Komparasi Algoritma K-Means Dan K-Medoids Dalam Clustering Penyebaran Kasus Covid 19

Abstract

Covid 19 is a rapid spread virus that has a negative impact on the province of XYZ. With the Indonesian government policy, four zone policies such as the red, green, yellow, orange zone, which each zone has a different meaning. The algorithm used in this study is K-Means and K-Medoids. Algorithm-Means group data by dividing them into several clusters based on the same characteristics. Whereas the K-Medoids algorithm selects real objects to represent the cluster. In this study, the two algorithms were compared using one dataset. The comparison is done by looking at the value of Davies-Bouldin Index (DBI) on the rapidminer. The best result of K-Means DBI is 0.078 while K-Medoids gives a value of 0.250 which is divided into 2 clusters