Sistem Rekomendasi Pekerjaan Berbasis Kompetensi Mahasiswa Menggunakan Pendekatan Content-Based Filtering - Intan Sulistyaningrum Sakkinah; Muhammad Ainul Fikri; Rosyida Arian Rahma (Jurnal Teknologi Informasi dan Multimedia, 2026)
Students often experience difficulties in obtaining job recommendations that match their academic competencies and personal interests. This problem is caused by the suboptimal utilization of stu-dents' academic data in the job matching process, resulting in recommendations that are often less relevant to students' profiles and abilities. Therefore, a job recommendation system is needed that can process academic data and user preferences to produce more appropriate job recommenda-tions that meet students' needs. This study aims to develop a web-based job recommendation sys-tem that utilizes Grade Point Average (GPA) with a Content-Based Filtering approach. The devel-oped system matches student profiles—including GPA, major, skills, and interests—with job characteristics in the information technology field. The level of match between student profiles and job profiles is calculated using the Cosine Similarity algorithm. Evaluation of system performance is carried out by implementing a 5-fold cross-validation scheme and using Top-3 Accuracy and Exact Accuracy metrics. The test results show that the system obtained an average Top-3 Accuracy score of 85.0% and Exact Accuracy of 54.7%. These results demonstrate that the developed system is capable of generating relevant job recommendations with a high level of consistency. Therefore, this system is expected to contribute to supporting students' decision-making process in planning career paths that align with their academic achievements and individual preferences.