Email this article 
Stock Prediction for Indonesia Stock Exchange with Long Short-Term Memory
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
Full Text:
PDFReferences
PT Kustodian Sentral Efek Indonesia, “Statistik Pasar Modal Indonesia Agustus 2023”. www.ksei.co.id. https://www.ksei.co.id/files/Statistik_Publik_Agustus_2023_v2.pdf (accessed 12 November, 2023).
A. Moghar and M. Hamiche, “Stock Market Prediction Using LSTM Recurrent Neural Network,” Procedia Computer Science, vol. 170, pp. 1168–1173, 2020, doi: 10.1016/j.procs.2020.03.049.
D.M.Q. Nelson, et al. "Stock Market’s Price Movement Prediction with LSTM Neural Networks" Conference Paper on International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, 2017.
M. Roondiwala, et al. "Predicting Stock Prices Using LSTM" Journal on International Journal of Science and Research (IJSR). Volume 6 Issue 4, April 2017.
M.A.I. Sunny, et al. "Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model" Conference Paper on Novel Intelligent and Leading Emerging Sciences Conference (NILES), 2020.
A. Yadav, et al. "Optimizing LSTM for Time Series Prediction in Indian Stock Market" Conference Paper on International Conference on Computational Intellegence and Data Science (ICCIDS), 2019.
A. Farzad, H. Mashayekhi, and H. Hassanpour, “A comparative performance analysis of different activation functions in LSTM networks for classification,” Neural Comput & Applic, vol. 31, no. 7, pp. 2507–2521, Jul. 2019, doi: 10.1007/s00521-017-3210-6.
Khoirudin, P. T. Pungkasanti, and N. Wakhidah, “ANTM.JK Stock Price Prediction Using Long Short-Term Memory (LSTM) Method During COVID-19 Pandemic,” in 2023 International Conference on Technology, Engineering, and Computing Applications (ICTECA), Semarang, Indonesia: IEEE, Dec. 2023, pp. 1–5. doi: 10.1109/ICTECA60133.2023.10490699.
K. Bagastio, R. S. Oetama, and A. Ramadhan, “Development of stock price prediction system using Flask framework and LSTM algorithm,” J. Infras. Policy. Dev., vol. 7, no. 3, Oct. 2023, doi: 10.24294/jipd.v7i3.2631.
M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, E. Salwana, and S. S., “Deep Learning for Stock Market Prediction,” Entropy, vol. 22, no. 8, p. 840, Jul. 2020, doi: 10.3390/e22080840.
G. Ding and L. Qin, “Study on the prediction of stock price based on the associated network model of LSTM,” Int. J. Mach. Learn. & Cyber., vol. 11, no. 6, pp. 1307–1317, Jun. 2020, doi: 10.1007/s13042-019-01041-1.
A. Geron “Hands-On Machine Learning with Scikit Learn, Keras, and TensorFlow” O’Rielly, 2019.
J. Rogel-Salazar “Advance Data Science and Analytics With Python” CRC Press, 2020.
https://idx.co.id/id/data-pasar/data-saham/indeks-saham
G. Athanasopoulos, and R.J. Hyndman, “Forecasting: Principles and Practice” Otext, 2018.
DOI: http://dx.doi.org/10.22441/fifo.2024.v16i1.010
Refbacks
- There are currently no refbacks.
Jurnal Ilmiah FIFO
 | Print ISSN: 2085-4315 |
|---|---|
| Online ISSN: 2502-8332 |
Sekretariat
Fakultas Ilmu Komputer
Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335
http://publikasi.mercubuana.ac.id/index.php/fifo
e-mail:[email protected]
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
.png)
