IMPLEMENTASI RAPIDMINER DENGAN METODE K-MEANS (STUDY KASUS: IMUNISASI CAMPAK PADA BALITA BERDASARKAN PROVINSI)

Abstract

Measles is one of the causes of death in children around the world which always increases every year. Although measles immunization programs have been implemented, the incidence of measles in children is still quite high. This study discusses the Implementation of Rapidminer with the K-Means Method (Case Study: Measles Immunization in Toddlers by Province). The increase in cases of measles in toddlers in Indonesia is a case that has never been separated from the government's attention. Data sources and research were obtained from the Central Statistics Agency (BPS). The data used in this study are data from 2004-2017 which consists of 34 provinces. The cluster process is divided into 3 (three) clusters, namely high cluster level (C1), medium cluster level (C2) and low cluster level (C3). So that the assessment for cases of immunization against measles based on high cluster province (C1) is 21 provinces for medium cluster (C2) of 12 provinces and for low cluster (C3) of 1 province. The results of the cluster can be used as input for the government, especially the provinces, so that provinces that enter the high cluster receive more attention and increase the socialization of measles immunization against children under five. Keywords: Data Mining, Measles, Clustering, K-means