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All Journal Inferensi Jurnal Litbang Provinsi Jawa Tengah
Sitanggang, Yuliana Ria Uli
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Public Health Social Sciences

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Clustering Area to Reduce Stunting Rates in Central Java Susanti, Desilia Wimbi; Tanur, Erwin; Sitanggang, Yuliana Ria Uli
JURNAL LITBANG PROVINSI JAWA TENGAH Vol 21 No 2 (2023): Jurnal Litbang Provinsi Jawa Tengah
Publisher : Badan Riset dan Inovasi Daerah Provinsi Jawa Tengah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36762/jurnaljateng.v21i2.1125

Abstract

Accelerating the reduction of the stunting rate is the government's focus until the end of 2024. Efforts to reduce the stunting rate need to pay attention to the variable factors that cause stunting under five children and the characteristics of the region so that optimal results can be obtained. This study aims to grouping regencies based on the causes of stunting in toddlers in Central Java Province. This research expected make a contribution to the Central Java Government in determining policies related to reducing stunting rates that are more appropriate by taking into account regional characteristics. The research design uses descriptive analysis and cluster analysis. The grouping of regencies in Central Java Province using cluster analysis resulted in three clusters, namely clusters with low, medium, and high stunting factors. Regencies in the first cluster need increased access to proper sanitation, JKN ownership, and increased welfare. The second cluster needs to focus on increasing JKN ownership, while the third cluster needs to focus on increasing the coverage of exclusive breastfeeding and early breastfeeding.
Small Area Estimation of Child Poverty on Java Island In 2021 (Comparison of EBLUP and Hierarchical Bayes) Istiana, Nofita; Tanur, Erwin; Ubaidillah, Azka; Sitanggang, Yuliana Ria Uli; Nainggolan, Rosalinda
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23311

Abstract

Information about child poverty is very important to ensure that children get their rights. Indonesia's decentralized system requires child poverty data in each district/city. Data provision at this level is constrained by a non-specific sample design used for certain age groups, so the sample age group for children is not always sufficient for each district/city. Therefore, direct estimation produces a high relative standard error (RSE), so it requires small area estimation (SAE). SAE that is often used is EBLUP, which assumes that the variable of interest is normally distributed. Child poverty data does not meet the normality assumption, so SAE with Hierarchical Bayes with Beta distribution (HB Beta) is proposed in this study. The result is direct estimation, EBLUP, and HB Beta produce relatively similar estimated values, but HB Beta has the lowest RSE.
Co-Authors Istiana, Nofita Nainggolan, Rosalinda Susanti, Desilia Wimbi Tanur, Erwin Ubaidillah, Azka