DATA MINING APPLICATION FOR CLUSTERING COVID-19 SPREAD AREAS IN DKI JAKARTA USING THE K-MEANS ALGORITHM

Abstract

Coronavirus Disease 2019 (coronavirus disease2019, abbreviated as COVID-19) is an infectious disease caused by SARS-Cov-2, a type of coronavirus. Covid-19 patients can experience fever, dry batik, and difficulty breathing. The infection spreads from one person to another through a splash (droplet) from the respiratory tract produced when coughing or sneezing.  The number of residents until 2019 reached 11,063,324 people spread across 6 cities consisting of 44 districts and 267 urban villages, making Covid-19 easy to spread. To be able to see the area of spread of Covid-19, it is necessary to group based on the attributes used consisting of Suspect Cases, Probable, Cases, Close Contacts, Confirmed Cases and Deaths. In this study, to cluster the data, the K-Means method and the Euclidean distance measurement method were used. This study produced a prototype application for grouping data on the distribution of Covid-19 patients. The result of the implementation of the K-Means Algorithm is that the Covid-19 spread cluster in DKI Jakarta is divided into 3 (three) clusters, namely cluster 1, cluster 2 and cluster 3. Cluster 1 is a medium case zone, Cluster 2 is a high case zone and Cluster 3 is a low case zone.