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Pemodelan Geographically Weighted Regression (GWR) dalam Prevalensi Obesitas Dewasa Kabupaten/Kota di Indonesia Tahun 2018 Prana, Fadhil Aqsa; Khairunnisa, Sherina Rafidah; Puspitasari, Dwi Ajeng; Mahendra, Yusa Okta; Pradastika, Kinanthi Ilham; Budiasih, Budiasih
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1786

Abstract

The results of Basic Health Survey show that the prevalence of adult obesity in Indonesia among people over 18 years of age has doubled from 10.5% in 2007 to 21.8% in 2018. We use data the prevalence of adult obesity as dependent variable and 10 independent variables of socio-economic, food security, and obesity-related risk factors in each regencies/cities in Indonesia. The results showed that there was spatial heterogeneity in prevalence of adult obesity in 514 regencies/cities in 2018. Using Geographically Weighted Regression (GWR), consumption of fatty foods had a positive effect on prevalence of adult obesity in all regions, in contrast to the average length of schooling, consumption of vegetables and soft drinks which only had effect in 501, 448, and 425 regencies/cities. Meanwhile, the poverty percentage, Food Security Index, consumption of instant noodles, and physical activity have a negative effect in 509, 458, 331, and 257 regencies/cities in Indonesia.
LOCALIZED DATA FOR EDUCATIONAL EQUITY: SMALL AREA ESTIMATION OF OUT-OF-SCHOOL CHILDREN IN BALI AND NUSA TENGGARA Khairunnisa, Sherina Rafidah; Ubaidillah, Azka; Hidayat, Ahmad Sovi; Septiyana, Alya Nur; Putri, Shalihati Melani; Prananggalih, Ahmad Tegar; Kusuma, Arya Candra; Syahidah, Shafiyah Asy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1179-1192

Abstract

This study aims to estimate the percentage of out-of-school children aged 7–17 years in Bali and Nusa Tenggara using the Small Area Estimation (SAE) method with a Hierarchical Bayes. One of the main challenges in education policy planning is the limited data available. National surveys, such as the National Socio-Economic Survey (Susenas), typically provide estimates only at the national and provincial levels, while more detailed data at the district level is often lacking. This limitation restricts the understanding of educational disparities at the local level and complicates the design of targeted policies. To address this issue, SAE Hierarchical Bayes provides a solution by producing more accurate district-level estimates, utilizing additional data without the need for new sampling. This method has proven to be cost-effective and efficient, particularly in regions with complex geography, such as Bali and Nusa Tenggara. The findings reveal that districts in East Nusa Tenggara generally exhibit a higher percentage of out-of-school children compared to the national average, indicating significant regional disparities that require attention. These findings highlight the urgency of improving educational infrastructure, particularly in underdeveloped areas of East Nusa Tenggara, to promote equitable access to education and reduce the number of children out of school