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All Journal Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Putri, Ajeng Kiana
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Decision Sciences, Operations Research & Management

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Grouping Diseases of Patients at RSU Mitra Medika Bandar Khalippa Medan Using the K-Medoids Clustering Method Putri, Ajeng Kiana; Nasution, Yusuf Ramadhan
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5583

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.
Co-Authors Yusuf Ramadhan Nasution, Yusuf Ramadhan