Descriptive Qualitative namely: a method used to analyze the existing monetary and financial data by applying theory or concept to facts. In the research, the data obtained and the proposed hypotheses will be analyzed and tested by means of analysis / statistical test of variance based structural equation or
RJOAS, 1(73), January 2018 89 Structural Equation Modeling (SEM) - Partial Least Square (PLS) using Smart PLS software program. The consideration of using SEM-PLS model, because of its ability to measure construct through its indicators, analyzing indicator variable, latent variable, and error of measurement and can do direct analysis (not one by one diregresi) relationship between independent variable (independent variable / exogenous) with dependent variable (dependent variable / endogenous). According to Imam Ghozali (2006: 18), Partial Least Square (PLS) is a powerful analytical method because it does not assume data to be measured by a certain scale, the number of small samples. The purpose of Partial Least Square (PLS) is to help researchers to get the value of latent variables for prediction purposes. RESULTS AND DISCUSSION Effect of Interest Rates on Conventional Bank Performance and Sharia Bank. The hypothesis of monetary variables (interest rate, exchange rate, and money supply in partial and simultaneous) simultaneously affect the internal variables (credit policy and Conventional Bank operational policy). According to result of hypothesis test known that external factor have not significant effect to performance of ank proved with value of P Value equal to 0,302 bigger than 0,05 and coefficient value 0,885. This means that external factor variables consisting of indicators: interest rate, exchange rate, and money supply have no effect on bank performance as measured by CAR, MPL, ROA and LDR indicators. The results of this study Kamalia Octaviyanti, Sunu Priyawan and Tri Ratnawati (2013) in their research have founded that external factors using inflation indicators, SBI rates, and exchange rates significantly influence the performance of banks as measured by Return On Assets (ROA) and Return On Equity (ROE) and supports the results of research by Diyanti and Widyarti (2012) who found that external factors measured using inflation variables significantly influence bank performance, also support the results of Angrish (2010) study which states that macroeconomic variables other than GDP significantly explain the performance of banking. Furthermore, this result also supports the findings of Sitorus (2004) and Dermiguc-Kunt and Huizinga (1997 and 2001) who found that external factors such as inflation, SBI rates, and exchange rates (exchange rate) have a significant effect on bank performance. However, this result does not support Setiawan's research (2009) which found that external factors of inflation did not significantly affect bank performance as measured by ROA ratio. This difference of findings is possible because the observation period used in the study is in relatively stable condition where only in 2008 the banking industry in Indonesia
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