(untitled) (INTI Nusa Mandiri, 2024)
The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.