IMPLEMENTASI ALGORITMA K-MEDOIDS UNTUK CLUSTERING WILAYAH TERINFEKSI KASUS COVID-19 DI DKI JAKARTA - Muh Arifandi; Arief Hermawan; Donny Avianto (Jurnal Teknologi Terapan, 2021)
In early March 2019, Indonesia was hit by the Covid-19 (Corona) outbreak. The increase in the number of patients infected with the Covid-19 virus is increasing day by day and is already difficult to control. Jakarta is no exception. To prevent the increase in cases of COVID-19, it is necessary to create a cluster or grouping of certain areas (Urban village) based on the number of positive, treated, recovered, died and isolated. This grouping will assist the DKI Jakarta government in providing appropriate handling according to the Urban village pattern. The data that will be used as a research study is the data on the distribution of the status of infected cases of Covid-19 in DKI Jakarta Province on May 20, 2021. The K-Medoids algorithm is a method that can determine a set of clusters among a group of data that is close to an object. Based on the research studies that have been carried out, it can be concluded that in the data mining technique, the total grouping of Covid-19 infected cases based on urban areas in DKI Jakarta Province uses the k-medoids algorithm with three clusters. Cluster 0, cluster 1, cluster 2. The highest Covid-19 infected cases in DKI Jakarta Province are shown in cluster 3 with 31 regions. The results of this grouping research will assist the DKI Jakarta government in providing appropriate handling according to the Urban village pattern. K-Medoids can be implemented using large amounts of data with complex attributes.