Pengklasterisasian Data Penyakit Hipertensi dengan Menggunakan Metode K-Means

The increasing number and variety of diseases suffered by the community due to lifestyle changes that are influenced by the progress of the times. Periodic disease data collection will increase the accumulation of data. This is often an error in the data search process so that it takes a long time to search the data. This study focuses on data collection on hypertension and aims to cluster the data. The method used in this study is CRISP-DM with a business understanding process, data understanding, data preparation, modeling, evaluation and deployment. The algorithm used in this clustering is the K-Means algorithm. The results of this study resulted in 2 clusters, namely cluster 0 Normal and cluster 1 Hypertension. The results of this study can provide information about the results of 2 clusters, namely cluster 0 Normal and cluster 1 Hypertension