Pengelompokan Penyakit pada Pasien Berdasarkan Usia dengan Metode K-Means Clustering

This research aims to cluster disease data based on patient age using the K-Means method at RSUD Dr. RM. Djoelham. In this case study, the clustering method with the K-Means algorithm is used to group patients based on patient age, address and type of disease. With this method, information can be obtained regarding patient grouping patterns based on age at Dr. RM. Djoelham, who helps identify the closest relationships between patient groups and provides insight into the distribution of disease across age groups, regions and types of disease suffered.This research was conducted at RSUD Dr. RM. Djoelham by loading data from patients treated at the hospital. The data used is 1,100 patient data from 2022-2024 which has been recorded by the hospital. This patient data will be analyzed using 3 variables in the research, namely Patient Age (C1), Address (C2), and Type of Disease (C3). With the results, cluster 1 contains 320 data, cluster 2 contains 326 data, and cluster 3 contains 454 data.

Authors

Publication Details

Journal
Bridge
Published
2024-09-18
DOI
https://doi.org/10.62951/bridge.v2i4.246
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Pengelompokan Penyakit pada Pasien Berdasarkan Usia dengan Metode K-Means Clustering

Magdalena Simanjuntak, Akim Manaor Hara Pardede, Maida Andriani
Bridge
Data Mining and Machine Learning Applications
article

Pengelompokan Penyakit pada Pasien Berdasarkan Usia dengan Metode K-Means Clustering

Magdalena Simanjuntak, Akim Manaor Hara Pardede, Maida Andriani
article en

Abstract

This research aims to cluster disease data based on patient age using the K-Means method at RSUD Dr. RM. Djoelham. In this case study, the clustering method with the K-Means algorithm is used to group patients based on patient age, address and type of disease. With this method, information can be obtained regarding patient grouping patterns based on age at Dr. RM. Djoelham, who helps identify the closest relationships between patient groups and provides insight into the distribution of disease across age groups, regions and types of disease suffered.This research was conducted at RSUD Dr. RM. Djoelham by loading data from patients treated at the hospital. The data used is 1,100 patient data from 2022-2024 which has been recorded by the hospital. This patient data will be analyzed using 3 variables in the research, namely Patient Age (C1), Address (C2), and Type of Disease (C3). With the results, cluster 1 contains 320 data, cluster 2 contains 326 data, and cluster 3 contains 454 data.

BridgeVol. 2(4)
Good health and well-being
Openalex Percentile: Top 68%
Data Mining and Machine Learning Applications
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