Pemodelan personalisasi rekomendasi buku dengan pendekatan association rule mining

Abstract

Introduction. Library X is an academic library in Jakarta, Indonesia. Library X has provided Online Public Access Catalog (OPAC) as a tool to provide information related to the collection. However, sometimes the information appears does not show high relevancy. One way to solve this problem is to develop user need based-book recommendation system. The purpose of this study is to create personalization model of book recommendations in Library X.Data Collection Method. The method used in this study was association rule mining using Apriori algorithm. Results and Discussions. The results showed that the book relationships for the minimum support was 0.1% and the minimum confidence was 10% and generated 42 association rules. It is noted that 657 (Accounting) and 658 (Management) are found to support for 2.6% with the confidence level for 14%.Conclusions. Book recommendation is formulated by selecting the rule with maximum support and confidence. The recommendation system is designed to be integrated to web application and user’s e-mail.