Implementasi Algoritma K-Means Clustering Dalam Pengelompokan Mutu Tanaman Jagung

Abstract

Several corn planting centers experienced a decrease in yields due to virus or pest attacks. The impact that occurred was a decrease in corn yields at a time when there was a large demand for corn from companies and communities in the Tanah Datar district. The corn harvest data processed in this research came from the Department of Agriculture and farmer groups in the Tanah Datar district. To solve this problem, researchers used the K-Means Clustering algorithm. This method groups similar data into the same group and vice versa. To test the results of this research so that they match what was expected, the researchers used the rapidminer application. From this research, three groups were obtained regarding the quality of corn planting, namely 'good', 'medium' and 'not good'. From the results of the research carried out, the results obtained were C0 with poor results which were influenced by the age of the seedlings, C1 with medium results which were influenced by planting distance and C2 with good results which were influenced by the use of pesticides, use of organic fertilizer and mounding