http://infor.seaninstitute.org/index.php/infokum/index JURNAL INFOKUM, Volume 9, No. 2,Juni 2021 ISSN : 2302-9706 INFOKUM is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License (CC BY-NC 4.0) 412 CLASSIFICATION OF MAJOR SELECTION BASED ON STUDENTS EXPERTISE USING C4.5 ALGORITHM N P Dharshinni 1* , Rr. Putri 2 ,Mardina Dameria 3 ,Eli Christian 4 , Doni Friando 5 ,Yan Very 6 Universitas Prima Indonesia, Indonesia *priyadharshinninaidu@gmail.com Abstract Article Info Selection and determination of majors is something that must be done by junior high school students when they want to enter senior high school. However, it is not uncommon for students to be confused in choosing the right major based on student expertise. The problems faced by many students who take majors because they follow their friends or parents and make it difficult for students to follow the available subjects according to the chosen majors and have an impact on student achievement. In analyzing the determination of the right major based on student expertise, the C4.5 algorithm is used. The classification of the C45 algorithm will produce a decision tree that can be used in determining the right direction. The results of the confusion matrix Classification of student value data in determining majors produce an accuracy value of 95.92%, class precision/class recall in Natural Sciences Major is 97.56%, class precision/class recall in Social Sciences Major is 87.50% and classification error is 4.08%. Decision tree results show the subject variables that influenced the selection of student majors were mathematics, science, ICT, skills, and tourism, The highest gain value lies in the Pariwista subject which is the root of the decision tree that is formed. The resulting rule is a math score above 82, a minimum science value of 84.5, a minimum ICT value of 85.5, so that students are more suitable for Natural Sciences Major. Meanwhile, if the value of mathematics is less than 82, tourism is less than 90.5 and skills are less than 84.5 then the student is more suitable for Social Sciences Major. Received 01 Juni 2021 Revised 20 Juni 2021 Accepted 30 Juni 2021 Keywords : Classification, Selection of Majors, Student Expertise, C4.5 Algorithm, Decision Tree 1. INTRODUCTION Student data is the most important asset for schools. Student data contains subject values, starting from the first semester to the final semester. The stored student values so that can be used as a determination of majors at the high school level. Not only the value of the subject, but the value of student expertise is also very encouraging in determining majors. Majoring in the process of selecting student groupings based on values, interests, and talents so that students can follow lessons with a focus (Novianti, Rismawan, & Bahri, 2016). The problem that is often encountered is that most students choose majors when entering senior high school because they follow their friends and parents without knowing