Work Readiness Prediction of Vocational High School Students Using a Genetic Algorithm-Optimized Random Forest and XGBoost as a Benchmark Model with Explainable AI (SHAP) - Syifa Amalia; Noor Latifah; Andy Prasetyo Utomo (Journal of Applied Informatics and Computing, 2026)
Work readiness among vocational high school (SMK) students remains a critical challenge in aligning educational outcomes with industry expectations. This study proposes a binary classification system using Genetic Algorithm (GA)-optimized Random Forest as the primary model and XGBoost as a benchmark. Two predictor variables are used: average practical score (X1) and attitude score (X2). The classification target is based on the minimum competency threshold (KKM = 80) regulated by the Indonesian Ministry of Education: a student is labeled Ready to Work when X1 >= 80 AND X2 >= 80. A boundary noise injection mechanism is applied to mitigate label determinism at the borderline zone. The dataset comprises 1,211 student records split into 968 training and 243 testing records. Evaluation employs hold-out testing, Stratified 5-Fold Cross Validation, overfitting detection, and McNemar's statistical significance test. SHAP interpretability is performed at three levels: global feature importance, beeswarm summary plot, and dependence plots. RF+GA achieved 90.12% accuracy, 0.9391 precision, 0.8640 recall, 0.9000 F1-score, and 0.9582 AUC-ROC. Cross-validation confirmed stable performance at 91.53% +/- 2.51%. Overfitting analysis showed a training-testing gap of only 1.72% (Not Detected). McNemar's test (p = 1.000) indicated statistically equivalent performance between both models. SHAP analysis revealed that attitude score (X2) is the dominant predictor (mean SHAP = 0.2611), followed by average practical score (X1, mean SHAP = 0.1628). Practical implications include an early warning system, targeted remedial programs, and individual guidance recommendations generated by the deployed Streamlit application.