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All Journal Prosiding Seminar Nasional Official Statistics
Rizky Zulkarnain
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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MODEL BETA-BINOMIAL UNTUK PENDUGAAN PROPORSI UNMET NEED MENURUT DOMAIN SOSIAL DEMOGRAFI Rizky Zulkarnain
Seminar Nasional Official Statistics Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (511.13 KB) | DOI: 10.34123/semnasoffstat.v2019i1.21

Abstract

Unmet need merupakan konsep yang berharga dalam program dan kebijakan Keluarga Berencana (KB) serta berkaitan dengan pencapaian target 3.7 Sustainable Development Goals (SDGs). Identifikasi dan pemahaman unmet need menurut karakteristik sosial demografi diperlukan agar program dan layanan yang diberikan dapat berjalan secara efektif. Di Indonesia, indikator unmet need diturunkan dari Survei Demografi dan Kesehatan Indonesia (SDKI). SDKI dirancang untuk dapat menyajikan estimasi pada level nasional dan provinsi. Pada level nasional, persentase unmet need dapat disajikan menurut karakteristik sosial demografi seperti umur, pendidikan dan kuantil kekayaan. Namun pada level provinsi, persentase unmet need hanya dapat disajikan secara agregat. Persentase unmet need menurut karakteristik sosial demografi tidak dapat disajikan pada level provinsi karena menghasilkan dugaan yang tidak dapat dipercaya. Hal ini disebabkan oleh tidak memadainya ukuran sampel ketika dirinci menurut karakteristik sosial demografi tertentu. Paper ini bertujuan untuk mengelaborasi penggunaan model beta-binomial untuk menduga proporsi unmet need menurut domain sosial demografi pada level provinsi. Terdapat tiga domain sosial demografi yang digunakan, yaitu menurut kelompok umur, pendidikan dan jenis pekerjaan. Lingkup penelitian hanya mencakup provinsi DKI Jakarta. Hasil penghitungan menunjukkan bahwa penduga beta-binomial memberikan presisi yang lebih baik dibandingkan penduga langsung. Hal ini tercermin dari nilai Root Mean Squared Error (RMSE) yang lebih kecil, baik dengan metode Naïve-Bayes maupun Bootstrap. Bahkan, untuk domain dengan ukuran sampel yang sangat kecil, penduga beta-binomial dapat menghasilkan presisi yang cukup memadai. Dengan demikian, disimpulkan bahwa model beta-binomial dapat digunakan untuk menangani permasalahan ketidakcukupan sampel dalam penyajian persentase unmet need menurut karakteristik sosial demografi di level provinsi.
KUALITAS UDARA DAN POTENSI TRANSMISI COVID-19 DI PULAU JAWA Rizky Zulkarnain; Karuniawati Dewi Ramadani
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.931 KB) | DOI: 10.34123/semnasoffstat.v2020i1.398

Abstract

The spreading of SARS-CoV-2 in Indonesia is increasingly worrying. COVID-19 pandemic has disrupted people’s lives, not only infected people but also the collateral survivors, that is people who are not infected but suffer disruption in their life due to a crisis. Java island is the epicenter of COVID-19 transmission in Indonesia. Several studies have shown that the incidence of COVID-19 has strong connections with the concentration of particulate matter in air. This study investigates the relationship between air quality and the risk of COVID-19 transmission in Java. Air quality is represented by NO2 concentration, while the risk of COVID-19 transmission is measured using COVID-19 Vigilance Index. This paper employs four models to identify the effect of NO2 exposure to the risk of COVID-19 transmission, where the population density is incorporated as control variable. Model-0 is the base model without cluster and spatial effects. Model-1 is the extension of Model-0 by adding spatial effect. Model-2 is the extension of Model-0 by adding cluster effect. While Model-3 is the complete model with spatial and cluster effects. The results showed that Model-3 is the best model in terms of explaining the relationship between NO2 concentration and the COVID-19 Vigilance Index. Model-3 improves the performance of base model substantially. Furthermore, estimation results suggest that NO2 concentration has positive connections with COVID-19 Vigilance Index, after cluster and spatial effects are considered. Regions with higher NO2 concentration (worse air quality) tend to have higher COVID-19 Vigilance Index (higher risk of COVID-19 transmission). These results have important implications for the mitigation strategies needed to handle COVID-19 transmission, especially in regions that have poor air quality.
Linkages Perekonomian Bali Rizky Zulkarnain; Nasiyatul Ulfah
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (637.352 KB) | DOI: 10.34123/semnasoffstat.v2021i1.845

Abstract

Several studies have been conducted to analyze multiplier effect of Bali economy. However, those studies merely focus on intersectoral linkages using Input-Output (IO) model, whereas regional production activities could be interdependent through various externalities and supply-chain networks. This study aims to analyze Bali economy by considering interregional linkage as well as intersectoral linkage. This study employs Inter Regional Input Output (IRIO) model. The IRIO table (17 industries x 34 provinces) was acquired from Statistics Indonesia (BPS). The results showed that there were several key sectors in Bali economy: Electricity and Gas, Transportation and Storage, Information and Communication, and Business Activities. Electricity and Gas had highest intersectoral linkage and output multiplier. Moreover, interregional analysis showed that final demand shock in Bali had significant impact to provinces in Java, especially East Java. On the other side, Bali economy was influenced by final demand shock in West Nusa Tenggara.
Estimasi PDB Mikroregional: Studi Kasus di Pulau Jawa Rizky Zulkarnain
Seminar Nasional Official Statistics Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (582.466 KB) | DOI: 10.34123/semnasoffstat.v2022i1.1257

Abstract

Sustainable development agenda requires accurate and granular data support. However, accurate and granular data are sparse, due to the limitation of various resources. This study explores the possibility of producing Gross Domestic Product (GDP) estimates at the more granular level, i.e. at 2.4-km grid cell (microregional GDP). To achieve the purpose, this study utilizes several high-resolution geospatial predictors, such as night-time lights, land cover, topography and the location of economic activities. This study covers Java islands only. The estimates of microregional GDP are produced using several machine learning models, such as LASSO, Elastic Net, Support Vector Machine and Random Forest. The results showed that Random Forest was the best model for estimating the microregional GDP, where the night-time lights was the best predictor. This study also validated the results using independent data sources, such as the Relative Wealth Index. Validation results showed that the microregional GDP estimates were quite reliable.
Co-Authors Karuniawati Dewi Ramadani Nasiyatul Ulfah