Prioritizing Pregnant Mother at Risk of Stunting: An Analytic Network Process Approach
Abstract
Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.
References
[2] A. Maturo, E. Sciarra, and A. G. S. Ventre, “Counselling: Decision Making, Consensus, and Mediation,” Procedia - Social and Behavioral Sciences, vol. 5. pp. 1770–1776, 2010. doi: 10.1016/j.sbspro.2010.07.362.
[3] A. Saleh, S. Syahrul, V. Hadju, I. Andriani, and I. Restika, “Role of Maternal in Preventing Stunting: a Systematic Review,” Gac Sanit, vol. 35, pp. S576–S582, Jan. 2021, doi: 10.1016/j.gaceta.2021.10.087.
[4] C. Berti and A. La Vecchia, “Temporal trend of child stunting prevalence and Food and Nutritional Surveillance System,” J Pediatr (Rio J), 2022, doi: 10.1016/j.jped.2022.10.001.
[5] S. H. Quamme and P. O. Iversen, “Prevalence of child stunting in Sub-Saharan Africa and its risk factors,” Clinical Nutrition Open Science, vol. 42. Elsevier B.V., pp. 49–61, Apr. 01, 2022. doi: 10.1016/j.nutos.2022.01.009.
[6] K. J. Singh, V. Chiero, M. Kriina, N. T. Alee, and K. Chauhan, “Identifying the trend of persistent cluster of stunting, wasting, and underweight among children under five years in northeastern states of India,” Clin Epidemiol Glob Health, vol. 18, Nov. 2022, doi: 10.1016/j.cegh.2022.101158.
[7] J. Castro-Bedriñana, D. Chirinos-Peinado, and G. De La Cruz-Calderón, “Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants,” Public Health in Practice, vol. 2, Nov. 2021, doi: 10.1016/j.puhip.2021.100112.
[8] M. I. H. Methun, A. Kabir, S. Islam, M. I. Hossain, and M. A. Darda, “A machine learning logistic classifier approach for identifying the determinants of Under-5 child morbidity in Bangladesh,” Clin Epidemiol Glob Health, vol. 12, Oct. 2021, doi: 10.1016/j.cegh.2021.100812.
[9] H. Shi et al., “Explainable machine learning model for predicting the occurrence of postoperative malnutrition in children with congenital heart disease,” Clinical Nutrition, vol. 41, no. 1, pp. 202–210, Jan. 2022, doi: 10.1016/j.clnu.2021.11.006.
[10] W. Zayat, H. S. Kilic, A. S. Yalcin, S. Zaim, and D. Delen, “Application of MADM methods in Industry 4.0: A literature review,” Comput Ind Eng, vol. 177, p. 109075, Mar. 2023, doi: 10.1016/j.cie.2023.109075.
[11] O. Shabalina, V. Guriev, S. Kosyakov, N. Dmitriev, and A. Davtian, “MADM System for the Development of Adaptable Mobile Applications for People with Intellectual Disabilities,” in 11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020, Institute of Electrical and Electronics Engineers Inc., Jul. 2020. doi: 10.1109/IISA50023.2020.9284409.
[12] A. Tchangani, “Social interactions issues in group decision-making,” in 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 245–252. doi: 10.1109/DASA51403.2020.9317263.
[13] M. S. Mubarik, S. H. A. Kazmi, and S. I. Zaman, “Application of gray DEMATEL-ANP in green-strategic sourcing,” Technol Soc, vol. 64, Feb. 2021, doi: 10.1016/j.techsoc.2020.101524.
[14] F. Kaytez, “Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis,” Renew Energy, vol. 196, pp. 1281–1293, Aug. 2022, doi: 10.1016/j.renene.2022.07.043.
[15] G. Büyüközkan and G. Çifçi, “A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers,” Expert Syst Appl, vol. 39, no. 3, pp. 3000–3011, Feb. 2012, doi: 10.1016/j.eswa.2011.08.162.
[16] W.-Y. Chiu, G.-H. Tzeng, and H.-L. Li, “A new hybrid MCDM model combining DANP with VIKOR to improve e-store business,” Knowl Based Syst, vol. 37, pp. 48–61, Jan. 2013, doi: 10.1016/j.knosys.2012.06.017.
[17] N. Dubey and A. Tanksale, “A study of barriers for adoption and growth of food banks in India using hybrid DEMATEL and Analytic Network Process,” Socioecon Plann Sci, vol. 79, Feb. 2022, doi: 10.1016/j.seps.2021.101124.
[18] E. Selerio, J. A. Caladcad, M. R. Catamco, E. M. Capinpin, and L. Ocampo, “Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process,” Socioecon Plann Sci, vol. 82, Aug. 2022, doi: 10.1016/j.seps.2021.101217.
[19] I. M. Lami and E. Todella, “A multi-methodological combination of the strategic choice approach and the analytic network process: From facts to values and vice versa,” Eur J Oper Res, vol. 307, no. 2, pp. 802–812, Jun. 2023, doi: 10.1016/j.ejor.2022.10.029.
[20] Y. Chen et al., “Analytic network process: Academic insights and perspectives analysis,” Journal of Cleaner Production, vol. 235. Elsevier Ltd, pp. 1276–1294, Oct. 20, 2019. doi: 10.1016/j.jclepro.2019.07.016.
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