CLASSIFICATION OF MAJOR SELECTION BASED ON STUDENTS EXPERTISE USING C4.5 ALGORITHM
Abstract
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.
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References
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