Metode Subtractive Fuzzy C-Means (SFCM) dalam Pengelompokan Kabupaten/Kota di Provinsi Sulawesi Selatan Berdasarkan Indikator Kemiskinan - Nurul Imania Kalla; Suwardi Annas; Muhammad Fahmuddin (Program Studi Statistika Fakultas MIPA UNM, 2022)
The percentage of poor people in South Sulawesi Province has increased in the last 3 years. It is necessary to handle poverty that by adjusting to the poverty level of each district. It is necessary to have a description of socio-economic conditions by grouping regency/city based on poverty indicators. The purpose of this grouping is determining the poverty level of each district so that it can be used as material for evaluating poverty problems. The clustering method often used in research was Fuzzy C-Means (FCM). In the development, FCM was combined with the Subtractive Clustering (SC) method to obtain the hybrid Subtractive Fuzzy C-Means (SFCM) method. The SFCM method has the advantage of speed, iteration, resulting in a data partition that is more stable and accurate when it is compared to the previous method. On this research, the SFCM method was applied with 4 variables from the poverty indicator data for South Sulawesi Province in 2021. The results of the analysis show that based on the Partition Coefficient Index (PC), the percentage of poverty in South Sulawesi Province was divided into three clusters, namely the low, medium and high poverty percentage clusters. Clusters of low poverty percentage consist of Bulukumba, Bantaeng, Takalar, Gowa, Soppeng, Wajo, Sidenreng Rappang, East Luwu, Makassar City, Parepare City, and Palopo City. Clusters of medium poverty percentage consist of Sinjai, Maros, Barru, and Enrekang. Meanwhile, Clusters of high poverty percentage consist of the Selayar Islands, Jeneponto, Pangkajene and Islands, Bone, Pinrang, Luwu, Tana Toraja, North Luwu, and North Toraja