PEMODELAN DEA AGGRESSIVE - BOOTSTRAP REGRESSION PADA FAKTOR YANG MEMPENGARUHI EFISIENSI PERBANKAN SYARIAH INDONESIA

Abstract

Data Envelopment Analysis is a non-parametric method developed based on linear programs with objective functions and weight functions. DEA is an analytical technique used to measure the efficiency of a process which the decision making units (DMUs) are homogeneous. The application of the DEA method in various fields yields in steps in determining policies in a process. This study aims to measure the efficiency of Indonesian Islamic banking by applying DEA method with aggressive weighting. The efficiency result of the method is then combined with bootstrap regression method to find out the variables that significantly influence the efficiency value. The data in the research process was taken from the monthly financial reports of Indonesian Islamic banking from 2018 to 2022 with a quantitative research approach and associative research type. The input variables used to measure efficiency include total assets, total labor, labor operating costs, total deposits, and fixed assets.While the output variables include total financing funds, net operating margin, and other operating income. Meanwhile, the independent variables to estimate the factors that influence the efficiency value of DEA include capital adequacy ratio, return on assets, company size, and financing to deposit ratio.The result of the analysis shows the goodness of the model which is the coefficient of determination worth 40.95%. Independent variables that significantly affect the efficiency of DEA aggressive are capital adequacy ratio, return on assets, and financing to deposit ratio

Keywords

• bootstrap regression, data envelopment analysis, efficiency
• data envelopment analysis, efisiensi, regresi bootstrap