Implementation of Hybrid Recommendations in the Standardized Student Internship Assessment System At ITPLN
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Abstract
Student internship assessment is an important aspect of higher education, requiring objective and accurate standards. This research examines implementing a hybrid recommendation system to improve the internship assessment process at ITPLN. The hybrid recommendation method combines content-based and collaborative approaches so that it can provide more relevant and personalized recommendations. Through analysis of previous assessment data and feedback from students and supervisors, this system is designed to assess student performance more comprehensively. The research results show that the use of a hybrid recommendation system can increase accuracy and fairness in assessments, as well as provide additional insight for supervisors in providing evaluations. Thus, this research contributes to the development of better assessment systems in the context of professional education, especially in the fields of engineering and technology. It is hoped that the implementation of this system can become a model for other institutions in optimizing the student internship assessment process.
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