Desain Sistem Rekomendasi “Genusian Course Academy” Menggunakan Pendekatan Hybrid Collaborative Filtering dan Content-Based Filtering

The recommendation system is a crucial component in enhancing user experience by providing relevant and personalized course suggestions based on individual preferences and needs. The Hybrid Collaborative Filtering approach combines Collaborative Filtering (CF), which analyzes user interaction patterns, with Content-Based Filtering (CBF), which evaluates course content similarity. Implementing this hybrid system is expected to address the limitations of each method, such as the cold start problem in CF and the limited recommendation variety in CBF. E-courses are a popular form of learning among the Indonesian community. The advantages of e-courses lie in the availability of explanatory videos, structured content, and ease of access. However, many users often struggle with choosing which training to take and selecting their next course after completing one. One solution to manage this information overload is through a recommendation system. This research aims to design a recommendation system within the academic environment known as Genusian Course Academy. The system is designed using a Hybrid Collaborative Filtering and Content-Based Filtering approach to enhance training recommendation accuracy for users. Collaborative Filtering is utilized to analyze user preferences based on historical data from other users, while Content-Based Filtering combines training attribute information to provide recommendations aligned with individual user preferences. The results of this study show testing system hit rate values with CF at 0, CBF at 0.16, and Hybrid at 1.00, and mean average precision testing values with CF at 0.85, CBF at 0.78, and Hybrid at 0.88. These values represent the outcomes of the recommendation system testing using Hybrid Collaborative Filtering and Content- Based Filtering approaches, achieving a website functionality rating of 90% through blackbox testing

Keywords: Genusian course Academy, Collaborative Filtering, Content-Based Filtering, recommendation system