INTEGRASI NAIVE BAYES DAN ITEM-BASED COLLABORATIVE FILTERING DALAM SISTEM PEMETAAN KOMPETENSI MAHASISWA
DOI:
https://doi.org/10.35143/jkt.v11i1.6612Keywords:
Item-Based Collaborative Filtering, Naive Bayes, Student Competency MappingAbstract
Preparing a strong portfolio is a crucial aspect for students in entering the workforce, one of which can be achieved through participation in various competitions. However, selecting competitions that align with student competencies remains a challenge due to the abundance of competition information, diversity in student interests and abilities, and limitations in budget, time, and resources. This study develops a recommendation system based on a Hybrid Recommendation System designed to map student competencies to relevant competition types. The system integrates the Naive Bayes method to classify student competencies and Item-Based Collaborative Filtering to calculate similarities between competition types based on other users’ preferences. The system is developed incrementally using the waterfall approach, including the stages of planning, analysis, design, implementation, and testing. The model follows standard machine learning workflows, comprising data collection, exploration and preprocessing, model building, performance evaluation, and method integration. The research data includes student profiles, competencies, and competition preferences collected through surveys and internal databases. Evaluation results indicate that the system successfully provides relevant competition recommendations with an accuracy rate of 70%. These results demonstrate the system’s contribution in assisting students to select competitions that match their competencies, presented in a user-friendly web-based application.Downloads
References
R. Setiamihardja, “Portofolio Assessment,” J. Pendidik. Dasar Kampus Cibiru, vol. 3, no. 2, pp. 1–2, 2012, [Online]. Available: https://ejournal.upi.edu/index.php/eduhumaniora/article/view/2806/1832
N. Asmilia, “Pentingnya Portofolio Investasi untuk Mengetahui Keanekaragaman Resiko dan Meningkatkan Minat Berinvestasi di Kalangan Generasi Milenial,” J. Pengabdi. Kpd. Masyaraka, vol. 4, no. 3, 2023.
M. M. Dewi, R. Andriani, and M. Nuraminudin, “Performance Analysis of the Item-Based Collaborative Filtering Model in Yogyakarta Tourism Recommendations,” vol. 9, no. 2, pp. 534–541, 2025.
J. Aisyiah and L. Cahyani, “Sistem Rekomendasi Program Studi Menggunakan Metode Hybrid Recommendation (Studi Kasus: MAN Sumenep),” pp. 59–72, 2023, doi: 10.30864/eksplora.v12i1.992.
D. S. Pradana, P. Prajoko, and G. P. Hartawan, “Perbandingan Algoritma Content-Based Filtering dan Collaborative Filtering dalam Rekomendasi Kegiatan Ekstrakurikuler Siswa,” Progresif J. Ilm. Komput., vol. 18, no. 2, p. 151, 2022, doi: 10.35889/progresif.v18i2.854.
M. I. Rizky, I. Asror, and Y. R. Murti, “Sistem Rekomendasi Program Studi untuk Siswa SMA Sederajat Menggunakan Metode Hybrid Recommendation dengan Content Based Filtering dan Collaborative Filtering,” in e-Proceeding of Engineering, 2020, p. 17.
L. Tommy, D. Novianto, and Y. S. Japriadi, “Sistem Rekomendasi Hybrid untuk Pemesanan Hidangan Berdasarkan Karakteristik dan Rating Hidangan,” J. Appl. Informatics Comput., vol. 4, no. 2, pp. 137–145, 2020, doi: 10.30871/jaic.v4i2.2687.
A. Yusmar, Implementasi metode collaborative filtering dengan pendekatan Item-Based untuk rekomendasi rumah makan menggunakan algoritma adjusted cosine similarity. 2020. [Online]. Available: https://repository.uinjkt.ac.id/dspace/handle/123456789/55992%0Ahttps://repository.uinjkt.ac.id/dspace/bitstream/123456789/55992/1/ADDINI YUSMAR-FST.pdf
A. A. Widjaja and H. N. Palit, “Hybrid Recommendation System untuk Peminjaman Buku Perpustakaan dengan Collaborative dan Content-Based Filtering,” J. Infra, vol. 10, no. 2, pp. 1–6, 2022, [Online]. Available: https://publication.petra.ac.id/index.php/teknik-informatika/article/view/12512
M. Nurizki, W. Apriandari, and A. Asriyanik, “Algoritma Naïve Bayes untuk Rekomendasi Seleksi Peserta Paskibraka Berbasis Website,” J. Inf. Syst. Res., vol. 4, no. 4, pp. 1486–1493, 2023, doi: 10.47065/josh.v4i4.3574.
Y. Dananjoyo and Y. Muflilah, “Sistem Rekomendasi Ukuran Baju Pada Aplikasi E-Commece Dengan Metode Naïve Bayes Yogo,” Jasmatika, vol. 3, no. 1, pp. 10–15, 2015.
V. Srividhya and R. Anitha, “Evaluating Preprocessing Techniques in Text Categor ization,” pp. 49–51, 2010.
L. Zhu and B. W. B, on Hybrid Recommendation Algorithms. Atlantis Press International BV. doi: 10.2991/978-94-6463-044-2.
Z. Zhang, T. Peng, and K. Shen, “Overview of Collaborative Filtering Recommendation Algorithms,” IOP Conf. Ser. Earth Environ. Sci., vol. 440, no. 2, 2020, doi: 10.1088/1755-1315/440/2/022063.
E. G. F. H. del Olmo, “Evaluation of recommender systems: A new approach,” Expert Syst. Appl., pp. 1–15, 2008.
V. Nakhipova et al., “Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction,” Int. J. Inf. Educ. Technol., vol. 14, no. 1, pp. 92–98, 2024, doi: 10.18178/ijiet.2024.14.1.2028.
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