Implementasi Metode Clustering dengan Algoritma DBSCAN Untuk Identifikasi Sentra Industri Berbasis Google Map - Yanto; Ahmad Homaidi; Ahmad Lutfi (Universitas Islam Raden Rahmat, Malang, 2024)
Identification and mapping of industrial centers is a strategic step in supporting sustainable regional planning and development. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm offers an effective approach in identifying industrial centers by analyzing the spatial distribution and density of data. This study aims to apply the DBSCAN algorithm in spatial data analysis to identify industrial centers in the Situbondo Regency area. The study uses a dataset of small and medium industries (IKM) from the Cooperative, Industry, and Trade Office of Situbondo Regency. The analysis results show that the DBSCAN algorithm successfully grouped industrial locations into several high-density clusters, identified as the main industrial centers. Further analysis reveals that the application of the DBSCAN algorithm can identify areas with potential for new industrial centers. These results can be used by the local government of Situbondo Regency as a basis for better regional development and economic planning.