CLUSTERING OF INPATIENTS USING THE K-MEANS METHOD AT RSIA SETYA BHAKTI

Authors

DOI:

https://doi.org/10.33480/pilar.v22i2.9032

Keywords:

clustering, data mining, disease groups, inpatients, KMeans

Abstract

Inpatient data is a valuable source of information for identifying patterns in patient characteristics within a hospital. However, such data have not been fully utilized to analyze patient characteristics. This study aims to cluster inpatient data from RSIA Setya Bhakti based on age, length of stay, patient category, and disease group using the K-Means algorithm. The research stages included problem identification, data collection, data cleaning, preprocessing, data transformation, normalization, determination of the optimal number of clusters using the Elbow method, implementation of the K-Means algorithm using RapidMiner, and interpretation of the clustering results. The dataset consisted of 923 inpatient medical records from January to April 2026. Based on the Elbow method, K = 3 was selected as the optimal number of clusters, resulting in three distinct patient groups. Cluster 0 consisted of 443 patients (47.99%), predominantly pediatric patients, with an average age of 6.84 years, an average length of stay of 1.70 days, and a predominance of infectious diseases. Cluster 1 consisted of 152 patients (16.47%), predominantly patients in the maternal category, with an average age of 44.18 years, an average length of stay of 1.72 days, and more diverse disease characteristics. Cluster 2 consisted of 328 patients (35.54%), predominantly patients in the maternal category with pregnancy-related cases, with an average age of 31.17 years and an average length of stay of 1.71 days. The contribution of this study is the identification of distinct inpatient characteristic patterns through clustering, providing descriptive information that can support the hospital in understanding the distribution and characteristics of its inpatient population.

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Author Biographies

  • Yuliana Putri Ananda, Universitas Bina Sarana Informatika

    Information Systems Study Program, Faculty of Engineering & Informatics

  • Al Fajri, Universitas Bina Sarana Informatika

    Information Systems Study Program, Faculty of Engineering & Informatics

  • Azka Jaisy Kahfi, Universitas Bina Sarana Informatika

    Information Systems Study Program,Faculty of Engineering & Informatics

  • Indah Suryani, Universitas Bina Sarana Informatika

    Information Systems Study Program, Faculty of Engineering & Informatics

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Published

2026-09-30

Issue

Vol. 22 No. 2 (2026): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2026

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Articles

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Copyright (c) 2026 Yuliana Putri Ananda, Al Fajri, Azka Jaisy Kahfi, Indah Suryani

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How to Cite

CLUSTERING OF INPATIENTS USING THE K-MEANS METHOD AT RSIA SETYA BHAKTI. (2026). Jurnal Pilar Nusa Mandiri, 22(2), 194-203. https://doi.org/10.33480/pilar.v22i2.9032