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All Journal Jurnal Ilmiah FIFO
Dian Wirawan
Universitas Mercu Buana
Computer Science & IT

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Stock Prediction for Indonesia Stock Exchange with Long Short-Term Memory Abdi Wahab; Ali Herdian; Dian Wirawan; Yuwan Jumaryadi; Syamsir Alam; Andrew Fiade
Jurnal Ilmiah FIFO Vol 16, No 1 (2024)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2024.v16i1.010

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.
Co-Authors Abdi Wahab Ali Herdian Andrew Fiade Jumaryadi, Yuwan Syamsir Alam