Analisis Clustering Penyakit Menular pada Manusia di Jakarta Timur Menggunakan Algoritma K-Means
DOI:
https://doi.org/10.35870/jtik.v9i1.3007Keywords:
K-Means Algorithm, Clustering, Infectious Diseases, East Jakarta, Data AnalysisAbstract
Humans are highly susceptible to various diseases without realizing their causes. The high incidence of infectious diseases in East Jakarta requires an analysis of distribution patterns to determine intervention priorities. This study aims to identify clusters of infectious diseases in East Jakarta, helping authorities plan effective prevention and treatment strategies. Data on infectious disease cases were obtained from the Central Statistics Agency of DKI Jakarta. The K-Means algorithm was used to cluster data based on variables such as period, region, type of disease, and number of cases. The results indicate several main clusters with distinct characteristics that can serve as a foundation for targeted strategies. From 2018 to 2021, diarrhea was predominant, making up 84.14% of cases in 2018 and 81.97% in 2019, pneumonia accounted for 32.92% in 2020, and TB Paru 33.63% in 2021. In conclusion, the K-Means algorithm effectively clusters infectious disease data and provides useful insights into disease distribution in East Jakarta, improving the impact of data-driven health programs.
Downloads
References
Bhunia, G. S., Roy, S. S., & Shit, P. K. (2021). Spatio-temporal analysis of COVID-19 in India – a geostatistical approach. Spatial Information Research, 29(5), 661–672. https://doi.org/10.1007/s41324-020-00376-0
Halim, C., Purnomo, H., & Wahyono, T. (2022). Analisis pengelompokan wilayah penyebaran COVID-19 di Indonesia dengan metode clustering menggunakan algoritma K-Means dan K-Medoids. INOVTEK Polbeng - Seri Informatika, 7(2). https://doi.org/10.35314/isi.v7i2.2566
Kurniawan, T., Wisjhnuadji, T., & Hanif, H. (2023). Data mining application for clustering COVID-19 spread areas in DKI Jakarta using the K-Means algorithm. Jurnal Limits, 20(1). https://doi.org/10.59134/jlmt.v20i1.325
Lessler, J., Salje, H., Grabowski, M. K., & Cummings, D. A. T. (2016). Measuring spatial dependence for infectious disease epidemiology. PLOS ONE, 11(5), e0155249. https://doi.org/10.1371/journal.pone.0155249
Mesquita, C. R., Enk, M. J., & Guimarães, R. J. d. P. S. e. (2021). Spatial analysis studies of endemic diseases for health surveillance: Application of scan statistics for surveillance of tuberculosis among residents of a metropolitan municipality aged 60 years and above. Ciência & Saúde Coletiva, 26(suppl 3), 5149–5156. https://doi.org/10.1590/1413-812320212611.3.09132020
Qomariasih, N. (2021). Implementasi K-Means clustering analysis untuk mengelompokkan kelurahan-kelurahan di DKI Jakarta berdasarkan jumlah positif COVID-19. Jurnal Syntax Transformation, 2(7). https://doi.org/10.46799/jst.v2i7.336
Rustam, S., Santoso, H., & Supriyanto, C. (2018). Optimasi K-Means clustering untuk identifikasi daerah endemik penyakit menular dengan algoritma particle swarm optimization di Kota Semarang. ILKOM Jurnal Ilmiah, 10(3), 251–259. https://doi.org/10.33096/ILKOM.V10I3.342.251-259
Santos, T. B. (2019). Aplikasi data mining untuk clustering daerah penyebaran penyakit diare di DKI Jakarta menggunakan algoritma K-Means. Jurnal Ilmiah FIFO, 11(2), 131. https://doi.org/10.22441/fifo.2019.v11i2.003
Sari, D. N. P., & Sukestiyarno, Y. L. (2021). Analisis cluster dengan metode K-Means pada persebaran kasus COVID-19 berdasarkan provinsi di Indonesia. Prisma: Prosiding Seminar Nasional Matematika, 4, 602–610.
Sari, R., Wanto, A., & Windarto, A. (2018). Implementasi RapidMiner dengan metode K-Means (studi kasus: imunisasi campak pada balita berdasarkan provinsi). KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 2(1). https://doi.org/10.30865/KOMIK.V2I1.930
Silvi, R. (2018). Analisis cluster dengan data outlier menggunakan centroid linkage dan K-Means clustering untuk pengelompokkan indikator HIV/AIDS di Indonesia. Journal of Multimedia, 4, 22–31. https://doi.org/10.15642/MANTIK.2018.4.1.22-31
Siringi, N., Mala, S., & Rawat, A. (2020). Study of K-Means clustering algorithm for identification of dengue fever hotspots. In Proceedings of the Conference on Emerging Technologies, 51–61. https://doi.org/10.1007/978-981-15-1420-3_6
Tukiyat, T., & Djohan, Y. (2022). Analisis penyebaran pandemi COVID-19 di Kota Jakarta menggunakan metode clustering K-Means dan Density-Based Spatial Clustering of Application with Noise (DBSCAN). Jurnal Informatika. https://doi.org/10.31294/inf.v9i1.11226
Yan, J., Cougoule, C., Lacroix-Lamandé, S., & Wiedemann, A. (2023). Mapping the scientific output of organoids for modeling animal and human infectious diseases: A bibliometric assessment. Research Square. https://doi.org/10.21203/rs.3.rs-3691844/v1.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Muhammad Arya Ramadhan, Edhy Poerwandono, Yuma Akbar, Aditya Zakaria Hidayat
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
