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

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Determinan Prevalensi Balita Stunting di Indonesia Tahun 2021 Priatmadani, Priatmadani; Anjarweni, Hanifah Ukti; Putri, Shalihati Melani; Pramana, Anugerah Surya; Palupi, Retno; Budiasih, Budiasih
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.v2023i1.1753

Abstract

Stunting is a disorder of child growth and development caused by inadequate nutritional intake and is characterized by below standard height. The stunting prevalence rate in Indonesia still exceeds the WHO recommendation, which is 20%, which is 24.4%. The problem of stunting in Indonesia is related to food security, economic and social conditions of the community. From this aspect, variables that are suspected of influencing stunting in Indonesia include food expenditure, food insecurity, PMT in KEK pregnant women, food insufficiency, level of education, and proper sanitation, the data of which was obtained from the Ministry of Health and BPS. The results of mapping the percentage of stunting show that areas that need to accelerate the reduction of stunting are in the eastern provinces of Indonesia and there is a spatial autocorrelation in the percentage of stunting between provinces in Indonesia. Based on the Spatial Autoregressive (SAR) model, only the education level variable has no significant effect and the percentage of stunting in a province in Indonesia will have a significant positive effect on the percentage of stunting in surrounding provinces.
MODELING FACTORS AFFECTING EDUCATED UNEMPLOYMENT ON JAVA ISLAND USING GEOGRAPHICALLY WEIGHTED POISSON REGRESSION MODEL Wicaksono, Ditto Satrio; Nuriyah, Sinta; Fajritia, Rahajeng; Yuniarti, Ni Putu Nita; Priatmadani, Priatmadani; Amelia, Laeli; Berliana, Sarni Maniar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0615-0626

Abstract

The eighth goal of the SDGs, which aim to promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all, addresses the problem of unemployment. Indonesia, the fourth-largest country in the world, keeps on dealing with unemployment and its negative consequences. Three provinces on the island of Java have higher unemployment rates for educated people than any other provinces. The purpose of this study is to examine the variables affecting educated unemployment in Java. This study uses cross-sectional data published from BPS-Statistics Indonesia website and the Indonesia Investment Coordinating Board (BKPM) for 119 regencies/cities across six provinces on Java Island in 2021. The predictor variables are Gross Regional Domestic Product (GRDP), investment, labor force participation rate, mean years of schooling, regency/city minimum wage, and inflation. The number of working-age population is used as an exposure measure. Four models were used to analyze the factors affecting educated unemployment on Java Island: Poisson regression model and Geographically Weighted Poisson Regression (GWPR) model, both with and without an exposure. Based on the smallest AIC/AICc, the best model is the GWPR model with an exposure. This model creates 11 groups of locations based on the same predictor variables that significantly affect educated unemployment
Co-Authors Amelia, Laeli Anjarweni, Hanifah Ukti Budiasih Budiasih Fajritia, Rahajeng Nuriyah, Sinta Pramana, Anugerah Surya Putri, Shalihati Melani Retno Palupi Sarni Maniar Berliana, Sarni Maniar Wicaksono, Ditto Satrio Yuniarti, Ni Putu Nita