TEHNIK DATA MINING DALAM MENGELOMPOKKAN KASUS PNEUMONIA PADA BALITA BERDASARKAN PROVINSI DI INDONESIA

Abstract

Pneumonia is an infectious disease that attacks the lungs, causing air sacs in the lungs to become inflamed and swollen. This health condition is often called a wet lung. The condition of a wet lung can be experienced by anyone caused by bacteria, viruses, and fungi that are easily transmitted through the air in sneezing or coughing conditions. But pneumonia in children can be very dangerous and cause death. This study aims to create a grouping model using the K-Means algorithm. The method used is Datamining Clustering K-Means. By using this algorithm the data that has been generated can be grouped into clusters based on these data. The data is sourced from the Indonesian Ministry of Health in 2017. The number of records used is 34 provinces divided into 2 clusters namely high and low clusters. From the calculation of K-Means, there were 3 provinces as the highest cluster and 31 provinces as a low cluster. The implementation process using the Rapidminer 5.3 application is used to help find accurate values. It is expected that with this research can be used as a reference to the government in tackling pneumonia especially in infants to be able to improve health services, supply drugs, and equipment for treatment and anticipate against pneumonia in provinces in Indonesia.

Keywords: Pneumonia, K-Means Algorithm, Data Mining