PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM)

Authors

  • hanifah nazhiroh unp
  • Dina Fitria Universitas Negeri Padang
  • Dony Permana Universitas Negeri Padang
  • Zilrahmi Universitas Negeri Padang

DOI:

https://doi.org/10.24036/ujsds/vol2-iss4/223

Keywords:

Forecasting, Long Short Term Memory (LSTM), MAPE

Abstract

The movement of the share price of PT Telkom (Tbk) fluctuates so it is necessary to do a 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 (Tbk) stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023). The purpose of this research is to determine the movement of PT.Telkom (Tbk) stock prices in 2024, to find out the difference in forecasting using PT.Telkom (Tbk) 2018-2023 stock price data with PT.Telkom (Tbk) stock price data after covid-19 2021-2023, and to determine the level of accuracy of forecasting PT.Telkom (Tbk) stock prices using the LSTM method. The results showed that both data have a small MAPE value. to forecast the share price of PT.Telkom for 1 year, PT.Telkom (Tbk) share price data for 2018-2023 is used which has more data to analyze long-term forecasting. From the analysis results obtained MAPE of 1.016% with the optimal parameter combination of neuron 4, batch size 64, and epoch 80. The results of forecasting the share price of PT.telkom (Tbk) in 2024 experienced very rapid fluctuations with an average share price of PT.Telkom (Tbk) in 2024 Rp 4,668 / sheet.

Published

2024-11-28

How to Cite

nazhiroh, hanifah, Dina Fitria, Dony Permana, & Zilrahmi. (2024). PT.Telkom (Tbk) Stock Price Forecasting Using Long Short Term Memory (LSTM) . UNP Journal of Statistics and Data Science, 2(4), 414–421. https://doi.org/10.24036/ujsds/vol2-iss4/223