Pengklasterisasian Data Penyakit Hipertensi dengan Menggunakan Metode K-Means


  • Fitria Rahmadayanti * Mail Institut Teknologi Pagar Alam, Pagar Alam, Indonesia
  • Inda Anggraini Institut Teknologi Pagar Alam, Pagar Alam, Indonesia
  • Tri Susanti Institut Teknologi Pagar Alam, Pagar Alam, Indonesia
    • (*) Corresponding Author
DOI: https://doi.org/10.47065/josh.v4i2.2905
Keywords: Hypertension Disease; Data Mining; Clustering; CRISP-DM; K-Means

Abstract

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

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Article History
Submitted: 2023-01-11
Published: 2023-01-31
Abstract View: 95 times
PDF Download: 110 times
How to Cite
Rahmadayanti, F., Anggraini, I., & Susanti, T. (2023). Pengklasterisasian Data Penyakit Hipertensi dengan Menggunakan Metode K-Means. Journal of Information System Research (JOSH), 4(2), 737-741. https://doi.org/10.47065/josh.v4i2.2905
Issue
Vol 4 No 2 (2023): January 2023
Pages: 737-741
Section
Articles

Copyright (c) 2023 Fitria Rahmadayanti, Inda Anggraini, Tri Susanti

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