<![CDATA[This study focuses on the development of a book recommendation system in the Library using the Apriori algorithm. By utilizing this algorithm, the library can analyze book borrowing transaction data to find patterns of student interest, which will help librarians in determining which books need to be recommended. Through the application of the Apriori algorithm, the system successfully identified 17 association rules that show the relationship between books that are often borrowed together. These rules have a minimum support of 5.5% and a confidence of 100%, indicating that this pattern is very strong and reliable. With the results of this study, it is hoped that librarians can be more efficient in recommending books to students, improving the reading experience, and maximizing the use of library collections. In the future, this system can be expanded by integrating user data for more personalized recommendations. ]]>
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