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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Seminar Nasional Official Statistics
Septiyana, Alya Nur
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pemodelan Geographically Weighted Regression pada Tingkat Pengangguran Terbuka di Pulau Jawa Tahun 2020 Septiyana, Alya Nur; Fatkhurrohman, Ikbal; Fikri, Fajriana Fadhlul; S, Riabela; Prananggalih, Ahmad Tegar; Bachtiar, Aji Bagus; ML, Dhitasya Salsabila; Berliana, Sarni Maniar
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.v2023i1.1789

Abstract

Geographically Weighted Regression (GWR) is a regression model that takes into account spatial effects in modeling the relationship between the response variable and the independent variable due to spatial heterogeneity in the data studied. The unemployment rate by regency/municipality in Java Island shows spatial heterogeneity so that the GWR modeling appropriate to be applied in determining the factors that influence the unemployment rate. The results show that the number of residents, the number of workers in the agricultural sector, the regional minimum wage, the mean years of schooling, and the labor force participation rate have different effects for different locations, while domestic investment has no significant effect on the unemployment rate either globally as well as locally. The application of the GWR model is better than the ordinary regression model based on the Akaike information criterion.
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
Co-Authors Bachtiar, Aji Bagus Fatkhurrohman, Ikbal Fikri, Fajriana Fadhlul Hidayat, Ahmad Sovi Khairunnisa, Sherina Rafidah Kusuma, Arya Candra ML, Dhitasya Salsabila Prananggalih, Ahmad Tegar Putri, Shalihati Melani S, Riabela Sarni Maniar Berliana, Sarni Maniar Syahidah, Shafiyah Asy Ubaidillah, Azka