Optimasi Sistem Pemantauan Akademik Siswa dengan Collaborative Filtering dan Decision Tree
DOI:
https://doi.org/10.61722/jssr.v2i6.2982Keywords:
Website, Machune Learnung, Collaborative Filtering, Decision Tree, Educational Information System.Abstract
This research aims to develop a student academic monitoring website that integrates Machine Learning technology, with Collaborative Filtering and Decision Tree methods. This website is designed to improve the quality of education by providing effective tools for students, teachers, and parents in monitoring and analyzing academic performance. The main feature of the website is an interactive dashboard that displays students' grades, attendance, and extracurricular participation data in an easy-to-understand format. The implementation of a personalized academic recommendation system using the Collaborative Filtering method enables the provision of learning material suggestions that suit the individual needs of students. The Decision Tree method is used to classify academic progress, helping to identify areas that require improvement.
References
Alsalman, Y.S. et al. (2019) ‘Using Decision Tree and Artificial Neural Network to Predict Students Academic Performance’, 2019 10th International Conference on Information and Communication Systems, ICICS 2019, pp. 104–109. Available at: https://doi.org/10.1109/IACS.2019.8809106.
Diana, R., Warni, H. and Sutabri, T. (2017) ‘Penggunaan Teknoogi Machine Laerning untuk Pelayanan Monitoring Kegiatan Belajar Mengajar pada SMK Bina Sriwijaya Palembang’, Jurnal Teknik Informatika, 5(1), pp. 41–50. Available at: https://jurnal.stmik-dci.ac.id/index.php/jutekin/article/view/709/630.
Gómez-Pulido, J.A., Durán-Domínguez, A. and Pajuelo-Holguera, F. (2020) ‘Optimizing latent factors and collaborative filtering for students’ performance prediction’, Applied Sciences (Switzerland), 10(16). Available at: https://doi.org/10.3390/app10165601.
Hidayati, D., 2022. Sistem Informasi Pendidikan dan Transformasi Digital. Cetakan pertama. Yogyakarta: UAD Press.
Matzavela, V. and Alepis, E. (2021) ‘Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments’, Computers and Education: Artificial Intelligence, 2. Available at: https://doi.org/10.1016/j.caeai.2021.100035.
Koren, Y., Rendle, S., Bell, R. (2022). Advances in Collaborative Filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. Available at: https://doi.org/10.1007/978-1-0716-2197-4_3
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