Library Book Loan Data Clustering Using K-Means Algorithm to Improve Book Loans

Denni Pratama, Sari Hermawan, Christina Juliane

Abstract


Entering the post-pandemic new standard era in 2022 as of June, the number of borrowed books at the Indonesian University of Education Library could be more optimal compared to the year before the pandemic (2018 - 2019). Lending in 2022 can still be increased by arranging the most borrowed books in one group. This research aims to classify books more optimally, which will be applied to book arrangement. Optimal book arrangement allows library visitors to find books more efficiently based on the books that are most often borrowed so that they are interested in borrowing other books in a group. Data mining is a term used to describe knowledge in a database from a repository by finding patterns and trends in data through examination with statistical and mathematical techniques. Clustering is a data mining method that can be used to determine the data clusters. One of the algorithms that can be used is K-Means. The clustering pattern obtained shows 2 (two) grouping clusters. Book titles in cluster 0 contain book titles related to research methodology, statistics, measurement scales, assessment, and learning evaluation. While cluster 1 tends to contain psychology, counseling, religion, philosophy, management, economics, and history. This data can be used by librarians in prioritizing purchasing a collection of books in the subsequent procurement.


Keywords


Clustering; Data Mining; K-Means; Library

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References


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DOI: https://doi.org/10.17509/edulib.v13i1.50003

DOI (PDF): https://doi.org/10.17509/edulib.v13i2.50003.g28326

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