Data-Driven K-Means Clustering Analysis for Stunting Risk Profiling of Pregnant Women
- Desvita Dian Nazella Universitas Dian Nuswantoro, Semarang, Indonesia
-
Heru Pramono Hadi
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Farrikh Al Zami Universitas Dian Nuswantoro, Semarang, Indonesia
- Ayu Ashari Universitas Dian Nuswantoro, Semarang, Indonesia
- Yupie Kusumawati Universitas Dian Nuswantoro, Semarang, Indonesia
- Suharnawi Suharnawi Universitas Dian Nuswantoro, Semarang, Indonesia
- Rama Aria Megantara Universitas Dian Nuswantoro, Semarang, Indonesia
- Muhammad Naufal Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
Stunting in children is influenced by maternal health conditions during pregnancy. This study aims to classify pregnant women to prevent stunting based on clinical, demographic, and environmental factors using the K-Means Clustering algorithm. A total of 229 data from the Primadona application (Disdalduk KB Kota Semarang) were analyzed using 14 normalized variables. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The Kruskal-Wallis test was performed to verify differences between clusters. This study resulted in seven clusters with different profiles, with a Silhouette Score of 0.134, Davies-Bouldin Index of 1.509, and Calinski-Harabasz Index of 29.54. These values indicate that the cluster structure is formed and reflects the variation in risk for pregnant women, although there is overlap due to differences in characteristics between individuals. The clustering successfully differentiated pregnant women with low to high risk, influenced by health and environmental factors. This study proves the effectiveness of K-Means in identifying stunting risk patterns in pregnant women and supports more targeted interventions, such as nutritional counseling, disease risk monitoring, education on cigarette smoke exposure, and referrals. Limitations of this study include the unbalanced distribution of data between and the use of cross-sectional data. Future research is recommended to improve pre-processing and compare other clustering methods such as K-Medoids or DBSCAN for more precise stunting risk analysis.
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References
S. K. Pranindita, A. Yuniastuti, and S. R. Rahayu, “Hubungan Faktor Maternal Ibu dengan Kejadian Stunting pada Balita Usia 24-59 Bulan di Kabupaten Grobogan,” Indonesian Journal of Public Health and Nutrition, vol. 5, no. 1, 2025, doi: https://doi.org/10.15294/ijphn.v5i1.28999.
World Health Organization, Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division. Geneva: World Health Organization, 2023. Accessed: Aug. 09, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789240068759
Kementerian Kesehatan Republik Indonesia, Laporan Kinerja Kementerian Kesehatan Republik Indonesia Tahun 2023. Jakarta: Kementerian Kesehatan Republik Indonesia, 2024.
L. Sulistianingrum, “Karakteristik dan tingkat pengetahuan ibu hamil dengan kejadian kurang energi kronis (KEK),” Midwifery J. MJ, vol. 3, no. 4, 2023, doi: 10.33024/mj.v3i4.13379.
N. K. Pane, U. H. Almadany, and E. Sujoko, “Status Gizi Ibu Hamil sebagai Prediktor Kejadian Stunting pada Anak Usia 24–59 Bulan di Kecamatan Padangsidimpuan Selatan,” PubHealth J. Kesehat. Masy., vol. 4, no. 1, pp. 46–53, Jul. 2025, doi: 10.56211/pubhealth.v4i1.1026.
W. Wulandari and W. D. Pangesti, “Prevalensi Preeklamsi dengan Komplikasi di Rumah Sakit Rujukan Kabupaten Banyumas Tahun 2017-2020,” J. Kebidanan Harapan Ibu Pekalongan, vol. 9, no. 1, pp. 1–15, Feb. 2022, doi: 10.37402/jurbidhip.vol9.iss1.168.
P. Hanum, S. Sumiaty, S. Sumiati, and S. Suryani, “Hubungan Kadar HB, Lila dan Berat Badan Ibu Saat Hamil Berisiko dengan Kejadian Stunting pada Anak Usia 1-3 Tahun,” MAHESA Malahayati Health Stud. J., vol. 4, no. 2, pp. 699–708, Feb. 2024, doi: 10.33024/mahesa.v4i2.13230.
D. Sartika, F. Elfaladonna, and A. Octarina, “Kombinasi hybrid K-means untuk klasterisasi multivariat dalam analisis stunting,” Jurnal Jaringan Sistem Informasi Robotik (JSR), vol. 9, no. 1, pp. 64-72, 2025.
J. Maulindar and E. P. Yudha, “Pengembangan Klastering Untuk Penanganan Ibu Hamil Menggunakan K-Means,” in Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023, Surakarta, Indonesia: Universitas Duta Bangsa Surakarta, Jul. 2023.
B. P. Wongso, M. E. Johan, and M. I. Fianty, “Empowering Pregnancy Risk Assessment: A Web-Based Classification Framework with K-Means Clustering Enhanced Models,” J. Inf. Syst. Inform., vol. 5, no. 4, pp. 1221–1239, Nov. 2023, doi: 10.51519/journalisi.v5i4.568.
R. Ishak, “Optimasi K-Means pada Clustering Penyakit Ibu Hamil Menggunakan Random Forest Optimization of K-Means in Disease Clustering of Pregnant Women Using Random Forest,” Jambura J. Electr. Electron. Eng., vol. 7, no. 1, Jan. 2025.
I. Indra, N. Nur, Muh. Iqram, and N. Inayah, “Perbandingan K-Means dan Hierarchical Clustering dalam Pengelompokan Daerah Beresiko Stunting,” ISI, vol. 8, no. 2, p. 356, Nov. 2023, doi: 10.35314/isi.v8i2.3612.
M. H. M. Rohman et al., “Clustering Analysis of Stunting Risk Factors Using K-Means and Principal Component Analysis: A Case Study in Indonesian Regency,” Sinkron, vol. 9, no. 1, pp. 65–77, 2025, doi: 10.33395/sinkron.v9i1.14311.
N. Alharbe, M. A. Rakrouki, and A. Aljohani, “A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm,” Processes, vol. 10, no. 6, p. 1199, Jun. 2022, doi: 10.3390/pr10061199.
A. Aljohani, “Optimizing Patient Stratification in Healthcare: A Comparative Analysis of Clustering Algorithms for EHR Data,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 173, Jul. 2024, doi: 10.1007/s44196-024-00568-8.
I. T. Utami, F. Suryaningrum, and D. Ispriyanti, “K-means cluster count optimization with silhouette index validation and Davies Bouldin index (case study: coverage of pregnant women, childbirth, and postpartum health services in Indonesia in 2020),” BAREKENG J. Ilmu Mat. Dan Terap., vol. 17, no. 2, pp. 0707–0716, Jun. 2023, doi: 10.30598/barekengvol17iss2pp0707-0716.
R. D. Syaputra and A. Solichin, “Pregnancy Risk Level Classification Using The CRISP-DM Method,” J. Ris. Inform., vol. 5, no. 1, pp. 537–548, Dec. 2022, doi: 10.34288/jri.v5i1.487.
H. Lv, L. Hu, M. Jiang, X. Liu, and Z. He, “Interpretable Clustering Ensemble,” Jun. 06, 2025, arXiv: arXiv:2506.05877. doi: 10.48550/arXiv.2506.05877.
S. Febriyanti and J. Nugraha, “Application of K-Medoids Clustering to Increase the 2020 Family Planning Program in Sleman Regency,” Enthusiastic Int. J. Appl. Stat. Data Sci., pp. 10–18, Apr. 2022, doi: 10.20885/enthusiastic.vol2.iss1.art2.
D. T. Setiyawan, B. Berlilana, and A. S. Barkah, “Comparative Analysis of DBSCAN, OPTICS, and Agglomerative Clustering Methods for Identifying Disease Distribution Patterns in Banjarnegara Community Health Centers,” J. Tek. Inform. (JUTIF), vol. 6, no. 3, pp. 1229–1240, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4577.
H. P. Hadi et al., “Mengungkap Heterogenitas Stunting pada Anak: Pendekatan Machine Learning untuk Intervensi yang Tepat Sasaran di Sambas, Indonesia,” Jatekom J. Apl. Teknol. Dan Komputasi, vol. 1, no. 2, pp. 94–108, Jun. 2025.
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Copyright (c) 2025 Desvita Dian Nazella, Heru Pramono Hadi, Farrikh Al Zami, Ayu Ashari, Yupie Kusumawati, Suharnawi Suharnawi, Rama Aria Megantara, Muhammad Naufal
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