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All Journal EKSAKTA: Journal of Sciences and Data Analysis Journal of Science and Applicative Technology Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Science and Technology Journal of Fundamental Mathematics and Applications (JFMA) Smart Society: Community Service and Empowerment Journal Indonesian Journal of Applied Mathematics
Mika Alvionita Sitinjak
Institut Teknologi Sumatera
Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Penerapan Metode Small Area Estimation dengan Pendekatan Hierarchical Bayesian (Studi Kasus: Tingkat Pengangguran Terbuka Di Provinsi Kepulauan Riau) S, Mika Alvionita
Indonesian Journal of Applied Mathematics Vol. 5 No. 2 (2025): Indonesian Journal of Applied Mathematics Vol. 5 No. 2 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35472/indojam.v5i2.2436

Abstract

The Open Unemployment Rate is an important indicator in describing the labor market conditions of a region. The Riau Islands Province recorded the highest open unemployment rate in Sumatra in 2024. However, direct estimation based on the survey is less accurate for small areas due to limited sample sizes. This study aims to estimate the OUR using the Small Area Estimation (SAE) approach based on a Hierarchical Bayesian (HB) Log-Normal–Normal model. The analysis results show that among several predictor variables used, only the Gross Regional Domestic Product (GRDP) has a significant effect on the OUR, with a regression coefficient of −1.2600. This indicates that an increase in GRDP is correlated with a decrease in the unemployment rate. The best model obtained is log(θi) = 1,5875 − 1,2600 x1i + vi with a Mean Squared Error of 0.309. For further development, it is recommended to conduct estimation at smaller area levels (sub-districts or villages) and to consider adding more predictor variables to improve model precision.
Co-Authors Ariyanto, Adisty Syawalda Christyan Tamaro Nadeak Danni Gathot Harbowo Dhea Sukma Agustiana Dimas Wahyu Saputro Fadia Dilla Sabine Fuji Lestari Irawati, Febri Dwi Lis Nuraini Luluk Muthoharoh Muliawati Muliawati, Triyana Muthoharoh, Luluk Napitupulu, Leonard Andreas Nuramaliyah, Nuramaliyah Rahwani, Putri Hazizah Rekzi P. Manullang Riri Lestari Satria, Ardika Sihaloho, Ryantoni Surya, Annisa Cahyani Syaiful, Achmad Taj Shavira Tirta Setiawan, Tirta