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Nofita Istiana
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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.
Hierarchical Bayes Application for Small Area Estimation with Error Measurement on Child Poverty in Sumatera Island Aisha Arthamevia; Zahra Rizky Fadilah; Rasya Az Zahra; Sausan Salsabila; Renandika Salsabiila Agistasari; Nofita Istiana
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 2 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i2.11540.2025

Abstract

Child poverty on Sumatera Island remains a significant issue, as four provinces recorded child poverty rates above the national average in 2021, increasing to five provinces in 2022. To support more effective and targeted policies, reliable estimates at the district/city level are required; however, direct estimates from the March 2023 Susenas data showed low precision, with 37 of 154 districts/cities having a Relative Standard Error (RSE) greater than 25%. To improve accuracy, this study applied Small Area Estimation using a Hierarchical Bayes model with Measurement Error on the Beta distribution (SAE HB ME Beta). Empirical findings revealed serious precision problems, particularly in Kepulauan Bangka Belitung, where all districts had RSE values above 25%, and in West Sumatera, which ranked second with more districts exceeding the threshold than those below it, including the highest overall RSE. When the model was initially estimated jointly for all provinces, one district in West Sumatera still had an RSE above 25% and estimates for Kepulauan Bangka Belitung failed to satisfy the internal consistency criterion. To address this heterogeneity, the model was re-estimated separately for West Sumatera and Kepulauan Bangka Belitung and for the remaining provinces. The final results show that all districts/cities achieved RSE ≤ 25% and met internal consistency requirements, indicating that the proposed approach improves the precision and reliability of district-level child poverty estimates across Sumatera Island.
Co-Authors Aisha Arthamevia Dolly Fernando Kevin Rizkika Setiawan Mahira Fachrunnisa Lubis Ni Putu Esti Utami Barsua Pembayun Otsu Indiana Rasya Az Zahra Renandika Salsabiila Agistasari Sausan Salsabila Zahra Rizky Fadilah