Main Article Content
Abstract
Stunting is a condition where a child’s height is under the average height of their age. Stunting will have an impact on the quality of human resources. The 2022 Indonesian Nutrition Status Survey reported that the prevalence of stunting in Indonesia reached 21.6%. This number decreased compared to the previous year. However, it remains below the government’s planned target of 14%. Therefore, appropriate methods are needed to model and identify the factors with the most significant impact on the data for each region studied. This research modeled the stunting problem using quantile regression. Quantile regression has several advantages, including the fact that it can be used on data with an inhomogeneous distribution and is not affected by outliers. The results showed that variables that had a significant effect on the prevalence of stunting using 0.95 quantile regression included babies receiving exclusive breast milk, percentage of family planning participants, percentage of households with access to adequate sanitation, low birth weight (LBW) babies, and percentage of toddlers who have Maternal and Child Health (MCH) books. It is hoped that this research can be utilized to carry out appropriate interventions to reduce the prevalence of stunting that occurs in Indonesia.
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References
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References
F. Agushybana, A. Pratiwi, P.L. Kurnia, N. Nandini, J. Santoso, and A. Setyo, “Reducing Stunting Prevalence: Causes, Impacts, and Strategies,” in BIO Web Conf., Nov. 2002, Art. no 00009, doi: 10.1051/bioconf/20225400009.
UNICEF Indonesia, “Laporan Tahunan 2023,” 2003. [Online]. Available: https://www.unicef.org/indonesia/media/21331/file/UNICEF%20Laporan%20Tahunan%202023.pdf.pdf
UNICEF Indonesia, “Laporan Tahunan Indonesia 2022,” 2022. [Online]. Available: https://www.unicef.org/indonesia/media/17686/file/Laporan%20Tahunan%20UNICEF%20Indonesia%202022%20(Single%20page).pdf
Kementerian Kesehatan Republik Indonesia, “Profil Kesehatan Indonesia 2022,” 2023. [Online]. Available: https://kemkes.go.id/id/profil-kesehatan-indonesia-2022
Badan Pusat Statistik, “Laporan Indeks Khusus Penanganan Stunting Kabupaten/Kota 2021-2022,” 2023. [Online]. Available: https://www.bps.go.id/id/publication/2023/12/19/37ee0674d0e89152b2425e9f/
laporan-indeks-khusus-penanganan-stunting-2021-2022.html
J. Mazucheli, B. Alves, A.F. Menezes, and V. Leiva, “An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data,” Comput. Methods Progr. Biomed., vol. 221, Jun. 2022, Art. no 106816, doi: 10.1016/j.cmpb.2022.106816.
V.K. Patidar, R. Wadhvani, S. Shukla, M. Gupta, and M. Gyanchandani, “Quantile regression comprehensive in machine learning: A review,” in 2023 IEEE Int. Stud. Conf. Elect. Electron. Comput. Sci. (SCEECS), 2024, pp. 1–6, doi: 10.1109/SCEECS57921.2023.10063026.
M.H. Chowdhury, M.F. Aktar, M.A. Islam, and N.M. Khan, “Factors associated with stunting status among under-5 years children in Bangladesh: Quantile regression modelling approach,” Child. Youth Serv. Rev., vol. 155, Dec. 2023, Art. no 107199, doi: 10.1016/j.childyouth.2023.107199
M. Siddiqa, A. Zubair, A. Kamal, M. Ijaz, and T. Abushal, “Prevalence and associated factors of stunting, wasting and underweight of children below five using quintile regression analysis (PDHS 2017–2018),” Sci. Rep., vol. 12, no. 1, Nov. 2022, Art. no 20326, doi: 10.1038/s41598-022-24063-2.
J.M.K. Aheto, “Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana,” BMC Public Health, vol. 20, May 2020, Art. no 644, doi: 10.1186/s12889-020-08782-7.
S.J. Staffa, D.M. Kohane, and D. Zurakowski, “Quantile regression and its applications: A primer for anesthesiologists,” Anesth. Analg., vol. 128, no. 4, pp. 820–830, 2019, doi: 10.1213/ANE.0000000000004017.
Y. Li, X. Li, M. Guo, C. Chen, P. Ni, and Z. Huang, “Regression analysis and its application to oil and gas exploration: A case study of hydrocarbon loss recovery and porosity prediction, China,” Energy Geosci., vol. 5, no. 4, Oct. 2024, Art. no 100333, doi: 10.1016/j.engeos.2024.100333.
X. Yan and X. G. Su, Linear Regression Analysis. Singapore, Singapore: World Scientific Publishing, 2009.
R.J. Freund, W.J. Wilson, and P. Sa, Regression Analysis: Statistical Modeling of a Response Variable. Cambridge, MA, USA: Academic Press, 2006.
E. Waldmann, “Quantile regression: A short story on how and why,” Stat. Model., vol. 18, no. 3–4, pp. 213-218, 2018, doi: 10.1177/1471082X18759142.
M. Maiti, “OLS versus quantile regression in extreme distributions,” Contad. Adm., vol. 64, no. 2, pp. 1–11, Apr.–Jun. 2019, doi: 10.22201/fca.24488410e.2018.1702.
R. Koenker, Quantiles Regression. Cambridge, England: Cambridge University Press, 2005.