Application of Spatial K-Means Clustering for Identifying Areas at High Risk of Tuberculosis (TB) in Indonesia in 2025
Abstract
This study aims to classify Indonesian provinces based on tuberculosis (TB) characteristics projected for 2025 using the K-Means Clustering method. The analysis covered 38 provinces using two indicators: the TB Case Detection Rate (JPTBC) and the TB Treatment Success Rate (JKPTBC). Data were standardized using the Z-score method prior to clustering. The optimal number of clusters was determined using the Silhouette Coefficient, resulting in seven distinct clusters for analysis. The findings reveal variations in TB characteristics across provinces, as evidenced by the spatial distribution of the seven clusters. The mapping indicates that TB conditions in Indonesia are heterogeneous, necessitating TB control strategies tailored to the specific characteristics of each provincial group. These clustering results can serve as a basis for prioritizing interventions and formulating TB control policies in Indonesia.
References
Baker, M., Das, D., Venugopal, K., & Howden-Chapman, P. (2008). Tuberculosis associated with household crowding in a developed country. Journal of Epidemiology and Community Health, 62(8), 715–721. https://doi.org/10.1136/jech.2007.063610
Faidah, D. Y., Destin, D., Anggina, F. A., & Caesar, M. I. (2025). Assessing the performance of K-means and DBSCAN clustering methods in tuberculosis mapping. Communications in Mathematical Biology and Neuroscience, 2025, Article 15. https://doi.org/10.28919/cmbn/9039
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Morgan Kaufmann.
Harling, G., & Castro, M. C. (2014). A spatial analysis of social and economic determinants of tuberculosis in Brazil. Health & Place, 25, 56–67. https://doi.org/10.1016/j.healthplace.2013.10.008
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28(1), 100–108. https://doi.org/10.2307/2346830
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Kementerian Kesehatan Republik Indonesia. (2024). Profil kesehatan Indonesia 2023. Kementerian Kesehatan Republik Indonesia.
Lönnroth, K., Jaramillo, E., Williams, B. G., Dye, C., & Raviglione, M. (2009). Drivers of tuberculosis epidemics: The role of risk factors and social determinants. Social Science & Medicine, 68(12), 2240–2246. https://doi.org/10.1016/j.socscimed.2009.03.041
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281–297). University of California Press.
Mar’ah, Z., Hafid, H., & Meliyana R, S. M. (2025). Epidemiologicalmapping of Tuberculosis in South Sulawesi Using Local Indicators of Spatial Association (Lisa) And K-Meansclustering. Sainsmat: Jurnal Ilmiah Ilmu Pengetahuan Alam, 14(01), 1–11. https://doi.org/10.35580/sainsmat141665022025
Milligan, G. W., & Cooper, M. C. (1988). A study of standardization of variables in cluster analysis. Journal of Classification, 5(2), 181–204. https://doi.org/10.1007/BF01897163
Munch, Z., Van Lill, S. W. P., Booysen, C. N., Zietsman, H. L., Enarson, D. A., & Beyers, N. (2003). Tuberculosis transmission patterns in a high-incidence area: A spatial analysis. The International Journal of Tuberculosis and Lung Disease: The Official Journal of the International Union Against Tuberculosis and Lung Disease, 7(3), 271–277.
Onozaki, I., Law, I., Sismanidis, C., Zignol, M., & Glaziou, P. (2015). National tuberculosis prevalence surveys in Asia, 1990–2012: An overview of results and lessons learned. Tropical Medicine & International Health, 20(9), 1128–1145. https://doi.org/10.1111/tmi.12534
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Shaweno, D., Karmakar, M., Alene, K. A., Ragonnet, R., Clements, A. C., Trauer, J. M., Denholm, J. T., & McBryde, E. S. (2018). Methods used in the spatial analysis of tuberculosis epidemiology: A systematic review. BMC Medicine, 16(1), 193. https://doi.org/10.1186/s12916-018-1178-4
Teibo, T. K. A., Andrade, R. L. de P., Rosa, R. J., Tavares, R. B. V., Berra, T. Z., & Arcêncio, R. A. (2023). Geo-spatial high-risk clusters of Tuberculosis in the global general population: A systematic review. BMC Public Health, 23(1), 1586. https://doi.org/10.1186/s12889-023-16493-y
Tiwari, N., Adhikari, C. M. S., Tewari, A., & Kandpal, V. (2006). Investigation of geo-spatial hotspots for the occurrence of tuberculosis in Almora district, India, using GIS and spatial scan statistic. International Journal of Health Geographics, 5, 33. https://doi.org/10.1186/1476-072X-5-33
Wang, H., Song, C., Wang, J., & Gao, P. (2024). A raster-based spatial clustering method with robustness to spatial outliers. Scientific Reports, 14, 4103.
World Health Organization. (2023). Global tuberculosis report 2023. World Health Organization.
World Health Organization. (2024). Global tuberculosis report 2024. World Health Organization.
World Health Organization. (2025). Global tuberculosis report 2025. World Health Organization. https://doi.org/10.2471/9789240116924
Copyright (c) 2026 International Journal of Science and Society
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
.png)
.jpg)
.png)
.png)
.png)
.png)
1.png)
.jpg)
-modified.png)
-modified.png)
-modified.png)
