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Arief Rachman Hakim
Department Of Statistics, Faculty Of Sciences And Mathematics, Diponegoro University
Decision Sciences, Operations Research & Management Other

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Journal : Media Statistika

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LIFE EXPECTANCY MODELING USING MODIFIED SPATIAL AUTOREGRESSIVE MODEL Hasbi Yasin; Budi Warsito; Arief Rachman Hakim; Rahmasari Nur Azizah
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.72-82

Abstract

The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.
MODELING LIFE EXPECTANCY IN CENTRAL JAVA USING SPATIAL DURBIN MODEL Arief Rachman Hakim; Hasbi Yasin; Agus Rusgiyono
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (720.681 KB) | DOI: 10.14710/medstat.12.2.152-163

Abstract

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.
PREDIKSI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS (MSP) Hasbi Yasin; Budi Warsito; Arief Rachman Hakim
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.776 KB) | DOI: 10.14710/medstat.12.1.39-49

Abstract

This research covers Spatial Extreme Value method application with Max-Stable Process (MSP) approach that will be used to analysis Extreme Rainfall in Semarang city. Extreme value sample are selected by Block Maxima methods, it will be estimated into Spatial Extreme Value form by including location factors. Then it transform to Frechet distribution because it has a heavy tail pattern. Max Stable Process (MSP) is an extension of the multivariate extreme value distribution into infinite dimension of the Extreme Value Theory. After the best model of extreme rainfall data in Semarang is obtained, then calculated the prediction of extreme rainfall with a certain time period. Predictions are calculated using a return level, predictions of extreme rainfall using the return period of the next two years, at the Semarang City Climatology Station predicted to be a maximum of 100.7539 mm. At the Tanjung Mas Rain Monitoring Station it is predicted that a maximum of 100.1052 mm, Ahmad Yani Rain Monitoring Station is predicted to be a maximum of 109.9379 mm. Maximum prediction of extreme rainfall can also be calculated for future t (time) periods.
PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Hasbi Yasin; Budi Warsito; Arief Rachman Hakim
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3437.176 KB) | DOI: 10.14710/medstat.11.1.53-64

Abstract

Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.Keywords: Banten, Economic growth, MGWR.
Co-Authors Ade Irma Prianti Agus Rusgiyono Alan Prahutama Anestasya Nur Azizah Arnisa Melani Kahar Budi Warsito Budi Warsito Chalimatus Sa'diah Hasbi Yasin Iut Triutami Lutfia Nuzula Lutfiah Maharani Siniwi Maghfiroh Hadadiah Mukrom Mifta Fara Sany Mustafid Mustafid Rahmasari Nur Azizah Rezky Dwi Hanifa Riza Rizqi Robbi Arisandi Rizki Faizatun Nisa Rukun Santoso Sisca Indah Pratiwi Sofiana Sofiana Stevanus Sandy Prasetyo Sugito Sugito Suparti Suparti Tatik Widiharih Tri Ernayanti