PREDICTING THE PERFORMANCE OF SHARIA BANK IN INDONESIA USING ARTIFICIAL NEURAL NETWORKS
This research provides an analysis of Islamic banking health using the Artificial Neural Network (ANN) approach, incorporating various theories such as Signalling Theory, Sharia Enterprise Theory (SET), Stewardship Theory, and Stakeholder Theory. Internal data of Islamic banks were evaluated with a focus on variables such as Return on Assets (ROA), Return on Equity (ROE), Financing to Deposit Ratio (FDR), Operating Expenses to Operating Income Ratio (BOPO), Net Interest Margin (NIM), Capital Adequacy Ratio (CAR), and Non-Performing Financing (NPF). The analysis results indicate that factors like Good Corporate Governance (GCG), Islamic Corporate Social Responsibility (ICSR), Zakat, and Sharia Compliance play crucial roles in influencing Islamic banking health. Policy recommendations include revising the selection of input variables, enhancing Sharia Governance, and re-evaluating the relevance of Sharia Supervisory Board (DPS) meetings. The implications of these findings provide guidance to improve the effectiveness of Islamic banking policies by considering additional variables. The research suggests that optimizing the model by incorporating additional variables such as Loan to Deposit Ratio (LDR) can enhance the accuracy of Islamic banking health evaluations through the ANN approach. Policy recommendations and research findings serve as a foundation for Islamic financial institutions to enhance their performance and operational sustainability.
ICSR, GCG, Zakat, Syariah Governance, Syariah Compliance, ANN.