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. π‘Šπ‘— = π‘Šπ‘— βˆ‘π‘Šπ‘— .............................................................................. (1) Description: π‘Š : 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 π‘Šπ‘—π‘› 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 π‘Š : 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 βˆ‘ 𝑆𝑖 .............................................................................. (3) Description: 𝑖 : Alternative 1,2,…,m. 𝑉 : Vector V 𝑆 : Vector S 𝑋 : Criteria value π‘Š : 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. (2018). 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