Analysis of Financial Distress Prediction Using Artificial Neural Network in Retail Companies Registered in Indonesia Stock Exchange
Since 2015, retail companies in Indonesia have experienced a decline, and corporate performance in the retail industry has decreased. This study aims to predict the financial distress of retail companies listed on the Indonesia Stock Exchange from 2015-2019, using the data mining method with an artificial neural network model. Current ratios, return on assets, and debt to assets ratio used input parameter predictions. The results show that these ratios are perfect for making predictions because they show a significant difference between companies that are declared to be experiencing financial distress and reported experiencing financial distress and used as training data for the prediction process. An excellent artificial neural network architecture predicts financial distress based on training data with a sample of 30 data training companies, namely 15 neurons in the input layer, 20 neurons in the hidden layer, and one neuron in the output layer. The results show that 4 out of 20 retail prediction companies experience financial distress.