Implementasi Algoritma K-Means Clustering untuk Identifikasi Lokasi Strategis Coffee Shop - Lahuri Gofarana Rohman; Cecep Nurul Alam; Beki Subaeki (Bulletin of Computer Science Research, 2025)
The rapid growth of coffee shops in Bandung City has led to increasingly fierce competition among business owners, particularly in choosing strategic locations. Inappropriate location selection can negatively impact customer attraction and business sustainability. This study aims to identify strategic areas for coffee shop development in Bandung City using the spatial-based K-Means Clustering algorithm. The data used consists of active food establishment locations obtained from the Open Data Kota Bandung portal, which includes latitude and longitude information. The K-Means algorithm with K-Means++ initialization was used to group the restaurant locations into three clusters based on geographical proximity. The clustering process was carried out in two iterations, beginning with the initial centroid determination, distance calculation using the Euclidean formula, and centroid updates until convergence. Final results show that the areas of Jl. Aceh Cluster 0 at coordinates (-6.911431, 107.622713), Jl. Setiabudi Cluster 1 at coordinates (-6.879891, 107.600774), and Jl. Kebon Jati Cluster 2 at coordinates (-6.917228, 107.598990) have different strategic potentials suited to specific coffee shop concepts. Evaluation was conducted through spatial distribution visualization, minimum distance analysis, and cluster stability. This study confirms that the K-Means method is effective in supporting spatial-based decision-making for business development.