<![CDATA[The church as a place for people to gather requires accurate information to store congregational data. With the Geographic Information System (GIS) it will be easier for church officials to map congregations that number more than hundreds of people and share locations of scheduled activities. The purpose of this study is to implement the K-Means algorithm to map the distribution of the GPPS Bethlehem congregation so that it can provide informative and easy-to-understand spatial visualization. The clustering algorithm method groups multiple data sets by explaining how data within a group has similar characteristics and how they differ from other groups. The data in the form of geographic coordinates (latitude and longitude) of the congregation's location was then processed using the K-Means algorithm with a predetermined number of clusters. The processing results showed that the data was successfully grouped into several clusters based on location proximity, each cluster having a centroid as the center point of the group, the centroid value changing at each iteration until it reached a convergent condition. Based on the research results, it can be concluded that: The clustering process produces several groups (clusters) that represent the distribution pattern of congregations in a particular area with a clear cluster center (centroid) and visualization using Leaflet.js is able to display the clustering results in the form of an interactive map that is informative and easy for users to understand.]]>
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