Penerapan Metode K-Means Clustering Untuk Mengelompokkan Ketahanan Pangan

Abstract

Food is a basic need that must be fulfilled and easily accessible by all people. After the end of the pandemic, it still caused several sectors to experience a decline, including the agricultural sector, which experienced a decline which resulted in crop yields also declining. The problem faced by several regions in Indonesia, one of which is the area of Indonesia, is the availability of food yields which has decreased and increased unstable due to lack of information about the grouping of harvest resilience each year. As a result, the food needs of the people in each region are not fulfilled. The purpose of this study was to classify regions with increasing and decreasing yields of food crops in Indonesia using the K-Medoids algorithm. The K-Means algorithm includes a clustering algorithm that is quite efficient in grouping small data and finding the most representative point and being able to overcome outliers. So that it can be used in grouping the effect of productivity and the level of food security.