Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms

Abu Tholib (Universitas Nurul Jadid)
M Noer Fadli Hidayat (Universitas Nurul Jadid)
Supri yono (Universitas Islam Negeri Maulana Malik Ibrahim Malang)
Resty Wulanningrum (Universitas Nusantara PGRI Kediri)
Erna Daniati (Universitas Nusantara PGRI Kediri)

Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Vol 2 No 2 (2023): September 2023

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.