Association rule mining for building book recommendation system in online public access catalog
Santi Mariana, Isti Surjandari, Arian Dhini, Asma Rosyidah, Puteri Prameswari · 2017
Improvement in service quality in Online Public Access Catalog (OPAC) of Universitas Indonesia's library is required due to its increasing use. Book recommendation system is one of the efforts that is conducted by Universitas Indonesia to improve it by providing related books the user may need. Thus, it is required to know how the books correlation is. This research uses data mining to process numerous data by using association analysis in loan records. It is able to find interesting relationship in a large set of data. In this research, there are two approaches in association analysis used to mine the association rules namely frequent itemset mining and infrequent itemset mining. Each approach is applied through an algorithm and both have showed its own results. Then, these results are evaluated and compared to find best rules to be input of book recommendation system.