Spatial Load Disparities in Cellular Networks: Integrating Geographic Information System, Minimum Spanning Tree, and Signal-Weighted K-Nearest Neighbor for Telkomsel Towers in Banten, Indonesia

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Abstract
The differential distribution of cellular towers of Telkomsel, Indonesia’s largest mobile network operator, in Banten Province, Indonesia, poses challenges to network performance and service reliability. Therefore, we developed a novel hybrid framework that integrates geographic information systems, minimum spanning tree modeling, and signal-weighted k-nearest neighbor classification to assess tower utilization and signal coverage. Leveraging geospatial data from 110 Telkomsel cellular towers and 1000 simulated user nodes, it was found that 2.73% of towers were overloaded and 189 signal blank spots were identified in rural and topographically complex areas. By incorporating both spatial topology and signal strength sensitivity, the developed method outperforms conventional spatial or machine learning approaches in preserving spatial fidelity and supporting infrastructure planning. Despite the use of simulated user data, the framework demonstrates high scalability and adaptability for integration with real-time network performance metrics, enabling dynamic and location-specific telecommunication optimization.

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