Implementasi Data Mining Naïve Bayes untuk Sistem Peringatan Dini Hasil Belajar Siswa Sekolah Dasar

Adyatma Ramadhani S; Atiqah Meutia Hilda; Muhammad Jafar Elly; Nani Mintarsih

doi:10.35760/ik.2025.v30i3.58

Authors

Adyatma Ramadhani S Author
Atiqah Meutia Hilda Author
Muhammad Jafar Elly Author
Nani Mintarsih Author

DOI:

https://doi.org/10.35760/ik.2025.v30i3.58

Keywords:

Data Mining, Classification, Naive Bayes, Student Learning Outcomes

Abstract

This study aims to apply data mining techniques using the Naïve Bayes algorithm to classify student learning outcomes as an early warning system. The classification model was developed using 90 student score records and grouped into two categories, namely “Good” and “Poor”. The experimental results show that the Naïve Bayes model achieved 100% accuracy, precision, and recall. These findings indicate that the proposed model is capable of consistently classifying student learning outcomes and has the potential to serve as a decision support tool for teachers and school administrators.

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