Spatial Clustering Analysis of Hand, Foot, and Mouth Disease in Jakarta using Local Indicator of Spatial Association Cluster Map and K-Means Clustering

Fatsa Vidyaningtyas Sabila; Yekti Widyaningsih

doi:10.31764/jtam.v9i3.30339

Spatial Clustering Analysis of Hand, Foot, and Mouth Disease in Jakarta using Local Indicator of Spatial Association Cluster Map and K-Means Clustering

Fatsa Vidyaningtyas Sabila, Yekti Widyaningsih

Abstract

Hand, Foot, and Mouth Disease (HFMD) is a infectious disease characterized by ulcers and blisters, primarily affecting children. The objective of this quantitative study is to identify areas with the highest HFMD cases (hotspot areas) in Jakarta in 2024 and to classify areas (districts) based on the number of HFMD cases and variables associated with the disease. The analysis employs the Local Indicator of Spatial Association (LISA) Cluster Map to detect spatial hotspots and K-Means Clustering to group districts by HFMD cases and related variables. LISA is a univariate method for detecting hotspots based on the local Moran’s Index that measures spatial dependence, whereas K-Means Clustering is a multivariate method for grouping individuals based on multiple variables. This study uses data from official government sources, including the number of HFMD cases, population density, average number of students per kindergarten, and average number of students per elementary school. The results of this study show that the LISA clustering reveals Kalideres and Cengkareng as High-High (H-H) clusters, while Tanah Abang, Menteng, and Senen form Low-Low (L-L) clusters. Makasar is classified as a Low-High (L-H) cluster. In contrast, the K-Means clustering groups districts into four clusters based on HFMD cases and related demographic factors, sorted in ascending order of HFMD cases. Areas with the lowest HFMD cases tend to have a moderate population density and fewer average number of students per kindergarten, while areas with the highest cases tend to have a lower population density but a higher average number of students per kindergarten. Areas classified as high cases HFMD by both methods, such as Cengkareng, should be prioritized for intervention. Cengkareng represents a district with the highest HFMD cases despite having a relatively low population density, along with a high average number of students per kindergarten and per elementary school.

Keywords

Hand Foot Mouth Disease; K-Means Clustering; Local Indicator of Spatial Association; Moran’s Index.

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References

Abidin, H. Z., Andreas, H., Gumilar, I., & Brinkman, J. J. (2015). Study on the risk and impacts of land subsidence in Jakarta. Proceedings of the International Association of Hydrological Sciences, 372, 115–120. https://doi.org/10.5194/piahs-372-115-2015

Anselin, L. (2001). Spatial Econometrics. In Baltagi, BH (Ed.), A companion to theoretical econometrics, Blackwell, Malden, MA.

Dinas Kependudukan dan Pencatatan Sipil Provinsi DKI Jakarta. (2025). Profil Perkembangan Kependudukan DKI Jakarta. https://kependudukancapil.jakarta.go.id/profile_perkembangan_adminduk/

Dinas Kesehatan Provinsi DKI Jakarta. (2025). Seksi Surveilans Epidemiologi dan Imunisasi. https://surveilans-dinkes.jakarta.go.id/sarsbaru/dashboard.php

Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/j.ins.2022.11.139

Jie, C., Jiyue, Z., Junhui, W., Yusheng, W., Huiping, S., & Kaiyan, L. (2020). Review on the Research of K-means Clustering Algorithm in Big Data. 2020 IEEE 3rd International Conference on Electronics and Communication Engineering, ICECE 2020, (pp. 107–111). https://doi.org/10.1109/ICECE51594.2020.9353036

Jollife, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. In Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065). Royal Society of London. https://doi.org/10.1098/rsta.2015.0202

Kemendikdasmen. (2025). Data Pokok Pendidikan (DAPODIK). https://dapo.dikdasmen.go.id/sp/1/010000

Lei, X., Cui, S., Zhao, Z., & Wang, J. (2015). Etiology, pathogenesis, antivirals and vaccines of hand, foot, and mouth disease. In National Science Review, 2(3), 268–284. Oxford University Press. https://doi.org/10.1093/nsr/nwv038

Lesage, J. P. (1999). The Theory and Practice of Spatial Econometrics. http://www.econ.utoledo.edu

Liu, J., Wang, H., Zhong, S., Zhang, X., Wu, Q., Luo, H., Luo, L., & Zhang, Z. (2024). Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis. JMIR Public Health and Surveillance, 10. https://doi.org/10.2196/58821

Markos, A., D’enza, A. I., & van de Velden, M. (2019). Beyond tandem analysis: Joint dimension reduction and clustering in R. Journal of Statistical Software, 91, Article 10. https://doi.org/10.18637/jss.v091.i10

Meng, D., Xu, J., & Zhao, J. (2021). Analysis and prediction of hand, foot and mouth disease incidence in China using Random Forest and XGBoost. PLoS ONE, 16(12), e0261629. https://doi.org/10.1371/journal.pone.0261629

Phung, D., Nguyen, H. X., Nguyen, H. L. T., Do, C. M., Tran, Q. D., & Chu, C. (2018). Spatiotemporal variation of hand-foot-mouth disease in relation to socioecological factors: A multiple-province analysis in Vietnam. Science of the Total Environment, 610–611, 983–991. https://doi.org/10.1016/j.scitotenv.2017.08.158

Qian, H., Huo, D., Wang, X., Jia, L., Li, X., Li, J., Gao, Z., Liu, B., Tian, Y., Wu, X., & Wang, Q. (2016). Detecting spatial-temporal cluster of hand foot and mouth disease in Beijing, China, 2009-2014. BMC Infectious Diseases, 16(1), 206. https://doi.org/10.1186/s12879-016-1547-6

Saputro, D. R. S., Widyaningsih, Y., Widyaningsih, P., Sutanto, & Widiastuti. (2021). Spatio-temporal patterns of dengue hemorrhagic fever (DHF) cases with local indicator of spatial association (LISA) and cluster map at areas risk in Java-Bali Indonesia. AIP Conference Proceedings, 2326(1), 020027. https://doi.org/10.1063/5.0040334

Sham, N. M., Krishnarajah, I., Akma Ibrahim, N., & Lye, M.-S. (2014). Temporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia. Geospatial Health, 8(2), 503–507. https://doi.org/10.4081/gh.2014.39

Suni, N. S. P. (2024). Mewaspadai lonjakan kasus hand, foot, mouth disease pascalibur Lebaran 2024. Info Singkat Pusat Analisis Keparlemenan DPR RI, 16(8/II), 1–8. https://berkas.dpr.go.id/pusaka/files/info_singkat/Info%20Singkat-XVI-8-II-P3DI-April-2024-1957.pdf

Suryowati, K., Bekti, R. D., & Faradila, A. (2018). A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation. IOP Conference Series: Materials Science and Engineering, 335(1), 012052. https://doi.org/10.1088/1757-899X/335/1/012052

Susanti, N., Purnamawati, S., Setiawaty, V., & Biomedis dan Teknologi Dasar Kesehatan Balitbangkes, P. (2014). Deteksi Penyebab dan Sebaran Kasus Kejadian Luar Biasa Hand Foot and Mouth Diseases (HFMD) Tahun 2008-2012.

Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018). Integration K-Means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster. IOP Conference Series: Materials Science and Engineering, 336(1), 012017. https://doi.org/10.1088/1757-899X/336/1/012017

Xu, L., Shi, Y., Rainey, J. J., Zhang, Z., Zhang, H., Zhao, J., Li, Y., Rao, H., Li, Y., Liao, Q., & Ma, Y. (2018). Epidemiological features and spatial clusters of hand, foot, and mouth disease in Qinghai Province, China, 2009-2015. BMC Infectious Diseases, 18(1), 351. https://doi.org/10.1186/s12879-018-3509-7

Yang, D. L., Li, W., Pan, M. H., Su, H. X., Li, Y. N., Tang, M. Y., & Song, X. K. (2022). Spatial analysis and influencing factors of pulmonary tuberculosis among students in Nanning, during 2012–2018. PLoS ONE, 17(5), e0268472. https://doi.org/10.1371/journal.pone.0268472

DOI: https://doi.org/10.31764/jtam.v9i3.30339