<![CDATA[In the rapidly evolving digital era, the need for accurate and personalized recommendation systems is increasingly important, particularly in digital libraries and online bookstores. This study aims to develop a book recommendation system using a collaborative filtering approach, which leverages user interaction data to suggest books that align with individual preferences. The system utilizes a user-based collaborative filtering method by calculating similarities between users based on their historical book ratings. The dataset used in this research is a simulated, anonymized dataset from a school library. Testing results indicate that the system is capable of delivering relevant recommendations with good accuracy, demonstrated by a low Mean Absolute Error (MAE) score and positive user feedback. This system allows users to discover books aligned with their interests more efficiently, thereby enhancing the overall reading experience.]]>
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