Murabahah Margin Determination Factors at Islamic Banks in Indonesia for the 2016 – 2020 period (Artificial Neural Network perspective) - Haswardi Haswardi; Rossje V Suryaputri; Eko Budi Nurdianto (Budapest International Research and Critics University, 2022)
The reseacrh has aims of analysis factor to determine of margin murabahah are cost operating of variable, third parts of funds (DPK), finacing deposit to ratio (FDR), net performing finance (NPF) and firm sizes as well as short term mismatch as variable on sharia banking from period 2016 – 2020. The quantitative approach has been used due to match on this research. The research using of type and shape data are secondary data have in the form of the data kinds are time series. The population has been used to period January 2016 to December 2020 using of statistical data to official banking from OJK has been focused with BUS and UUS are noted. The data sample has been taken by using to saturate sampling methods. And then the research a usage to based on Artificial Neural Network (ANN) application. The results of the study using N(6-1-1) in processing the best results to obtain the results of the margin level on murabahah which is influenced by the classification of factors according to the Network Diagram, in the input layer it is used in the research form. And these types include Operational Costs (BOPO), Third Party Funds (DPK), Financing Deposit Ratio (FDR), Non Performing Finance (NPF) and Company Size and Short Term Mismatch. The results showed that the determining factor that influenced the murabahah margin the most influential was the third party fund variable with importance of 31.04%, the next financing deposit ratio with importance was 27.21%, non-performing finance with importance was 17.18%, the ratio liquidity in the form of short term mismatch of 12.92%, company size of 11.48% and operating costs with importance of 0.14%. This result is also in line with Islamic banking statistics published by the Financial Services Authority (OJK) in 2021 that the increase in TPF from 2016 to 2020 is directly proportional to the increase in financing, so it can be assumed that the increase in TPF has an effect on financing.