PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM)
Hanifah Nazhiroh, Dina Fitria, Dony Permana, Zilrahmi Zilrahmi · UNP Journal of Statistics and Data Science · 2024
The movement of the share price of PT.Telkom (Tbk) fluctuates so it is necessary to do forecasting analysis. Forecasting the share price of PT.Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN)method. In this study using PT.Telkom Stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023) this is done to see if a significant price decline has an impact on forecasting the stock price for the next period. Both data have a very small difference in MAPE value. The data that has the smallest MAPE value is PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023) with a MAPE of 0,93% with an optimal parameter combination of neurons, batch size 64, and epoch 80.