Implementation of the Web-Based K-Means Clustering Algorithm on Hypertension Levels in the Elderly at the Bungah District Health Center

This research was carried out at the community health center in Bungah sub - district with the aim that so that patients receive appropriate treatment, a web-based system is needed to group hypertension data into several classes (clusters). The method used in data grouping is the K-Means Clustering method . In this study, 100 hypertension data were grouped into 4 clusters . The hypertension data used consists of two attributes, namely systole and diastole, the working mechanism is to normalize the data first, then the system groups the data into groups that have the same characteristics. The system will display the results of the clustering process which consists of four (4) clusters , namely cluster 1 Isolated Systolic Hypertension, cluster 2 Grade 1 (mild hypertension), cluster 3 Grade 2 (moderate hypertension), and cluster 4 Grade 3 (high hypertension). The data used for clustering is 100 data from blood pressure checks of patients at the Community Health Center using a blood pressure monitor. The results of the last iteration of 100 hypertension data in the system were used as parameters for calculating the level of effectiveness, by comparing the data from the clustering test results to the data from diagnosis a which produced an effectiveness value of 80%.