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Aditya Desta Saputra
Department of Information Systems, Universitas Stikubank Semarang, Indonesia

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Predicting Student Academic Performance Using the Naïve Bayes Algorithm (Case Study: SMK Pelayaran “AKPELNI” Semarang) Aditya Desta Saputra; Herny Februariyanti
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.95

Abstract

Predicting student academic performance can help schools identify students who may require early academic support. This study evaluates a Naïve Bayes classification approach using attendance and parental socioeconomic factors to predict academic performance at SMK Pelayaran “AKPELNI” Semarang. The study used 210 student records from the 2024/2025 academic year. Academic performance was determined from the average scores in Indonesian Language, Mathematics, English, and vocational subjects and classified into Low, Medium, and High categories. These subject scores were excluded as predictors to maintain independence between the predictors and the target label. The predictors consisted of total attendance, father’s education and income, and mother’s education and income. Missing parental education values were handled using mode imputation. The data were divided using a stratified 80:20 train-test split. Categorical Naïve Bayes with Laplace smoothing (α=1) and uniform class priors was applied. On the 42-record test set, the model achieved 66.67% accuracy, 39.74% balanced accuracy, and 33.88% macro F1. The Medium class achieved an F1 score of 79.41%, the High class 22.22%, while the Low class was not detected. These findings indicate that attendance and parental socioeconomic factors alone are insufficient for reliable minority-class prediction and should not yet be used as a standalone early warning system.
Co-Authors Herny Februariyanti