Grouping Diseases of Patients at RSU Mitra Medika Bandar Khalippa Medan Using the K-Medoids Clustering Method

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Published Aug 26, 2024
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5583
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Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER

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Ajeng Kiana Putri
Universitas Islam Negeri Sumatera Utara
Yusuf Ramadhan Nasution
Universitar Islam Negeri Sumatera Utara

Abstract

The aim of this research is to apply the methodK-Medoisin categorizing the illnesses of patients at the RSUBandar Khalippa Medika PartnersMedan. And to produce a system for grouping patient data based on Rapidminer and Google Colab on patient diseases.Based on the results of research on the application of the K-Medoids algorithm, it was found that the grouping of patient diseases at RSU Mitra Medika Bandar Khalippa used the RapidMiner application with a C0 (High) cluster of 3 diseases, a C1 (Medium) cluster of 6 diseases and a C2 (Low) cluster of 1 disease. Meanwhile, using the Google Colabs application with a C0 (High) cluster of 3 diseases, a C1 (Medium) cluster of 4 diseases and a C2 (Low) cluster of 3 diseases. The results of grouping patient disease data at RSU Mitra Medika Bandar Khalippa using RapidMiner, it was found that the disease with the highest grouping (C0)is a diseasePulmonary tuberculosis, Essential Hypertension and Diabetes Mellitus. Whereasgrouping patient disease datawith Google Colabs it was found that the disease with the highest grouping (C0)is a diseaseBronchus Or Lung, Trachea, Bronchus And Lung and Pleural Effusion.


Keywords: Disease Grouping, RSU, MethodsK-Medois.

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How to Cite
Putri, A. K., & Nasution, Y. R. (2024). Grouping Diseases of Patients at RSU Mitra Medika Bandar Khalippa Medan Using the K-Medoids Clustering Method. Jurnal Sistem Informasi Dan Ilmu Komputer, 8(1), 69–84. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5583

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