Library Book Lending Recommendation Using Association Rules with Frequent Pattern Growth (FP-Growth) Algorithm

Abstrak

College libraries are libraries managed by higher education institutions such as university libraries. The library functions as an information center management forum for students which includes learning resource functions, access functions, librarian functions, ethical functions, and evaluation functions. Along with changes in information search behavior in the era of information technology, libraries continue to try to improve services to meet student needs. Students prefer to read through e-books rather than reading books or library collections. There is a paradigm that is believed by students as the basis for leaving physical collections and then switching to electronics. The paradigm is caused by the complex needs of students and the desire to obtain information instantly, giving rise to dependence on search engines such as Google. Library book loan transaction data can solve the problem of finding collections as literature for students. Data mining can obtain hidden information patterns from large library transaction data. Data mining techniques, namely association rules using the FP-Growth algorithm, can find recommendations for library books that are borrowed simultaneously. The FP-Growth algorithm produces book recommendations that can be borrowed simultaneously by students. Association rules from Padang State University library book loan transaction data totaling 5,090 transactions. With a minimum support value of 0.0009 the book title with the highest confidence value is the book title 'Professional Teacher: Mastering Teaching Methods and Skills' and 'Participatory Learning Methods and Techniques' are recommended to be borrowed simultaneously.