A Hybrid SOM-LSTM Model for Childhood Stunting Risk Classification: A Case Study in a Maternity Hospital in Medan
DOI:
10.33395/sinkron.v10i1.15434Keywords:
Stunting, Stunting, Big Data, Self-Organizing Map, Long Short-Term Memory, Artificial IntelligenceAbstract
Childhood stunting presents a persistent global and national health challenge, significantly impacting human resource quality. Clinical settings, particularly maternity hospitals, records a high volume of stunting cases, generating large-scale health data that necessitates advanced analytical methods for accurate risk identification. This paper proposes an integrated Self-Organizing Map (SOM)-Long Short-Term Memory (LSTM) model for robust stunting risk classification. SOM effectively clusters diverse patient data based on characteristic similarities, while LSTM leverages historical health records to predict future stunting probability. This approach aims to enhance predictive accuracy and facilitate proactive medical intervention. Simulation and evaluation results demonstrate that the proposed SOM-LSTM model significantly improves the reliability of early stunting risk detection compared to conventional methods, contributing to data-driven decision-making and advancing AI-based hospital transformation.
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Bappenas. (2019). RENCANA PEMBANGUNAN JANGKA MENENGAH NASIONAL 2020-2024. 315. https://perpustakaan.bappenas.go.id/e-library/file_upload/koleksi/migrasi-data-publikasi/file/RP_RKP/Narasi RPJMN IV 2020-2024_Revisi 14 Agustus 2019.pdf
Begashaw, G. B., Zewotir, T., & Fenta, H. M. (2025). A deep learning approach for classifying and predicting children’s nutritional status in Ethiopia using LSTM-FC neural networks. BioData Mining, 18(1), 11. https://doi.org/10.1186/s13040-025-00425-0
D.Ruthvik, D. N. R. (2024). AdvanceDeeplearningformalnutritionassessment. International Journal of Information Technology and Computer Engineering, 12, 42-59. Malnutrition remains a critical global health challenge, with profound implications for both physical and cognitive development across various populations. Its effects are particularly severe in vulnerable groups, including children, who are at a crucial
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
Griffin, J. M., Mathew, J., Gasparics, A., Vértesy, G., Uytdenhouwen, I., Chaouadi, R., & Fitzpatrick, M. E. (2022). Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel. Applied Sciences, 12(8), 3721. https://doi.org/10.3390/app12083721
Guo, F., Li, S., Zhao, G., Hu, H., Zhang, Z., Yue, S., Zhang, H., & Xu, Y. (2024). A SOM-LSTM combined model for groundwater level prediction in karst critical zone aquifers considering connectivity characteristics. Environmental Earth Sciences, 83(9), 267. https://doi.org/10.1007/s12665-024-11567-5
Han, N., Qiao, S., Yuan, G., Huang, P., Liu, D., & Yue, K. (2019). A novel Chinese herbal medicine clustering algorithm via artificial bee colony optimization. Artificial Intelligence in Medicine, 101, 101760. https://doi.org/10.1016/j.artmed.2019.101760
Hasdyna, N., Dinata, R. K., Rahmi, & Fajri, T. I. (2024). Hybrid Machine Learning for Stunting Prevalence: A Novel Comprehensive Approach to Its Classification, Prediction, and Clustering Optimization in Aceh, Indonesia. Informatics, 11(4), 89. https://doi.org/10.3390/informatics11040089
Kementerian Kesehatan Republik Indonesia. (2023). Laporan Hasil Survei Kesehatan Indonesia (SKI) 2023. https://www.badankebijakan.kemkes.go.id/ski-2023-dalam-angka/
Kohonen, T. (2013). Essentials of the self-organizing map. Neural Networks, 37, 52–65. https://doi.org/10.1016/j.neunet.2012.09.018
Ma, Y., Yang, J., Feng, J., Wang, H., Li, Y., & Li, Y. (2022). Load data recovery method based on SOM-LSTM neural network. Energy Reports, 8, 129–136. https://doi.org/10.1016/j.egyr.2021.11.070
Ndagijimana, S., Kabano, I. H., Masabo, E., & Ntaganda, J. M. (2023). Prediction of Stunting Among Under-5 Children in Rwanda Using Machine Learning Techniques. Journal of Preventive Medicine and Public Health, 56(1), 41–49. https://doi.org/10.3961/jpmph.22.388
Novalina, N., Aksar Tarigan, I. A., Kayla Kameela, F., & Rizkinia, M. (2025). Benchmarking machine learning algorithm for stunting risk prediction in Indonesia. Bulletin of Electrical Engineering and Informatics, 14(3), 2252–2263. https://doi.org/10.11591/eei.v14i3.8997
Pamungkas, P., & Hidayathillah, A. P. (2024). PENGEMBANGAN MODEL KASITING BERBASIS ARTIFICIAL INTELLIGENCE TERHADAP DETEKSI DINI STUNTING BAYI PADA KADER KESEHATAN. PREPOTIF : JURNAL KESEHATAN MASYARAKAT, 8(3), 5829–5835. https://doi.org/10.31004/prepotif.v8i3.34697
Sekretariat Wakil Presiden Republik Indonesia. (2021). Strategi Nasional Percepatan Pencegahan Anak Kerdil (Stunting). https://stunting.go.id/
Sinaga, F. M., Pipin, S. J., Winardi, S., Tarigan, K. M., & Brahmana, A. P. (2023). Analyzing Sentiment with Self-Organizing Map and Long Short-Term Memory Algorithms. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(1), 131–142. https://doi.org/10.30812/matrik.v23i1.3332
Soni, A., Fahey, N., Ash, A., Bhutta, Z., Li, W., Simas, T. M., Nimbalkar, S., & Allison, J. (2022). Predictive algorithm to stratify newborns at-risk for child undernutrition in India: Secondary analysis of the National Family Health Survey-4. Journal of Global Health, 12, 04040. https://doi.org/10.7189/jogh.12.04040
Sugihartono Tri, Wijaya Benny, Marini, Alkayes Ahmad Faqih, A. H. A. (2025). Optimizing Stunting Detection through SMOTE and Machine Learning: a Comparative Study of XGBoost, Random Forest, SVM, and k-NN. Journal of Applied Data Sciences, 6, 667–682. https://bright-journal.org/Journal/index.php/JADS/article/view/494/346
Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition Monitoring using Machine Learning: A Review of Theory, Applications, and Recent Advances. Expert Systems with Applications, 221, 119738. https://doi.org/10.1016/j.eswa.2023.119738
Tang, Y., Yu, F., Pedrycz, W., Yang, X., Wang, J., & Liu, S. (2022). Building Trend Fuzzy Granulation-Based LSTM Recurrent Neural Network for Long-Term Time-Series Forecasting. IEEE Transactions on Fuzzy Systems, 30(6), 1599–1613. https://doi.org/10.1109/TFUZZ.2021.3062723
Waqas, M., & Humphries, U. W. (2024). A critical review of RNN and LSTM variants in hydrological time series predictions. MethodsX, 13, 102946. https://doi.org/10.1016/j.mex.2024.102946
Wei, J., Zhuo, L., Pan, S., Lian, X., Yao, X., & Fu, X. (2023). HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA–protein interactions. Computers in Biology and Medicine, 157, 106783. https://doi.org/10.1016/j.compbiomed.2023.106783
World Health Organization. (2021). Levels and trends in child malnutrition: UNICEF/WHO/The World Bank Group joint child malnutrition estimates: key findings of the 2021 edition. https://www.who.int/publications/i/item/9789240025257
World Health Organization. (2025). SDG Target 2.2: End all forms of malnutrition. The Global Health Observatory. https://www.who.int/data/gho/data/themes/topics/sdg-target-2_2-malnutrition
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Copyright (c) 2026 Frans Mikael Sinaga, Ferawaty, Kelvin
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