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 (Faculty of Computer Science (FILKOM) Brawijaya University, 2026)
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.