<![CDATA[This research investigates the effectiveness of the Naïve Bayes and C4.5 algorithms in analyzing book borrowing patterns at the Pringsewu Muhammadiyah University Library. As libraries evolve into important educational resource centers, understanding user borrowing behavior becomes critical for effective collection management and service improvement. This research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) to guide the research stages, including business understanding, data understanding, preparation, modeling, evaluation, and implementation. A dataset consisting of 5,586 records and ten attributes related to book lending was used, with thorough data cleaning and preprocessing performed. The performance of both algorithms was evaluated using K-fold cross validation, resulting in a C4.5 accuracy of 96.26% compared to 91.44% for Naïve Bayes. These results demonstrate that C4.5 excels at capturing complex relationships in data, providing valuable insights into user preferences and improving library services. This research highlights the potential of data mining techniques to improve library management and suggests directions for future research, including exploration of advanced machine learning algorithms and expansion of data sets for broader libraries.]]>
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