Application Of K-Means Clustering in Grouping Citizen Welfare Based on Sub-Districts In Surakarta City

Abstract

The purpose of this article is to classify the welfare of sub-districts in Surakarta City using the K-means Clustering method. The K-means Clustering method has the ability to group large enough data in a very fast and efficient time. This method is included in the partition method which is based on the center point. This algorithm requires three parameters, namely the number of clusters, cluster initialization, and system distance. The data is taken from Surakarta City in Figures 2021, and the data loaded is 2020 data.