Mochammad Abdul Mukid, Mochammad Abdul
Diponegoro University

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SOLVING A SYSTEM OF LINEAR EQUATIONS BY QR FACTORIZATION METHOD FOR TEMPERATURE AND ALTITUDE REGRESSION MODEL AGAINST SPONTANEOUS-POTENTIAL Widowati, Widowati; Setyawan, Agus; Mustafid, Mustafid; Nur, Muhammad; Sudarno, Sudarno; Harmoko, Udi; Adhy, Satriyo; Gunawan, Gunawan; Subagio, Agus; Tjahjana, Heru; Sulpiani, Ririn; Riyanto, Djalal Er; Suhartono, Suhartono; Mukid, Mochammad Abdul; Suseno, Jatmiko Endro
JURNAL SAINS DAN MATEMATIKA Volume 22 Issue 3 Year 2014
Publisher : JURNAL SAINS DAN MATEMATIKA

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Abstract

Many real problems can be represented in the form of multiple linear regression equation. One of those is the relationship between the variables of temperature and altitude of the spontaneous-potential. In order to determine the parameters of the regression equation, the least squares method was used. From here, there was obtained the system of linear equations. In this paper, to solve systems of linear equations, the exact method was used as the exact solution is certainly better than the approached solution. The method used was the QR factorization method. At the QR factorization, the system of linear equations was written in form of matrix equation. Then, the coefficient matrix which the number of rows is m and number of columns is n with linearly independent columns was factored into the matrix Q which has the same size with the matrix A, with orthonormal columns and matrix R was upper triangular. Furthermore, by backward substitution, it could be obtained the exact solution of linear equation system. As verification of this proposed method, a case study was given using data of temperature, altitude, and spontaneous-potential in the geothermal manifestations area, Gedongsongo, Mount Ungaran Semarang. From here, it was obtained the parameters of exact multiple linear regression model which states the relationship between temperature and altitude toward the spontaneous-potential.
PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL Alvitiani, Siska; Yasin, Hasbi; Mukid, Mochammad Abdul
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Departemen Statistika FSM Undip

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Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model