Work

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

Year: 2024

Type: article

Abstract: 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.

Source: UNP Journal of Statistics and Data Science

DOI: 10.24036/ujsds/vol2-iss4/223

Authors: Hanifah Nazhiroh, Dina Fitria, Dony Permana, Zilrahmi

Institution: State University of Padang

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4405088237

FWCI:

Cites:

Cited by:

Related to: 10