Utilization of K-Means Clustering to Examine Library User Segmentation's Impact on Student Graduation Rates

This research aims to optimize library services through user segmentation analysis and to determine the correlation between user activities and student graduation levels using the K-Means clustering algorithm. By analyzing this relationship, the study seeks to enhance both library service effectiveness and student academic success. The methodology involves several stages: data collection, preprocessing (including data merging and cleaning), and data modeling. The K-Means clustering method is specifically used to segment library users based on various factors and to identify patterns that correlate with student graduation levels. The dataset comprises circulation data and student graduation data from the UIN Padangsidimpuan Academic Information System (SIAKAD), including variables such as study duration, Grade Point Average (GPA), and loan levels. The analysis resulted in the identification of three distinct clusters: Cluster 0, Cluster 1, and Cluster 2. These clusters are characterized by different lengths of study, GPA, and library loan levels, providing insights into the preferences and needs of library users. The findings suggest that tailored library services, informed by these clusters, can potentially enhance student engagement and academic performance. For example, libraries could develop specialized discussion services related to student research, thereby improving thesis quality and research outcomes. Additionally, optimizing digital-based library services can further support student success.