Application of the Classification and Regression Tree (CART) Algorithm for Classifying Student Majors at MAN 1 OKU Timur

Abstract

The classification of new student majors represents a vital process to align students’ educational trajectories with their aptitudes and interests. MAN 1 OKU Timur, an Islamic senior high school under the Ministry of Religious Affairs (Kementerian Agama), categorizes students into three primary academic tracks: Natural Sciences, Social Sciences, and Religion. Traditionally, the classification process relied on a simple summation and ranking of academic scores, a method prone to subjective bias and misclassification. This study implements a data mining approach utilizing the Classification and Regression Tree (CART) algorithm to enhance the objectivity and precision of the major selection process. Using data from 379 students of the 2020/2021 academic year, the CART model achieved an accuracy rate of 88.65%, demonstrating high reliability in predicting appropriate majors based on students’ academic performance and stated preferences. The results indicate that the CART algorithm can serve as an effective decision-support tool for the classification of new students at MAN 1 OKU Timur.