Pengembangan Sistem Rekomendasi Program Studi Multikelas Menggunakan Algoritma Random Forest
- Renita Astri Universitas Dharma Andalas, Padang, Indonesia
-
Ahmad Kamal
*
Institut Bisnis dan Teknologi Pelita Indonesia, Pekanbaru,
Indonesia
https://orcid.org/0009-0004-3768-8010
- Zulfahmi Zulfahmi Universitas Dharma Andalas, Padang, Indonesia
- Faradika Faradika Universitas Dharma Andalas, Padang, Indonesia (*) Corresponding Author
Abstract
Choosing a major is a crucial decision for prospective students entering higher education. An inappropriate choice may lead to low learning motivation, poor academic performance, and career mismatches. This study aims to develop a multiclass majors recommendation system based on machine learning using the Random Forest algorithm. The dataset consists of 855 student and alumni records from 10 majors at Dharma Andalas University (UNIDHA), including academic attributes (subject grades, GPA, entrance test results) and non-academic attributes (gender, high school major, interest, and alumni career field). The model was trained using an 80:20 train-test ratio and evaluated using accuracy, precision, recall, F1-score, and macro-average AUC. The results show that the Random Forest outperforms Decision Tree, K-Nearest Neighbor, and Naive Bayes, achieving an accuracy of 0.920 and AUC of 0.972. These findings demonstrate that ensemble-based algorithms are highly effective for multiclass recommendation problems and can serve as a foundation for academic and career guidance systems in universities.
Downloads
References
Akbar, A., Yogi, Ananto, & Pratama, S. (2023). Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning Untuk Model Klasifikasi Calon Mahasiswa Baru. Journal of Information Technology and Society, 1, 11–14. https://doi.org/10.35438/jits.v1i1.20
Asbara, N., & Alviani, V. (2023). Perancangan Sistem Pendukung Keputusan dalam Penentuan Program Studi Berbasis Online. Journal of Information System Research (JOSH), 4, 988–997. https://doi.org/10.47065/josh.v4i3.3241
Astri, R., Hung, L. P., Sura, S. B., & Kamal, A. (2025a). Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines. 9(4), 879–885.
Astri, R., Hung, L. P., Sura, S. B., & Kamal, A. (2025b). Improving the Accuracy of Tourism Recommendation System Based on Neural Collaborative Filtering Renita. 9(4), 886–893.
Astri, R., Hung, L. P., Sura, S. B., Kamal, A., & Yuliet, R. (2023). Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic. E3S Web of Conferences, 464. https://doi.org/10.1051/e3sconf/202346406002
Astri, R., Kamal, A., & Sura, S. (2022). Coffee Shop Recommendation System Using an Item-Based Collaborative Filtering Approach. 2022 International Symposium on Information Technology and Digital Innovation (ISITDI), 65–67. https://doi.org/10.1109/ISITDI55734.2022.9944403
Atalla, S., Daradkeh, M., Gawanmeh, A., Khalil, H., Mansoor, W., Miniaoui, S., & Himeur, Y. (2023). An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling. Mathematics, 11, 1098. https://doi.org/10.3390/math11051098
Faradika, F., Astri, R., & Zulfahmi, Z. (2020). Sistem Informasi Penjadwalan Otomatis Media Sosial Instagram Untuk Mendukung Promosi Program Studi Di Universitas Dharma Andalas. Jurnal Teknologi Dan Sistem Informasi Bisnis, 2(2), 225–230. https://doi.org/10.47233/jteksis.v2i2.149
Femas Aji Saputra, Adityawarman Adityawarman, & Salwa Rahman Nursyabani. (2024). Analisis Dampak Kesalahan Pemilihan Jurusan terhadap Prestasi Akademik dan Kesejahteraan Psikologis Mahasiswa. Corona: Jurnal Ilmu Kesehatan Umum, Psikolog, Keperawatan Dan Kebidanan, 2(2), 180–192. https://doi.org/10.61132/corona.v2i2.418
Isma’il, M., Aliyu, G., Abdulmumin, I., & Adamu, S. (2020). An Autonomous Courses Recommender System For Undergraduate Using Machine Learning Techniques. https://doi.org/10.1109/ICMCECS47690.2020.240882
Kamal, A., Sura, S. B., Hung, L. P., Astri, R., & Kurniati, T. (2023). Empowerment of MSME crafts recommendation system with content-based filtering method on social commerce after the pandemic. E3S Web of Conferences, 464. https://doi.org/10.1051/e3sconf/202346412001
Müllner, P., Schmerda, S., Theiler, D., Lindstaedt, S., & Kowald, D. (2022). Towards employing recommender systems for supporting data and algorithm sharing. https://doi.org/10.1145/3565011.3569055
Pratama, A., Aryanto, R., & Pratama, A. (2022). Model Klasifikasi Calon Mahasiswa Baru Untuk Sistem Rekomendasi Program Studi Sarjana Berbasis Machine Learning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 9, 725. https://doi.org/10.25126/jtiik.2022934311
Singgih Purnomo, & Rayhan Gunaningrat. (2022). Determinants of Student Interest in Choosing a Study Program. International Journal of Social Science, 1(6), 873–878. https://doi.org/10.53625/ijss.v1i6.1899
Sularno, S., Astri, R., Zulfahmi, Z., Prima Mulya, D., Faradika, F., & Anggraini, P. (2023). Pelatihan Digital Marketing Dalam Membentuk Branding Sekolah. Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat, 2(1), 25–29. https://doi.org/10.47233/jpmittc.v2i1.527
Zayed, Y., Salman, Y., & Hasasneh, A. (2022). A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student Data. Applied Sciences, 12, 1–14. https://doi.org/10.3390/app122412525
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Pengembangan Sistem Rekomendasi Program Studi Multikelas Menggunakan Algoritma Random Forest
Pages: 567-577
Copyright (c) 2025 Renita Astri, Ahmad Kamal, Zulfahmi Zulfahmi, Faradika Faradika
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).
