Implementation of Long Short-Term Memory Algorithm for Stock Price Prediction of BBCA and BBRI
DOI:
https://doi.org/10.61769/telematika.v19i2.701Keywords:
BBCA, BBRI, Long Short-Term Memory (LSTM), predictions, stockAbstract
Investing in equity instruments carries a high level of risk because stock movements in the market are difficult to predict. Historical data analysis can be a solution for investors in forecasting future stock price movements. In addition to increasing awareness of the importance of investment, technology also helps in decision-making. This research uses the Long Short-Term Memory (LSTM) algorithm to predict stock prices. The data is taken from the Yahoo Finance website; the variables used are only stock closing data. The stages include literature study, data collection, data sharing, data preprocessing, model building, denormalization, and evaluation. The most optimal results were obtained from the model built on PT Bank Rakyat Indonesia, Tbk. (BBRI) with a training data RMSE value of 37.037 and a testing data RMSE of 80.128. Meanwhile, testing using the LSTM algorithm on PT Bank Central Asia, Tbk (BBCA) obtained a training data RMSE value of 36.905 and a testing data RMSE of 99.9. Furthermore, the best model is used to predict PT BCA and PT BRI stock prices in the next month.
References
D. Dinanti, “Analisis perbandingan metode double moving average dengan double exponential smoothing pada peramalan harga saham perbankan,” Farabi: Jurnal Matematika dan Pendidikan Matematika, vol. 6, no. 1, hlm. 105–112, 2023.
R. Julian dan M. R. Pribadi, “Peramalan harga saham pertambangan pada Bursa Efek Indonesia (BEI) menggunakan long short-term memory (LSTM),” Jatisi (Jurnal Teknik Informatika dan Sistem Informasi), vol. 8, no. 3, hlm. 1570–1580, 2021. DOI: 10.35957/jatisi.v8i3.1159.
L. Wiranda dan M. Sadikin, “Penerapan long short-term memory pada data time series untuk memprediksi penjualan produk PT Metiska Farma,” Jurnal Nasional Pendidikan Teknik Informatika (Janapati), vol. 8, no. 3, hlm. 184–196, 2019.
M. N. Wathani, K. Kusrini, dan K. Kusnawi, “Prediksi tren pergerakan harga saham PT Bank Central Asia, Tbk. dengan menggunakan algoritme long shot term memory (LSTM),” Infotek: Jurnal Informatika dan Teknologi, vol. 6, no. 2, hlm. 502–512, 2023. DOI: 10.29408/jit.v6i2.19824.
B. A. Aprian, Y. Azhar, dan V. R. Setya Nastiti, “Prediksi pendapatan kargo menggunakan arsitektur long short term memory,” Jurnal Komputer Terapan, vol. 6, no. 2, hlm. 148–157, 2020. DOI: 10.35143/jkt.v6i2.3621.
P. Aji Riyantoko, T. Maulana Fahruddin, K. Maulida Hindrayani, dan E. Maya Safitri, “Analisis prediksi harga saham sektor perbankan menggunakan algoritme long short-terms memory (LSTM),” Seminar Nasional Informatika (Semnasif), vol. 1, no. 1, 2020, hlm. 427–435. [Daring]. Tersedia: http://www.jurnal.upnyk.ac.id/index.php/semnasif/article/view/4135
G. Budiprasetyo, M. Hani’ah, dan D. Z. Aflah, “Prediksi harga saham syariah menggunakan algoritme long short-term memory (LSTM),” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 3, hlm. 164–172, 2023. DOI: 10.25077/teknosi.v8i3.2022.164-172.
Y. Setiawan, T. Tarno, dan P. Kartikasari, “Prediksi harga jual kakao dengan metode long short-term memory menggunakan metode optimasi root mean square propagation dan adaptive moment estimation dilengkapi GUI Rshiny,” Jurnal Gaussian, vol. 11, no. 1, hlm. 99–107, 2022. DOI: 10.14710/j.gauss.v11i1.33994.
A. S. B. Karno, “Prediksi data time series saham Bank BRI dengan mesin belajar LSTM (long short-term memory),” Journal of Informatic and Information Security, vol. 1, no. 1, hlm. 1–8, 2020. DOI: 10.31599/jiforty.v1i1.133.
D. I. Puteri, “Implementasi long short-term memory (LSTM) dan bidirectional long short-term memory (BiLSTM) dalam prediksi harga saham syariah,” Euler: Jurnal Ilmiah Matematika, Sains dan Teknologi, vol. 11, no. 1, hlm. 35–43, 2023. DOI: 10.34312/euler.v11i1.19791.
T. G. Lasijan, R. Santoso, dan A. R. Hakim, “Prediksi harga emas dunia menggunakan metode long-short term memory,” Jurnal Gaussian, vol. 12, no. 2, hlm. 287–295, 2023. DOI: 10.14710/j.gauss.12.2.287-295.
B. A. H. Kholifatullah dan A. Prihanto, “Penerapan metode long short-term memory untuk klasifikasi pada hate speech,” Journal of Informatics and Computer Science (Jinacs), vol. 04, hlm. 292–297, 2023. DOI: 10.26740/jinacs.v4n03.p292-297.
T. Bastian Sianturi, I. Cholissodin, dan N. Yudistira, “Penerapan algoritme long short-term memory (LSTM) berbasis multifungsi aktivasi terbobot dalam prediksi harga Ethereum,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 7, no. 3, hlm. 1101–1107, 2023.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Nur Zuzzaifa, Sulistyo Dwi Sancoko
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
