COMPARISON OF DECISION TREE AND RANDOM FOREST ALGORITHMS IN PREDICTING STUDENT GRADUATION BASED ON ACADEMIC DATA

This research aims to compare the performance of the Decision Tree and Random Forest algorithms in predicting student graduation based on academic data. By utilizing data such as Grade Point Average (GPA), the number of credit hours, and course grades, this study focuses on analyzing the accuracy of both algorithms in predicting students who are at risk of not graduating on time. The results of the study indicate that the Random Forest algorithm achieves higher accuracy compared to the Decision Tree, particularly in terms of recall and precision. While Decision Tree is simpler and easier to interpret, it tends to have overfitting issues that can affect prediction results. In contrast, Random Forest overcomes these issues by producing more stable predictions through an ensemble process. This study is expected to contribute to the development of student graduation prediction systems in educational institutions. As such, institutions can use these findings as a foundation for designing intervention strategies for students at risk of not graduating on time.