Implementation of the K-means Clustering Method on Stunting Case in Indonesia

Abstract

Method K - means clustering is one technique that will partition the data into groups, so that the data which have the same characteristics are grouped into the same group. Clustering can be done on various types of data including clustering in order to
determine the province which is the priority for stunting reduction. This study aims to classify provinces based on the prevalence
of stunting for infants 0-59 months, exclusive breastfeeding, weigh 4 times, adequacy of energy and protein using the K-Means
clustering method. The results showed that the optimal cluster formed was 4 clusters, in which group 1 was a cluster whose province had good nutritional status with an alow prevalence of stunting with a percentage of exclusive breastfeeding, weighing 4 times, adequate energy and high protein. While cluster 4 is a cluster that needs priority attention since the average value of stunting high-value percentage of energy adequacy low protein,although it has a value of percentage of ASI exclusive and toddlers weighing 4 times high enough that consists of Nusa Tenggara Barat, Nusa Tenggara East, Aceh, South Sulawesi, West Sulawesi and Banten. Provinces with stunting values, exclusive breastfeeding, weighing toddlers more than 4 times, adequacy of energy and protein that are the same height tend to group together, while provinces with low scores also group themselves.