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) πΈπ‘›π‘‘π‘Ÿπ‘œπ‘π‘¦(𝑆) = βˆ‘ βˆ’π‘›π‘–=1 𝑝𝑖 βˆ— π‘™π‘œπ‘”2 𝑝𝑖 .......................... (1) 4. Calculating the gain value. πΊπ‘Žπ‘–π‘›(𝑆, 𝐴) = 𝑆 βˆ’ βˆ‘ |𝑆𝑖| |𝑆| 𝑛 𝑖=1 βˆ— 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 NaΓ―ve 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 gainβ€”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 β€œMINAT” 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) π‘¨π’„π’„π’–π’“π’‚π’„π’š = 𝑑𝑝 + 𝑑𝑛 𝑑𝑝 + 𝑑𝑛 + 𝑓𝑝 + 𝑓𝑛 𝑋 100% = 1971 + 0 1971 + 0 + 29 + 0 𝑋 100% = πŸ—πŸ–, πŸ“πŸ“% 𝑹𝒆𝒄𝒂𝒍𝒍 = 𝑑𝑝 + 𝑑𝑛 𝑓𝑛 + 𝑑𝑝 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 rateβ€”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. (2018). Algoritma C4.5 Untuk Memprediksi Mahasiswa Yang Mengulang Mata Kuliah (Studi Kasus Di Amik Labuhan Batu). Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 11–22. https://doi.org/10.24176/simet.v9i1.1627 Baktiar, A. (2022). Decission Tree Sebagai Metode Penentuan Penjurusan Perguruan Tinggi Berdasarkan Minat Dan Bakat Melalui Data Raport Dengan Uji Algoritma C4 . 5. Jurnal Pilar Teknologi, 7(1), 40–45. https://doi.org/10.33319/piltek.v7i1.110 Darmawan, E. (2018). C4.5 Algorithm Application for Prediction of Self Candidate New Students in Higher Education. Jurnal Online Informatika, 3(1), 22. https://doi.org/10.15575/join.v3i1.171 Khairina, D. M., Ramadhani, F., Maharani, S., & Hatta, H. R. (2015). Department Recommendations for Prospective Students Vocational High School of Information Technology with NaΓ―ve Bayes Method. 2nd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 92–96. https://doi.org/10.1109/ICITACEE.2015.743 7777 Kurniasari, R., & Fatmawati, A. (2019). Penerapan Algoritma C4.5 Untuk Penjurusan Siswa Sekolah Menengah Atas. Jurnal Ilmiah Komputer Dan Informatika (KOMPUTA), 8(1), 19–27. https://doi.org/10.34010/KOMPUTA.V8I1.3 045 Larose, D. T. (2005). Discovering Knowledge in Data: An Introduction to Data Mining. In Discovering Knowledge in Data: An Introduction to Data Mining (2nd ed., pp. 1– 222). John Willey & Sons Inc. https://doi.org/10.1002/0471687545 Luvia, Y. S., Windarto, A. P., Solikhun, S., & Hartama, D. (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 NaΓ―ve 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