ANALISIS SPASIAL PENGARUH TINGKAT PENGANGGURAN TERHADAP KEMISKINAN DI INDONESIA (Studi Kasus Provinsi Jawa Tengah)

Rahmawati, Rita; Safitri, Diah; Fairuzdhiya, Octafinnanda Ummu

doi:10.14710/medstat.8.1.23-30

Cited by 11 publications

(8 citation statements)

References 4 publications

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“…Hasil Penelitian ini sesuai dengan (Rahmawati et al, 2015) Kurnia Sari Dewi, Wiwin Priana (2021), Leonita & Sari (2019) Masrofah (2020) pengangguran berpengaruh positif terhadap kemiskinan. Pengangguran memiliki korelasi yang erat dengan kemiskinan karena ketika sesorang menganggur maka dari itu tidak ada pendapatan yang dihasilkan untuk memenuhi kebutuhan hidupnya dan apabila itu terjadi maka dia digolongkan orang miskin.…”

Section: Pengaruh Pertumbuhan Ekonomi Terhadap Kemiskinanunclassified

Pengaruh islamic human development index dan pertumbuhan ekonomi terhadap kemiskinan dengan pengangguran sebagai variabel intervening

Viollani

Siswanto

Suprayitno

2022

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Poverty is no longer a foreign problem to be discussed, moreover this problem has become an international problem, including Indonesia. This problem was exacerbated by the outbreak of the deadly disease Covid-19 which caused the Indonesian economy to slump further, not least from the economic sector that went out of business and high layoffs occurred. Many efforts must be made to reduce poverty, such as increasing human resource development, increasing economic growth and reducing unemployment. This study aims to determine the effect of the Islamic Human Development Index and economic growth on poverty with unemployment as an intervening variable in Indonesia in 2016-2020. This study uses quantitative research methods, and path testing as an analytical tool. The data used is panel data from 34 provinces spread across Indonesia with a total observation of 136 samples. The direct test results show that I-HDI and economic growth have a significant negative effect on unemployment, while the indirect test results show that I-HDI and economic growth have a significant negative effect on poverty, while the unemployment variable can mediate the I-HDI relationship on poverty and economic growth. against poverty.

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Section: Pengaruh Pertumbuhan Ekonomi Terhadap Kemiskinanunclassified

Pengaruh islamic human development index dan pertumbuhan ekonomi terhadap kemiskinan dengan pengangguran sebagai variabel intervening

Viollani

Siswanto

Suprayitno

2022

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“…Namun, aspek kemiskinan bukan hanya dipengaruhi oleh peubah-peubah penjelas saja, tetapi juga dipengaruhi oleh efek spasial (Zhou dan Liu, 2019). Kebijakan pemerintah dalam penanggulangan kemiskinan dengan analisis spasial dapat dilakukan dengan lebih komprehensif karena memperhatikan keadaan "tetangga" dari wilayah tersebut (Rahmawati et al, 2015).…”

Section: Pendahuluanunclassified

Pemodelan Kemiskinan Di Jawa Menggunakan Bayesian Spasial Probit Pendekatan Integrated Nested Laplace Approximation (Inla)

Maulina

Djuraidah²,

Kurnia³

2019

Medstat

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Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few. The Bayes Method is a parameter estimation method that utilizes initial information (prior) and sample information so that it can provide predictions that have a higher accuracy than the classical methods. Bayes inference using INLA approach provides faster computation than MCMC and possible uses large data sets. This study aims to model Javanese poverty using the Bayesian Spatial Probit with the INLA approach with three weighting matrices, namely K-Nearest Neighbor (KNN), Inverse Distance, and Exponential Distance. Furthermore, the result showed poverty analysis in Java based on the best model is using Bayesian SAR Probit INLA with KNN weighting matrix produced the highest level of classification accuracy, with specificity is 85.45%, sensitivity is 93.75%, and accuracy is 89.92%.

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“…According to LeSage (1999) in Rahmawati (2015), the general model of spatial regression is as follows:…”

Section: Spatial Regressionmentioning

confidence: 99%

Modeling Life Expectancy in Central Java Using Spatial Durbin Model

Hakim

Yasin

Rusgiyono

2019

Medstat

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Central Java in 2017 was one of the provinces with high life expectancy, ranking second. Life expectancy of Central Java Province in 2017 is 74.08% per year. The fields of education, health and socio-economics, are several factors that are thought to influence the life expectancy in an area. To find out what factors that the regression analysis method can use to find out what factors influence the life expectancy. But in observations found data that have a spatial effect (location) called spatial data, a spatial regression method was developed such as linear regression analysis by adding spatial effects. One form of spatial regression is Spatial Durbin Model (SDM) which has a form like the Spatial Autoregressive Model (SAR). The difference between the two if in the SAR model the effect of spatial lag taken into account in the model is only on the response variable (Y) but in the SDM method, effect of spatial lag on the predictor variable (X) and response (Y) are also taken into account. Selection of the best model using Mean Square Error (MSE), obtained by the MSE value of 1.156411, the number mentioned is relatively small 0, which indicates that the model is quite good.

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ANALISIS SPASIAL PENGARUH TINGKAT PENGANGGURAN TERHADAP KEMISKINAN DI INDONESIA (Studi Kasus Provinsi Jawa Tengah)

Cited by 11 publications

References 4 publications

Pengaruh islamic human development index dan pertumbuhan ekonomi terhadap kemiskinan dengan pengangguran sebagai variabel intervening

Pengaruh islamic human development index dan pertumbuhan ekonomi terhadap kemiskinan dengan pengangguran sebagai variabel intervening

Pemodelan Kemiskinan Di Jawa Menggunakan Bayesian Spasial Probit Pendekatan Integrated Nested Laplace Approximation (Inla)

Modeling Life Expectancy in Central Java Using Spatial Durbin Model

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