The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction

Abstract

This research conducted classification testing on the study case of student graduation prediction in a university. It aims to assist the university in maintaining academic development and in finding solutions for improving timely graduation. This study combined two methods, i.e., Naive Bayes and Particle Swarm Optimization, to produce a better level of accuracy. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. That will be further enhanced using the Particle Swarm Optimization method to produce a better level of accuracy. There are 10 (ten) samples in this study randomly selected from the alumni data of UIGM students in 2011-2014. From the test results, this research resulted in an accuracy value of 90% from the Naive Bayes algorithm testing, after testing the Naive Bayes with Particle Swarm Optimization, which produced an accuracy value of 100%. The conclusion obtained from the results is the Naive Bayes method has a higher accuracy value if combined with Particle Swarm Optimization. Thus the university can more easily predict whether or not the students graduate on time for the upcoming graduation period. The results of this test prove that to predict student graduation using the Naive Bayes method with Particle Swarm Optimization is appropriate.