Klasterisasi Data Rekam Medis Pasien Menggunakan Metode  K-Means Clustering Di Rumah Sakit Widodo Ngawi

Harfina  Dilawati; Heru Widianto; Agustinus  Kuswiadji

doi:10.37148/bios.v5i2.134

Harfina Dilawati STIKES Bhakti Husada Mulia
Heru Widianto STIKES Bhakti Husada Mulia
Agustinus Kuswiadji STIKES Bhakti Husada Mulia

DOI: https://doi.org/10.37148/bios.v5i2.134

Keywords: Medical Records, Clustering, Data Mining, K-Means Clustering, RStudio

Abstract

Medical record management is the most vital aspect of hospital operations. Medical records serve as a crucial source of information in patient healthcare services. Through data mining, knowledge extraction and data analysis can be carried out to find relationships, data structures, patterns, and regularities between data. The purpose of this study is to cluster using the RStudio application with the K-means clustering technique and identify disease prevalence patterns in Ngawi Regency. The methodology used is quantitative descriptive using secondary data, samples taken from hospitalization data from October to December 2023 totaling 3171 and divided into six variables, namely gender, age, sub-district, diagnosis, length of treatment and payment method. There are four clustering results, Cluster 1 amounted to 524 patients (19%), Cluster 2 amounted to 831 patients (30%), Cluster 3 amounted to 940 patients (33%) and Cluster 4 amounted to 512 patients (18%). Based on visits from each sub-district in Ngawi Regency, the total number of inpatient disease cases is fever (74), gastrointestinal diseases (64), stroke (43), and degenerative diseases such as diabetes (64), heart disease (7), neoplasms (6), asthma (4), systemic diseases (3), and external diseases (10). The study's findings can serve as a foundation for statistical management to inform decisions aimed at enhancing services and inpatient facilities

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