<![CDATA[Product recommendation systems play an important role in helping users find products that match their interests and needs on e-commerce platforms. This research aims to compare the effectiveness of two popular methods in recommendation systems, namely Content-Based Filtering and Collaborative Filtering. The research method used is quantitative with data collection through questionnaires which are then analysed using evaluation metrics such as precision, recall, and F1-score to measure the accuracy level of each method. The results show that Content-Based Filtering provides more accurate recommendations than Collaborative Filtering in the context of this research. This finding indicates that product characteristics relevant to user preferences have a more dominant influence in generating appropriate recommendations, compared to other user preferences. This research makes an important contribution to the development of a more effective recommendation system to improve user experience in finding relevant products on e-commerce platforms. Thus, the results of this study can serve as a reference for recommendation system developers in choosing the most suitable method to improve user satisfaction and system performance.]]>
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