DESIGN OF STUDENT GRADUATION PREDICTION SYSTEM USING NAIVE BAYES AND WEBSITE-BASED DECISION TREE

Abstract

In college, the aspect that affects quality is the length of time students study. Therefore, a system is needed to predict student graduation whether they graduate at the specified time or late. This research aims to design and implement a website-based Student Graduation Prediction System Using Naive Bayes and Decision Tree Methods. The data used to predict graduation in the form of assessments obtained in the form of Assignment Scores, Midterm Exam Scores, Final Exam Scores, Total Scores, and Gender. In this research, the Naive Bayes method is used to calculate the probability of student graduation based on the attributes in the dataset. In addition, the Decision Tree algorithm, specifically the C4.5 algorithm, is applied to build a decision tree model that can efficiently predict student graduation. This system is web-based using PHP programming language, the database used is MySQL. System evaluation is done through accuracy, precision, and recall measurements, which provide an overview of the extent to which the system can predict graduation correctly. In addition, the prediction results of student graduation consist of two, namely On Time and Late. The results of this study indicate that the Student Graduation Prediction System using data on Gender, Assignment Scores, Midterm Exam Scores, Final Exam Scores, and Total Scores is able to provide a fairly accurate prediction of student graduation.