Application of K-Medoids Clustering Method on Disease Clustering Based on Patient Medical Records

Dian Fatika, Bustami Bustami, Yesy Afrillia

Abstract


Dr. Fauziah Bireuen Regional General Hospital (RSUD) faces daily challenges in managing the ever-increasing medical record data. Currently, the medical record data only consists of reports containing information on the number of patients and their diseases, which are then archived without further processing to generate valuable information. This research aims to cluster diseases based on patient medical records using the K-Medoids Clustering method, thereby providing information on the patterns of disease spread in various regions of the Bireuen Regency. The data used are patient medical records from RSUD Dr. Fauziah Bireuen from 2021–2023, focusing on five common diseases: stroke, hypertension, schizophrenia, dyspepsia, and pneumonia. We conducted Clustering in 17 sub-districts in Bireuen Regency using the K-Medoids method and determined the optimal number of clusters using the Elbow method. The research results show that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The results showed that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The cluster distribution for stroke disease consists of 7 sub-districts in the high cluster, 7 in the medium, and 3 in the low. Hypertension disease consists of 6 sub-districts in the high cluster, 3 in the medium, and 8 in the low. Schizophrenia disease comprises seven sub-districts in the high cluster, 8 in the medium, and 2 in the low. Dyspepsia disease includes six sub-districts in the high cluster, 2 in the medium, and 9 in the low. Meanwhile, pneumonia disease consists of 8 sub-districts in the high cluster, 5 in the medium, and 4 in the low.


Keywords


Clustering, K-Medoids, Medical Record, Elbow

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References


Kemenkes, “Laporan Riskesdas 2018 Nasional.pdf,” Lembaga Penerbit Balitbangkes. p. hal 156, 2018.

PERMENKES RI No 269/MENKES/PER/III/2008, “permenkes ri 269/MENKES/PER/III/2008,” Permenkes Ri No 269/Menkes/Per/Iii/2008, vol. 2008. p. 7, 2008.

Angelina M. T. I. Sambi Ua et al., “Penggunaan Bahasa Pemrograman Python Dalam Analisis Faktor Penyebab Kanker Paru-Paru,” J. Publ. Tek. Inform., vol. 2, no. 2, pp. 88–99, 2023, doi: 10.55606/jupti.v2i2.1742.

M. N. P. Pamulang, M. N. Aini, and U. Enri3, “Komparasi Distance Measure Pada K-Medoids Clustering untuk Pengelompokkan Penyakit ISPA,” Edumatic J. Pendidik. Inform., vol. 5, no. 1, pp. 99–107, 2021, doi: 10.29408/edumatic.v5i1.3359.

D. Permata Sari, “Pengelompokkan Penyakit Berdasarkan Lingkungan Dengan Algoritma K-Means Pada Puskesmas Sungai Tarab 2,” JOISIE (Journal Inf. Syst. Informatics Eng., vol. 5, no. 2, pp. 75–81, 2021, doi: 10.35145/joisie.v5i2.1700.

H. Pohan, M. Zarlis, E. Irawan, H. Okprana, and Y. Pranayama, “Penerapan Algoritma K-Medoids dalam Pengelompokan Balita Stunting di Indonesia,” JUKI J. Komput. dan Inform., vol. 3, no. 2, pp. 97–104, 2021, doi: 10.53842/juki.v3i2.69.

E. M. P. Hermanto, H. B. Rochmanto, and R. Agustin, “Pemetaan Program Indonesia Sehat dengan Pendekatan Keluarga (PIS PK) di Kabupaten Bondowoso dengan K-Medoids,” J. Stat. dan Komputasi, vol. 2, no. 2, pp. 83–92, 2023, doi: 10.32665/statkom.v2i2.2307.

T. A. Munandar, “Penerapan Algoritma Clustering Untuk Pengelompokan Tingkat Kemiskinan Provinsi Banten,” JSiI (Jurnal Sist. Informasi), vol. 9, no. 2, pp. 109–114, 2022, doi: 10.30656/jsii.v9i2.5099.

M. Minarni, E. I. Sari, A. Syahrani, and P. Mandarani, “Klasterisasi Penyakit Menggunakan Algoritma K-Medoids pada Dinas Kesehatan Kabupaten Agam,” J. Nas. Pendidik. Tek. Inform., vol. 10, no. 3, p. 137, 2021, doi: 10.23887/janapati.v10i3.34904.

B. Nurseptia, N. Sulistiyowati, and A. Voutama, “Pemetaan Tingkat Kekerasan Pada Anak Dan Perempuan Menggunakan Algoritma K-Medoids (Studi Kasus: P2TP2A DKI Jakarta),” vol. 10, no. 4, pp. 244–255, 2023.

J. Wandana, S. Defit, and S. Sumijan, “Klasterisasi Data Rekam Medis Pasien Pengguna Layanan BPJS Kesehatan Menggunakan Metode K-Means,” J. Inf. dan Teknol., vol. 2, pp. 4–9, 2020, doi: 10.37034/jidt.v2i4.73.

A. Suprianto, H. Latipa Sari, and R. Zulfiandry, “Perbandingan Algoritma K-Means Dan K-Medoid Dalam Pengelompokan Data Pasien Berdasarkan Rekam Medis Di Puskesmas M. Thaha Bengkulu Selatan,” J. Sci. Soc. Res., vol. 4307, no. 3, pp. 580–586, 2023.

R. T. S. Muhammad Hariyanto, “Clustering pada Data Mining untuk Mengetahui Potensi Penyebaran Penyakit DBD Menggunakan Metode Algoritma K-Means dan Metode Perhitungan Jarak Euclidean Distance,” Sist. Komput. dan Tek. Inform., vol. 1, no. 1, pp. 117–122, 2018.

L. Rosnita, Y. Afrillia, R. P. Fhonna, and U. Ilyatin, “Development of Web-Based Tracer Alumni Information System,” J. Comput. Sci. Inf. Technol. Telecommun. Eng., vol. 2, no. 2, pp. 202–210, 2021, doi: 10.30596/jcositte.v2i2.7845.

R. Bayu Prasetyo, Y. Agus Pranoto, and R. Primaswara Prasetya, “Implementasi Data Mining Menggunakan Algoritma K-Means Clustering Penyakit Pasien Rawat Jalan Pada Klinik Dr. Atirah Desa Sioyong, Sulteng,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 4, pp. 2144–2151, 2023, doi: 10.36040/jati.v7i4.7419.

N. A. Maori and E. Evanita, “Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 14, no. 2, pp. 277–288, 2023, doi: 10.24176/simet.v14i2.9630.

S. Sidabutar, Edpid. 2020.

Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” J. Teknol. dan Sist. Inf., vol. 2, no. 2, p. 100, 2021.

N. Nurdin, S. Fitriani, Z. Yunizar, and B. Bustami, “Clustering the Distribution of COVID-19 in Aceh Province Using the Fuzzy C-Means Algorithm,” JTAM (Jurnal Teor. dan Apl. Mat., vol. 6, no. 3, p. 665, 2022, doi: 10.31764/jtam.v6i3.8576.

N. R. Aeni, A. Nilogiri, and R. Umilasari, “Algoritma Partitioning Around Medoids Dalam Mengelompokkan Provinsi Di Indonesia Berdasarkan Indeks Kinerja Davies Bouldin




DOI: https://doi.org/10.52088/ijesty.v5i1.679

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International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674