Implementasi Naive Bayes untuk Memprediksi Prestasi Belajar Siswa MTs Fathurrahman Padang Tualang

Alfin Noval Permana; Juwita Adinda; Alfin Noval Permana

doi:10.61132/merkurius.v4i4.1698

Authors

Alfin Noval Permana Institut Teknologi dan Bisnis Indonesia
Juwita Adinda Institut Teknologi dan Bisnis Indonesia
Alfin Noval Permana Institut Teknologi dan Bisnis Indonesia

DOI:

https://doi.org/10.61132/merkurius.v4i4.1698

Keywords:

Academic Prediction, Learning Achievement, Madrasah Tsanawiyah, Naive Bayes, Student Classification

Abstract

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.

References

Alsaaidah, B., Al-Diabat, M., Al-Oqaily, A., & Alzu’bi, A. (2022). Educational data mining system for predicting students’ academic performance using Naive Bayes classifier. Journal of Intelligent Systems, 31(1), 419–433.

Ardiansyah, R., & Suryana, T. (2023). Penerapan algoritma Naive Bayes untuk klasifikasi prestasi belajar siswa sekolah menengah pertama. Jurnal Informatika dan Sistem Informasi, 4(2), 89–97.

Fitriani, N., Maulana, I., & Sari, R. (2024). Klasifikasi prestasi akademik mahasiswa menggunakan Gaussian Naive Bayes dengan evaluasi k-fold cross validation. Jurnal Ilmiah Teknologi Informasi Terapan, 10(2), 115–123.

Hasanah, N., & Mulyani, S. (2024). Penerapan Gaussian Naive Bayes pada prediksi prestasi belajar siswa SMP menggunakan data nilai akademik. Jurnal Sistem Informasi dan Teknologi, 6(1), 45–53.

Mardiyanto, A., Kurniawan, B., & Putri, D. (2025). Implementasi data mining untuk prediksi keberhasilan akademik siswa madrasah menggunakan metode klasifikasi probabilistik. Jurnal Pendidikan Islam dan Teknologi, 3(1), 22–34.

Nugroho, A., & Prasetyo, E. (2022). Implementasi Naive Bayes untuk prediksi kelulusan mahasiswa berdasarkan data akademik. Jurnal Informatika dan Rekayasa Perangkat Lunak, 4(1), 22–31.

Prasetya, D. A., & Wibowo, F. (2023). Evaluasi model machine learning untuk prediksi nilai akademik siswa sekolah dasar dan menengah. Jurnal Teknologi dan Sistem Komputer, 11(3), 178–186.

Ramadhani, F., & Fitria, L. (2022). Data mining untuk prediksi prestasi akademik: Tinjauan sistematis. Jurnal Ilmu Komputer dan Informasi, 15(1), 1–15.

Rish, I. (2021). An empirical study of the Naive Bayes classifier. Journal of Universal Computer Science, 27(1), 1–15.

Santoso, H., Prabowo, A., & Hidayah, I. (2024). Comparative study of machine learning algorithms for student academic performance prediction in Indonesian secondary schools. Indonesian Journal of Computing and Cybernetics Systems, 18(1), 55–67.

Septiani, R., & Kurniawan, H. (2021). Analisis perbandingan metode klasifikasi data mining untuk prediksi prestasi siswa. Jurnal Pendidikan Teknologi dan Kejuruan, 27(2), 175–183.

Sukmawati, R., & Hermawan, D. (2023). Sistem pendukung keputusan prediksi prestasi belajar siswa menggunakan Naive Bayes berbasis web. Jurnal Rekayasa Sistem dan Teknologi Informasi, 7(1), 40–48.

Wahyuningsih, S., Hartati, S., & Nugroho, E. (2025). Penerapan Naive Bayes Gaussian dalam klasifikasi potensi akademik siswa madrasah tsanawiyah berbasis data historis. Jurnal Sistem Cerdas dan Komputasi, 8(1), 1–12.

Wijaya, B., Santoso, E., & Purnomo, A. (2023). Prediksi prestasi belajar siswa sekolah dasar menggunakan algoritma Naive Bayes: Studi kasus di Kota Semarang. Jurnal Ilmiah Informatika, 11(2), 45–54.

Zhang, H. (2021). The optimality of Naive Bayes revisited: A statistical perspective. Computational Statistics & Data Analysis, 154, 107098.