Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JISICOM (Journal of Information System, Infomatics and Computing)
Exsel Zeth Lopulalan
Universitas Papua
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
PERBANDINGAN KINERJA NAÏVE BAYES DAN RANDOM FOREST DALAM PREDIKSI PERFORMA AKADEMIK SISWA Marcelino Alberki Kabuhung; Exsel Zeth Lopulalan; Michael Sabandar; Arif Handika
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2433

Abstract

Predicting student academic performance has become one of the important applications of machine learning in the field of education. This study aims to compare the performance of Naïve Bayes and Random Forest algorithms in predicting student academic performance using a data mining approach. The dataset used in this research is the Student Performance Dataset obtained from the UCI Machine Learning Repository, which contains academic, social, behavioral, and demographic student data. The research method applies the CRISP-DM framework consisting of data understanding, data preparation, modeling, and evaluation stages. The preprocessing stage includes categorical variable encoding, removal of G1 and G2 variables to avoid data leakage, and transformation of the target variable into binary classification. The dataset was divided into 80% training data and 20% testing data. Model evaluation was conducted using accuracy, precision, recall, f1-score, and confusion matrix. The results show that Random Forest achieved an accuracy of 72.15%, outperforming Naïve Bayes with 70.88%. Feature importance analysis indicates that absences, failures, age, and goout are the most influential factors affecting student academic performance. This study concludes that Random Forest provides better performance in classifying student academic performance and is capable of providing interpretation of factors influencing prediction results.
Co-Authors Arif Handika Marcelino Alberki Kabuhung Michael Sabandar