Segmentasi Risiko Kesehatan Bayi dan Balita Menggunakan Algoritma K-Means
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.504Keywords:
K Means Clustering; Infant Health Risk; Low Birth Weight; Jakarta; Public Health SegmentationAbstract
Urban disparities in maternal and child health remain a critical challenge in achieving the Sustainable Development Goals (SDGs). This study aims to map the disparity of health risks among infants and toddlers across 44 subdistricts in DKI Jakarta by analyzing three key indicators: prevalence of low birth weight (LBW), infant mortality, and undernutrition. Cross-sectional data from 2024 (n=176) were normalized using Min-Max scaling to minimize scale bias. The clustering process using the K-Means algorithm was conducted after determining the optimal number of clusters (k=5) through the Elbow method. Cluster validation employed three metrics—Silhouette Score (0.65), Davies-Bouldin Index (0.45), and Calinski-Harabasz Index (82.2)—demonstrating the model's consistency. Stability analysis through subsampling further confirmed the reliability of the results (standard deviation <0.1). Five risk patterns were identified: (1) two low-risk clusters (LBW <1.0%; undernutrition <2%), (2) two moderate-risk clusters (LBW 1.0–1.75%; infant mortality 0.5–3%), and (3) one high-risk cluster (LBW >1.75%; undernutrition >8%). The subdistricts of Jagakarsa and Kepulauan Seribu were identified as priority intervention hotspots due to high comorbidity risks. The findings indicate that the K-Means approach is effective in supporting evidence-based resource allocation policies, particularly in optimizing NICU services and nutrition supplementation programs in high-risk areas. This spatially based approach also facilitates more intuitive visualization for targeted and efficient planning of local health programs.
Downloads
References
WHO, “Health-related Sustainable Development Goals and targets in the Eastern,” Jun. 2021. [Online]. Available: www.emro.who.int
R. Gemilasari, L. Zeffira, R. Malik, and V. T. Septiana, “Karakteristik Bayi Dengan Berat Badan Lahir Rendah (BBLR),” Scientific Journal, vol. 3, no. 1, pp. 16–26, Jan. 2024, doi: https://doi.org/10.56260/sciena.v3i1.125.
C. Crum, J. Sundquist, and K. Sundquist, “Risk of Hypertension Into Adulthood in Persons Born Prematurely: a National Cohort Study,” Eur Heart J, vol. 41, no. 16, pp. 1551–1553, Apr. 2020, doi: 10.1093/eurheartj/ehaa118.
A. E. K. Wollum, E. K. Berdal, J. M. Iversen, M. S. Indredavik, and K. A. I. Evensen, “Outcomes and predictors of Functioning, Mental Health, and Health-Related Quality of Life in Adults Born With Very Low Birth Weight: a Prospective Longitudinal Cohort Study,” BMC Pediatr, vol. 22, no. 1, pp. 1–12, Dec. 2022, doi: 10.1186/s12887-022-03676-6.
D. Vlahov et al., “Urban as a Determinant of Health,” Journal of Urban Health, vol. 84, no. 1, pp. 16–25, May 2007, doi: 10.1007/s11524-007-9169-3.
A. B. Lawson, Bayesian Disease Mapping Third Edition, Third. Philadelphia: Taylor & Francis Group, 2018.
K. Wagstaff and C. Cardie, “Constrained K-means Clustering with Background Knowledge,” San Francisco: Proceedings of the Eighteenth International Conference on Machine Learning, Jun. 2001, pp. 577–584.
M. A. Robbani, G. Firmansyah, A. M. Widodo, and B. Tjahjono, “Clustering of Child Stunting Data in Tangerang Regency Using Comparison of K-Means, Hierarchical Clustering and DBSCAN Methods Attribution-Share Alike 4.0 International (CC BY-SA 4.0),” Asian Journal of Social and Humanities, vol. 2, p. 2024, Sep. 2024, doi: https://doi.org/10.59888/ajosh.v2i12.422.
A. I. Silitonga, Z. A. Nabila, C. R. Z. Lubis, S. Nurdina, and Haryadi, “Klasterisasigizi Buruk Dan Stuntingdi Provinsi Sumatera Utara Menggunakan K-Means Clustering,” Jurnal METHODIKA, vol. 10, no. 2, pp. 13–18, Sep. 2024, doi: https://doi.org/10.46880/mtk.v10i2.3147.
H. Maryani, A. Rizkianti, and N. Izza, “Classification of Healthy Family Indicators in Indonesia Based on a K-Means Cluster Analysis,” Journal of Preventive Medicine and Public Health, vol. 57, no. 3, pp. 234–240, Nov. 2023, doi: https://doi.org/10.3961/jpmph.23.497.
R. Facendola, G. Ottomano Palmisano, A. De Boni, C. Acciani, and R. Roma, “Determinants of The Adherence to Mediterranean Diet: Application of The K-means Cluster Analysis Profiling Children in The Metropolitan City of Bari,” Front Sustain Food Syst, vol. 7, no. 1, pp. 1–17, Jan. 2024, doi: 10.3389/fsufs.2023.1329090.
T. A. Munandar and A. Yunizar Yusuf Pratama, “Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm,” MATRIK?: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 23, no. 2, pp. 285–296, Feb. 2024, doi: 10.30812/matrik.v23i2.3352.
S. Zelasky, C. L. Martin, C. Weaver, L. K. Baxter, and K. M. Rappazzo, “Identifying Groups of Children’s Social Mobility Opportunity for Public Health Applications Using K-Means Clustering,” Heliyon, vol. 9, no. 9, pp. 1–11, Sep. 2023, doi: 10.1016/j.heliyon.2023.e20250.
E. Irfiani, S. Sulistia Rani, S. Nusa Mandiri Jl Kramat Raya No, and J. Pusat, “Algoritma K-Means Clustering Untuk Menentukan Nilai Gizi Balita,” Jurnal Sistem dan Teknologi Informasi, vol. 6, no. 4, pp. 17–27, Oct. 2018, doi: https://doi.org/10.26418/justin.v6i4.29024.
S. Raschka and V. Mirjalili, Python Machine Learning Third Edition Machine Learning and Deep Learning With Python, Scikit-Learn, And TensorFlow 2, Third. Birmingham: Packt, 2019. [Online]. Available: www.packt.com
P. Bickel, P. Diggle, S. Fienberg, U. Gather, I. Olkin, and S. Zeger, Springer Series in Statistics. New York: Springer, 2009. [Online]. Available: http://www.springer.com/series/692
M. Kuhn and K. Johnson, Applied Predictive Modeling. London: Springer, 2023.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. California: Morgan Kaufmann, 2006.
W. Triningsih, “Tata Laksana Perawatan Bayi Berat Badan Lahir Rendah,” RSUP Dr. Sardjito Yogyakarta.
UNICEF, “Good Nutrition is The Bedrock of Child Survival and Development,” UNICEF.
KEMENKES, “Profil Kesehatan Indonesia 2022,” Jakarta, 2022. Accessed: May 16, 2025. [Online]. Available: http://www.kemkes/go.id
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Segmentasi Risiko Kesehatan Bayi dan Balita Menggunakan Algoritma K-Means
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Lalu Mutawalli, Mohammad Taufan Asri Zaen, Ahmad Tantoni, Indi Febriana Suhriani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
