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All Journal Al-Jabar : Jurnal Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Seminar Nasional Official Statistics
Wicaksono, Ditto Satrio
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Implementasi Geographically Weighted Regression (GWR) pada Determinasi Faktor Produksi Beras di Indonesia Tahun 2021 Wicaksono, Ditto Satrio; Kusumasari, Pamelina Alisha; Fajar, Huda M.; Fajritia, Rahajeng; Puspita Anggraini, I Gusti Ayu; 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.v2023i1.1714

Abstract

Food security is one of the nation’s responsibility in ensuring the appropriate and affordable foods for its citizens, especially rice. However, the rising number of agricultural land function conversion together with inequality rice production in every provinces still challenge our government in achieving the national food security. Understanding this situation, this research is conducted to analyze the factors influencing rice production in each province in Indonesia in 2021. To achieve this purpose, descriptive analysis and inferential analysis are employed, utilizing the Geographically Weighted Regression due to the spatial factors involved in each province and the occurrence of spatial heterogeneity from the global model. GWR model shows a better performance compared to global model in modelling the rice production growth in Indonesia. The results shows that there are six regional groups based on significant variables. Furthermore, labor still be the main factor in increasing rice production across all provinces also the intensity of fertility must be considered in order to achieve food security in Indonesia.
Utilize imagery and crowdsourced data on spatial employment modelling Pusponegoro, Novi Hidayat; Rachmawati, Ro'fah Nur; Siallagan, Maria A. Hasiholan; Wicaksono, Ditto Satrio
Al-Jabar: Jurnal Pendidikan Matematika Vol 15 No 2 (2024): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v15i2.24518

Abstract

Background: Spatial employment modeling investigates employment distribution, patterns, influencing factors, neighboring area impact, and regional policy efficacy. Conventional studies often rely on traditional data sources, which may overlook critical employment-related phenomena. In 2022, Java recorded the lowest labor absorption rate in Indonesia, necessitating a new approach.Aim: This study combines imagery, crowdsourced data, and official statistics to identify factors influencing labor absorption in Java Island.Method: Geographically Weighted Regression (GWR) was employed to account for spatial effects in the data.Results: The model reveals that nighttime light intensity in urban and agricultural areas, along with environmental quality, significantly enhances labor absorption across Java. Internet facilities, universities, and the number of micro and small industries also positively influence most districts/cities.Conclusion: Incorporating new data sources offers valuable insights for understanding employment patterns and can enrich employment research frameworks.
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 Budiasih Budiasih Fajar, Huda M. Fajritia, Rahajeng Kusumasari, Pamelina Alisha Novi Hidayat Pusponegoro Nuriyah, Sinta Priatmadani, Priatmadani Puspita Anggraini, I Gusti Ayu Rachmawati, Ro'fah Nur Sarni Maniar Berliana, Sarni Maniar Siallagan, Maria A. Hasiholan Yuniarti, Ni Putu Nita