ABSTRACT
Jakarta as the center of the capital city of Indonesia has a very high mobility and population density. This has resulted in the spread of COVID-19 cases also have a very high increasing trend. Regional clustering and the detection of variables that affect COVID-19 deaths can be an early warning or the basis for government policies in handling the spread of disease outbreaks. This study aims to classify areas at the subdistrict level in Jakarta based on distribution of COVID-19 cases using the K-Means method. After the regional clusters were formed, Bayesian regression analysis was carried in each cluster and sub-district to identify variables that had an effect on COVID-19 deaths. The number of deaths is assumed to have Normal distribution, and statistical inference in Bayesian regression using the Integrated Nested Laplace Approximation (INLA) approach. This study produced several interesting results including: (1) there are 4 clusters that indicate areas prone to spread with a high case rate, fairly high risk, low risk to very low risk areas. (2) most of Jakarta's sub-districts, which is about 45%, are included in areas with a fairly high risk of spreading. (3) In general, the number of recovered cases is a significant variable on the majority decrease number of COVID-19 deaths in each cluster. © 2021 IEEE.