K-Means Cluster as a Reading Interest Analysis Tool in the North Sumatra Provincial Library

Abstract

This study aims to apply the K-Means algorithm in analyzing students' reading interests in regional libraries in North Sumatra. As library collections and information sources increase, understanding readers' preferences has become a key element in improving the quality of library services and collections. The K-Means algorithm was chosen because of its ability to group data based on similar characteristics, thus providing deeper insight into students' reading interest patterns. The data used in this study were obtained from the visitor information database per library, which includes the number of visits, types of books frequently borrowed, duration of visits, and categories of the most popular reading materials. The data were processed through several stages, namely preprocessing to clean and align the data, clustering using the K-Means algorithm, and cluster analysis. The analysis revealed that students can be grouped into several categories based on their reading interest patterns, such as groups with high interest in scientific literature, groups that predominantly read entertainment and fiction books, and groups with low levels of reading interest. Furthermore, the results of this study also show that students' reading interests fluctuate over time. The results of this study have practical implications for library managers in developing more targeted strategies, such as literacy programs, procuring books that suit visitors' needs, and optimizing data-based services. Therefore, this study is expected to help efforts to increase the number of visits and the level of user satisfaction.