Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency | Zendy

Winarni Suwarso | Zendy

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Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency

Author(s) -

Winarni Suwarso

Publication year - 2018

Publication title -

jurnal simada (sistem informasi dan manajemen basis data)

Language(s) - English

Resource type - Journals

eISSN - 2621-0827

pISSN - 2615-7292

DOI - 10.30873/simada.v1i2.1134

Subject(s) - cluster analysis , fuzzy logic , division (mathematics) , paddy field , fuzzy clustering , agricultural engineering , agriculture , production (economics) , mathematics , agricultural science , computer science , engineering , statistics , geography , environmental science , artificial intelligence , economics , arithmetic , archaeology , macroeconomics

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

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