Analisis pengaruh sentimen terhadap akurasi prediksi harga close market cryptocurrency menggunakan Deep Learning berbasis GRU

Muhammad Alif Fadillah; Fauzi Niko Allianzah; Arya Maulana; Agam Gunawan; Fajar Delli Wihartiko

doi:10.24246/aiti.v23i3.496-510

Authors

Muhammad Alif Fadillah Universitas Pakuan https://orcid.org/0009-0006-6263-502X
Fauzi Niko Allianzah Universitas Pakuan https://orcid.org/0009-0007-3139-4419
Arya Maulana Universitas Pakuan
Agam Gunawan Universitas Pakuan
Fajar Delli Wihartiko Universitas Pakuan

DOI:

https://doi.org/10.24246/aiti.v23i3.496-510

Keywords:

cryptocurrency, price prediction, market sentiment, deep learning, GRU

Abstract

The volatile, dynamic fluctuations in cryptocurrency prices make it difficult to predict closing prices in time-series modeling. In addition to historical price data, the dynamics of the crypto market are also influenced by investor sentiment shaped by digital sources such as news and online media, reflecting their perceptions, expectations, and reactions to market conditions that impact price direction. This study develops a cryptocurrency closing price prediction model using a deep learning approach with Gated Recurrent Units (GRU) that integrates market price and sentiment data into a single integrated framework. The research stages include data collection via APIs, selection of relevant data, preprocessing such as normalization and the creation of time-series sequences, construction of the prediction model, and evaluation and visualization of results. The findings indicate that the model effectively captures the temporal patterns of price movements, generating predictions that align with the actual trends of Bitcoin and Ethereum. The inclusion of sentiment data provides supporting signals to represent market dynamics shaped by investor behavior. Thus, the integration of price and sentiment data yields predictions that are more accurate and better representative of the cryptocurrency market.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

A. Pranolo et al., “Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques,” ILKOM Jurnal Ilmiah, vol. 16, no. 2, pp. 210–220, Aug. 2024, doi: 10.33096/ilkom.v16i2.2333.210-220.

I. P. B. Priadinata, I. G. I. Sudipa, N. P. S. Meinarni, I. M. L. Radhitya, and I. K. D. G. Supartha, “Comparative Analysis of LSTM, GRU, and Bi-LSTM Deep Learning Models for Time Series Cryptocurrency Price Forecasting,” sinkron, vol. 9, no. 3, pp. 1024–1035, Jul. 2025, doi: 10.33395/sinkron.v9i3.14795.

H. S. Htay, M. Ghahremani, and S. Shiaeles, “Enhancing Bitcoin Price Prediction with Deep Learning: Integrating Social Media Sentiment and Historical Data,” Applied Sciences (Switzerland), vol. 15, no. 3, Feb. 2025, doi: 10.3390/app15031554.

D. Grubišić, B. Škrabić Perić, and M. Jadrić, “Bitcoin price prediction using sentiment analysis,” ST-OPEN, vol. 6, pp. 1–15, Oct. 2025, doi: 10.48188/so.6.12.

M. J. Hamayel and A. Y. Owda, "A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and Bi-LSTM Algorithms," AI, vol. 2, no. 4, pp. 477–496, Oct. 2021. Link: https://www.mdpi.com/2673-2688/2/4/30

S. Alghamdi, S. Alqethami, T. Alsubait, and H. Alhakami, “Cryptocurrency Price Prediction using Forecasting and Sentiment Analysis.” DOI:10.14569/IJACSA.2022.01310105

N. M. Aruan, D. Anisa, and K. Tambunan, "Pendekatan Algoritma Support Vector Regression Dalam Memprediksi Harga Cryptocurrency (Studi Kasus: Binance)," JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 10, no. 3, pp. 531–541, Sep. 2023. Link: https://jurnal.mdp.ac.id/index.php/jatisi/article/view/5071

J. David, S. Binnewies, and B. Stantic, "Cryptocurrency Price Prediction Algorithms: A Survey and Future Directions," Forecasting, vol. 6, no. 3, pp. 637–671, Aug. 2024. Link: https://www.mdpi.com/2571-9394/6/3/34

K. Wołk, "Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting," Data, vol. 7, no. 3, art. 137, Mar. 2023. Link: https://www.mdpi.com/2504-2289/7/3/137

A. Bouteska, M. Z. Abedin, P. Hájek, and K. Yuan, “Cryptocurrency price forecasting: A comparative analysis of ensemble learning and deep learning methods,” International Review of Financial Analysis, vol. 92, Art. no. 103055, 2024, doi: 10.1016/j.irfa.2023.103055.

A. Kraaijeveld and E. De Smedt, “The predictive power of public Twitter sentiment for forecasting cryptocurrency prices,” Journal of International Financial Markets, Institutions & Money, vol. 65, Art. no. 101188, 2020, doi: 10.1016/j.intfin.2020.101188.

M. Melina et al., “Cryptocurrency Time Series Forecasting Model Using Gru Algorithm Based On Machine Learning,” Barekeng, vol. 19, no. 2, pp. 1317–1328, Jun. 2025, doi: 10.30598/barekengvol19iss2pp1317-1328.

P. L. Seabe, C. R. B. Moutsinga, and E. Pindza, “Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach” Fractal and Fractional, vol. 7, no. 2, art. 203, 2023, doi: 10.3390/fractalfract7020203.

D. Perera, J. Lim, S. Gunraku, and W. Lim, “Cryptocurrency analysis: Price prediction of cryptocurrency using user sentiments and quantitative data,” in Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART), vol. 3, 2024, pp. 210–217, doi: 10.5220/0012315100003636.

F. Feizian and B. Amiri, “Cryptocurrency price prediction model based on sentiment analysis and social influence,” IEEE Access, vol. 11, pp. 142177–142195, 2023, doi: 10.1109/ACCESS.2023.3342688.