Pengelompokan Wilayah berdasarkan Penyandang Masalah Kesejahteraan Sosial (PMKS) dengan Optimasi Algoritme K-Means menggunakan Self Organizing Map (SOM) - Iskarimah Hidayatin; Sigit Adinugroho; Candra Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Persons with Social Welfare Problems are people, families, groups or someone who cannot function socially because there is a spiritual, physical or social difficulty. Grouping regions based on PMKS is very important to do to provide an overview of PMKS problems with the policy objectives taken right on target. Self Organizing Map (SOM) algorithm for determining the number of clusters and initial centroids while the K-Means algorithm for determining the cluster end result. The research flow, that is, the data is normalized, then the SOM process then to K-Means, then testing and analysis are carried out. SOM parameter testing using silhouette coefficient obtained the best parameter is the learning rate value of 0.2, beta at 0.8, r (neighboring) of 0, the number of clusters by 2, and epoch by 50. K-Means algorithm optimization using SOM is better than algorithm K-Means based on the silhouette coefficient value. The silhouette coefficient value in SOM is 0.21882702 while K-Means has a value of 0.201911102. Analysis of the results obtained K-Means algorithm optimization with SOM is cluster 1 with a total of 26 districts / cities by having similarities in the variable Social Problem Migrant Workers, Families with social psychological problems, and children who are victims of violence / who are treated wrongly have an average value high average and cluster 0 with the number of 12 districts / cities with similarities in variables other than cluster 1.