APPLICATION OF THE QUEST AND CHAID METHODS IN CLASSIFYING STUDENT GRADUATION

Abstract

Graduation is the final result of the learning process during the course. Student graduation time is affected by many factors. Whether or not the time of student graduation is appropriate is an important thing that must be considered. Graduating well and on time is one measure of success in the learning process. This research aims to build a student graduation classification model by applying the QUEST (Quick, Unbiased, and Efficient, Statistical Tree) and CHAID (Chi-squared Automatic Interaction Detection) methods, examining the factors that affect student graduation, and comparing the classification results of the two methods. Both methods produce output in the form of tree diagrams, making it easier to interpret. Based on the classification tree formed from the two methods, four final nodes of the classification tree were generated, and three categories were grouped. Factors that affect student graduation include age and IPK. The classification results show that the percentage of classification accuracy for student graduation with QUEST and CHAID methods is 76.1%.