Clustering of COVID-19 vaccination recipients in DKI Jakarta using the K-Medoids Algorithm

Abstract

The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained in the simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209. The clusters obtained are clusters 0 to cluster 5. The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters.