PENERAPAN METODE ST-DBSCAN MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION PADA KLASTERISASI IPLM PROVINSI DI INDONESIA

Abstract

This research proposes an approach that uses the Particle Swarm Optimization (PSO) algo-
rithm to optimize the parameter values of the Spatio Temporal Density Based Spatial Clustering of
Applications with Noise (ST-DBSCAN) method for the clustering of provinces in Indonesia based
on factors affecting the Community Literacy Development Index (IPLM). The data used includes 7
variables with 68 observations. The use of the PSO algorithm results in optimal parameter values
based on the Silhouette Coefficient (0.104994), including parameters such as epsilon 1 of 2.73,
epsilon of 2, and MinPts of 5. Silhouette Coefficient evaluation shows that PSO-ST-DBSCAN is
capable of clustering the region into 4 main clusters and one noise cluster, where cluster 0 contains
13 provinces, cluster 1 contains 3 provinces, cluster 2 contains 4 provinces, cluster 3 contains 3
provinces, and the noise cluster contains 11 provinces.