<![CDATA[Location selection errors are one of the main factors for the failure of MSMEs, but most existing geographic information systems are still limited to distribution visualization without AI-based analytical capabilities. This study proposes a GeoAI approach that integrates geo-tagging with the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to produce a spatial cluster-based MSME location recommendation system. The system is built using the Django Framework with PostgreSQL as a spatial database, and developed using the Extreme Programming (XP) method. The case study was conducted on coffee shop and barber shop MSMEs in Depok City. DBSCAN was applied to 220 barber shop data and 241 coffee shop data to identify business concentration zones as the basis for location recommendations. The clustering evaluation results showed a Silhouette Score of 0.509 and a DBI of 0.551 for barber shops, and 0.217 and 0.743 for coffee shops, with a system functional accuracy of 90.90%. This research shows that the GeoAI approach based on spatial clustering is able to transform MSME geospatial data into actionable location intelligence, and has the potential to be adopted as a decision-making tool by business actors and local governments in MSME ecosystem planning. ]]>
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