Implementasi dan Analisis Algoritma Content-Based Filtering Pada Sistem Rekomendasi Produk Tas pada Basis Data MySQL - Pranata, Aryoga; Sulianta, Feri (Jurnal Ilmiah Global Education, 2025)
Recommendation systems have become a crucial component in various digital platforms to enhance user experience by providing relevant product suggestions. This research aims to implement and analyze the Content-Based Filtering (CBF) algorithm in a product recommendation system using the MySQL database. The CBF algorithm works by recommending products similar to those already liked or purchased by the user based on the features of those products. In this context, features such as product category, brand, and text description are used to generate relevant recommendations. The implementation of this algorithm involves using Natural Language Processing (NLP) techniques to extract features from product descriptions stored in the database. The first phase of this research involves collecting and processing product data to ensure consistency and readiness for further analysis. Key features of each product are then extracted and their similarities calculated using the CBF algorithm. Subsequently, the recommendation results are tested and evaluated using performance metrics such as Precision and Recall to determine the system's effectiveness in providing relevant and beneficial recommendations to users. The research findings indicate that the CBF algorithm can provide fairly accurate and relevant product recommendations, enhancing user satisfaction by offering product choices that match their preferences. Performance evaluation also demonstrates that the system is effective in recognizing user preference patterns and providing useful suggestions. Additionally, the use of the MySQL database offers advantages in efficient data management and processing. With this recommendation system, it is expected to improve user satisfaction and engagement in the e-commerce platform. The use of CBF techniques enables the system to continually learn and adapt to user preferences, providing increasingly relevant recommendations over time.