Perbandingan Algoritma K-Means dan Fuzzy C-Means untuk Clustering Puskesmas Berdasarkan Gizi Balita di Surabaya - Kurnia, Alvin (Jurnal PROCESSOR, 2023)
Diseased environments and low public awareness of environmental hygiene in several cities in Indonesia make many people, especially young children, susceptible to various diseases. The 63 Puskesmas in Surabaya City have weighed 148,720 toddlers. There are 4.2% of toddlers with malnutrition, 0.1% of toddlers with malnutrition and 4.5% of toddlers with stunting. Clustering is a technique in data mining that aims to group objects (data) into several clusters or groups so that similar objects are united into the same cluster. This method is to implement, test, and evaluate the K-Means and Fuzzy C-Means algorithms in clustering. so the results of this study are to classify health centers based on toddler nutrition in the city of Surabaya. The best silhouette coefficient results from K-Means with the Normalization process is 0.51833215835383. While the best result on the Fuzzy C-Means algorithm with the Normalization process is 0.49666983222478. So it can be concluded that the K-Means Algorithm is better at clustering Puskesmas based on toddler nutrition in Surabaya City.