Analisis Sebaran Spasial dan Profil Pemancar Radio Menggunakan Algoritma K-Means dan DBSCAN
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1300Keywords:
DBSCAN; K-Means Algorithm; Radio Spectrum Licensing; Radio Transmitter; Spatial DistributionAbstract
The rapid growth of radio transmitters has caused frequency spectrum congestion and high license rejection rates in urban areas, while conventional manual licensing evaluation focused on single-criterion analysis struggles to map potential interference accumulation and detect isolated locations. This research aims to analyze the spatial distribution and technical profile of radio transmitters using K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms. A comprehensive dataset comprising 38,776 radio station license records was processed using Z-score standardized spatial attributes (geographic coordinates) and technical parameters (operating frequency, Effective Radiated Power, antenna height, and bandwidth). First-stage K-Means modeling (k=4) segmented four macro profiles: Cluster 0 represents urban high-frequency microwave backhauls (mean frequency 14.72 GHz) with the lowest licensing approval rate (22.28%); Cluster 1 encompasses low-frequency mobile cellular infrastructure (mean frequency 1.95 GHz) with the highest approval rate (83.79%); while Cluster 2 and Cluster 3 cover eastern terrain backhauls (mean frequency 9.22 GHz, approval rate 26.69%) and western mountainous link networks (mean frequency 9.94 GHz, approval rate 25.03%). Furthermore, second-stage spatial clustering using DBSCAN with a 3 km Haversine search radius identified 221 local high-density hotspots (primary mega-hotspot encompassing 14,864 transmitters) and isolated 1,581 noise points (4.08%) representing remote locations. The integration of K-Means and DBSCAN effectively reveals hidden technical patterns and geographic density variations, providing a data-driven zoning framework for automated radio frequency spectrum licensing evaluation.
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