Naïve Bayes Decision Support for Junior High Student Graduation Prediction: Kartika VI Jayapura Study

Authors

  • Daniel Bienfield Manahan Siahaan Universitas Sepuluh Nopember Papua, Kota Jayapura, Papua, Indonesia
  • Heru Sutejo Universitas Sepuluh Nopember Papua, Kota Jayapura, Papua, Indonesia https://orcid.org/0009-0009-6836-5990

DOI:

https://doi.org/10.51903/fs4qx719

Keywords:

Naïve Bayes, Data Mining, Decision Support, Graduation Prediction, Junior High School Students

Abstract

his study aims to implement the Naïve Bayes algorithm as a decision support tool for predicting junior high school student graduation at SMP Kartika VI Jayapura. Student graduation is an important indicator in assessing the success of the learning process at school. However, students’ academic and non-academic data are often not optimally utilized to support decision-making. Therefore, this study develops a prediction model to help the school identify students who are likely to graduate and students who are at risk of not graduating at an earlier stage. The dataset used in this study consists of 289 student records, divided into 202 training records and 87 testing records. The predictor attributes include Mathematics Score, Indonesian Language Score, English Language Score, Science Score, Social Science Score, Attitude, Attendance Percentage, School Rule Violations, Extracurricular Activeness, and Participation in Tutoring, while the predicted label is Graduation Status. The research process consists of data collection, data cleaning, modelling, and evaluation using RapidMiner. The results show that the Naïve Bayes model achieves an accuracy of 82.76%, indicating that the model performs fairly well in predicting student graduation. However, the evaluation also shows that the model is stronger in identifying the Graduated category than the Not Graduated category. Therefore, this model is more appropriate to be used as an early warning and decision support tool, not as the sole basis for determining student graduation. The prediction results can help the school identify students at risk of not graduating so that guidance, mentoring, and academic support can be provided more accurately and effectively.

References

[1] D. A. Punkastyo, F. Septian, and A. Syaripudin, “Implementasi Data Mining menggunakan Algoritma Naïve Bayes untuk Prediksi Kelulusan Siswa,” Journal of System and Computer Engineering (JSCE), vol. 5, no. 1, pp. 24–35, Jan. 2024, doi: 10.61628/jsce.v5i1.1073.

[2] T. Adrian and N. Suarna, “Implementasi Data Mining untuk Mengklasifikasi Hasil Kelulusan Madrasah menggunakan Algoritma Naive Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 1000–1004, Mar. 2024, doi: 10.36040/JATI.V8I1.8892.

[3] R. M. Apsari, “Penerapan Metode Naïve bayes dalam Memprediksi Prestasi Siswa,” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), vol. 4, no. 2, pp. 38–46, Aug. 2024, doi: 10.55382/JURNALPUSTAKAAI.V3I3.760.

[4] A. David Imanuel, N. Nawaningtyas Pusparini, and A. Sani, “Klasifikasi untuk Memprediksi Tingkat Kelulusan Mahasiswa STMIK Widuri Menggunakan Algoritma Naive Bayes,” JURNAL ILMIAH INFORMATIKA, vol. 12, no. 01, pp. 1–7, Mar. 2024, doi: 10.33884/jif.v12i01.8201.

[5] M. Hakim et al., “Analisis Prediksi Kelulusan Mahasiswa Universitas Dinamika Bangsa menggunakan Metode Naïve Bayes,” ejournal.unama.ac.idMF Hakim, S SaputraJurnal Informatika Dan Rekayasa Komputer (JAKAKOM), 2025•ejournal.unama.ac.id, doi: 10.33998/JAKAKOM.2025.5.1.1999.

[6] A. Hussen et al., “Modeling of Student Graduation Prediction using the Naive Bayes Classifier Algorithm,” ieeexplore.ieee.orgAAME Hussen, A Saikhu2024 3rd International Conference on Creative Communication and, 2024•ieeexplore.ieee.org, Accessed: Jun. 09, 2026. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10701117/

[7] Junaidi, R. Valicia Anggela, and I. Fadhli, “Prediksi Kelulusan Tepat Waktu Mahasiswa Menggunakan Metode Data Mining dengan Algoritma Naive Bayes,” Jurnal Edik Informatika Penelitian Bidang Komputer Sains dan Pendidikan Informatika, vol. 9, no. 2, pp. 65–74, Sep. 2023, doi: 10.22202/EI.2023.V9I2.7324.

[8] U. Indahyanti, N. L. Azizah, and H. Setiawan, “Educational Data Mining on Student Academic Performance Prediction: A Survey: Educational Data Mining pada Prediksi Kinerja Akademik Mahasiswa : Sebuah Survey,” Procedia of Social Sciences and Humanities, vol. 3, pp. 1442–1447, Jul. 2022, doi: 10.21070/PSSH.V3I.344.

[9] T. Le Quy, T. H. Nguyen, G. Friege, and E. Ntoutsi, “Evaluation of Group Fairness Measures in Student Performance Prediction Problems,” SpringerT Le Quy, TH Nguyen, G Friege, E NtoutsiJoint European Conference on Machine Learning and Knowledge Discovery in Databases, 2022•Springer, vol. 1752 CCIS, pp. 119–136, 2023, doi: 10.1007/978-3-031-23618-1_8.

[10] F. R. Al-Giffary and M. Martanto, “Klasifikasi Kelulusan Siswa Tahun 2024 Menggunakan Metode Decision Tree (Studi Kasus SMA Islam Alazhar 5 Cirebon),” Jurnal Manajamen Informatika Jayakarta, vol. 4, no. 2, p. 195, Apr. 2024, doi: 10.52362/jmijayakarta.v4i2.1408.

[11] M. R. Nugroho, I. E. Hendrawan, and P. P. Purwantoro, “Penerapan Algoritma K-Means untuk Klasterisasi Data Obat pada Rumah Sakit ASRI,” NUANSA INFORMATIKA, vol. 16, no. 1, pp. 125–133, Jan. 2022, doi: 10.25134/NUANSA.V16I1.5294.

[12] E. Fitriana et al., “Clustering Kos dengan Algoritma K-Means Untuk Rekomendasi Tempat Berdasarkan Harga dan Fasilitas,” Jurnal Media Informatika, vol. 6, no. 3, pp. 1990–1995, Jun. 2025, doi: 10.55338/JUMIN.V6I3.6188.

[13] S. Rachardian and E. Sediyono, “Prediksi Kelulusan Tepat Waktu Mahasiswa untuk Pemantauan Program Studi Menggunakan Metode Data Mining,” AITI, vol. 21, no. 2, pp. 168–182, Sep. 2024, doi: 10.24246/aiti.v21i2.168-182.

[14] A. Wijaya, Anita, M. C. Manurung, and Y. D. S. Sitanggang, “Penerapan Data Mining untuk Prediksi Kelulusan Siswa Sekolah Dasar Menggunakan Algoritma Naïve Bayes Classifier,” Jurnal Algoritma, vol. 23, no. 1, May 2026, doi: 10.33364/algoritma/v.23-1.3448.

[15] W. Lastari and J. Jasmir, “Penerapan Data Mining Untuk Memprediksi Prestasi Siswa SMA pada Dinas Pendidikan Provinsi Jambi,” Jurnal Manajemen Sistem Informasi, vol. 8, no. 2, pp. 310–321, Jun. 2023, Accessed: Jul. 22, 2026. [Online]. Available: https://ejournal.unama.ac.id/index.php/jurnalmsi/article/view/864

[16] A. David Imanuel, N. Nawaningtyas Pusparini, and A. Sani, “Klasifikasi untuk Memprediksi Tingkat Kelulusan Mahasiswa STMIK Widuri Menggunakan Algoritma Naive Bayes,” JURNAL ILMIAH INFORMATIKA, vol. 12, no. 01, pp. 1–7, Mar. 2024, doi: 10.33884/jif.v12i01.8201.

[17] Pusparini et al., “Klasifikasi untuk Memprediksi Tingkat Kelulusan Mahasiswa STMIK Widuri Menggunakan Algoritma Naive Bayes,” ejournal.upbatam.ac.idAD Imanuel, NN Pusparini, A SaniJurnal Ilmiah Informatika, 2024•ejournal.upbatam.ac.id, vol. 12, no. 01, pp. 1–7, Mar. 2024, doi: 10.33884/JIF.V12I01.8201.

[18] S. M. Hudzaifah, D. Gunawan, and D. Iskandar, “Implementasi Algoritma Naïve Bayes dalam Memprediksi Tingkat Kelulusan Siswa pada Sertifikasi Mikrotik Certified Network Associate (MTCNA),” Jurnal Indonesia : Manajemen Informatika dan Komunikasi, vol. 5, no. 3, pp. 3225–3236, Sep. 2024, doi: 10.35870/jimik.v5i3.1012.

[19] F. Fauziah and A. S. Ramadhantya, “Penggunaan Rapidminer untuk Memprediksi Kelulusan Mahasiswa Dengan Algorithm Naive Bayes,” Jurnal Teknologi Informasi, vol. 10, no. 1, pp. 52–60, Jun. 2024, doi: 10.52643/JTI.V10I1.4671.

[20] A. Riyanto and E. S. Ompusunggu, “Implementasi Data Mining untuk Mengklasifikasi Hasil Belajar Siswa/i dengan Metode Naïve Bayes,” Jurnal Teknologi Dan Ilmu Komputer Prima (JUTIKOMP), vol. 7, no. 2, pp. 129–141, Oct. 2024, doi: 10.34012/JUTIKOMP.V7I2.5237.

[21] H. Latifah and Sri Mujiyono, “Perbandingan Algoritma Naive Bayes , K-NN , ID3 , dan SVM dalam Menentukan Prediksi Kelulusan Siswa di SMK Muhamadiah Majenang,” Jurnal Mahasiswa Teknik Informatika, vol. 2, no. 1, pp. 38–45, Apr. 2023, doi: 10.35473/jamastika.v2i1.1871.

[22] Ramadhani Zidan Arifin, Hasbi Firmansyah, and Wahyu Asriyani, “Prediksi Kelulusan Siswa Berdasarkan Data Demografis dan Akademik pada Dataset Student Performance,” Jurnal Pengabdian Masyarakat dan Riset Pendidikan, vol. 4, no. 2, pp. 13300–13307, Dec. 2025, doi: 10.31004/jerkin.v4i2.4251.

[23] L. Baridah and R. A. Putri, “Predicting Student Learning Outcomes in Vocational Computer and Network Engineering Using Naïve Bayes,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 8, no. 3, pp. 667–676, Nov. 2025, doi: 10.24014/IJAIDM.V8I3.38333.

[24] R. Arifin, H. Firmansyah, “Prediksi Kelulusan Siswa berdasarkan Data Demografis dan Akademik pada Dataset Student Performance: Penelitian,” jerkin.orgRZ Arifin, H Firmansyah, W AsriyaniJurnal Pengabdian Masyarakat dan Riset Pendidikan, 2025•jerkin.org, vol. 4, no. 2, pp. 13300–13307, Dec. 2025, doi: 10.31004/JERKIN.V4I2.4251.

[25] E. Kuznetsova and M. Nkosi, “Adaptive Fuzzy Logic Integration for Optimizing Decision Support Systems under Data Uncertainty,” Journal of Technology Informatics and Engineering, vol. 4, no. 3, pp. 463–477, Dec. 2025, doi: 10.51903/JTIE.V4I3.456.

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Published

2026-10-28

Issue

Vol. 5 No. 3 (2026): November

Section

Decision Support Systems

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Copyright (c) 2026 Daniel Bienfield Manahan Siahaan; Heru Sutejo

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