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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 ().
MENATA LITERASI BERBASIS DATA STATISTIK MELALUI ANALISIS REGRESI PADA INDEKS PEMBANGUNAN LITERASI MASYARAKAT INDONESIA 2024 Antonius Kapitan; Karuna, Elisabeth Evelin
Pendas : Jurnal Ilmiah Pendidikan Dasar Vol. 11 No. 01 (2026): Volume 11 No. 01 Maret 2026 Public
Publisher : Program Studi Pendidikan Guru Sekolah Dasar FKIP Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/jp.v11i01.43016

Abstract

Tingkat literasi masyarakat Indonesia hingga tahun 2024 masih menunjukkan kesenjangan antardaerah, yang terlihat dari terbatasnya akses layanan perpustakaan, ketidakmerataan kualitas kolek The literacy rate of Indonesians until 2024 still shows regional disparities, as evidenced by limited access to library services, uneven collection quality, and a shortage of trained library staff. This condition indicates that literacy development has not been fully supported by an adequate supporting ecosystem. Referring to the components of the Community Literacy Development Index (IPLM) established by BPS, this study attempts to organize an understanding of literacy based on data through a statistical approach, specifically multiple regression analysis, to identify the main factors that influence national literacy achievement. This study analyzes three key elements of the IPLM, namely the equitable distribution of library services, the adequacy of collections, and the adequacy of library staff as predictors of community literacy levels. The regression results show that these three variables have a significant effect both simultaneously and partially. The Adjusted R-squared value of 0.8735 indicates that 87.35% of the variation in community literacy can be explained by these three components, so this model has very strong predictive power. These findings confirm that literacy development depends not only on the reading ability of the community, but also on the quality of the surrounding information ecosystem. This study concludes that national literacy strengthening strategies must be directed at improving the equitable distribution of library services, optimizing the quality and diversity of collections, and strengthening the capacity of library staff. Using a statistical data-based approach, this study provides an empirical basis for organizing Indonesian literacy in a more systematic, inclusive, and sustainable manner. si, serta minimnya tenaga perpustakaan terlatih. Kondisi ini menandakan bahwa pembangunan literasi belum sepenuhnya ditopang oleh ekosistem pendukung yang memadai. Mengacu pada komponen Indeks Pembangunan Literasi Masyarakat (IPLM) yang ditetapkan BPS, penelitian ini berupaya menata pemahaman literasi berbasis data melalui pendekatan statistik, khususnya analisis regresi berganda, untuk mengidentifikasi faktor-faktor utama yang memengaruhi capaian literasi nasional. Penelitian ini menganalisis tiga unsur kunci IPLM, yaitu pemerataan layanan perpustakaan, ketercukupan koleksi, dan ketercukupan tenaga perpustakaan sebagai prediktor tingkat literasi masyarakat. Hasil regresi menunjukkan bahwa ketiga variabel tersebut berpengaruh signifikan baik secara simultan maupun parsial. Nilai Adjusted R-squared sebesar 0.8735 mengindikasikan bahwa 87.35% variasi literasi masyarakat dapat dijelaskan oleh ketiga komponen tersebut, sehingga model ini memiliki daya prediksi yang sangat kuat. Temuan ini menegaskan bahwa pembangunan literasi tidak hanya bergantung pada kemampuan membaca masyarakat, tetapi pada kualitas ekosistem informasi yang melingkupinya. Penelitian ini menyimpulkan bahwa strategi penguatan literasi nasional harus diarahkan pada peningkatan pemerataan layanan perpustakaan, optimalisasi kualitas dan keragaman koleksi, serta penguatan kapasitas tenaga perpustakaan. Dengan pendekatan berbasis data statistik, penelitian ini memberikan landasan empiris untuk menata literasi masyarakat Indonesia secara lebih sistematis, inklusif, dan berkelanjutan.