Explainable GIS-Based Decision Support System for Internet Infrastructure Planning Using Spatial K-Means Clustering
Received: 4 February 2026 | Revised: 24 February 2026, 2 March 2026, and 14 March 2026 | Accepted: 15 March 2026 | Online: 4 April 2026
Corresponding author: Irena Santi Widjaja
Abstract
This study aimed to develop an explainable GIS-based decision-support system for planning Internet infrastructure tailored to regional spatial features. Spatial K-Means clustering is applied to demographic, socioeconomic, and network infrastructure data to identify geographic clustering patterns. Experiments using various clusters (k = 3 and k = 5) were used to evaluate the model's ability to distinguish regional characteristics. The results show that the proposed method reveals significant regional differences, with population densities from 445 to 3515 people/km2, Internet penetration rates between 52.4% and 82.7%, and Base Transceiver Stations (BTSs) ranging from 2.9 to 12.3 units per cluster. Integrating interactive GIS visualization with explainable analytics enhances the transparency of results and helps identify priority development regions, transition regions, and regions with mature infrastructure. These findings offer practical implications for formulating internet infrastructure development policies that are more targeted and tailored to regional needs.
Keywords:
spatial clustering, explainable, infrastructure planning, regional analysis, policy-oriented analyticsDownloads
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Copyright (c) 2026 Irena Santi Widjaja, R. Rizal Isnanto, Maman Somantri
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