Pengelompokan Pupuk Bersubsidi Di Kabupaten Bojonegoro Menggunakan Metode K -Means Clustering

Abstract

Subsidized fertilizer is a form of the government's commitment to increasing agricultural productivity, increasing food production and sustainable food security, now the price of fertilizer in Bojonegoro district is getting more expensive, this encourages researchers to raise it as the title of the thesis. The purpose of the K-Means Clustering method is to increase knowledge and understanding of computer-based systems. The K-Means algorithm is an iterative clustering algorithm that partitions datasets into a number of K Clusters that have been set at the beginning. The K-Means algorithm is simple to implement and execute, relatively fast, adaptable, commonly used in practice. From the problem of clustering subsidized fertilizers in Bojonegoro Regency, it can be solved using the ¬K-Means method. The K-Means algorithm can precisely group large amounts of data. The determination of the central point (centroid) in the initial process of the K-Means algorithm has a significant influence on the cluster yield. Based on the research conducted, the authors tested the best cluster grouping using the Davies Bouldin Index (DBI). It was found that the results of 3 clusters were the best with a DBI value of 0.527 while 4 clusters with a DBI value of 0.649 and 5 clusters with a DBI value of 0.677. The smaller the resulting value in the test, the better the result will be. Meanwhile, the Mean Square Error (MSE) produced in fertilizer clustering is 0.588 and RMSE is 0.962.