APPLICATION OF DATA MINING WITH K-MEANS CLUSTERING ALGORITHM FOR HYPERTENSION DISEASE CLASSIFICATION AT PUSKESMAS LUBUK BUAYA PADANG IN 2024

Abstract

Lubuk Buaya Health Center faces a high challenge in treating hypertension with an increasing prevalence of cases. This study aims to group patients based on certain characteristics using the K-Means Clustering algorithm in data mining, in order to provide deeper insights and support more effective medical interventions. The type of research used is descriptive quantitative research with observation method. The population of this study was medical record documents of hypertension patients with a total population of 1,897 data. The sample used was 330 data selected using the Slovin formula. Data collection was done through observation and interviews with officers. The results showed that the number of severe hypertension patients was 73 patients, moderate level was 12 patients, and low level was 248 patients. From these results, it can be concluded that the k-means clustering method successfully classifies the level of hypertension in Lubuk Buaya Health Center.