Adaptive Neuro-Fuzzy Inference System for Predicting Stock Price of ITMG Issuer

ITMG stock price reflects investor perception of its ability to earn and grow its profits in the future. The prediction is beneficial for determining the movement of the stock price of financial exchange, therefore both issuers and investors could take important steps regarding the financial condition. In this study, a stock price prediction was carried out using the Adaptive Neuro Fuzzy Inference System (ANFIS) method with the Fuzzy Inference System (FIS) Grid Partitioning type. The initial step was to determine the parameters that produce gbellmf as a membership function type, 300 epochs, and a learning rate of 0.03. The calculation of accuracy was carried out using the RMSE calculation. The experiments conducted found that the smallest RMSE calculation result was 0.021942 with 80% of training data. Other issuers can use this method because it can predict stock prices quite well.