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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Riset Statistika Journal of Statistics and Data Science
Karuna, Elisabeth Evelin
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Novianti, Pepi; Crisdianto, Riki; Karuna, Elisabeth Evelin; Fairuzindah, Athaya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1251-1262

Abstract

Bengkulu Province ranks second in the category of the highest percentage of poor people in the Sumatra region, at 14.62% in March 2022, and sixth in Indonesia, which is undoubtedly one of the fundamental problems that requires mutual attention. The phenomenon of high poverty in Bengkulu Province is inseparable from the lives of people whose main livelihood is in the agricultural sector, especially tenant farmers. Therefore, in this study, the Copula and Gaussian Copula Marginal Regression (GCMR) methods are applied to determine how the agricultural sector affects poverty in Bengkulu Province using secondary data obtained from the Bengkulu Provincial Statistics Agency (Susenas 2022). The results show that the Copula model can identify various types of dependency between the number of poor households in each district/city in Bengkulu Province in 2022 and each of the variables, namely the Number of Agricultural Business Households , the Growth Rate of the Agricultural Sector , the Human Development Index , and the Open Unemployment Rate ( ) by considering the different characteristics of dependency such as top-tail, bottom-tail, or negative dependency. Meanwhile, the GCMR model can provide the direction of influence of the independent variables on the dependent variable Y, where it can be seen that the variables , , and have a negative influence on the variable , whie the variable has a positive impact on the variable . Therefore, in general, it can be concluded that either positive or negative dependencies identified by the Copula model can influence the resulting GCMR model by providing more profound complexity regarding the relationship between the variables analyzed.
A Panel Data Spatial Regression Approach for Modeling Poverty Data In Southern Sumatra Hidayati, Nurul; Karuna, Elisabeth Evelin; Alwansyah, Muhammad Arib
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.41288

Abstract

This research examines the use of spatial panel data regression approach to model poverty data in the Southern Sumatra region. The main objective of the study is to model poverty in the Southern Sumatra region using spatial panel data regression. Panel data from districts/cities in South Sumatra, Jambi, Lampung, Bengkulu, and Bangka Belitung during the 2015-2021 period were used in the analysis. The spatial panel models used in this study are panel SAR regression and panel SEM. The results show that the spatial panel data approach is better at explaining variations in poverty levels compared to non-spatial models. A significant spatial spillover effect was found, where the poverty level of an area is influenced by the conditions of its neighboring areas. The results of the analysis show that the best model to use in modeling the Poverty Percentage data in the Southern Sumatra region is the Spatial Autoregressive Fixed Effect (SAR-FE) model based on the smallest AIC and BIC values. Factors such as average years of schooling and life expectancy are proven to have a significant influence on the percentage of poverty in the SAR Fixed Effect model.
SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Jomecho, Tommy; Oktarina, Cinta Rizky; Pratiwi, Stevy Cahya; Karuna, Elisabeth Evelin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0759-0772

Abstract

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().
Model Regresi Data Panel Robust terhadap Kemiskinan di Provinsi Sulawesi Selatan Mahrani; Karuna, Elisabeth Evelin; El Darman, Atiqa Azza
Jurnal Riset Statistika Volume 5, No. 2, Desember 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i2.8637

Abstract

Abstract. Poverty is one of the fundamental issues in socio-economic development in Indonesia, which remains the main focus of regional policies. Variations in poverty levels between districts/cities in South Sulawesi Province reflect differences in development performance influenced by economic growth and the Human Development Index (HDI). This article aims to analyze the influence of these two factors on poverty using a Panel Data Regression approach with Heteroskedasticity-Consistent Estimator (Robust Standard Errors). Theoretically, this method was developed to produce parameter estimates that remain consistent even when classical assumptions are violated, particularly heteroskedasticity and cross-section unit correlation. The data used covers 24 districts/cities in South Sulawesi Province during the 2020–2024 period. The analysis was conducted using Fixed Effect (FE) and Random Effect (RE) models, accompanied by a Hausman test to determine the best model, as well as Pesaran CD, Breusch-Pagan LM, and Wooldridge diagnostic tests. The results of the article show that HDI has a negative and significant effect on poverty levels, while economic growth has no significant effect. The robust approach has been proven to increase the reliability of estimation results in socioeconomic data conditions that do not meet the assumption of homoscedasticity. These findings indicate that improving the quality of human resources through education and health is a key strategy in reducing poverty levels in South Sulawesi. Abstrak. Kemiskinan merupakan salah satu persoalan fundamental dalam pembangunan sosial ekonomi di Indonesia yang masih menjadi fokus utama kebijakan daerah. Kemiskinan di Provinsi Sulawesi Selatan masih menunjukkan variasi antarwilayah yang dipengaruhi oleh pertumbuhan ekonomi dan Indeks Pembangunan Manusia (IPM). Penelitian ini menganalisis pengaruh kedua variabel tersebut menggunakan regresi data panel dengan Driscoll–Kraay Robust Standard Errors, yang dipilih karena mampu mengatasi heteroskedastisitas, autokorelasi, dan cross-sectional dependence. Data mencakup 24 kabupaten/kota selama periode 2020–2024. Model Fixed Effect (FE) dan Random Effect (RE) diuji, dan pemilihan model dilakukan menggunakan uji Hausman. Hasil penelitian menunjukkan bahwa IPM berpengaruh negatif dan signifikan terhadap kemiskinan, sementara pertumbuhan ekonomi tidak signifikan. Pendekatan robust ini meningkatkan reliabilitas hasil estimasi dan memberikan gambaran empiris yang relevan bagi kebijakan pengentasan kemiskinan di Sulawesi Selatan. Artikel ini menggunakan metode Regresi Data Panel dengan Driscoll–Kraay Robust Standard Errors untuk memastikan konsistensi standard error ketika terjadi heteroskedastisitas dan cross-sectional dependence. Analisis mencakup 24 kabupaten/kota di Provinsi Sulawesi Selatan periode 2020–2024 menggunakan model Fixed Effect (FE), Random Effect (RE), dan Uji Hausman. Hasil menunjukkan bahwa IPM berpengaruh negatif dan signifikan terhadap kemiskinan, sementara PDRB tidak berpengaruh signifikan. Pendekatan Driscoll–Kraay meningkatkan reliabilitas estimasi dibanding robust konvensional.
Co-Authors Alwansyah, Muhammad Arib Crisdianto, Riki El Darman, Atiqa Azza Fairuzindah, Athaya Jomecho, Tommy Mahrani Nurul Hidayati Oktarina, Cinta Rizky Pepi Novianti Pratiwi, Stevy Cahya Rini, Dyah Setyo Sigit Nugroho