The purpose of this study is to determine the effect of the Financing to Deposit Ratio (FDR), Non Performing Financing (NPF) and Operational Cost variable to Operational Revenue (BOPO) toward Return On Asset (ROA) at Islamic Bank in Indonesia period 2013-2017 partially and simultaneously. This study uses a quantitative approach. Samples were determined using purposive sampling technique and the number of selected samples was 13 Islamic Commercial Banks. This study uses regression analysis with panel data tests to determine the relationship between exogenous variables and endogenous variables. The result of this research shows that BOPO is partially has significant influence to the profitability . Meanwhile, FDR and NPF are partially have insignificant influence to the profitability. While simultaneously, FDR, NPF and BOPO have significant influence to the profitability of Islamic bank with the coefficient of determination is 80,48% while the remaining 19,52% is influenced by other variables not included in this research.Keywords: ROA, FDR, NPF, BOPO and Islamic Bank

Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural network is also discussed as for comparison purposes. The main interest of the forecasts consists of three days up to five days ahead predictions for daily data. The pure autoregressive model with an order 2, or AR (2) with a MAPE value of 1.27% is found to be an appropriate model for forecasting the Malaysian peak daily load for the 3 days ahead prediction. ANFIS model gives a better MAPE value when weekends' data were excluded. Regression models with ARMA errors are found to be good models for forecasting different day types. The selection of these models is depended on the smallest value of AIC statistic and the forecasting accuracy criteria.

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