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All Journal Journal of Information Technology and Computer Science
Arsy Kurnia Fitri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Naive Bayes with SMOTE for Predicting the Competitiveness of Vocational School Graduates on Imbalanced Data (Case Study: SMK Negeri 3 Malang) Arsy Kurnia Fitri; Wijoyo, Satrio Hadi; Hariyanti, Uun
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111870

Abstract

Vocational high schools (SMK) aim to produce work-ready graduates. However, the open unemployment rate (TPT) for SMK graduates remains high at 9.01%, indicating a significant competency gap. This study designs a model to predict graduates workforce competitiveness using the Naive Bayes algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE is employed to address the class imbalance between capable and incapable graduates. The study follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, utilizing academic scores and tracer study datasets. Evaluation results demonstrate that applying SMOTE with a 70:30 train-test split successfully increased model accuracy to 97%. Notably, the model effectively detects the minority class with a Recall of 90%. Furthermore, cross-validation yielded an average accuracy of 97.66%, demonstrating stable performance. Finally, the model was implemented as a web-based dashboard to serve as an early warning system for schools.
Co-Authors Satrio Hadi Wijoyo Uun Hariyanti, Uun