<![CDATA[This study aims to analyze membership activity at the Rimba Baca Library in South Jakarta using the K-Means algorithm. The background of this research is the library's need to understand membership patterns and improve services based on visit and book borrowing data. The dataset for this study consists of 81 membership records collected from 2023 to 2024. The methodology involved collecting visit and book borrowing data, then applying the K-Means algorithm to cluster members based on their activity levels. The results of the study indicate the presence of three clusters with different characteristics. Cluster 1 comprises very active members, while Clusters 0 and 2 exhibit lower levels of activity. These findings provide insights for the library to develop more effective service strategies, such as special promotions and programs to increase activity among less active member groups. Additionally, the study shows that membership types allowing for more book borrowings do not necessarily correlate with high activity levels. With this information, the library can enhance member engagement and optimize the use of existing resources, thereby creating a more dynamic and interactive environment for all visitors]]>
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