Integrasi Moodle API dan LLM dalam Otomasi Monitoring Capaian Pembelajaran Lulusan Berbasis OBE
DOI:
https://doi.org/10.36596/jitu.v10i1.2307Keywords:
machine learning, Outcome-Based Education, Face-Api.js, OpenAI APIAbstract
The implementation of Outcome-Based Education (OBE) necessitates transparency and accountability in mapping Program Learning Outcomes (PLO). Nevertheless, the manual mapping process from an extensive array of courses (60 Course Learning Outcomes/CLO) to 10 PLOs frequently encounters administrative constraints and a high risk of human error. This study aims to design and develop an integrated OBE monitoring system utilizing the Moodle API for grade synchronization and the OpenAI API for academic documentation assistance. Adopting the Design Science Research (DSR) methodology, the system was implemented using native PHP. The findings demonstrate that Moodle API integration facilitates the real-time automation of grade retrieval per sub-CLO. Simultaneously, the application of Mermaid.js effectively transforms these data into dynamic traceability visualizations. Moreover, the implementation of the OpenAI API (GPT) provides significant cognitive assistance for faculty members in drafting Semester Learning Plans (RPS) aligned with competency standards. This system establishes a robust data infrastructure for institutions to perform accurate and transparent curriculum evaluations.
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