Analisis Kinerja Algoritma Fuzzy C-Means dan K-Means pada Data Kemiskinan - Aniq Noviciatie Ulfah; Shofwatul ‘Uyun (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2015)
One of the local government of Gunungkidul efforts to realize program in order to alleviate poverty is to perform the data collection poverty of its citizens. The local government of Gunungkidul has formulated a collection by weighting against 15 indicators into three groups. The amount of data and indicators to be used will certainly lead to difficulties in implementation, ineffective and less objective. Therefore we need automation in the process of clustering data on poverty. This study aims to analyze the performance of the FCM algorithm and K-means are implemented in the data on poverty in Girijati Purwosari village into 3 clusters. Some of the steps that must be done prior to clustering, first performed pretreatment includes data cleaning and data transformation for clustering is then performed using the second algorithm. The suitability of data between FCM algorithm and the calculation of poverty indicators in the Girijati village is 50 % and for the K - Means algorithm is 83.33 % . Therefore, K- Means algorithm is more appropriately used in data classification of poverty based on the three criteria of poverty, beside FCM algorithm.