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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Muhammad Arib Alwansyah
Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, Universitas Negeri Jakarta, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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BETA REGRESSION MODELING ON POVERTY DATA IN INDONESIA 2019 - 2022 Muhammad Arib Alwansyah; Sigit Nugroho; Ramya Rachmawati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2099-2116

Abstract

The Central Statistics Agency (BPS) reported that the percentage of poor people in Indonesia increased from 2019 to 2021, reaching 10.14 percent. This condition highlights the need for an analytical approach capable of accurately modeling percentage data that are naturally bounded between 0 and 1. This study introduces a new approach by applying the Beta regression model to analyze the factors influencing poverty levels across Indonesian provinces. The novelty of this research lies in the application of the Beta regression model to panel data on poverty, which remains rarely explored in empirical studies on Indonesia’s socio-economic indicators. The model was chosen because it provides more efficient and unbiased parameter estimates than the ordinary least squares (OLS) method, especially when the dependent variable exhibits asymmetry and heteroskedasticity. Parameter estimation was conducted using the Maximum Likelihood Estimation (MLE) method with the Newton–Raphson iterative algorithm to ensure convergence and estimation efficiency. The data used in this study are provincial-level poverty data sourced from official publications by the BPS. The analysis results indicate that the model meets the model suitability criteria for 2019 and 2020. Factors that significantly influenced the percentage of poor people in both years included the percentage of the population with health insurance and the literacy rate. Meanwhile, in 2021 and 2022, factors that significantly influenced the percentage of the poor population included the average years of schooling, the percentage of the population with health insurance, and the literacy rate. This study contributes to the field of applied statistics by demonstrating that the Beta regression model offers a novel and robust alternative for analyzing bounded and asymmetric socio-economic data. Furthermore, it provides new empirical insights into the statistical modeling of poverty in Indonesia, offering a methodological advancement over traditional regression approaches.
Outlier Handling in Applied Regression: Performance Comparison Between Least Trimmed Squares and Maximum Likelihood-Type Estimators Cinta Rizki Oktarina; Andini Setyo Anggraeni; Muhammad Arib Alwansyah; Reza Pahlepi
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.01

Abstract

Poverty analysis often relies on regression models whose performance can deteriorate in the presence of outliers, leading to biased estimates and unreliable conclusions. This study aims to evaluate the effectiveness of robust regression methods compared with Ordinary Least Squares (OLS) when modeling poverty levels across 154 regions in Sumatra. Four socioeconomic indicators were used as predictors, and outlier detection was conducted using the DFFITS approach. After identifying deviations from normality and the presence of influential observations, two robust estimation techniques M-estimation and Least Trimmed Squares (LTS) were applied to improve model stability. The results show that while all predictors significantly influence poverty, the LTS estimator provides the most accurate and robust performance, yielding the smallest Mean Squared Error (MSE) and an R-squared value of 53.37%. These findings demonstrate that LTS is better suited than OLS and M-estimation for handling data contamination and offers a more reliable approach for modeling poverty determinants
Co-Authors Andini Setyo Anggraeni Cinta Rizki Oktarina Ramya Rachmawati Reza Pahlepi Sigit Nugroho