UNIB Statistics Lecturer Presents Small Area Estimation Model for Poverty Analysis in Bengkulu at MaGeStiC 2025 / Dosen Statistika UNIB Paparkan Model Small Area Estimation untuk Analisis Kemiskinan di Bengkulu pada MaGeStiC 2025

Jember, September 18, 2025 – A lecturer from the Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Bengkulu, Dr. Etis Sunandi, S.Si., M.Si., participated as a presenter at The 2nd International Conference on Mathematics, Geometry, Statistics, and Computation (MaGeStiC 2025) organized by the University of Jember. On this occasion, he presented his research titled “Bias Reduction using Small Area Estimation of the Beta-Binomial Model for Poverty Data Analysis in Bengkulu Province.”

The study was conducted in collaboration with Sigit Nugroho, Dr. Idhia Sriliana, Titin Siswantining (University of Indonesia), Laily Nissa Atul Mualifah (IPB University), and several undergraduate students from the University of Bengkulu.

In his presentation, Dr. Etis Sunandi explained that the Small Area Estimation (SAE) model based on the Beta-Binomial Adjusted Profile Hierarchical Likelihood (SAE-BB-APHL) framework was developed to address bias and overdispersion issues in subdistrict-level poverty data in Bengkulu Province. Utilizing SUSENAS 2022 and PODES 2022 data, the model incorporates auxiliary variables such as the number of BPJS participants, employment status, education level, and number of health facilities to produce more accurate estimates.

The research findings indicate that the SAE-BB-APHL method yields smaller mean squared error (MSE) values compared to direct estimation methods, resulting in more precise poverty mapping in areas with limited sample sizes. This model proves effective in supporting data-driven development planning at the regional level.

This study is part of a Fundamental Research Grant funded by the Directorate of Research, Technology, and Community Service (DRTPM), Ministry of Education, Culture, Research, and Technology (Kemdikbudristek) through the DPPM Kemdiktisaintek scheme, under contract number 2856/UN30.15/PT/2025.

Dr. Etis Sunandi’s participation in MaGeStiC 2025 represents a significant contribution from the Statistics Department of the University of Bengkulu to the development of quantitative methods for socio-economic issues, while also strengthening UNIB’s role in the international academic community in applied statistics and data science.

Through this participation, it is expected that the research outcomes will be more broadly applied in sectoral data analysis—particularly in poverty mapping and data-driven policy development in Bengkulu Province and other regions of Indonesia.

Jember, 18 September 2025 – Dosen Departemen Matematika, Fakultas MIPA Universitas Bengkulu, Dr. Etis Sunandi, S.Si., M.Si., berpartisipasi sebagai pemakalah dalam The 2nd International Conference on Mathematics, Geometry, Statistics, and Computation (MaGeStiC 2025) yang diselenggarakan oleh Universitas Jember. Pada kesempatan tersebut, beliau mempresentasikan hasil penelitian berjudul “Bias Reduction using Small Area Estimation of the Beta-Binomial Model for Poverty Data Analysis in Bengkulu Province.”

Penelitian ini dilakukan bersama tim kolaborator, yaitu Sigit Nugroho, Dr. Idhia Sriliana, Titin Siswantining (Universitas Indonesia), Laily Nissa Atul Mualifah (IPB University), serta mahasiswa bimbingan dari Universitas Bengkulu.

Dalam paparannya, Dr. Etis Sunandi menjelaskan bahwa model Small Area Estimation (SAE) berbasis Beta-Binomial Adjusted Profile Hierarchical Likelihood (SAE-BB-APHL) dikembangkan untuk mengatasi permasalahan bias dan overdispersi pada data kemiskinan tingkat kecamatan di Provinsi Bengkulu. Dengan menggunakan data SUSENAS 2022 dan PODES 2022, model ini memanfaatkan variabel bantu seperti jumlah peserta BPJS, status pekerjaan, pendidikan, dan jumlah fasilitas kesehatan untuk menghasilkan estimasi yang lebih akurat.

Hasil penelitian menunjukkan bahwa metode SAE-BB-APHL mampu menghasilkan nilai galat (MSE) yang lebih kecil dibandingkan metode estimasi langsung (direct estimation), sehingga memberikan hasil pemetaan kemiskinan yang lebih presisi di wilayah dengan sampel terbatas. Model ini dinilai efektif untuk mendukung perencanaan pembangunan berbasis data di daerah.

Penelitian ini merupakan bagian dari penelitian Fundamental Reguler yang didanai oleh Direktorat Riset, Teknologi, dan Pengabdian kepada Masyarakat (DRTPM) Kemdikbudristek melalui skema DPPM Kemdiktisaintek, dengan nomor kontrak 2856/UN30.15/PT/2025.

Kehadiran Dr. Etis Sunandi dalam MaGeStiC 2025 menjadi bentuk kontribusi nyata dosen Statistika Universitas Bengkulu dalam pengembangan metode kuantitatif untuk isu-isu sosial ekonomi, sekaligus memperkuat peran UNIB dalam komunitas akademik internasional di bidang statistika terapan dan data science.

Melalui partisipasi ini, diharapkan hasil penelitian dapat diterapkan lebih luas dalam analisis data sektoral, terutama untuk mendukung pemetaan kemiskinan dan kebijakan pembangunan berbasis data di Provinsi Bengkulu dan wilayah lainnya di Indonesia.