Using Apriori Algorithm to Analyze Library Borrowing Patterns at SMK Negeri 1 Cirebon

This study aims to identify book borrowing patterns in the SMKN 1 Cirebon City library using the association rule method with the apriori algorithm. The apriori algorithm is a data mining method that finds association relationships between items in an extensive database. In this study, book borrowing transaction data is processed to determine the combination of books often borrowed together. The analysis begins with processing book borrowing transaction data, followed by applying the apriori algorithm to find frequent itemsets and association rules with high support and confidence values. The analysis results show that students often carry specific book borrowing patterns, such as those who borrow Python programming books borrow data science with Python books. This pattern is expected to be a recommendation for the library in managing book inventory, arranging bookshelves, and developing more effective and efficient borrowing strategies so that the library can rearrange the bookshelves by placing books that are often borrowed together in the nearest location. Also, the library can determine the types of books that need to be added based on the connected borrowing pattern. Thus, applying the association rule method using the apriori algorithm can help libraries understand students' book-borrowing habits and improve the quality of library services.