Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency - Winarni Suwarso (Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data), 2018)
Based on the data of rice crops from BPS-Statistics of Bekasi Regency in the field of Food Crops, there are several sub-districts in Bekasi Regency with varying rice yields. Therefore, it is necessary to group the sub-districts with the highest potential of rice producers. Therefore, a method is needed to facilitate the classification of paddy producing districts. By Fuzzy C-Means clustering method, the division of rice-producing sub-districts can be done based on the area of rice harvest (Ha) and rice production (ton). In this research, clustering of potential sub-districts using the Fuzzy C-Means algorithm is aimed at facilitating the grouping of a sub-district with the largest and low rice yields. The result is an illustration that shows the subdistrict grouping based on the results of paddy farming.Keywords: Clustering; Data Mining; Fuzzy C-Means Algorithm