Development of Academic Community Recommendation System Using Content-Based Filtering at UIN Malang Informatics Engineering Study Program
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Abstract
The mismatch between the number and quality of Information and Communication Technology (ICT) talents and industry needs in Indonesia creates significant challenges, especially for Informatics Engineering students who often experience difficulties in determining the appropriate professional field. This research aims to develop a content-based filtering-based academic community recommendation system to help students choose communities that are relevant to their interests, skills and experience. The system uses TF-IDF and cosine similarity methods to match student profiles with community descriptions. Data was collected from 48 students and 10 academic communities in the Informatics Engineering Study Program of UIN Malang, and processed through preprocessing stages before modeling. Evaluation results using the System Usability Scale (SUS) resulted in a score of 76, which is categorized in the “good” level, However, users indicated the need for improved guidance in navigating the system. This system is expected to be an innovative solution to increase student participation in appropriate academic communities, as well as support the development of their potential and readiness for the world of work
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References
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[6] X. L. Zheng, C. C. Chen, J. L. Hung, W. He, F. X. Hong, and Z. Lin, “A Hybrid Trust-Based Recommender System for Online Communities of Practice,” IEEE Transactions on Learning Technologies, vol. 8, no. 4, pp. 345–356, 2015, doi: 10.1109/TLT.2015.2419262.
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[8] J. Talaghzi, M. Bellafkih, A. Bennane, M. M. Himmi, and M. Amraouy, “A Combined E-Learning Course Recommender System,” International Journal of Emerging Technologies in Learning, vol. 18, no. 6, pp. 53–70, 2023, doi: 10.3991/ijet.v18i06.36987.
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[10] R. H. Mondi and A. Wijayanto, “Recommendation System With Content-Based Filtering Method for Culinary Tourism in Mangan Application,” ITSMART: Jurnal Ilmiah Teknologi dan Informasi, vol. 8, no. 2, pp. 65–72, 2019.
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[14] Q. Y. Shambour, M. M. Al-Zyoud, A. H. Hussein, and Q. M. Kharma, “A doctor recommender system based on collaborative and content filtering,” International Journal of Electrical and Computer Engineering, vol. 13, no. 1, pp. 884–893, 2023, doi: 10.11591/ijece.v13i1.pp884-893.
[15] A. Al-Ajlan and N. Alshareef, “Recommender System for Arabic Content Using Sentiment Analysis of User Reviews,” Electronics (Switzerland), vol. 12, no. 13, 2023, doi: 10.3390/electronics12132785.
[16] R. Hidayat and S. Minati, “Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation,” IJID (International Journal on Informatics for Development), vol. 8, no. 1, p. 47, Jun. 2019, doi: 10.14421/ijid.2019.08108.
[17] E. Poerbaningtyas and R. Maulidi, “Penerapan Metode Cosine Similarity Pada Sistem Informasi Retrival Pencarian Terjemahan Ayat-Ayat Suci Al-Qur’An,” J-Intech, vol. 11, no. 2, pp. 325–335, 2023, doi: 10.32664/j-intech.v11i2.1020.
[18] T. V. Yadalam, V. M. Gowda, V. S. Kumar, D. Girish, and M. Namratha, “Career recommendation systems using Content based Filtering,” Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, no. Icces, pp. 660–665, 2020, doi: 10.1109/ICCES48766.2020.09137992.
[19] John Brooke, “SUS: A Retrospective,” Journal of usability studies, vol. 8, no. 2, pp. 29–40, 2013.
[20] A. Bangor, P. T. Kortum, and J. T. Miller, “An empirical evaluation of the system usability scale,” International Journal of Human-Computer Interaction, vol. 24, no. 6, pp. 574–594, 2008, doi: 10.1080/10447310802205776.