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.1] ; l1: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.639 0.639 lovebird pied 42 42 l2: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.3] ; l4: [kc1.5], [kc2.3], [kc3.5], [kc4.5], [kc5.7], [kc6.5] 0.748 0.748 lovebird pied 18 43 l6: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.1] ; l1: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.671 0.671 lovebird biru 34 44 l3: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.7], [kc6.3] ; l5: [kc1.1], [kc2.1], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.768 0.768 lovebird golden cherry 15 45 l5: [kc1.1], [kc2.3], [kc3.3], [kc4.3], [kc5.3], [kc6.5] ; l6: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.7], [kc6.5] 0.6575 0.657 lovebird biru 38 46 l1: [kc1.3], [kc2.5], [kc3.1], [kc4.5], [kc5.3], [kc6.3] ; l2: [kc1.1], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.1] 0.560 0.560 lovebird albino 50 47 l1: [kc1.1], [kc2.5], [kc3.1], [kc4.5], [kc5.3], [kc6.3] ; l2: [kc1.1], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.1] ; l3: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.1], [kc6.3] ; l4: [kc1.3], [kc2.5], [kc3.5], [kc4.1], [kc5.5], [kc6.5] ; l5: [kc1.3], [kc2.5], [kc3.1], [kc4.3], [kc5.7], [kc6.1] ; l6 : [kc1.1], [kc2.5], [kc3.3], [kc4.1], [kc5.7], [kc6.5] 0.239 0.239 lovebird golden cherry 64 102 no alternative code & preference value system values manual value highest rank desc 48 l4: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.5] ; l5: [kc1.1], [kc2.5], [kc3.1], [kc4.1], [kc5.1], [kc6.1] ; 0.789 0.789 lovebird pied 10 49 l4: [kc1.3], [kc2.5], [kc3.1], [kc4.1], [kc5.5], [kc6.5] ; l2: [kc1.3], [kc2.5], [kc3.3], [kc4.3], [kc5.5], [kc6.3] 0.612 0.612 lovebird lutino 47 50 l1: [kc1.5], [kc2.5], [kc3.3], [kc4.1], [kc5.5], [kc6.5] ; l3: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.1], [kc6.5] 0.515 0.515 lovebird golden cherry 54 51 l1: [kc1.1], [kc2.5], [kc3.5], [kc4.3], [kc5.1], [kc6.1] ; l4: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.5] ; l5: [kc1.1], [kc2.5], [kc3.1], [kc4.3], [kc5.3], [kc6.1] 0.4267 0.427 lovebird pied 60 52 l2: [kc1.1], [kc2.3], [kc3.1], [kc4.1], [kc5.5], [kc6.1] ; l4: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.7], [kc6.1] 0.792 0.791 lovebird pied 8 53 l2: [kc1.1], [kc2.3], [kc3.1], [kc4.1], [kc5.1], [kc6.1] ; l5: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.1], [kc6.5] 0.787 0.787 lovebird lutino 11 54 l3: [kc1.3], [kc2.1], [kc3.3], [kc4.1], [kc5.1], [kc6.1] ; l1: [kc1.3], [kc2.5], [kc3.5], [kc4.5], [kc5.5], [kc6.3] 0.668 0.668 lovebird albino 35 55 l3: [kc1.3], [kc2.3], [kc3.3], [kc4.1], [kc5.5], [kc6.1] ; l5: [kc1.5], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.5] 0.640 0.640 lovebird cinnamon 41 56 l4: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.3], [kc6.1] ; l1: [kc1.3], [kc2.1], [kc3.3], [kc4.1], [kc5.1], [kc6.1] 0.690 0.690 lovebird pied 31 57 l2: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.5], [kc6.1] ; l4: [kc1.5], [kc2.5], [kc3.5], [kc4.5], [kc5.1], [kc6.5] 0.746 0.746 lovebird pied 19 58 l6: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.5], [kc6.3] ; l1: [kc1.1], [kc2.3], [kc3.1], [kc4.3], [kc5.1], [kc6.5] 0.741 0.741 lovebird biru 21 59 l3: [kc1.1], [kc2.5], [kc3.1], [kc4.3], [kc5.3], [kc6.5] ; l5: [kc1.1], [kc2.3], [kc3.3], [kc4.5], [kc5.7], [kc6.1] ; l6: [kc1.3], [kc2.3], [kc3.5], [kc4.1], [kc5.1], [kc6.5] 0.4338 0.434 lovebird biru 58 60 l5: [kc1.5], [kc2.3], [kc3.3], [kc4.3], [kc5.3], [kc6.1] ; l6: [kc1.5], [kc2.5], [kc3.1], [kc4.3], [kc5.7], [kc6.5] 0.5179 0.518 lovebird cinnamon 53 61 l4: [kc1.3], [kc2.5], [kc3.5], [kc4.1], [kc5.5], [kc6.5] ; l2: [kc1.3], [kc2.5], [kc3.3], [kc4.3], [kc5.7], [kc6.3] 0.506 0.506 lovebird lutino 55 62 l1: [kc1.1], [kc2.5], [kc3.3], [kc4.1], [kc5.5], [kc6.3] ; l3: [kc1.5], [kc2.1], [kc3.5], [kc4.3], [kc5.7], [kc6.5] 0.687 0.687 lovebird golden cherry 32 63 l1: [kc1.1], [kc2.5], [kc3.1], [kc4.1], [kc5.3], [kc6.1] ; l4: [kc1.5], [kc2.5], [kc3.3], [kc4.5], [kc5.7], [kc6.5] 0.784 0.784 lovebird albino 13 64 l2: [kc1.5], [kc2.1], [kc3.5], [kc4.5], [kc5.1], [kc6.5] ; l4: [kc1.3], [kc2.1], [kc3.1], [kc4.1], [kc5.1], [kc6.3] 0.708 0.708 lovebird lutino 26 referring to table 14 contains the results of 64 data testing scenarios input alternative sample data based on lovebird type and sub-criteria obtained an accuracy rate of 100% with true test data as much as 64 out of 64 total tests by manual calculation and system calculation. good quality lovebird determination tests were conducted as many as 64 tests of evidence of different input samples. of the 64 best rankings, the first rank lovebird is lovebird pied with a value 0.837. from the experiment, the table was created containing alternative codes for the bird's name and weight preference criteria, the results of manual calculation values, the results of system calculation values, the highest ranking of the bird's name, and description of the rating of each lovebird. table 15. basic accuracy percentage assessment category level percentage value very low 0% 20% low 21% 40% moderate 41% 60% high 61% 80% very high 81% 100% in table 15(kurniawan, 2017) shows the accuracy of the system accuracy which refers to the test results from table 14 it can be concluded that there are 3 levels of categories where as many as 4 users or about 6% of 64 tests with low accuracy category levels in the results of lovebird immersion best with a percentage value of 21%-40%. furthermore, as many as 12 users or about 19% of 64 tests with a moderate accuracy category level in the best lovebird communication results with a percentage value of 41%-60% then as many as 48 users or 103 about 75% of 64 tests with a high accuracy category level in the results of lovebird communication best with a percentage value of 61%-80%. and there are no users whose percentage value is below 20% or very low and above 81% or very high that has been recommended by the system or manually. conclusion based on the results of research and design obtained a website-based application system on identifying the selection of quality lovebirds designed with native php programming and mysql as databases. this application contains 6 criteria data consisting of each of 3 physical subcriteria of lovebird and 6 alternative data of selected lovebird names in the same species. in addition, this application also combines 2 methods namely analytical hierarchy process and weighted product with test conclusions on 64 alternative sample evidence based on lovebird type and sub-criteria both in system calculations and manual calculations that result in low accuracy category levels. with a percentage value of 21%-40%. furthermore, as many as 12 users or about 19% of 64 tests with a moderate accuracy category level with a percentage value of 41%-60% then as many as 48 users or about 75% of 64 tests with a high accuracy category level in the best lovebird communication results with a percentage value of 61%-80%. this decision support system as an application that produces the highest ranking in helping recommend the decision of determining the best quality lovebird. this designed application can still be developed even better and is recommended for its development to be used with other methods and implementations reference amalia, e. l., rda, r. a., & pratama, a. n. 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