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All Journal Prosiding Seminar Nasional Official Statistics
Yogi Ramadhan
Universitas Jenderal Soedirman
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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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).
Implementasi Small Area Estimation Hierarchical Bayes - Beta Difference Benchmark dalam Estimasi NEET Lulusan Perguruan Tinggi Salis, Dian Rahmawati; Japany, Adham Malay; Rodliyah, Ratih; Ibad, Syaikhul; Pulungan, Ridson Al farizal; Ramadhan, Yogi
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.v2024i1.2285

Abstract

The survey data generated by BPS serves as the primary data source for calculating various SDGs indicators. However, not all indicators can be reliably estimated, particularly at detailed disaggregation levels. Some indicators face issues due to sample inadequacy, resulting in high Relative Standard Errors (RSEs) if estimated directly. One such indicator is the percentage of young college graduates who are neither in education, employment, nor training (NEET). This indicator is only available at the provincial level, with disaggregation based on other characteristics only available at national level. Therefore, this study aims to estimate NEET among college graduates at the regency/city level in Sumatra Island for the year 2023 using the SAE HB Beta model. To maintain consistency with direct estimates at the provincial level, which have shown sufficiently low RSEs, a benchmarking process will be conducted using the difference benchmark method. Based on the findings, the difference benchmark method enhances the validity of the estimation results using the SAE HB Beta model.
Co-Authors Ibad, Syaikhul Japany, Adham Malay Pulungan, Ridson Al farizal Rodliyah, Ratih Salis, Dian Rahmawati Ubaidillah, Azka