Hyperparameter Optimization of Naive Bayes for Supervisor Recommendation in Computer Science
-
Muhammad Nabil Sinaga
*
Universitas Islam Negeri Sumatera Utara, Medan,
Indonesia
- Rakhmat Kurniawan R Universitas Islam Negeri Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
The increasing number of students in the Department of Computer Science at UIN Sumatera Utara has made the process of selecting thesis supervisors more complex and time-consuming. This study aims to develop a system that automatically recommends the most suitable supervisor based on the similarity between thesis titles and lecturers’ areas of expertise. The proposed model applies text preprocessing techniques such as case folding, tokenization, stopword removal, and keyword extraction to transform thesis titles into meaningful features. These features are then classified using the Naive Bayes algorithm to predict the probability of each lecturer being the most relevant supervisor. The dataset consists of 794 thesis titles and 25 lecturers collected from 2019–2024. The model was evaluated using an 80:20 data split, achieving an accuracy of 87.3% with stable precision and recall scores, demonstrating reliable performance in supervisor recommendations. This enhanced Naive Bayes model can assist academic departments in ensuring a fairer and more efficient supervisor assignment process.
Downloads
References
Aisyiah, J., & Cahyani, L. (2024). Sistem Rekomendasi Program Studi Menggunakan Metode Hybrid Recommendation (Studi Kasus: MAN Sumenep). Jurnal Eksplora Informatika, 12(1), 59–72. https://doi.org/10.30864/eksplora.v12i1.992
Ali Fauzi, M., Arifin, A. Z., Gosaria, S. C., & Prabowo, I. S. (2021). Indonesian news classification using naïve bayes and two-phase feature selection model. Indonesian Journal of Electrical Engineering and Computer Science, 8(3), 610–615. https://doi.org/10.11591/ijeecs.v8.i3.pp610-615
Asfi, M., & Fitrianingsih, N. (2020). Implementasi Algoritma Naive Bayes Classifier sebagai Sistem Rekomendasi Pembimbing Skripsi. Jurnal Nasional Informatika dan Teknologi Jaringan, 5, 45–50.
Fatayat, & Nugroho, R. A. (2021). Analisa Penentuan Dosen Pembimbing Tugas Akhir Mahasiswa Menggunakan Naive Bayes Classifier. Simtika, 4(3), 1–7. http://ejournal.undhari.ac.id/index.php/simtika/article/view/527
Gunantohadi, T., & Crysdian, C. (2022). Review Penerapan Metode Klasifikasi Pada Sistem Rekomendasi Sosial Kemasyarakatan. Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM), 3(2), 84–91. https://doi.org/10.31102/jatim.v3i2.1578
Hairani, H., & Mujahid, M. (2022). Recommendations of Thesis Supervisor using the Cosine Similarity Method. Sistemasi, 11(3), 646. https://doi.org/10.32520/stmsi.v11i3.2003
Irmayanti, A., & Ruspita, D. (2024). Rancangan Aplikasi Kasir Toko Kelontong Berbasis Website Menggunakan Metode Waterfall. IKRA-ITH Informatika : Jurnal Komputer dan Informatika, 9(1), 56–61. https://doi.org/10.37817/ikraith-informatika.v9i1.4376
Lestari, S., & Wardana, S. S. (2025). Optimasi Sistem Rekomendasi Musik Berbasis Naïve Bayes: Studi Kasus pada Pengguna Musik di Spotify. Jurnal Indonesia : Manajemen Informatika dan Komunikasi, 6(3), 1939–1949. https://doi.org/10.63447/jimik.v6i3.1600
Marsani Asfi, N. F. (2022). Implementasi Algoritma Naive Bayes Classifier sebagai Sistem Rekomendasi Pembimbing Skripsi. InfoTekJar, 5.
Perkasa, K. B. P. Y., & Eka Purwiantono, F. (2023). Sistem Rekomendasi Jurusan Menggunakan Algoritma Naïve Bayes Gaussian Berbasis Web. J-Intech, 11(2), 361–370. https://doi.org/10.32664/j-intech.v11i2.1090
Pratama, H. I., Aisah, S. N., & Akbar, F. (2025). Rancangan Sistem Rekomendasi Topik Tugas Akhir dengan Naive Bayes Classifier (Studi Kasus Departemen Sistem Informasi, Universitas Andalas). Jurnal Nasional Teknologi dan Sistem Informasi, 11(2), 200–206. https://doi.org/10.25077/teknosi.v11i2.2025.200-206
Pratama, M. K. B., Dewi, Y. P., Kusumawati, T. I. J., & Pebrianti, D. (2024). Designing a laboratory assistant attendance system using Radio Frequency Identification (RFID) technology based on IOT. Jurnal Inovasi dan Teknologi Pembelajaran, 11(1), 44–55. https://doi.org/10.17977/um031v11i12024p044
Rasyid, R. M. A. K., Riyanto, A., Widyawati, R., & Istiningsih, I. (2023). Implementasi Algoritma Naïve Bayes untuk Sistem Rekomendasi Pemilihan Fakultas di Universitas Amikom Yogyakarta. Jikom: Jurnal Informatika dan Komputer, 13(1), 1–9. https://doi.org/10.55794/jikom.v13i1.93
Resmalawati, C., Hamrul, H., & Rachmini, S. A. (2023). Sistem Pendukung Keputusan Penentu Dosen Pembimbing Studi Kasus Teknik Informatika Universitas Sulawesi Barat Menggunakan Algoritma Naïve Bayes. Prosiding Seminar Nasional Rekayasa Keteknikan & Informatika, Senarai, 47–56.
Risky, M. A. Z., & Yuhandri, Y. (2021). Optimalisasi dalam Penetrasi Testing Keamanan Website Menggunakan Teknik SQL Injection dan XSS. Jurnal Sistim Informasi dan Teknologi, 3, 215–220. https://doi.org/10.37034/jsisfotek.v3i4.68
Sriani, S., & Nabila, A. (2024). Implementasi Deep Learning Untuk Mengidentifikasi Umur Manusia Menggunakan Convolutional Neural Network (Cnn). Jurnal Informatika dan Teknik Elektro Terapan, 12(3), 1836–1843. https://doi.org/10.23960/jitet.v12i3.4457
Swanjaya, D., Kom, M., & Rochana, S. (2024). Perancangan Sistem Rekomendasi Jenis Parfum dengan Metode Naive Bayes Classifier. Journal INOTEK, 8, 218–225.
Yogo Dananjoyo, Y. M. (2024). Sistem Rekomendasi Ukuran Baju Pada Aplikasi E-Commece Dengan Metode Naïve Bayes. 32(22), 344–349.
Yulindawati, Y., Lailiyah, S., Yusnita, A., & Hafifah, A. (2024). Rekomendasi Pemilihan Judul Tugas Akhir Menggunakan Metode Naïve Bayes. Journal of Information System Management (JOISM), 5(2), 171–175. https://doi.org/10.24076/joism.2024v5i2.1383
Zaiha, F. H. (2021). Identifikasi Faktor Penghambat Mahasiswa Tingkat Akhir Dalam Menyelesaikan Tugas Akhir Di Program Studi Pendidikan Jasmani Universitas Muhammadiyah Kotabumi. 167–186.
Zulaikah, D. (2024). Implementasi Naive Bayes Classifier Pada Sistem Rekomendasi Parfum Toko “Rajawali. https://repository.unpkediri.ac.id/id/eprint/14852%0Ahttp://repository.unpkediri.ac.id/14852/3/RAMA_55201_2013020183_0723098303_0713028801_01_front_ref.pdf
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Hyperparameter Optimization of Naive Bayes for Supervisor Recommendation in Computer Science
Pages: 528-538
Copyright (c) 2025 Muhammad Nabil Sinaga, Rakhmat Kurniawan R
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
