Evaluasi Model Jaringan Saraf Tiruan Berbasis LSTM dalam Memprediksi Fluktuasi Harga Bitcoin

  • Sudriyanto Sudriyanto Universitas Nurul Jadid
  • Mochammad Faid Universitas Nurul Jadid
  • Kamil Malik Universitas Nurul Jadid
  • Ahmad Supriadi Universitas Nurul Jadid
Keywords: LSTM Neural Networks, Bitcoin Price Prediction, Cryptocurrency Financial Analysis

Abstract

Amid the highly volatile fluctuations in the cryptocurrency market, the ability to accurately predict Bitcoin prices becomes crucial for investors and financial analysts. This study aims to develop a predictive model using Long Short-Term Memory (LSTM) Neural Networks, a specific form of recurrent neural network, to predict Bitcoin prices. Historical data on daily closing prices of Bitcoin from 2015 to 2023 was used to train and test the model. Following data preprocessing, which included normalization and the creation of a time series dataset, the LSTM model was constructed with two LSTM layers and two dense layers to enhance the predictive analysis. The model was trained with the data split into 80% for training and 20% for testing. Results show that the LSTM model was able to produce fairly accurate predictions with a low loss value on the test data. Further evaluation through comparison with baseline models showed significant improvements in predictive accuracy. This research demonstrates the potential application of advanced machine learning techniques in financial analysis, particularly in predicting the prices of highly volatile assets like Bitcoin. With continuous improvements to the model architecture and parameter optimization, Bitcoin price predictions could become more reliable, helping stakeholders make more informed investment decisions.

References

[1] S. Saadah and H. Salsabila, “Prediksi Harga Bitcoin Menggunakan Metode Random Forest,” Jurnal Komputer Terapan, vol. 7, no. 1, 2021, doi: 10.35143/jkt.v7i1.4618.
[2] T. Ariwibowo, “Efektivitas Analisis Teknikal Untuk Profitabilitas Cryptocurrency di Spot Market (Analisis Profitabilitas Criptocurrency di Spot Market Menggunakan Pendekatan Analisis Teknikal),” Jurnal Ekonomi Manajemen Sistem Informasi, vol. 4, no. 1, 2022, doi: 10.31933/jemsi.v4i1.1154.
[3] M. Aghashahi and S. Bamdad, “Analysis of different artificial neural networks for Bitcoin price prediction,” International Journal of Management Science and Engineering Management, vol. 18, no. 2, 2023, doi: 10.1080/17509653.2022.2032442.
[4] M. L. Rasdi Rere, Hariyanto, and Rozi, “Studi prediksi harga bitcoin menggunakan recurrent neural network,” Seminar Nasional Teknologi Informasi dan Komunikasi STI&K (SeNTIK), vol. 6, no. 1, 2022.
[5] Y. Li and W. Dai, “Bitcoin price forecasting method based on CNN‐LSTM hybrid neural network model,” The Journal of Engineering, vol. 2020, no. 13, 2020, doi: 10.1049/joe.2019.1203.
[6] D. M. Gunarto, S. Sa’adah, and D. Q. Utama, “Predicting Cryptocurrency Price Using RNN and LSTM Method,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 1, 2023, doi: 10.32736/sisfokom.v12i1.1554.
[7] F. Syahro and N. Fitriani, “Perbandingan Performa Model Machine Learning Support Vector Machine, Neural Network, Dan K-Nearest Neighbors Dalam Prediksi Harga Saham,” Jurnal Advanced Research Informatika, vol. 2, no. 1, 2023, doi: 10.24929/jars.v2i1.2983.
[8] H. Malik, N. Fatema, and A. Iqbal, “Intelligent Data Analytics for Wind Speed Forecasting for Wind Power Production Using Long Short-Term Memory (LSTM) Network,” in Intelligent Data-Analytics for Condition Monitoring, 2021. doi: 10.1016/b978-0-323-85510-5.00008-9.
[9] CoinMarketCap, “Cryptocurrency Prices, Charts And Market Capitalizations | CoinMarketCap,” CoinMarketCap. 2022.
[10] S. Gomzin, “How Bitcoin Works,” in Crypto Basics, 2022. doi: 10.1007/978-1-4842-8321-9_2.
[11] S. K. Panda, A. R. Sathya, and S. Das, “Bitcoin: Beginning of the Cryptocurrency Era,” in Intelligent Systems Reference Library, vol. 237, 2023. doi: 10.1007/978-3-031-22835-3_2.
[12] Y. Liu and A. Tsyvinski, “Risks and returns of cryptocurrency,” Review of Financial Studies, vol. 34, no. 6, 2021, doi: 10.1093/rfs/hhaa113.
[13] F. Fang et al., “Cryptocurrency trading: a comprehensive survey,” Financial Innovation, vol. 8, no. 1. 2022. doi: 10.1186/s40854-021-00321-6.
[14] yahoo finance, “Bitcoin USD (BTC-USD),” https://finance.yahoo.com/quote/BTC-USD/history?period1=1520553600&period2=1715235081.
[15] Ferdiansyah, S. H. Othman, R. Z. M. Radzi, D. Stiawan, and T. Sutikno, “Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy,” IAES International Journal of Artificial Intelligence, vol. 12, no. 1, 2023, doi: 10.11591/ijai.v12.i1.pp251-261.
Published
2024-06-04
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