Comparison of K-Means and K-Medoids Algorithms for Clustering the Spread of the COVID-19 Outbreak in Indonesia

Abstract

The spread of Corono Virus 19 is very fast through effective human-to-human transmission through close contact and respiratory droplets such as coughing or sneezing. Various studies have been conducted to deal with COVID 19, but until now it has not been found how to stop the spread of this virus. Based on data obtained from the covid19.go.id page accessed on January 1, 2021 which was updated by the Ministry of Health, the overall number of confirmed cases was 1,078,314 active cases reaching 175,095 or 16.2% of confirmed cases, recovered 873,221 or 81.0% of cases confirmed, died 29,998 or 2.8% of the confirmed cases. In this study, comparing the two algorithms in the dataset which aims to analyze grouping patterns and determine the best method of data processing. The data used comes from the Ministry of Health, there are 4 attributes including confirmed cases, treatment, recovery and death, in this study only 2 attributes are used, namely confirmed cases and death. From the results of data analysis and processing through a comparison between the K-Means method and the K-Medoids for grouping the spread of the corona 19 virus in Indonesia, with the Davies Boulden index value from the K2 to K9 values, it turns out that the K-Means method gets the smallest value at the K-value. 5 is 0.064, while K-Medoids at the k-2 value is 0.411. Thus, from the two methods used, it can be found that the best method for clustering the spread of the corona 19 virus outbreak in Indonesia is the K-Means method.