Prediction of Library Book Borrowing Patterns Using The Random Forest Algorithm

Libraries play a crucial role in supporting the improvement of public literacy by providing reading materials tailored to users' needs and interests. One of the challenges faced by the Bukit Batu District Public Library is that the collection acquisition analysis process is not yet based on comprehensive borrowing patterns, potentially resulting in inaccurate results. This study aims to predict book borrowing patterns and classify collections into popular and unpopular categories using the Random Forest algorithm. Historical book borrowing data from 2019 to 2024 was used as the primary source in the model training and testing process. Testing was conducted with three data sharing ratios, namely 70:30, 80:20, and 90:10, which resulted in prediction accuracy of 89.19%, 88.69%, and 86.74%, respectively. Based on the analysis results, mathematics books were identified as the most popular collection with 146 borrowings, while social studies books were categorized as unpopular with 122 borrowings. These findings are expected to serve as a reference for libraries in formulating more effective, efficient, and data-based collection management strategies, thereby increasing the relevance and attractiveness of collections for users and supporting the optimization of library services.