Implementasi Naive Bayes untuk Memprediksi Prestasi Belajar Siswa MTs Fathurrahman Padang Tualang - Alfin Noval Permana; Juwita Adinda; Roberto Kaban (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2026)
Student learning achievement is an important indicator in evaluating the success of the learning process in madrasah. This study aims to implement the Naive Bayes algorithm in predicting the learning achievement of Grade VII, VIII, and IX students at MTs Fathurrahman Padang Tualang. The research data uses 77 students from three grade levels; 38 students (49.35%) are classified as Achieving and 39 students (50.65%) as Underachieving. Model evaluation using the Hold-Out Split Data method (80% training, 20% testing) achieved Accuracy of 93.75%, Precision of 100.00%, and Recall of 87.50%, confirming the model's high reliability. The UAS variable is the strongest predictor with a mean difference of 13.59 points between classes (μAchieving = 80.92 vs μUnderachieving = 67.33). This research proves that Naive Bayes is an effective and efficient classification algorithm for predicting student learning achievement across grade levels in madrasah tsanawiyah. The proposed model can support educators in identifying students with potential academic difficulties, enabling early intervention and more targeted learning strategies. Furthermore, the implementation of predictive analytics provides valuable insights for improving academic management and supporting data-driven decision-making in educational institutions.