Modeling Factors That Influence the Percentage of Stunting Children in Indonesia Using Panel Data Regression

Sartono, Firyal Almasah Kamilia (2023) Modeling Factors That Influence the Percentage of Stunting Children in Indonesia Using Panel Data Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Children's survival, health, and development depend on high-quality nourishment. Stunting is a nutritional problem identified with a children's height being measured, then compared with a standard, and the results are below normal. Stunting can have long-term effects such as decreased cognitive ability and learning achievement and decreased immunity so that people become sick easily, and are at high risk of developing diabetes, etc. The government targets the stunting rate to drop to 14 percent in 2024. Meanwhile, in 2021, the prevalence of stunting is still at 24.4 percent. To find out the factors causing the percentage of stunting in children in Indonesia from 2017 to 2021, panel data regression is used in this study. The panel data regression method is a method by combining the cross-section data and time series data. Variables used in this study are the percentage of exclusive breastfeeding, percentage of households with access to proper sanitation, percentage of visits of pregnant women (K4), percentage of households with access to proper drinking water, and percentage of complete basic immunization in infants with the 34 provinces in Indonesia as the research unit. The result of this study proves that there is no multicollinearity between the independent variables. The best estimation model for analyzing the percentage of stunting in children under five years old in Indonesia is the FEM between individuals and times. The variables that have a significant effect are exclusive breastfeeding, households with access to proper sanitation, and visits of pregnant women with an adjusted

Item Type:	Thesis (Other)
Uncontrolled Keywords:	children, panel data regression, stunting
Subjects:	Q Science > QA Mathematics > QA278.2 Regression Analysis. Logistic regression
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Firyal Almasah Kamilia Sartono
Date Deposited:	14 Sep 2023 04:46
Last Modified:	14 Sep 2023 04:46
URI:	http://repository.its.ac.id/id/eprint/104613

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