Penerapan Algoritma K-Means Clustering Untuk Memetakan 4G BTS (Base Tranceiver Station) yang Mengalami Congestion di Kabupaten Bekasi - Handala Simetris Harahap; Eriska Febrianto (DPPM Universitas Pelita Bangsa, 2023)
The rapid development of wireless communication technology up to the fourth generation (4G) requires structured and systematic network data management. Datasets consisting of the number of Base Transceiver Stations (BTS), Physical Resource Block Utilization (PRB Utilization), and Downlink User Throughput (DLUT) need to be analyzed to identify areas with potential congestion and to support the planning of new BTS locations. This study aims to apply the Elbow method and K-Means Clustering to determine 4G BTS sites indicated to experience congestion in Bekasi Regency. The Elbow method was employed to identify the optimal number of clusters, resulting in K = 3. Subsequently, the K-Means algorithm was used to classify BTS based on network load levels. The results show that cluster 1 (C1), categorized as high load, consists of 153 BTS or approximately 40%, cluster 2 (C2), categorized as medium load, includes 155 BTS or about 40%, and cluster 3 (C3), categorized as low load, comprises 77 BTS or around 20%. These findings are expected to support decision-making in network optimization and 4G BTS development planning in the studied area.