Intelligent System for Student Industrial Internship Placement Recommendations Using the Case-Based Reasoning Method
DOI:
https://doi.org/10.36520/joofin.v1.i1.2Keywords:
Case-Based Reasoning, Recommendation System, Internship Placement, Student Placement, Decision Support System, Similarity MatchingAbstract
Industrial internship placement requires matching student competencies, interests, academic performance, and practical skills with the needs of host organizations. Manual placement is vulnerable to inconsistent judgments and may fail to reuse knowledge from successful previous placements. This study develops an intelligent recommendation framework using Case-Based Reasoning (CBR) to support student industrial internship placement. Each historical placement is represented as a case containing a student profile, host-organization requirements, and placement outcome. The retrieval stage applies weighted local similarity and global similarity to rank prior cases; the reuse, revise, and retain stages adapt recommendations, allow expert validation, and preserve validated cases as new organizational knowledge. Because the source proposal does not contain the completed empirical dataset, the prototype is demonstrated with an explicitly synthetic case base to verify the calculation flow rather than to claim field accuracy. The worked evaluation shows that the framework produces traceable rankings and exposes the contribution of each criterion to the final recommendation. The approach provides a transparent foundation for a web-based decision-support system and can be extended with institutional internship data for prospective validation.
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Copyright (c) 2026 Tomy Satria Alasi(Author)
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