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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Inferensi Prosiding Seminar Nasional Official Statistics
Ubaidillah, Azka
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Estimasi Tingkat Kemiskinan Anak Level Kabupaten/Kota di Provinsi Banten Tahun 2018-2021 dengan Small Area Estimation (SAE) Rao-Yu Pendekatan Hierarchical Bayes Salis, Dian Rahmawati; Ubaidillah, Azka
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.v2023i1.1709

Abstract

ABSTRACT Child poverty have a significant impact on the future quality of the adult population. Estimating the level of child poverty accurately, especially at regency/city level, is crucial for targeted policy interventions. Direct estimations based on SUSENAS data have a Relative Standard Error (RSE) value of more than 25%, necessitating the use of an indirect method called small area estimation (SAE). The province of Banten has consistently had the lowest Gross Participation Rate (GPR) for Early Childhood Education (ECE) among the provinces in Java over the years which can be interpreted as an early indication of limited access to children's education due to poverty. In this study, the level of child poverty in the districts/cities of Banten Province was estimated using the hierarchical bayes Rao-Yu model with normal and beta distribution approach. The results of this study indicate that although it produces the best precision, the SAE Rao-Yu HB Beta estimation has results with a smaller level of consistency than the normal SAE Rao-Yu HB estimation.
Pendugaan Persentase Penduduk Miskin Ekstrem Menggunakan Small Area Estimation dengan Partitioning Around Medoids Clustering Ramadhan, Yogi; Ubaidillah, Azka
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.v2023i1.1717

Abstract

Eradication of extreme poverty is one of the goals to be achieved among the global goals (SDGs). Eradicating extreme poverty is inseparable from the role of governments as policymakers. Policy creation requires high-precision data. Aggregate extreme poverty data are collected based on the National Socio-Economic Survey (Susenas), based on Susenas results in March 2022 East Java is one of the provinces with a high number of people living below the extreme poverty line. Besides that, the high RSE in estimating the percentage of people in extreme poverty in regency/city in East Java province makes the precision low. Low precision results in inaccurate estimated data and should not be used, especially for policy making. One way to improve accuracy is to use Small Area Estimation (SAE). The most commonly used SAE model is EBLUP, and for unsampled area estimation, the estimation can use clusters of information. Problems that arise in forming clusters are outliers in the observed data, which can lead to forming errors within the clusters. A cluster of algorithms that can be used to overcome these problems is Partitioning Around Medoids (PAM).
Pendugaan Angka Partisipasi Kasar Pendidikan Anak Usia Dini Berdasarkan Jenis Kelamin dan Level Kabupaten/Kota di Provinsi Jawa Tengah Tahun 2022 dengan Pendekatan Model Hierarchical Bayes Berdistribusi Beta dengan Measurement Error Rodliyah, Ratih; Ubaidillah, Azka
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.v2023i1.1906

Abstract

Quality education is one of the goals of the 2030 Sustainable Development Goals (SDGs). Services to education can be provided from an early age to prepare for further formal education. Susenas produced an estimator of the Gross Enrollment Rate (APK) for Early Childhood Education (PAUD) to describe the capacity of the country's education system to accommodate students in the early age group. Small area statistics urgently needed to create policies that are more right on target. Central Java province has APK PAUD based on sex which is still inadequate for all districts/cities based on the Relative Standard Error (RSE). Therefore, this study applies indirect estimation to improve the precision of the direct estimator using Small Area Estimation (SAE) with measurement error (ME) using Hierarchical Bayes (HB) Normal distribution and Hierarchical Bayes (HB) Beta distribution. The results of this study indicate that the estimated value using the HB ME Beta method is more precise than direct estimation and SAE HB ME Norm estimation.
Pendugaan Area Kecil untuk Persentase Balita Miskin Tingkat Kabupaten/Kota di Provinsi Papua Tahun 2023 Menggunakan Pendekatan EBLUP dengan Informasi Klaster Fusur, Alma Rohmah; Ubaidillah, Azka
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.1981

Abstract

The development of a country depends on the quality of its human resources. In 2020, Indonesia's Human Capital Index (HCI) is 0.54. This figure is relatively low compared to other Southeast Asian countries. One of the causes of Indonesia's low HCI rate is poverty. In 2022, the highest percentage of poor children is in Papua. In addition, under-five are the highest poor group among other age groups. Direct estimation often results in less precise guesses due to insufficient sample size. Even direct estimation cannot estimated when an area has no sample. This study uses SAE EBLUP method with cluster information to get the estimated percentage of under-five poverty. The results show that 7 districts/cities in Papua Province have a direct estimator RSE of more than 25%. Using cluster analysis, direct estimators with RSE for unsampled areas were produced, and EBLUP estimators with cluster information were proven to be better than direct estimators from the RSE produced lower than the direct estimator RSE.
Pendugaan Area Kecil Tingkat Pengangguran Terbuka Level Kecamatan Di Provinsi Kepulauan Riau Tahun 2022 Puspita, Desak Nyoman Febrina Ambara; Ubaidillah, Azka
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.1982

Abstract

Kepulauan Riau is the province with the second highest Unemployment Rate (TPT) in 2022. Kepulauan Riau’s TPT has doubled In last decade. It was 5,08 percent in August 2012 to 10,34 percent in August 2020. Kepulauan Riau has 31,8 percent of desperate unemployment. Unemployment alleviation policy is more efficient if it is focused on sub-districts area. However, TPT is available up to district level. Sakernas August 2022 does not have sufficient sample size to estimate TPT in the sub-district level. Therefore, this study aims to estimate TPT at sub-district level in Kepulauan Riau in 2022 using Small Area Estimation (SAE) with additional information from the Potensi Desa 2021. The results showed that Hierarchical Bayes Beta SAE produced sub-district TPT estimators with smaller relative standard errors than direct estimation. Sub-districts with high TPT values in Kepulauan Riau are spread in urban areas so that they require special policies in reducing unemployment.
SMALL AREA ESTIMATION OF THE PERCENTAGE HOUSEHOLDS WITH FOOD EXPENDITURE SHARE MORE THAN 65 PERCENT IN LOW EXPENDITURE GROUP Anjarwati, Niken Alfina; Ubaidillah, Azka
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/barekengvol19iss2pp927-936

Abstract

The right to get adequate food is a human right that must be fulfilled. Food insecurity is a problem that arises from not fulfilling food needs physically or economically. Food insecurity and poverty are interrelated. The United Nations prioritizes the elimination of poverty as the first goal and achieving food security as the second goal in the Sustainable Development Goals. In 2022, East Java had the highest percentage of households with a food expenditure share more than 65 percent in Java. The availability of data by expenditure group illustrates the economic status of households will assist the government in making targeted policies. However, the calculation of direct estimates at the regency level has not shown good precision, characterized by estimates with an RSE >25 percent. Therefore, this study aims to implement SAE HB Beta to improve the precision of the direct estimator. The result shows that SAE HB Beta produces a more precise estimation.
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
Small Area Estimation of Child Poverty on Java Island In 2021 (Comparison of EBLUP and Hierarchical Bayes) Istiana, Nofita; Tanur, Erwin; Ubaidillah, Azka; Sitanggang, Yuliana Ria Uli; Nainggolan, Rosalinda
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23311

Abstract

Information about child poverty is very important to ensure that children get their rights. Indonesia's decentralized system requires child poverty data in each district/city. Data provision at this level is constrained by a non-specific sample design used for certain age groups, so the sample age group for children is not always sufficient for each district/city. Therefore, direct estimation produces a high relative standard error (RSE), so it requires small area estimation (SAE). SAE that is often used is EBLUP, which assumes that the variable of interest is normally distributed. Child poverty data does not meet the normality assumption, so SAE with Hierarchical Bayes with Beta distribution (HB Beta) is proposed in this study. The result is direct estimation, EBLUP, and HB Beta produce relatively similar estimated values, but HB Beta has the lowest RSE.
Co-Authors Anjarwati, Niken Alfina Fusur, Alma Rohmah Hidayat, Ahmad Sovi Istiana, Nofita Khairunnisa, Sherina Rafidah Kusuma, Arya Candra Nainggolan, Rosalinda Prananggalih, Ahmad Tegar Puspita, Desak Nyoman Febrina Ambara Putri, Shalihati Melani Rodliyah, Ratih Salis, Dian Rahmawati Septiyana, Alya Nur Sitanggang, Yuliana Ria Uli Syahidah, Shafiyah Asy Tanur, Erwin Yogi Ramadhan