Credit Scoring Modeling of Indonesian Micro, Small and Medium Enterprises using Neuro-Fuzzy Algorithm

The paper describes the implementation of a neuro-fuzzy with additional accelerated Levenberg-Marquardt Algorithm that can be used to predict credit score of Micro, Small and Medium Enterprises (MSMEs) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with suitable membership function is used in the sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g., mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low RMSE value with given training data of neuro-fuzzy network, are further fine-tuned during the network training. Finally, the above training algorithm was validated on TS type MISO neuro-fuzzy structure for credit score prediction application of MSMEs.