Dalam analisis pengeompokan (cluster), banyak kelompok menjadi suatu masalah yang berarti. Beberapa peneliti memiliki banyak kelompok sesuai dengan kebutuhan dalam penelitiannya. FCM melakukan pengelompokan dengan prinsip meminimumkan fungsi pengelompokannya dimana salah satu parameternya adalah fungsi keanggotaan dalam fuzzy (sebagai pembobot) yang disebut juga dengan fuzzier. Penelitian ini bertujuan untuk mengkaji metode pengelompokan dengan Fuzzy C-Means Clustering dan penerapannya dalam pengelompokan Kabupaten/Kota di Sulawesi Selatan berdasarkan Faktor-faktor Penyebab Gizi Buruk yakni sarana dan tenaga kesehatan, kependudukan, perekonomian yang rendah, serta asupan gizi yang rendah. Dari hasil analisis pengelompokan Fuzzy C-Means dengan 2 cluster diperoleh fungsi objektif sebesar 1079141921,2224. Dimana kelompok pertama terdiri dari 18 kabupaten/kota sedangkan kelompok kedua terdiri atas6 kabupaten.Kata Kunci:Cluster, Fuzzy-C-Means, Fuzzier In the analysis of clustering, many groups became an issue. Some researchers chose many groups that match the needs of their research. FCM performs grouping with the principle of minimising its categorization function where one of the parameters is a membership function in fuzzy (as weighing), also known as with fuzzier .This research aimed to study the methods of grouping with Fuzzy C-Means Clustering and its application in the classification of grouping at Regency/City of South Sulawesi based on factors of Causes of Malnutrition i.e. in terms of facilities and health workers, population, economy, and low nutrient intake that is low. From the results of the analysis of the classification with Fuzzy C-Means with 2 clusters with the objective function respectively is 1079141921.2224. When the first group of 18 district while the second group consists of 6 counties.Keywords:Cluster, Fuzzy C-Meanas, Fuzzier
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