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All Journal Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Oktaviana, Lisda
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Analisis Faktor-Faktor yang Memengaruhi Stunting di Indonesia Tahun 2021 Hardinata, Rizki; Oktaviana, Lisda; Husain, Farah Fadhilah; Putri, Syofmarlianisyah; Kartiasih, Fitri
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.v2023i1.1867

Abstract

Stunting is a problem that arises from a lack of nutrition and can interfere with the growth and development of children. The high prevalence of stunting can be an indicator of the low quality of a country's human resources. The impacts include limited cognitive abilities, low productivity, and a high risk of disease, which have a long-term negative impact on the preservation of the country. Therefore, stunting is one of the targets of the Sustainable Development Goals (SDGs). The purpose of this study was to analyze the factors that influence the prevalence of stunting in Indonesia in 2021. This study used a cross-sectional approach with secondary data from the Central Bureau of Statistics and the Indonesian Ministry of Health. The research methods used are descriptive analysis and inferential analysis with multiple linear regression. The results showed that the proportion of low-birth-weight babies significantly increased the prevalence of stunting. On the other hand, the proportion of households that had access to sanitation allowed it to significantly reduce the prevalence of stunting in Indonesia.
Disaggregating the Hidden: Small Area Estimates of Child Labor in Bali Province Agung, Ahmad Nadifa Al; Sari, Arlita Dwina Firlana; Azarine, Clarissa; Oktaviana, Lisda; Aqsha, Zidan Akbar Al; Istiana, Nofita
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.433

Abstract

Child labor remains a critical concern in Indonesia, including in Bali Province, which exhibits a higher prevalence than the national average. However, efforts to formulate effective local policies are often hindered by the unreliability of child labor statistics at the regency/municipality level, primarily due to high Relative Standard Error (RSE) values. This study seeks to estimate more reliable proportion of child labor at the regency level in Bali through the application of Small Area Estimation (SAE). The analysis utilizes data from the August 2024 Sakernas survey, supplemented with contextual variables from the 2024 PODES dataset. The SAE approach employed was the Hierarchical Bayes method with a Beta distribution (HB-Beta). The findings indicate that the HB-Beta model yields better accurate estimates, as evidenced by RSE values below 25% across all regencies. This demonstrates the potential of the HB-Beta model produces more accurate estimates than direct estimates, as it can better reflect differences between regency and help design more effective local policies to reduce child labor.
Co-Authors Agung, Ahmad Nadifa Al Aqsha, Zidan Akbar Al Azarine, Clarissa Fitri Kartiasih Hardinata, Rizki Husain, Farah Fadhilah Istiana, Nofita Putri, Syofmarlianisyah Sari, Arlita Dwina Firlana