Analisis Penerapan Data Mining Terhadap Kasus Positif Covid-19 Menggunakan Metode K-Means Clustering

Abstract

This study has problems such as the absence of the use of the K-means clustering algorithm for data on positive COVID-19 cases in the Indonesian province. The purpose of this study is to apply the K-means clustering method in finding the closest distance to produce the lowest and highest clusters of data on positive COVID-19 cases in the Indonesian province. K-means is one of the algorithms in the non-hierarchical Clustering technique that tries to partition the existing data in the form of one or more clusters. The results obtained from the k-means clustering method produced 2 clusters, namely the lowest cluster C1 = 30 items while the highest cluster C2 = 4 items. This research can be used as a reference and can be further developed with other clustering methods or algorithms such as k-medoid in order to get a comparison of results and steps to use algorithms related to clustering.