Analisis Persebaran Penyakit di Wilayah Menggunakan Algoritma K-Means Berbasis Data Kunjungan Fasilitas Kesehatan

Zatin Suhaira(1), Rizki Muliono(2),


(1) Universitas Medan Area
(2) Universitas Medan Area
DOI: https://doi.org/10.34007/incoding.v5i2.983

Keywords


K-Means; Health Facilities; Medical Records; Cluster; Silhouette Score; Davies-Bouldin Index

Full Text:

PDF

References


G. B. Mentari and Susilawati, “Faktor-faktor Yang Mempengaruhi Akses Pelayanan Kesehatan di Indonesia,” J. Heal. Sains, vol. 3, no. 8.5.2017, pp. 2003–2005, 2022.

A. S. Mentari and B. Besral, “Analisis Faktor Higiene Sebagai Sumber Penularan Hepatitis a Di Indonesia: Literature Review,” J. Cahaya Mandalika ISSN 2721-4796, pp. 2293–2301, 2024, [Online]. Available: https://ojs.cahayamandalika.com/index.php/jcm/article/view/3209

S. Sofia, E. T. Ardianto, N. Muna, and S. Sabran, “Analisis Aspek Keamanan Informasi Data Pasien Pada Penerapan RME di Fasilitas Kesehatan,” J. Rekam Med. Manaj. Inf. Kesehat., vol. 1, no. 2, pp. 94–103, 2022, doi: 10.47134/rmik.v1i2.29.

R. Marbun, R. Ariyanti, and V. Dea, “Peningkatan Pengetahun Masyarakat Terkait Pentingnya Rekam Medis Bagi Pasien Di Fasilitas Pelayanan Kesehatan,” SELAPARANG J. Pengabdi. Masy. Berkemajuan, vol. 5, no. 1, p. 163, 2021, doi: 10.31764/jpmb.v5i1.6427.

W. T. Ina, Y. Mesakh, and S. I. Pella, “Klusterisasi Penyakit Endemis Pada Kecamatan Sabu Barat, Kabupaten Sabu Raijua Menggunakan Algoritma K-Means,” J. Media Elektro, vol. XI, no. 1, pp. 39–44, 2022, doi: 10.35508/jme.v11i1.6508.

M. A. Sembiring, “Penerapan Metode Algoritma K-Means Clustering Untuk Pemetaan Penyebaran Penyakit Demam Berdarah Dengue (DBD),” J. Sci. Soc. Res., vol. 4, no. 3, p. 336, 2021, doi: 10.54314/jssr.v4i3.712.

F. Muhammad, N. M. Maghfur, and A. Voutama, “Sentiment Analysis Dataset on COVID-19 Variant News,” Systematics, vol. 4, no. 1, pp. 382–391, 2022.

A. Amato and V. Di Lecce, “Data preprocessing impact on machine learning algorithm performance,” Open Comput. Sci., vol. 13, no. 1, p. 20220278, 2023.

M. Dzaki Salman, N. Rizki Pratama, M. A. Nakhlah Farid, A. Agung Setiawan, F. Zalianti, and I. Bil Huda, “MALCOM: Indonesian Journal of Machine Learning and Computer Science Comparison of K-Means and K-Medoids Clustering Algorithm Performance in Grouping Schools in Riau Province Based on Availability of Facilities and Infrastructure,” vol. 5, no. July, pp. 797–806, 2025.

F. C. Oettl, J. F. Oeding, R. Feldt, C. Ley, M. T. Hirschmann, and K. Samuelsson, “The artificial intelligence advantage: Supercharging exploratory data analysis,” Knee Surgery, Sport. Traumatol. Arthrosc., vol. 32, no. 11, pp. 3039–3042, 2024, doi: 10.1002/ksa.12389.

R. Muliono and Z. Sembiring, “Data Mining Clustering Menggunakan Algoritma K-Means Untuk Klasterisasi Tingkat Tridarma Pengajaran Dosen,” J. Comput. Eng. Syst. Sci., vol. 4, no. 2, pp. 2502–714, 2019.

B. Chong, “K-means clustering algorithm: a brief review,” Acad. J. Comput. Inf. Sci., vol. 4, no. 5, pp. 37–40, 2021, doi: 10.25236/ajcis.2021.040506.

P. M. Hasugian, B. Sinaga, J. Manurung, and S. A. Al Hashim, “Best Cluster Optimization with Combination of K-Means Algorithm And Elbow Method Towards Rice Production Status Determination,” Int. J. Artif. Intell. Res., vol. 5, no. 1, pp. 102–110, 2021, doi: 10.29099/ijair.v6i1.232.

V. A. Permadi, S. P. Tahalea, and R. P. Agusdin, “K-Means and Elbow Method for Cluster Analysis of Elementary School Data,” Prog. Pendidik., vol. 4, no. 1, pp. 50–57, 2023, doi: 10.29303/prospek.v4i1.328.

I. Irwan, W. Sanusi, A. S. Anwar, and A. Rahman, “The Implementation of Spatial Model with K-Means Clustering Method to Cluster Flood Affected Areas in Bone Regency,” ARRUS J. Soc. Sci. Humanit., vol. 3, no. 2, pp. 186–195, 2023, doi: 10.35877/soshum1771.

M. Wahyudi, S. Solikhun, and L. Pujiastuti, “Komparasi K-Means Clustering dan K-Medoids Clustering dalam Mengelompokkan Produksi Susu Segar di Indonesia Berdasarkan Nilai DBI,” J. Bumigora Inf. Technol., vol. 4, no. 2, pp. 243–254, 2022, doi: 10.30812/bite.v4i2.2104.

G. Vardakas, I. Papakostas, and A. Likas, “Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters,” 2024, [Online]. Available: http://arxiv.org/abs/2402.00608




DOI: https://doi.org/10.34007/incoding.v5i2.983

Article Metrics

Abstract view : 77 times
PDF - 8 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 INCODING: Journal of Informatics and Computer Science Engineering