Density Based Spatial Clustering of Application with Noise on Tuberculosis Cases in Indonesia

Mahrani

doi:10.59890/ijaamr.v4i5.223

Authors

Mahrani Universitas Negeri Makassar

DOI:

https://doi.org/10.59890/ijaamr.v4i5.223

Keywords:

DBSCAN Algorithm, Density-Based Clustering, Tuberculosis

Abstract

This study aims to identify the clustering pattern of tuberculosis treatment outcomes in Indonesia in the 2025 research context using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method. This study contributes to public health data mining by applying density-based clustering to classify provincial tuberculosis treatment outcome patterns and detect outlier regions. This quantitative study used secondary tuberculosis data from 34 provinces in Indonesia, observed and analyzed during the 2025 research period. The variables included healed, complete treatment, died, failed treatment, loss to follow-up, and not evaluated indicators. Data were analyzed using normalization, principal component analysis, DBSCAN clustering, and Davies-Bouldin Index validation. The optimal parameters were ε = 0.05 and MinPts = 2, producing two clusters and one noise region. The findings support targeted tuberculosis surveillance and regional health planning

References

Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2014). Statistics for business and economics. www.nelson.com

Dahmouni, A., El Moutaouakil, K., & Satori, K. (2018). Clustering and jarque-bera normality test to face recognition. Procedia Computer Science, 127, 246–255. https://doi.org/10.1016/j.procs.2018.01.120

Daszykowski, M., & Walczak, B. (2009). Density-Based Clustering Methods.

Gnimassoun, J. E., Ricky N’DRI, A. K., & Legrand KOFFI, Dagou Dangui Augustin Sylvain. (2024). Efficient Workflow Scheduling for Minimizing Data Transfers and Enhancing Resource Utilization in Cloud IaaS Platforms. INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH, 12(11). https://doi.org/10.47191/ijmcr/v12i11.01

Latifi-Pakdehi, A., & Daneshpour, N. (2021). DBHC: A DBSCAN-based hierarchical clustering algorithm. Data and Knowledge Engineering, 135. https://doi.org/10.1016/j.datak.2021.101922

Loh, W. K., & Park, Y. H. (2014). A survey on density-based clustering algorithms. Lecture Notes in Electrical Engineering, 280 LNEE, 775–780. https://doi.org/10.1007/978-3-642-41671-2_98

Martínez-Ratón, Y., & Velasco, E. (2024). Density-functional theory for clustering of two-dimensional hard particle fluids. Journal of Molecular Liquids, 397. https://doi.org/10.1016/j.molliq.2024.124044

Monshizadeh, M., Khatri, V., Kantola, R., & Yan, Z. (2022). A deep density based and self-determining clustering approach to label unknown traffic. Journal of Network and Computer Applications, 207. https://doi.org/10.1016/j.jnca.2022.103513

Pöelitz, C., & Andrienko, N. (2010). Finding arbitrary shaped clusters with related extents in space and time. www.flickr.com

Rochman, E. M. S., Miswanto, & Suprajitno, H. (2022a). COMPARISON OF CLUSTERING IN TUBERCULOSIS USING FUZZY C-MEANS AND K-MEANS METHODS. Communications in Mathematical Biology and Neuroscience, 2022. https://doi.org/10.28919/cmbn/7335

Rochman, E. M. S., Miswanto, & Suprajitno, H. (2022b). COMPARISON OF CLUSTERING IN TUBERCULOSIS USING FUZZY C-MEANS AND K-MEANS METHODS. Communications in Mathematical Biology and Neuroscience, 2022. https://doi.org/10.28919/cmbn/7335

Sanyaolu, A. (2019). Tuberculosis: A Review of Current Trends. Epidemiology International Journal, 3(2). https://doi.org/10.23880/eij-16000123

Spiegel, M. R., & Stephens, L. J. (2008). Schaum’s outline of statistics (4th ed.). https://doi.org/10.1036/0071485848

Zhang, R., Qiu, J., Guo, M., Cui, H., & Chen, X. (2022). An Adjusting Strategy after DBSCAN. IFAC-PapersOnLine, 55(3), 219–222. https://doi.org/10.1016/j.ifacol.2022.05.038

Zhang, T. fan, Li, Z., Yuan, Q., & Wang, Y. ning. (2022). A spatial distance-based spatial clustering algorithm for sparse image data. Alexandria Engineering Journal, 61(12), 12609–12622. https://doi.org/10.1016/j.aej.2022.06.045

Zhu, Q., Tang, X., & Elahi, A. (2021). Application of the novel harmony search optimization algorithm for DBSCAN clustering. Expert Systems with Applications, 178. https://doi.org/10.1016/j.eswa.2021.115054