CLUSTERING DAERAH TERDAMPAK SAMPAH DI INDONESIA MENGGUNAKAN ALGORITMA DBSCAN.
CLUSTERING DAERAH TERDAMPAK SAMPAH DI INDONESIA MENGGUNAKAN ALGORITMA DBSCAN.
Dessy Santi,W. Maharani,2 Authors,Agustinus Kali
TLDR
The results show that DBSCAN effectively groups waste-affected areas into several clusters based on data density and spatial proximity, which can serve as a foundation for determining policy priorities for regional and national waste management.
Abstract
The waste problem in Indonesia is a complex and evolving environmental issue, particularly in areas with high population density and economic activity. This study aims to cluster regions affected by waste issues using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN was chosen for its ability to identify spatial patterns and detect outliers without requiring a predefined number of clusters. The data used includes spatial and non-spatial information related to waste volume and regional characteristics across various provinces in Indonesia. The results show that DBSCAN effectively groups waste-affected areas into several clusters based on data density and spatial proximity. These clusters can serve as a foundation for determining policy priorities for regional and national waste management. This research is expected to contribute to the development of more targeted and data-driven waste management strategies.