Sufi presents at MYCE 2022
Graduating student Sufi Dahlan presented his final year project at the international conference, Current trends in Islamic technology: through research and innovation
Sufi presented his final year project on 9th June 2022 at the International Convention Centre during the international conference titled, ANALYZING BOOK BORROWING PATTERNS IN DEWAN BAHASA DAN PUSTAKA BRUNEI USING APRIORI ALGORITHM
This paper aims to find the associations between the books that have been borrowed in the Dewan Bahasa dan Pustaka's (DBP) libraries to determine the common borrowing behaviors in Brunei libraries through data mining. The concept of data mining is commonly associated with knowledge discovery, which refers to analyzing data from various perspectives and encapsulating it into practical information. One of the highly regarded techniques for data mining for library services is the association mining rule. Association rule mining is a method that seeks out commonly occurring patterns, correlations, or relationships in datasets from many types of databases, such as relational databases, transactional databases, and other types of repositories. The Apriori Algorithm is a well-known algorithm for performing frequent itemset searches with the association mining rule. The algorithm processes data of book borrowing transactions collected by the library and converts them into valuable information by using knowledge about the previously known frequent itemset. The book borrowing data is processed, cleaned, and transformed into a book transaction dataset as Apriori input. This paper uses two association rule mining metrics to determine the results' relationships: support and confidence. The results show that library users are more inclined toward borrowing Malay-titled books and academic past exam papers with a support value of around 0.5%. The top 5 rules are presented, sorted by their support values which reflect the likelihood of users borrowing academic past exam papers with an average confidence value of around 70%.
Keywords: association rules, machine learning, data mining
Universiti Brunei Darussalam
Jalan Tungku Link, BE1410
Negara Brunei Darussalam