scholarly journals MEMODELKAN ANGKA GIZI BURUK DI PROVINSI BALI DENGAN PENDEKATAN REGRESI SPASIAL

2021 ◽  
Vol 10 (2) ◽  
pp. 103
Author(s):  
ANAK AGUNG ISTRI AYU PRATAMI ◽  
I KOMANG GDE SUKARSA ◽  
NI LUH PUTU SUCIPTAWATI ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Birth Weight ◽  
Exclusive Breastfeeding ◽  
Spatial Regression ◽  
Geographic Location ◽  
Regression Method ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Error ◽  
Regression Methods ◽  
Spatial Autoregressive

Nutritional problems in toddler are still a serious problem in various districts/cities in Indonesia. The case of malnutrition in Bali Province vary in many regions and hypothesized to be influenced by geographic location, which is often known as spatial heterogeneity. To overcome this problem, a spatial regression method is used on this research. This study aims to model the factors that are hypothesized affect malnourished toddlers in Bali Province using spatial regression methods, i.e. spatial autoregressive model (SAR) and spatial error model (SEM). Both models have 5 predictors variable, i.e. the percentage of toddlers aged between 6 - 59 months who received vitamin A, the percentage of babies with low birth weight (LBW), the percentage of households with clean and healthy living behavior (PHBS), the percentage of children under five receiving exclusive breastfeeding, and the percentage of toddler health services, which are obtained from Bali Provincial Health Office. The results showed SEM method produced smaller AIC value and higher , with  and  AIC values ??of 96.24% and 60.84, respectively.

scholarly journals Dampak Spillover Pekerja Migran Indonesia (PMI) Asal Jawa Tengah Terhadap Pertumbuhan Ekonomi Propinsi Jawa Tengah

2019 ◽  
Vol 1 (2) ◽  
pp. 191-207
Author(s):  
Caroline Caroline ◽  
FX Sugiyanto ◽  
Achmad Syakir Kurnia ◽  
Etty Puji Lestari ◽  
Ceacilia Srimindarti
Keyword(s):  
Saudi Arabia ◽  
Autoregressive Model ◽  
Euclidean Distance ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Durbin Model ◽  
Spatial Error ◽  
Spatial Autoregressive

Spillover tenaga kerja Propinsi Jawa Tengah tahun 2019 cukup tinggi 60.432 pekerja setelah Propinsi Jawa Timur, yaitu 68.740 pekerja. Spillover tenaga kerja Propinsi Jawa Tengah diduga karena jumlah penduduk yang banyak di Propinsi Jawa Tengah. Faktor pendorong Pekerja Migran Indonesia (PMI) berniat bekerja keluar negeri adalah untuk memperoleh pendapatan yang layak sehingga selisih pendapatan dan biaya hidupnya dapat dikirim keluarganya di Indonesia. Tujuan penelitian ini adalah menganalisis dampak spillover Pekerja Migran Indonesia (PMI) terhadap pertumbuhan ekonomi Propinsi Jawa Tengah. Metode penelitian ini menggunakan matriks bobot spasial dengan pendekatan Euclidean Distance untuk menghitung Spatial Autoregressive Model (SAR), Spatial Error Model (SAR), dan Spatial Durbin Model (SDM). Simpulan Hasil penelitian ini adalah sebagai berikut : Spillover tenaga kerja Propinsi Jawa Tengah yang diwujudkan dalam bentuk pekerja Migran Indonesia asal Jawa Tengah kebanyakan berasal dari Kabupaten Cilacap, Kabupaten Kendal, Kabupaten Brebes, Kabupaten Banyumas, Kabupaten Pati, Kabupaten Grobogan, Kabupaten Kebumen, Kabupaten Wonosobo, dan Kabupaten Batang dengan tingkat pendidikan Sekolah Menengah Pertama (SMP) dengan jenis kelamin wanita kebanyakan bekerja pada negara Negara Hongkong, Negara Taiwan, Negara Malaysia, Negara Singapura, Negara Korea Selatan, Negara Brunai Darussalam, dan Negara Saudi Arabia.

scholarly journals Analisis Faktor-Faktor yang Mempengaruhi Penyebaran Penyakit Demam Berdarah Dengue (Dbd) di Provinsi Jawa Tengah dengan Metode Spatial Autoregressive Model dan Spatial Durbin Model

2018 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Arkadina Prismatika Noviandini Taryono ◽  
Dwi Ispriyanti ◽  
Alan Prahutama
Keyword(s):  
Autoregressive Model ◽  
Spatial Regression ◽  
Hemorrhagic Fever ◽  
Spatial Autoregressive Model ◽  
Spatial Durbin Model ◽  
Spatial Error ◽  
Independent Variables ◽  
Central Java ◽  
Spatial Autoregressive ◽  
Years Of Schooling

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. Moran’s I test results stated that there are spatial dependencies between dependent and independent variables. The best model produced is the SAR model because it has the smallest AIC value of 49.61

scholarly journals Perbandingan Spatial Autoregressive Model dan Spatial Error Model dalam Pemodelan Indeks Pembangunan Manusia di Provinsi Jawa Timur

2021 ◽  
Vol 5 (1) ◽  
pp. 41-50
Author(s):  
Desie Rahmawati ◽  
Hardian Bimanto
Keyword(s):  
Lagrange Multiplier ◽  
Autoregressive Model ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Error ◽  
Spatial Autoregressive

Indeks Pembangunan Manusia (IPM) merupakan indikator untuk mengukur keberhasilan upaya pembangunan kualitas hidup manusia yang telah dicapai. Pertumbuhan IPM di suatu wilayah dapat dipengaruhi oleh faktor geografis yaitu besarnya angka IPM di suatu wilayah dapat memengaruhi angka IPM pada wilayah yang berdekatan sehingga faktor geografis diduga dapat memengaruhi dan memberikan efek dependensi spasial pada nilai IPM di Provinsi Jawa Timur. Penelitian ini bertujuan untuk melakukan pemodelan pada faktor yang berpengaruh terhadap Indeks Pembangunan Manusia di Provinsi Jawa Timur. Unit pengamatan pada penelitian ini adalah 38 kabupaten/kota di Provinsi Jawa Timur. Data yang digunakan adalah data sekunder dari Badan Pusat Statistik Jawa Timur tahun 2017. Metode analisis yang digunakan dalam penelitian ini adalah metode Spatial Autoregressive Model (SAR) dan Spatial Error Model (SEM). Hasil penelitian menunjukkan bahwa berdasarkan nilai uji Lagrange Multiplier (lag) dan Lagrange Multiplier (error) terdapat dependensi lag dan error. Variabel prediktor yang secara signifikan berpengaruh terhadap nilai IPM pada model SAR dan SEM antara lain Angka Harapan Hidup, Rata-rata Lama Sekolah, Angka Harapan Lama Sekolah dan Kemampuan daya beli masyarakat. Berdasarkan hasil penelitian didapatkan model SEM dengan nilai R2 terbesar dan nilai AIC terkecil sehingga model SEM lebih baik digunakan untuk menganalisis nilai IPM di Provinsi Jawa Timur dibandingkan model SAR dan model regresi OLS.

scholarly journals Pemodelan dengan Spatial Autoregressive (SAR) pada Angka Putus Sekolah Bagi Anak Usia Wajib Belajar di Provinsi Sulawesi Selatan

2020 ◽  
Vol 3 (1) ◽  
pp. 44
Author(s):  
Rika Nasir ◽  
Suwardi Annas ◽  
Muhammad Nusrang
Keyword(s):  
Autoregressive Model ◽  
Moving Average ◽  
Error Model ◽  
Spatial Error Model ◽  
Spatial Autoregressive Model ◽  
Spatial Error ◽  
Spatial Autoregressive ◽  
Spatial Moving Average

Abstract. Regresi spasial merupakan pengembangan dari regresi klasik. Pengembangan ini berdasarkan adanya pengaruh tempat atau spasial dari data yang dianalisis. Beberapa model regresi spasial adalah Spatial Autoregressive (SAR), Spatial Error Model (SEM) dan Spatial Moving Average (SARMA). Penelitian ini menggunakan analisis model SAR terhadap angka putus sekolah di Sulawesi Selatan. Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik Provinsi Sulawesi Selatan tahun 2018. Penelitian ini dilakukan untuk mengetahui model Spatial Autoregressive (SAR) pada data banyaknya angka putus sekolah yang terjadi di Provinsi Sulawesi Selatan, serta mengenalisis faktor-faktor yang memberikan pengaruh signifikan terhadap pertumbuhan angka putus sekolah. Hasil penelitian ini memperoleh model yaitu ; sehingga faktor-faktor yang berpengaruh secara signifikan terhadap angka putus sekolah di Sulawesi Selatan adalah pengeluaran per kapita, rasio murid terhadap sekolah dan jumlah penduduk miskin.Keywords: Regresi Spasial, Spatial Autoregressive Model (SAR), Angka Putus Sekolah

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

scholarly journals Model Regresi Spasial dan Aplikasinya pada Kasus Tingkat Kemiskinan Kabupaten Soppeng

2018 ◽  
Vol 4 (2) ◽  
pp. 102
Author(s):  
Anggi Ananda Putri ◽  
Wahidah Sanusi ◽  
Sukarna Sukarna
Keyword(s):  
Social Issues ◽  
Well Being ◽  
Error Model ◽  
Poor Household ◽  
Spatial Dependency ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Sar Model ◽  
Spatial Autoregressive ◽  
Independent Variable

Poverty is one of the major problem that frequently faced by human. Begin from poverty, consequently emerged several social issues, such as homeless, beggar, defendant, and prostitution. On this research were conducted modeling poverty degree in Soppeng with using number of poor household as the dependent variable. Modeling were done by using area approach which is a Spatial Autoregressive (SAR) model and Spatial Error Model (SEM). As for the independent variable used on this research is the number of health services, school facility, population density, social well being disable, and the distance on village and centre of Soppeng.  Regarding to the analysis of Spatial Autoregressive (SAR) and Spatial Error Model (SEM) shows that there is a spatial dependency lag and error on number of poor household variable. As for the independent variable which have the significancy account for 5% on Spatial Autoregressive (SAR) and Spatial Error Model (SEM) are every variables with a number R2= 90,9% on SAR and R2= 90,1% on SEM.

scholarly journals PEMODELAN KASUS DIARE DI PROVINSI BALI DENGAN METODE ANALISIS REGRESI SPASIAL

2021 ◽  
Vol 10 (2) ◽  
pp. 95
Author(s):  
I.G.A. DIAH SULASIH ◽  
MADE SUSILAWATI ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Regression Analysis ◽  
Spatial Dependence ◽  
Spatial Regression ◽  
Spatial Effect ◽  
Model Estimation ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Bowel Movements ◽  
Spatial Regression Analysis ◽  
The Relationship

Diarrhea is a disease that occurs due to changes in the frequency of bowel movements and can cause death. In 2018, 115.889 cases of diarrhea were found in Bali Province. Information on the relationship between locations indicates the spatial effect in the model. Model estimation was done by using spatial regression analysis. This study aims to determine what factors influence diarrhea cases in Bali Province. The results show that the number of diarrhea cases in a district is influenced by the surrounding districts. This is reinforced in the Moran’s I test which shows spatial dependence. In the analysis of the Spatial Error Model (SEM), it was obtained that the value of  was 57,69% and the variables that significantly affected diarrhea cases in Bali Province were population density and sanitation facilities

scholarly journals Innovación en Ecuador: un enfoque espacial/Innovation in Ecuador: a spatial approach

2020 ◽  
Author(s):  
V Morales-Oñate ◽  
B Morales-Oñate
Keyword(s):  
Government Support ◽  
Spatial Error Model ◽  
Spatial Error ◽  
Spatial Spillovers ◽  
Spatial Influence ◽  
Spatial Approach ◽  
Palabras Clave ◽  
Proposed Model ◽  
Private Banks ◽  
Spatial Autoregressive

Este trabajo explora la distribución espacial del éxito innovador de las empresas en Ecuador entre 2012 y 2014. Los datos cuentan con una muestra de 6275 empresas con representatividad provincial. En base a esta información, los objetivos que persigue esta investigación están orientados a i) establecer si existe o no relaciones espaciales en las provincias del Ecuador y ii) resaltar políticas estatales que contribuyan a la innovación. Los resultados muestran que existe influencia espacial en el éxito innovador. Asimismo, el modelo planteado sugiere políticas orientadas a la innovación mediante apoyo del Gobierno así como financiamiento por parte de la banca privada. This paper explores the spatial distribution of the innovative success of companies in Ecuador between 2012 and 2014. The data has a sample of 6275 companies with provincial representation. Based on this information, the objectives pursued by this research are aimed at i) establishing whether or not there are spatial relationships in the provinces of Ecuador and ii) highlighting state policies that contribute to innovation. The results show that there is a spatial influence on innovative success. Likewise, the proposed model suggests policies oriented towards innovation through government support as well as financing from private banks. Palabras clave: Spillovers espaciales, Modelo espacial autorregresivo, Modelo de error espacial, Modelo espacial de Durbin. Keywords: Spatial spillovers, Spatial autoregressive model, Spatial Error Model, Spatial Durbin Model.

scholarly journals The Effect of Land Use/Cover Change on Soil Erosion Change by Spatial Regression in Changwu County on the Loess Plateau in China

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1209
Author(s):  
Shichuan Yu ◽  
Fei Wang ◽  
Mei Qu ◽  
Binhou Yu ◽  
Zhong Zhao
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Regression ◽  
Ordinary Least Squares ◽  
Spatial Error Model ◽  
The Loess Plateau ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Geographically Weighted Regression Model ◽  
Lag Model

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km2 and 64.93 km2, respectively. In 1997–2007, the planted land increase was mainly through the conversion of cropland. In 2007–2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km−2 year−1, 1305.17 ton km−2 year−1, 1310.60 ton km−2 year−1, and 1891.46 ton km−2 year−1 in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.

scholarly journals Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments

2019 ◽  
pp. 004912411988246 ◽  
Author(s):  
Tobias Rüttenauer
Keyword(s):  
Monte Carlo ◽  
Spatial Data ◽  
Regression Models ◽  
Spatial Regression ◽  
Spillover Effects ◽  
Spatial Error ◽  
Monte Carlo Experiments ◽  
Spatial Autoregressive ◽  
Spatial Regression Models ◽  
Variable Bias

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive and spatial error specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) and the simple spatial lag of X (SLX) provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect “spillover” effects, several—quite realistic—situations exist in which the SLX outperforms the more complex SDM and SDEM specifications.

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