Implementasi CNN-BiLSTM untuk Prediksi Harga Saham Bank Syariah di Indonesia

Mushliha Mushliha

Abstract


Stock price forecasting plays a crucial role in stock investment. Accuracy in predicting stock prices can provide significant financial benefits and help reduce investment risks. Stock price data are time series with high-frequency characteristics, non-linearity, and long memory, which makes stock price prediction a complex challenge. This research proposes a method for predicting the stock prices of Islamic banks in Indonesia using CNN-BiLSTM. This method aims to improve prediction accuracy by utilizing the feature extraction capabilities of CNN and the ability of BiLSTM to understand the temporal sequences of stock data. The data used in this research are the closing stock prices of Bank Syariah Indonesia (BSI), Bank Tabungan Pensiunan Negara Syariah (BTPN Syariah), and Bank Panin Dubai Syariah (PDSB) from January 2, 2020, to July 4, 2024. Testing these three stocks yielded MAPE values of 2.376%, 2.092%, and 0.629%, respectively. The study results show that the CNN-BiLSTM prediction model produced has very good accuracy in predicting stock prices.

Keywords


Forecasting; Time Series; Stock Price; CNN-BiLSTM

Full Text:

PDF

References


Z. Hu, W. Liu, J. Bian, X. Liu, and T.Y. Liu, “Listening to Chaotic Whispers,” in Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Feb. 2018, pp. 261–269, doi: 10.1145/3159652.3159690.

K. Adam, A. Marcet, and J. P. Nicolini, “Stock Market Volatility and Learning,” Journal of Finance, vol. 71, no. 1, pp. 33–82, 2016, doi: 10.1111/jofi.12364.

W. Nuij, V. Milea, F. Hogenboom, F. Frasincar, and U. Kaymak, “An Automated Framework for Incorporating News into Stock Trading Strategies,” IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 823–835, Apr. 2014, doi: 10.1109/tkde.2013.133.

M. Qiu and Y. Song, “Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model,” Plos One, vol. 11, no. 5, p. e0155133, May 2016, doi: 10.1371/journal.pone.0155133.

R. Vanaga and B. Sloka, “Financial and capital market commission financing: Aspects and challenges,” Journal of Logistics, Informatics and Service Science, vol. 7, no. 1, pp. 17–30, 2020, doi: 10.33168/LISS.2020.0102.

M. M. Kumbure, C. Lohrmann, P. Luukka, and J. Porras, “Machine learning techniques and data for stock market forecasting: A literature review,” Expert Systems with Applications, vol. 197, p. 116659, Jul. 2022, doi: 10.1016/j.eswa.2022.116659.

T. Prasetyo et al., “Perbandingan Kinerja Metode Arima, Multi-Layer Perceptron, Dan Random Forest Dalam Peramalan Harga Logam Mulia Berjangka Yang Mengandung Pencilan,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 2, pp. 265–274, 2024, doi: 10.25126/jtiik.2024117392.

T. Phuoc, P. T. K. Anh, P. H. Tam, and C. V. Nguyen, “Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam,” Humanities and Social Sciences Communications, vol. 11, no. 1, Mar. 2024, doi: 10.1057/s41599-024-02807-x.

A. Nilsen, “Perbandingan Model RNN, Model LSTM, dan Model GRU dalam Memprediksi Harga Saham-Saham LQ45,” Jurnal Statistika Dan Aplikasinya, vol. 6, no. 1, pp. 137–147, Jun. 2022, doi: 10.21009/jsa.06113.

M. A. D. Suyudi, E. C. Djamal, and A. Maspupah, “Prediksi Harga Saham menggunakan Metode Recurrent Neural Network,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATi), Aug. 2019, pp. 34-38 [Online]. Available: https://journal.uii.ac.id/Snati/article/view/13398.

B. H. Mahendra, L. Chaerani, and G. Gumay, “Analisis Perbandingan Prediksi Harga Saham menggunakan Algoritma Artificial Neural Network dan Linear Regression,” Jurnal Ilmiah Komputasi, vol. 22, no. 2, pp. 303–312, 2023, doi: 10.32409/jikstik.22.2.3357.

A. S. B. Karno, “Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory),” Journal of Informatic and Information Security, vol. 1, no. 1, pp. 1–8, 2020, doi: 10.31599/jiforty.v1i1.133.

N. Afrianto, D. H. Fudholi, and S. Rani, “Prediksi Harga Saham Menggunakan BiLSTM dengan Faktor Sentimen Publik,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 1, pp. 41–46, 2022, doi: 10.29207/resti.v6i1.3676.

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, pp. 35–43, May 2023, doi: 10.34312/euler.v11i1.19791.

S. Chen and H. He, “Stock Prediction Using Convolutional Neural Network,” IOP Conference Series. Materials Science and Engineering, vol. 435, p. 012026, Nov. 2018, doi: 10.1088/1757-899x/435/1/012026.

M. Yang and J. Wang, “Adaptability of Financial Time Series Prediction Based on BiLSTM,” in Procedia Computer Science, vol. 199, Jan. 2022, pp. 18–25, doi: 10.1016/j.procs.2022.01.003.

J. Luo and X. Zhang, “Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction,” Applied Intelligence, vol. 52, no. 1, pp. 1076–1091, 2022, doi: 10.1007/s10489-021-02503-2.

S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Sep. 2017, doi: 10.1109/icacci.2017.8126078.

P. Qori, D. Oktafani, and I. Kharisudin, “Analisis Peramalan dengan Long Short Term Memory pada Data Kasus Covid-19 di Provinsi Jawa Tengah”, Prisma, vol. 5, pp. 752-758, Feb. 2022.

Q. Wu, F. Guan, C. Lv, and Y. Huang, “Ultra-short-term multi-step wind power forecasting based on CNN-LSTM,” IET Renewable Power Generation, vol. 15, no. 5, pp. 1019–1029, 2021, doi: 10.1049/rpg2.12085.

Y. Sun, Q. Sun, and S. Zhu, “Prediction of Shanghai Stock Index Based on Investor Sentiment and CNN-LSTM Model,” Journal of Systems Science and Information, vol. 10, no. 6, pp. 620–632, 2022, doi: 10.21078/JSSI-2022-620-13.

W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, “A CNN-LSTM-Based Model to Forecast Stock Prices,” Complexity, vol. 2020, pp. 1–10, Nov. 2020, doi: 10.1155/2020/6622927.

A. Staffini, “A CNN–BiLSTM Architecture for Macroeconomic Time Series Forecasting,” in Engineering Proceedings, vol. 33, no. 1, Jun. 2023, p. 33, doi: 10.3390/engproc2023039033.

T. Mariyani and I. Rosyida, “Implementasi Metode Double Exponential Smoothing untuk Peramalan Luas Panen Padi di Kabupaten Pati dengan Bantuan Software Minitab 16”, Prisma, vol. 6, pp. 707-713, Mar. 2023.

R. E. Wahyuni, “Optimasi Prediksi Inflasi dengan Neural Network Pada Tahap Windowing Adakah Pengaruh Perbedaan Window Size,” Technologia Jurnal Ilmiah, vol. 12, no. 3, p. 176, Jul. 2021, doi: 10.31602/tji.v12i3.5181.




DOI: https://doi.org/10.37905/jjom.v6i2.26509



Copyright (c) 2024 Mushliha

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Jambura Journal of Mathematics has been indexed by

>>>More Indexing<<<


Creative Commons License

Jambura Journal of Mathematics (e-ISSN: 2656-1344) by Department of Mathematics Universitas Negeri Gorontalo is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Powered by Public Knowledge Project OJS. 


Editorial Office


Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B. J. Habibie, Moutong, Tilongkabila, Kabupaten Bone Bolango, Gorontalo, Indonesia
Email: info.jjom@ung.ac.id.