Enhancing Student Collaboration in Academic Projects Through a Content-Based Filtering Recommender System
DOI:
https://doi.org/10.59395/ijadis.v6i2.1329Keywords:
Content Based Filtering, Recommender System, Mean Average Precission, System Usability ScaleAbstract
The Informatics Engineering Study Program at UIN Maulana Malik Ibrahim Malang facilitates students in developing their interests and talents through 10 academic communities that serve as forums for knowledge exchange and innovation in IT project development. However, a challenge arises in assigning suitable students to appropriate projects, resulting in many projects being completed by a limited set of students. To address this, a recommender system for academic project members was developed using the Content-Based Filtering method. This system assists project initiators in selecting competent team members based on students’ prior experiences, considering the similarity between project requirements and student profiles. A dataset of 198 student-completed projects was used, with preprocessing, TF-IDF, and cosine similarity applied in the recommendation process. The system was implemented using the Flask framework with Python and HTML. Evaluation was conducted using the SUS method for usability (achieving a score of 79, categorized as excellent) and MAP for model performance across three scenarios. Scenario one (random community) scored 0.92, scenario two (same community) scored 0.79, and scenario three (comparison with actual members) scored 0.98. The results indicate that broader search scopes yield more accurate recommendations. This research contributes to the improvement of collaborative IT project in academic environments by enabling data-driven student member selection. The proposed system has the potential to be adopted by other academic institutions facing similar team formation challenges.
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Copyright (c) 2025 Aldian Faizzul Anwar, Ririen Kusumawati, M. Ainul Yaqin, Irwan Budi Santoso, Abdurrozaq Ashshiddiqi Zuhri
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