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Kevin Rizkika Setiawan
Politeknik Statistika STIS
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Journal : JSTAR

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PENDEKATAN SMALL AREA ESTIMATION UNTUK PEMETAAN PEKERJA DISABILITAS DI NUSA TENGGARA SEBAGAI DUKUNGAN STATISTIK BAGI DASA CITA NTT Ni Putu Esti Utami Barsua; Pembayun Otsu Indiana; Mahira Fachrunnisa Lubis; Kevin Rizkika Setiawan; Dolly Fernando; Nofita Istiana
Jurnal Statistika Terapan (ISSN 2807-6214) Vol 5 No 2 (2025): Jurnal Statistika Terapan
Publisher : Badan Pusat Statistik Provinsi NTT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64930/jstar.v5i2.126

Abstract

This paper examines the estimation of the number of workers with disabilities in the Nusa Tenggara region using Sakernas 2024 data. The limited sample sizes in several districts lead to high sampling errors, necessitating a more reliable small-area statistical approach (Small Area Estimation). The unavailability of accurate small-area labor statistics for persons with disabilities hampers evidence-based regional development planning and inclusive policymaking. This study applies the Small Area Estimation (SAE) method using a Hierarchical Bayesian (HB) Poisson–Gamma model to handle count data with overdispersion—an approach that remains rarely applied in Indonesian labor statistics. The model is developed by integrating Sakernas data with auxiliary information from PODES and the Ministry of Education. Estimation is conducted through Bayesian inference using Markov Chain Monte Carlo (MCMC) simulation. The HB Poisson–Gamma model effectively reduces the Relative Standard Error (RSE) from an average of 44.6% in direct estimation to below 10% across 32 districts in Nusa Tenggara. These results demonstrate the model’s ability to improve data reliability and support inclusive employment policies aligned with regional development priorities.
Co-Authors Dolly Fernando Mahira Fachrunnisa Lubis Ni Putu Esti Utami Barsua Nofita Istiana Pembayun Otsu Indiana