ANALISIS PENGARUH NPL, BOPO,LDR, DAN NOPFE TERHADAP CAR (Studi Empiris : Bank Pembangunan Daerahse- Indonesia Periode 2012-2015)

Abstract

This study aimed to verify the effect of Non-Performing Loans (NPL), Operating Expenses to Operating Income (OEOI), Loan to Deposit Ratio (LDR), and Net Open Position in Foreign Exchange (NOPFE) of the Capital Adequacy Ratio (CAR). The writer used the secondary data which comes from the financial statements of the Bank Pembangunan Daerah (BPD), published by the Otoritas Jasa Keuangan (OJK), in the period of 2012 to 2015.The writer also used a purposive sampling as the method of the study with the provisions that the bank has a complete financial data which relevance to the variables, there are total 8 samplings from 26 Bank Pembangunan Daerah (BPD). The analysis tool used the classical assumption test, multiple linear regression analysis, the coefficient of determination, T-Test statistical and F-Test statistical. The result of the study shows that there is no deviation of classical assumption, indicating that the available data has been qualified to be used as the multiple linear regression model. Based on the result of the regression testing, the writer obtained a regression equation: CAR = 10,450 + 3,729 NPL – 4,171 OIOE – 1,846 LDR – 1,823 NOPFE.The hypothesis testing the writer concluded that: Non-Performing Loans (NPL) has significant positive effect on Capital Adequacy Ratio (CAR), Operating Expenses to Operating Income (OIOE) has significant negative effect on Capital Adequacy Ratio (CAR), and Loan to Deposit Ratio (LDR) and Net Open Position in Foreign Exchange (NOPFE) has no effect on Capital Adequacy Ratio (CAR). The value of Adusted R Square is 19.8 % , this means that the amount of influence exerted by variables Non Performing Loans (NPL), Operating Expenses to Operating Income (OIOE), Loan to Deposits Ratio (LDR), and Net Open Position in Foreign Exchange (NOPFE) to the changes that occur in the Capital Adequacy Ratio (CAR) is 19.8 % , while the 80,2% is explaining by other variables outside the model.