Optimalisasi Random Forest dengan Penyelarasan Temporal untuk Identifikasi Faktor Determinan Stunting Nusa Tenggara Barat

Lalu Mutawalli; Mohammad  Taufan Asri Zaen; Ahmad  Tantoni; Muhammad  Fauzi Zulkarnaen

doi:10.47065/bit.v7i2.2648

Lalu Mutawalli * STMIK Lombok, Indonesia
Mohammad Taufan Asri Zaen STMIK Lombok, Indonesia
Ahmad Tantoni STMIK Lombok, Indonesia
Muhammad Fauzi Zulkarnaen STMIK Lombok, Indonesia

DOI: https://doi.org/10.47065/bit.v7i2.2648

Keywords: stunting; random forest; spatio-temporal; determinant factors; posyandu

Abstract

Stunting remains a major public health issue in West Nusa Tenggara, with key challenges including low data integrity caused by reporting discontinuities, spatial heterogeneity, and statistical noise that obscure accurate determinant identification. These issues are particularly evident during the 2023–2024 reporting transition, where inconsistencies distort the relationship between community health worker performance and stunting prevalence. This study applies a Spatio-temporal approach incorporating temporal alignment, normalization, and Random Forest regression. The analysis includes Moderated Regression Analysis using Ordinary Least Squares, R², and Global Moran's I. The objective is to improve accuracy in identifying determinants of stunting reduction and evaluate community health worker effectiveness using integrated data. Results show consistent decline in stunting prevalence during 2018–2024, with significant reductions in Central of Lombok (-42.2%), West Lombok (-39.4%), and Bima City (-47.7%), while increases occurred in Bima Regency (+8.5%) and Mataram City (+33.6%). Model performance improves significantly after data alignment, with R² exceeding 0.70. Feature importance identifies cadre workload ratio and child weighing participation as dominant predictors (p = 0.073), indicating a discovery effect. Spatial analysis yields Global Moran's I of -0.0615, suggesting a dispersed pattern. Overall, integrating temporal data alignment with machine learning and spatial analysis enhances determinant identification and supports data-driven policy for stunting reduction in West Nusa Tenggara.

References

WHO, “Stunting in a nutshell,” World Health Organization (WHO). Accessed: Dec. 17, 2025. [Online]. Available: https://www.who.int/news/item/19-11-2015-stunting-in-a-nutshell

IKP, “Strategi NTB Putus Rantai Stunting: Dari Meja Makan Hingga Program Desa Berdaya,” Bidang Informasi dan Komunikasi Publik (IKP) Dinas Kominfotik Provinsi. Accessed: Feb. 12, 2026. [Online]. Available: https://ntbprov.go.id/post/strategi-ntb-putus-rantai-stunting-dari-meja-makan-hingga-program-desa-berdaya#

M. De Onis et al., “Prevalence thresholds for wasting, overweight and stunting in children under 5 years,” Public Health Nutr., vol. 22, no. 1, pp. 175–179, Oct. 2018, doi: 10.1017/S1368980018002434.

IKP, “Stunting NTB 13,39% di Bawah Nasional, Iqbal: Ini Kerja Bersama Kab/Kota,” Bidang Informasi dan Komunikasi Publik (IKP) Dinas Kominfotik Provinsi. Accessed: Feb. 12, 2026. [Online]. Available: https://ntbprov.go.id/post/stunting-ntb-1339-di-bawah-nasional-iqbal-ini-kerja-bersama-kabkota

L. Hanifah and A. W. Astuti, “Promotive and Preventive Efforts of Health Workers in Reducing Stunting: A Scoping Review,” Jurnal Aisyah : Jurnal Ilmu Kesehatan, vol. 8, no. 1, pp. 297–308, Mar. 2023, doi: 10.30604/jika.v8i1.1603.

M. Lukman, A. Yamin, and R. D. Susanti, “Community Empowerment to Prevent Stunting Through Integrated Health Promotion in Cilayung Village, Jatinangor Sub-District,” Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM), vol. 6, no. 3, pp. 1027–1038, Mar. 2023, doi: 10.33024/jkpm.v6i3.8729.

N. F. Sahamony, T. Terttiaavini, and H. Rianto, “Analisis Perbandingan Kinerja Model Machine Learning untuk Memprediksi Risiko Stunting pada Pertumbuhan Anak,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 413–422, Apr. 2024, doi: 10.57152/malcom.v4i2.1210.

M. Yanto, F. Hadi, and S. Arlis, “Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children,” Jurnal Rekayasa Sistem dan Teknologi Informasi (RESTI), vol. 7, no. 6, pp. 1378–1386, Dec. 2023, doi: 10.29207/resti.v7i6.5278.

S. P. Manongga, H. Hendry, and D. H. F. Manongga, “Predicting a stunting prevalence using semi-supervised learning models in east nusa tenggara,” International Journal of Innovative Computing, Information and Control, vol. 19, no. 4, pp. 1073–1086, Aug. 2023, doi: 10.24507/ijicic.19.04.1073.

A. H. Mubarok, P. Pujiono, D. Setiawan, D. F. Wicaksono, and E. Rimawati, “Parameter Testing on Random Forest Algorithm for Stunting Prediction,” SINKRON: Jurnal dan Penelitian Teknik Informatika, vol. 9, no. 1, pp. 107–116, Jan. 2025, doi: 10.33395/sinkron.v9i1.14264.

Moh. A. E. Pratama, S. Hendra, H. R. Ngemba, R. Nur, R. Azhar, and R. Laila, “Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 2, pp. 200–209, Jun. 2024, doi: 10.32736/sisfokom.v13i2.2097.

T. I. A. Fanny Dewi, N. N. Wirawan, and N. Muslihah, “Determinants of stunting in urban and rural areas of Indonesia: A systematic review,” AcTion: Aceh Nutrition Journal, vol. 10, no. 3, pp. 860–874, Sep. 2025, doi: 10.30867/action.v10i3.2690.

A. Wetty Sinurat, K. Manurung, R. Sembiring, K. Rochadi, and J. Sinaga, “Factors Associated with the Incidence of Stunting in Early Childhood Children in the Working Area of Buhit Community Health Centre, Pangururan District, Samosir Regency, 2024,” Journal of Pharmaceutical and Sciences, vol. 2025, no. 3, pp. 1970–1985, Sep. 2025, doi: 10.36490/journal-jps.com.

M. L. Arqam et al., “Integrating education-based interventions and machine learning for stunting prevention: A case study in East Lombok, Indonesia,” Dialogues in Health, vol. 8, no. 1, pp. 1–18, Jun. 2026, doi: 10.1016/j.dialog.2025.100264.

S. R. Giyarsih et al., “The potential role of farmer women’s groups as community-based urban farming in addressing the risk of stunting,” Discover Food, vol. 6, no. 1, pp. 1–18, Dec. 2026, doi: 10.1007/s44187-025-00728-5.

A. Arifuddin, A. Fauzan, R. Bagus, F. Hakim, and A. F. Nur, “Spatial Pattern Analysis and Determinants of Stunting Prevalence in Central Sulawesi, Indonesia: Using Linear Regression, Local Moran’s I, and Random Forest Approaches,” Healthy Tadulako Journal (Jurnal Kesehatan Tadulako, vol. 11, no. 3, pp. 504–516, Jul. 2025, doi: 10.22487/ht.

M. A. Morid, O. R. L. Sheng, and J. Dunbar, “Time Series Prediction Using Deep Learning Methods in Healthcare,” ACM Trans. Manag. Inf. Syst., vol. 14, no. 1, pp. 1–29, Apr. 2023, doi: 10.1145/3531326.

Moh. A. E. Pratama, S. Hendra, H. R. Ngemba, R. Nur, R. Azhar, and R. Laila, “Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 2, pp. 200–209, Jun. 2024, doi: 10.32736/sisfokom.v13i2.2097.

E. K. Anku and H. O. Duah, “Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms,” PLoS One, vol. 19, no. 2, pp. 1–16, Feb. 2024, doi: 10.1371/journal.pone.0296625.

T. T. Tamir, M. A. Techane, M. T. Dessie, and K. A. Atalell, “Applied nutritional investigation spatial variation and determinants of stunting among children aged less than 5 y in Ethiopia: A spatial and multilevel analysis of Ethiopian Demographic and Health Survey 2019,” Nutrition, vol. 103, pp. 1–9, Jun. 2022, doi: 10.1016/j.nut.2022.111786.

Dinas Kesehatan, “Jumlah Kader Posyandu di Nusa Tenggara Barat,” NTB Satu Data. Accessed: Feb. 13, 2026. [Online]. Available: https://data.ntbprov.go.id/dataset/9e7557c6-0613-4dfa-b3db-d9815cfee0b6/show

Dinas Kesehatan, “Jumlah dan Persentase Balita Ditimbang di Nusa Tenggara Barat,” NTB Satu Data. Accessed: Feb. 13, 2026. [Online]. Available: https://data.ntbprov.go.id/dataset/9d291da4-a2a5-4982-a801-3e4bb115b257/show

Dinas Kesehatan, “Jumlah dan Persentase Balita Stunting di Nusa Tenggara Barat,” NTB Satu Data. Accessed: Feb. 13, 2026. [Online]. Available: https://data.ntbprov.go.id/dataset/9d27497f-eb7b-4513-814f-244f656bf52e/show

M. J. Crawley, The R Book. New Jersey: John Wiley & Son, 2007. doi: 10.1002/9780470515075.

T. Hengl, M. Nussbaum, M. N. Wright, G. B. M. Heuvelink, and B. Gräler, “Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables,” PeerJ, vol. 2018, no. 8, pp. 1–49, Aug. 2018, doi: 10.7717/peerj.5518.

E. Scornet, G. Biau, and J. P. Vert, “Consistency of random forests,” Ann. Stat., vol. 43, no. 4, pp. 1716–1741, Aug. 2015, doi: 10.1214/15-AOS1321.

J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed. New Jersey: Lawrence Erlbaum Associate, 2003. Accessed: Feb. 14, 2026. [Online]. Available: https://eli.johogo.com/Class/CCU/SEM/_Applied%20Multiple%20Regression-Correlation%20Analysis%20for%20the%20Behavioral%20Sciences_Cohen.pdf

R. M. Dodhia, J. Cohen, P. Cohen, S. G. West, and L. S. Aiken, “A Review of Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.),” Journal of Educational and Behavioral Statistics, vol. 30, no. 2, pp. 227–229, Aug. 2005, doi: https://doi.org/10.3102/10769986030002227.

O. Schabenberger and C. A. Gotway, “Statistical Methods for Spatial Data Analysis,” in Mathematical Geology, vol. 38, Roussos Dimitrakopoulos, Ed., New York: Chapman and Hall/CRC, 2006, pp. 511–513. doi: 10.1201/9781315275086.

Y. Chen, “Spatial autocorrelation equation based on Moran’s index,” Nature Research, Beijing, Dec. 2023. doi: 10.1038/s41598-023-45947-x.

S. Sofiyulloh, M. Rahmaniati, and F. Nugraha, “Spatial-Temporal Study of Stunting Risk in East Java 2021 - 2023,” Medical Technology and Public Health Journal, vol. 9, no. 2, pp. 163–174, Sep. 2025, doi: 10.33086/mtphj.v9i2.6921.

L. Gopalakrishnan, S. Patil, L. Fernald, D. Walker, and N. Diamond-Smith, “Factors associated with Anganwadi Workers’ service delivery of Integrated Child Development Services (ICDS) in rural India: A cross-sectional analysis of household and community health worker surveys,” PLOS ONE: Public Library of Science, vol. 20, no. 7, pp. 1–16, Jul. 2025, doi: 10.1371/journal.pone.0326971.

A. Singh et al., “Learning from health system actor and caregiver experiences in Ghana and Nepal to strengthen growth monitoring and promotion,” PLOS ONE: Public Library of Science, vol. 18, no. 3, pp. 1–19, Mar. 2023, doi: 10.1371/journal.pone.0282807.

K. Scott et al., “What do we know about community-based health worker programs? A systematic review of existing reviews on community health workers,” Hum. Resour. Health, vol. 16, no. 1, pp. 1–7, Aug. 2018, doi: 10.1186/s12960-018-0304-x.

A. B. Zemariam et al., “Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms,” PLOS ONE: Public Library of Science, vol. 20, no. 1, pp. 1–27, Jan. 2025, doi: 10.1371/journal.pone.0316452.

S. H. Marpaung, F. M. Sinaga, K. H. Rambe, F. P. Simamora, and K. Kelvin, “Random Forest Optimization Using Recursive Feature Elimination for Stunting Classification,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 8, no. 1, pp. 281–287, Mar. 2025, doi: 10.24014/ijaidm.v8i1.35295.

T. Sugihartono, B. Wijaya, Marini, A. F. Alkayes, and H. A. Anugrah, “Optimizing Stunting Detection through SMOTE and Machine Learning: a Comparative Study of XGBoost, Random Forest, SVM, and k-NN,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 667–682, Jan. 2025, doi: 10.47738/jads.v6i1.494.

H. Shen, H. Zhao, and Y. Jiang, “Machine Learning Algorithms for Predicting Stunting among Under-Five Children in Papua New Guinea,” Children: Multidisciplinary Digital Publishing Institute (MDPI), vol. 10, no. 1, pp. 1–18, Oct. 2023, doi: 10.3390/children10101638.

F. Xie et al., “Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies,” J. Biomed. Inform., vol. 126, no. 1, pp. 1–4, Feb. 2022, doi: 10.1016/j.jbi.2021.103980.

M. K. Ayele, G. A. Baye, S. H. Yesuf, A. A. Engda, and E. T. Mitiku, “Predicting stunting status among under five children in ethiopia using ensemblemachine learning algorithms,” Sci. Rep., vol. 15, no. 1, pp. 1–11, Dec. 2025, doi: 10.1038/s41598-025-03206-1.

M. Myatt et al., “Children who are both wasted and stunted are also underweight and have a high risk of death: A descriptive epidemiology of multiple anthropometric deficits using data from 51 countries,” Archives of Public Health, vol. 76, no. 1, pp. 1–11, Jul. 2018, doi: 10.1186/s13690-018-0277-1.

Y.-C. Kuo and Y.-J. Tseng, “MedM2T: A MultiModal Framework for Time-Aware Modeling with Electronic Health Record and Electrocardiogram Data,” Journal of Biomedical and Health Informatics (JBHI), pp. 1–9, Nov. 2025, doi: doi.org/10.48550/arXiv.2510.27321.