Implementasi Multi-Platform Naïve Bayes untuk Prediksi Keberhasilan Mahasiswa secara Real Time

Nurfitria Khoirunnisa; Haryati Haryati

doi:10.31544/jtera.v11.i1.2026.61-68

Abstract

Educational Data Mining has emerged as a critical technology for predictive analytics in higher education. However, implementation consistency across different computational platforms remains an understudied area. Student graduation prediction requires robust algorithmic approaches with validated performance across multiple computational environments to support reliable data-driven academic decision making. This research implements and evaluates the performance of Naïve Bayes algorithm in Educational Data Mining for student graduation prediction. The study provides comprehensive multi-platform comparative analysis between WEKA and Python implementations with rigorous statistical validation. Academic data from graduated students of D-3 Information Systems Study Program, Politeknik Negeri Subang were utilized as the training dataset. Implementation was conducted using a dual-platform approach: WEKA and Python with scikit-learn library. Performance evaluation employed stratified 10-fold cross-validation with comprehensive metrics including accuracy, precision, recall, F1-score, and Kappa statistic. Comparative analysis was conducted against Decision Tree, Support Vector Machine, Random Forest, and Logistic Regression algorithms. The results demonstrate that Naïve Bayes achieved adequate accuracy with exceptional cross-platform consistency. Comparative analysis revealed competitive performance with superior computational efficiency. Multi-platform implementation validates Naïve Bayes effectiveness for Educational Data Mining applications with consistent performance across computational environments, making it suitable for real-time educational analytics deployment in vocational education institutions.

Keywords

educational data mining naïve bayes algorithm multi-platform implementation performance evaluation student graduation

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