<![CDATA[Equitable distribution of public facilities and infrastructure is a crucial challenge in regional governance in Aek Kuasan District, Asahan Regency. Currently, data inventories of educational, health, and religious facilities are still managed conventionally in tabular format or as a recapitulation of physical documents. This method makes it difficult for sub-district governments and stakeholders to identify spatial distribution patterns of facilities, often resulting in disparities in accessibility between villages, such as significant differences in facility density between densely populated residential areas and plantations. This study aims to design a web-based Geographic Information System (GIS) capable of automatically visualizing and analyzing public facility clustering. The method used is the K-Means Clustering algorithm with Euclidean distance measurement to partition the region into several clusters based on proximity of location coordinates. The frontend is built using React.js and the Leaflet map library for interactive visualization, while the backend uses Node.js to handle all the algorithm's computational logic, including distance calculations and centroid determination. Data storage is performed using a PostgreSQL relational database that stores location attributes in double-precision numeric format to ensure coordinate accuracy without relying on the database's spatial extensions. System testing results indicate that the application is capable of precisely mapping the locations of infrastructure facilities in seven villages/sub-districts in Aek Kuasan District. The implementation of the K-Means algorithm successfully classified areas into dense and sparse cluster zones, visualized through differences in marker colors on the digital map. This analytical information is expected to provide an empirical basis for the Aek Kuasan District Government in making more equitable and targeted development planning decisions.]]>
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