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<![CDATA[Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga ]]>
<![CDATA[Azka Ubaidillah]]>;
<![CDATA[Anang Kurnia]]>;
<![CDATA[Kusman Sadik]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 9 No 1 (2017): Journal of Statistical Application and Computational Statistics ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v9i1.91
<![CDATA[Household per capita expenditure data is one of the important information as an approach to measure the level of prosperity in an area. Such data is needed by the government, both at the central and regional levels in formulating, implementing and evaluating the implementation of development programs. This research is aimed at modeling the household per capita expenditure data which takes into account the specificity of BPS data which has a hierarchical structure, and data distribution pattern which has the right skewed characteristic. The modeling is done by using the three parameters of Log-normal distribution (LN3P) and the three parameters of Log-logistics (LL3P) with a single level (unilevel) and two levels (multilevel) structure. The parameter estimation process is done by Markov Chain Monte Carlo (MCMC) method and Gibbs Sampling algorithm. The results showed that on the unilevel model, the LL3P model is better than the LN3P model. While in multilevel model, LN3P model is better than LL3P model. The results also show that the best model for modeling household per capita expenditure data is the LN3P multilevel model with the smallest Deviance Information Criterion (DIC) value.]]>
<![CDATA[PENERAPAN MODEL REGRESI THRESHOLD UNTUK PENINGKATAN EFISIENSI DALAM PENDUGAAN AREA KECIL ]]>
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2019i1.121
<![CDATA[Pendugaan area kecil (SAE) dewasa ini berkembang cukup pesat seiring dengan meningkatnya kebutuhan atas penyediaan statistik yang terpercaya di area kecil, yaitu area dengan jumlah contoh (sampel) yang sedikit atau tidak mencukupi untuk dilakukan pendugaan secara langsung. Metode SAE dapat meningkatkan efektivitas contoh dengan “meminjam” kekuatan dari informasi area yang bertetanggaan dan pengaruh peubah penyertanya. Dalam aplikasinya, model Fay-Herriot menggunakan pendekatan Empirical Best Linear Prediction (EBLUP) banyak dilakukan karena sifat modelnya yang sederhana. Salah satu sifat sederhana dari model EBLUP adalah penggunaan hubungan linier antara peubah yang diamati dengan peubah penyertanya. Namun sering dijumpai bahwa hubungan linier tersebut belum cukup untuk meningkatkan efisiensi model SAE sebagai akibat pola yang terbentuk antara peubah amatan dan peubah penyertanya tidak linier. Paper ini menjelaskan salah satu alternatif cara untuk meningkatkan efisiensi model SAE dengan menerapkan model regresi threshold. Dari hasil simulasi dan aplikasi data pengeluaran perkapita makanan tingkat kabupaten/kota di Jawa Tengah tahun 2015 diperoleh keterangan bahwa model regresi threshold menghasilkan pendugaan dengan RMSE (root mean square error) dan RSE (relative standard error) yang lebih kecil dibandingkan model EBLUP. Hal ini menunjukkan bahwa penerapan model regresi threshold mampu untuk meningkatkan efisiensi dalam pendugaan area kecil.]]>
<![CDATA[PENDUGAAN AREA KECIL UNTUK ANGKA PARTISIPASI KASAR PENDIDIKAN DASAR DAN MENENGAH TINGKAT KABUPATEN/KOTA DI PROVINSI JAWA TENGAH TAHUN 2018 ]]>
<![CDATA[Gusti Firmando]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2020i1.466
<![CDATA[Pada Maret tahun 2018, Angka Partisipasi Kasar (APK) di Indonesia untuk pendidikan dasar dan menengah adalah sebesar: APK SD/sederajat 108,61%, APK SMP/sederajat 91,52%, sedangkan APK SMA/sederajat 80,68%. Capaian tersebut masih jauh dari target Rencana Pembangunan Jangka Menengah Nasional (RPJMN) 2014-2019. Salah satu provinsi yang memiliki APK pendidikan dasar dan menengah di bawah target RPJMN adalah Provinsi Jawa Tengah. Upaya yang dapat dilakukan untuk mewujudkan target tersebut adalah dengan mengetahui capaian APK pendidikan dasar dan menengah di level kabupaten/kota berdasarkan hasil Susenas September sehingga kontrol dapat dilakukan dua kali dalam setahun. Namun, langkah ini akan memerlukan penambahan jumlah sampel yang menyebabkan diperlukannya waktu, biaya, tenaga dan pemikiran yang lebih besar. Untuk mengatasi hal tersebut, Small Area Estimation (SAE) dapat digunakan untuk menghasilkan presisi yang memadai tanpa melakukan penambahan jumlah sampel. SAE merupakan metode pendugaan parameter-parameter subpopulasi yang memiliki ukuran sampel kecil. Metode SAE yang banyak digunakan adalah Empirical Best Linear Unbiased Predictor (EBLUP). Namun, model ini belum memasukkan pengaruh spasial ke dalam model. Model Fay-Herriot yang memerhatikan efek spasial dikenal dengan Spatial Empirical Best Linear Unbiased Predictor (SEBLUP). Hasil penelitian menunjukkan bahwa metode EBLUP lebih baik dalam mengestimasi APK SD/sederajat dan APK SMA/sederajat, dan metode SEBLUP lebih baik dalam mengestimasi APK SMP/sederajat.]]>
<![CDATA[Pendugaan Area Kecil Angka Partisipasi Kasar Perguruan Tinggi di Indonesia Menggunakan Model Subarea Twofold dengan Pendekatan Hierarchical Bayes ]]>
<![CDATA[Reyhan Saadi]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1217
<![CDATA[Gross Enrolment Rate of higher education (APK PT) is an indicator that can be used to measure the achievement of the SDGs program in the context of education. Unfortunately, this data with good quality can only be presented up to the provincial level because the number of SUSENAS samples is designed to be only sufficient to present data up to the provincial level. These limitations makes it difficult to provide good quality district APK PT data. In fact, in formulating policies, APK-PT data is needed with availability to the level of small areas such as districts as a comparison. The SAE method can be an alternative way to improve the quality of the APK-PT estimation results at the small area level. Along with the increasing need for small area data, currently an SAE model has been developed with ability to estimate at two levels of a small area, namely twofold subarea model. Seeing this opportunity, researchers are interested in estimating the APK-PT of districts and provinces in Indonesia with the twofold subarea model using the HB approach. The estimation results shows that this model is able to produce better APK-PT estimates in both regency and province level.]]>
<![CDATA[Pendugaan Area Kecil Persentase Anak-anak Usia Kurang dari 18 Tahun yang Hidup di Bawah Garis Kemiskinan Tingkat Kabupaten/Kota di Indonesia Tahun 2020 ]]>
<![CDATA[Jayanti Wulansari]]>;
<![CDATA[Novia Permatasari]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1467
<![CDATA[The rapid spread of the COVID-19 pandemic has caused a global crisis, including in Indonesia. One group at high risk of being affected during a crisis is children. Since their nature is dependent on others, decreasing income in the household makes children vulnerable to poverty. Child poverty is a condition where children cannot meet their basic needs. The percentage of children under 18 years living in poverty is one of the child poverty indicators. Unfortunately, this indicator is only available at the national and provincial levels. Data availability for small areas is required to support the acceleration of poverty alleviation, which is the goal of the SDGs and the 2020-2024 RPJMN. This study aims to obtain an estimate of the percentage of children under 18 living in poverty at the regencies/municipalities level using the SAE EBLUP. The result shows that the SAE EBLUP method is able to produce estimation with smaller and better MSE and RSE than the direct estimation.]]>
<![CDATA[Pendugaan Persentase Rumah Tangga yang Memiliki Akses Terhadap Air Minum Layak, Sanitasi Layak, serta Rumah Layak Huni dan Terjangkau pada Level Kecamatan Di Provinsi Papua Tahun 2019 Menggunakan Model Fay Herriot Multivariat ]]>
<![CDATA[Manda Syari Utami]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1498
<![CDATA[Access to proper drinking water, proper sanitation and affordable and livable houses is a problem that has become the focus of many countries, especially Indonesia. The success of these indicators is in line with the availability of data not only at the national, provincial, and regency/city levels but also at the smaller levels. Information on indicators is obtained from the Susenas of BPS a survey designed to estimate up to the regency/city level, so it does not meet the minimum sample requirement to estimate to the lower level. The Small Area Estimation (SAE) method can increase the effectiveness of the sample size by borrowing the power of the accompanying variable that has a relationship with the variable to be estimated. (Benavent, 2016) proposed the SAE method using the multivariate Fay Herriot EBLUP model by utilizing the correlation of the variables to be studied. This study aims to estimate and map the ownership data on access to proper drinking water, proper sanitation and affordable and livable houses at the district level in Papua. The results showed that the estimation of ownership indicators of access to proper drinking water, proper sanitation and affordable and livable housing using the multivariate Fay Herriot EBLUP method had a smaller relative standard error (RSE) compared to univariate and direct estimates.]]>
<![CDATA[Analisis Kesuksesan Sistem Informasi dengan Pendekatan Model Kesuksesan Sistem Informasi DeLone & McLean ]]>
<![CDATA[Ajeng Wahyu Tri Yulinda]]>;
<![CDATA[Azka Ubaidillah]]>;
<![CDATA[Yunarso Anang]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1520
<![CDATA[SIPADU-STIS is an information system used at the Politeknik Statistika STIS to support the implementation of academic activities on campus. SIPADU-STIS was developed in 2010 and has been used as the main information system at Politeknik Statistika STIS up to now. However, there is no specific research has been conducted on the factors that influence the success of the SIPADU-STIS. The information system that has been implemented needs to be studied for its success and the factors that influence it. The criteria in this study refers to the DeLone & McLean Information System Success Model that has been modified. The aim is to determine the variables that influence the success of SIPADU. The focus of this research is SIPADU-STIS Web Portal for Students. The variables studied include system quality, information quality, service quality, user satisfaction, and net benefits. The result is that all predictor variables have a significant positive effect and provide validation to the model. There are some recommendations based on the analysis that would be a concern for the on-going SIPADU development.]]>
<![CDATA[Pembangunan Package R untuk Small Area Estimation Pendekatan Nonparametrik Berbasis Kernel Nadaraya-Watson ]]>
<![CDATA[Wicak Surya Hasani]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2022i1.1545
<![CDATA[The limited sample in survey activities is an obstacle to providing data in smaller domains and areas. Small Area Estimation (SAE) can be solve this problem. However, this indirect estimation technique requires the assumption of a linear relationship between the mean of a small area and the accompanying variables. This problem can be solved by using a nonparametric approach, one of the nonparametric approaches that can be used is the Nadaraya-Watson Kernel base. To facilitate the implementation, the researcher built a Package R for Small Area Estimation of a nonparametric approach based on the Nadaraya-Watson Kernel with the package name "saekernel". The results show that the "saekernel" package that has been built is suitable and feasible to use. The package that has been built is also applied to the BPS survey, which is to estimate per capita expenditure at the sub-district level in the D.I Yogyakarta Province based on data National Socio-Economic Survey (Susenas) from the March 2019.]]>
<![CDATA[Estimation of Education Indicators in East Java Using Multivariate Fay-Herriot Model ]]>
<![CDATA[Novia Permatasari]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/icdsos.v2021i1.51
<![CDATA[Education is an important aspect in improving human resources. Data availability of education indicators in a low administrative level is needed as a basis for education planning in that region. The problem of sample size when provide a low administrative level data can be overcome by indirect estimation, namely Small Area Estimation (SAE). SAE is able to increase the effectiveness of the survey sample size by using the strength of neighbouring areas and information from auxiliary variables related to the variables of interest. We obtain simulation study to compare multivariate model to univariate model and implement multivariate model to estimate three education indicators which are obtained from the National Socio-Economic Surveys by Statistics Indonesia. Simulation results are in line with previous studies, where the multivariate Fay-Herriot model with p variable has smaller of mean squares error (MSE) than the univariate model. The model implementation to estimate CrudeParticipation Rate (APK), School Participation Rate (APS), and Pure Participation Rate (APM) also shows that the multivariate model produces smaller RRMSE than the direct estimates. It can be concluded that multivariate model is able to produce more efficient estimates than direct estimation and univariate model.]]>
<![CDATA[R Package Development for Difference Benchmarking in Small Area Estimation Fay-Herriot Model ]]>
<![CDATA[Zaza Yuda Perwira]]>;
<![CDATA[Azka Ubaidillah]]>
<![CDATA[ Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/icdsos.v2021i1.69
<![CDATA[In recent decades, the use of small area estimation (SAE) for producing official statistics has been widely recognized by many National Statistics Offices including BPS-Statistics Indonesia. For official statistics usage, the aggregation of small area estimates is expected to be numerically consistent and more efficient than the aggregation of the unbiased direct estimates that cannot be guaranteed by Fay-Herriot model. Simulation experiments are performed to assess the behaviour of the difference benchmarking method Fay-Herriot model and to compare the mean squared error (MSE). The result shows that the difference benchmarking method can produce a consistent aggregation towards the direct estimation. Furthermore, an R package was built to implement the method that is easier to be used and is already available in the CRAN website. The package has been evaluated using validity (simulation), performance, case studies, and usability tests. These evaluations show that the package is suitable for use. Implementation of the methodology is also be applied to estimate average household consumption per capita expenditure in districts in D.I. Yogyakarta province, Indonesia 2019]]>
