Penerapan Model Regresi Spasial Dalam Menentukan Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia Di Kabupaten/Kota Provinsi Sumatera Barat

Samadi, Hari; Asdi, Yudiantri; Efendi,

doi:10.25077/jmu.6.4.80-89.2017

Cited by 6 publications

(8 citation statements)

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“…Spatial regression can be used as a solution in modeling data that has an element of location in it. Spatial regression is an analysis to evaluate the relationship between one variable and several other variables by paying attention to spatial / regional influences (Samadi, et al, 2017). Location or regional factors are thought to influence and give the effect of spatial/regional dependence on the poverty rate in South Sumatra Province.…”

Section: Metode Penelitian Metode Analisismentioning

confidence: 99%

Analisis Pola Spasial dan Determinan Kemiskinan di Provinsi Sumatera Selatan

Nazipawati,

Mulyaningsih

2024

Ecombis. Rev. J. Ilm. Eco. and Bussines.

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The purpose of this study was to determine the spatial pattern of the poverty rate and to determine the factors that may have a spatial influence on the poverty rate in South Sumatra Province. The factors that are suspected to influence and have a spatial (regional) dependency effect on the poverty rate are economic growth, population, length of roads in good condition, number of households that have access to decent drinking water, number of villages that can receive internet signals and Gross Regional Domestic Product per capita 2021 year in 15 districts/cities. The data used in this study were sourced from the South Sumatra Central Bureau of Statistics and spatial identity data sourced from https://gadm.org/download_country_v3.html. The analysis method used is spatial regression analysis using the Stata 17 application. The results of this study show that based on the map there are groupings or clusters of poverty rates. However, there is no spatial dependency effect on the poverty rate in South Sumatra Province. Economic growth has a negative but insignificant effect on the poverty rate, population has a negative and significant effect on the poverty rate, the length of highways in good condition has a positive but insignificant effect on the poverty rate, the number of households with access to safe drinking water has a negative but insignificant effect on the poverty rate, the number of villages that can receive internet signals has a positive and significant effect on the poverty rate, and GRDP per capita has a positive and significant effect on the poverty rate in South Sumatra Province.

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Section: Metode Penelitian Metode Analisismentioning

confidence: 99%

Analisis Pola Spasial dan Determinan Kemiskinan di Provinsi Sumatera Selatan

Nazipawati,

Mulyaningsih

2024

Ecombis. Rev. J. Ilm. Eco. and Bussines.

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“…Spasial merupakan sesuatu yang mempunyai hubungan dengan wilayah. Model Spatial Autoregressive (SAR) merupakan model yang menggabungkan konsep regresi linier dengan pergeseran spasial pada variabel respon ketika terdapat korelasi spasial (Samadi, 2017). Model ini juga menggambarkan pengaruh suatu variabel yang merupakan faktor kejadian Covid-19 terhadap kematian akibat Covid-19.…”

Section: Pendahuluanunclassified

Model Spatial Autoregressive pada Tingkat Angka Kematian Korban Covid-19 di Provinsi Riau

Rahmadeni,

Dina

2024

zeta

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Riau province has the most cases on Sumatra island and has more COVID 19 cases, the third province in Indonesia. Spread of COVID 19 disease in spatial regression analysis with multiple Lagrange spatial lags to determine the dependence of spatial lags using spatial autoregression. This method can identify spatial autocorrelation in the spread pattern of COVID 19 disease in the county as well as determine the cause of COVID 19 mortality. Results of autoregression analysis According to space, there are three variables that significantly affect the COVID 19 mortality rate: poverty, unemployment and population density. With the SAR model results, koefisisen was determined to be 98.91%. This means that poverty, unemployment and population density are the factors responsible for 98. 91% of the COVID 19 mortality rate. The rest (1. 09%) is influenced by other factors outside the model.

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“…The spatial error coefficient (Ɛ) shows the correlation level of the influence of spatial error from one region to other areas around it (Anselin, 2005;Baltagi et al, 2003;LeSage & Pace, 2009;Samadi et al, 2017).…”

Section: Spatial Error Model (Sem)mentioning

confidence: 99%

A spatial analysis of non-performance financing determinants in Islamic banks in Indonesia

Suprayitno

Hardiani

2021

JEKI

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Purpose -This study aims to analyze the non-performance financing determinant of Islamic Banking (IB) in Indonesia through spatial analysis. These determinants consist of macroeconomic and microeconomic variables Methodology -Samples in this research were selected using the purposive sampling technique with the criteria of Islamic banks registered in the Financial Services Authority (OJK) that released quarterly data reports and financial reports during 2015-2020. There are 7 Islamic banks that met the criteria.Findings -Results showed that the ROA, Inflation, GDP, and BI Rate simultaneously affected the NPF level, while FDR and CAR do not affect the NPF level of Islamic commercial banks. Meanwhile, partially FDR, BOPO, CAR, ROA, Inflation, GDP, and BI Rate affect the NPF level. In addition, the spatial analysis showed that based on the global distribution, there is a significant spatial effect through the geographical location between one bank and other.Originality -This research provides empirical data related to the determinants of non-performing financing, using the spatial analysis approach. Moreover, this research also uses the most updated data, Islamic and conventional banks during 2015-2020Research limitations -Related to the samples of Islamic banks, this research only investigates those registered in OJK, not all Islamic banks in Indonesia.

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Penerapan Model Regresi Spasial Dalam Menentukan Faktor-Faktor Yang Mempengaruhi Indeks Pembangunan Manusia Di Kabupaten/Kota Provinsi Sumatera Barat

Cited by 6 publications

References 0 publications

Analisis Pola Spasial dan Determinan Kemiskinan di Provinsi Sumatera Selatan

Analisis Pola Spasial dan Determinan Kemiskinan di Provinsi Sumatera Selatan

Model Spatial Autoregressive pada Tingkat Angka Kematian Korban Covid-19 di Provinsi Riau

A spatial analysis of non-performance financing determinants in Islamic banks in Indonesia

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