Stock Prediction for Indonesia Stock Exchange with Long Short-Term Memory

Abdi Wahab; Ali Herdian; Dian Wirawan; Yuwan Jumaryadi; Syamsir Alam; Andrew Fiade

doi:10.22441/fifo.2024.v16i1.010

Stock Prediction for Indonesia Stock Exchange with Long Short-Term Memory

Abdi Wahab, Ali Herdian, Dian Wirawan, Yuwan Jumaryadi, Syamsir Alam, Andrew Fiade

Abstract

Predicting stock prices through different analyses and techniques is highly challenging. The task is complicated further by fluctuating market conditions and the impact of news, necessitating the consideration of numerous factors. The advancements in machine learning and deep learning have led many researchers to use algorithms like RNN with LSTM for predictions. In this study, we aim to predict stock prices on the Indonesia Stock Exchange using LSTM, focusing on optimizing the hidden layer and activation function. We focus on some stock data with good liquidation in the Indonesia Stock Exchange. The comparison performance between models proposed in this research will be the method in this research. The result showed that the LSTM model with hyperbolic tan activation method performed better than the LSTM model with sigmoid activation method. The future research based on this research, we can compare several other activation methods.

Keywords

Stock Prediction; LSTM; Hidden Layer; Activation Function

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DOI: http://dx.doi.org/10.22441/fifo.2024.v16i1.010

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Jurnal Ilmiah FIFO

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