Analysis of k-medoids clustering on toddler immunization in north sumatra province

Abstract

Immunization is a process to increase the body's immune system by inserting a vaccine, namely a virus or bacteria that has been weakened, killed, or parts of the bacteria (virus) have been modified. The purpose of this study was to group children's immunization data according to districts/cities in North Sumatra Province using the K-Medoids Data Mining algorithm. The K-Medoids algorithm or pattern known as PAM (Partitioning Around Medoids) uses the partitioning clustering method to group a set of n objects into a number of K clusters. The data in this study is sourced from the Central Bureau of Statistics of North Sumatra. The grouping was carried out based on the number of recipients of the DPT-HB and Measles/MR vaccines from 33 districts/cities in North Sumatra Province. The results of the study are expected to be taken into consideration for local governments, especially the Ministry of Health in the distribution of immunization for children under five with the type of immunization to achieve the national target set by the Ministry of Health, which is 79.1%.