Klasterisasi Wilayah Prioritas Vaksin Menggunakan Algoritma K-Means Clustering - Rahmat Kurniawan; Muhammad Siddik Hasibuan; Riska Hasibuan (STMIK Budi Darma, 2023)
K-means clustering algorithm is a clustering method which is done by partition (partitional clustering). The use of clusters intends to partition a number of objects into groups where each object is into the closest group so that it will produce groups with significant differences. In addition, efforts have been made by the government to prevent wider disease transmission, among others, by implementing large-scale social restrictions and monitoring areas where there is a lot of migration of local and foreign residents as well as vaccinating. Looking at the data on the number of people who were infected, died from the Covid-19 virus until they recovered, which occurred in various regions in Indonesia. For this reason, it is necessary to cluster the area from the red, yellow, and green zones, which means for the red zone itself, it means that the area is a danger area and an area with a large number of viruses infected. This study aims to solve the problem, namely to produce applications that can provide information about priority areas for vaccines in North Sumatra, clustering the North Sumatra area, knowing vaccine priority areas using the K- Means algorithm. To find out the results of using the K-Means algorithm application, namely the application can classify Covid 19 cases in each Regency/City in North Sumatra Province into clusters of C1 (High), C2 (Medium) and C3 (Low). Based on the results of the clustering, Medan is in the C1 cluster with a distance value of 0.00 so that it can be prioritized for covid 19 vaccination activities. In the C2 (Medium) clustering there is one Regency, namely Deli Serdang with a 4th iteration value with a distance of 0.00. In the C3 (Low) cluster. Covid-19 case data from march-November 2021 in Nort Sumatra Province was calculated in 4 iterations until there there were no more data changes in the clustering process.