Perbandingan Kinerja Model LSTM dengan dan tanpa Indeks Fear & Greed dalam Prediksi Harga Bitcoin Berbasis Data Time Series
-
Mada Rabbani Syah
*
Universitas Muhammadiyah Malang, Malang,
Indonesia
- Vinna Rahmayanti Setyaning Nastiti Universitas Muhammadiyah Malang, Malang, Indonesia (*) Corresponding Author
Abstract
Bitcoin is a highly volatile digital asset whose price movements are influenced by historical market patterns and investor sentiment, making accurate prediction a significant challenge for conventional forecasting approaches. This study compares the predictive performance of the Long Short-Term Memory (LSTM) model in forecasting Bitcoin prices under two configurations: without and with the integration of the Crypto Fear & Greed Index as a market sentiment feature. The dataset consists of time series data including opening, highest, lowest, and closing prices obtained from the CoinGecko API, along with daily sentiment scores collected from the Alternative.me API. The data preprocessing stages include normalization using the Min-Max Scaler and sequence construction using the sliding window method, both of which are standard practices in deep learning-based time series forecasting. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) as quantitative error metrics. The experimental results show that the LSTM model without the Fear & Greed Index produced an MAE of 1011.34, an RMSE of 1340.55, and a MAPE of 1.20%, while the model incorporating the Fear & Greed Index achieved an MAE of 979.95, an RMSE of 1349.07, and a MAPE of 1.18%. These findings indicate that the inclusion of market sentiment improves overall prediction accuracy, as reflected by the reduction in MAE and MAPE, despite a marginal increase in RMSE. The main contribution of this study is providing empirical evidence regarding the impact of integrating the Crypto Fear & Greed Index as a market sentiment indicator on improving the performance of LSTM models for Bitcoin price prediction, as well as offering a direct comparison between models based solely on historical price data and models that combine historical price data with market sentiment information. This study concludes that combining deep learning techniques with measurable sentiment indicators provides a more comprehensive and effective framework for Bitcoin price prediction.
Downloads
References
S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008 [Online]. Available: https://bitcoin.org/bitcoin.pdf.
A. Roy, “In El Salvador, More People Have Bitcoin Wallets Than Traditional Bank Accounts,” Forbes, 2021. [Online]. Available: https://www.forbes.com/sites/theapothecary/2021/10/07/in-el-salvador-more-people-have-bitcoin-wallets-than-traditional-bank-accounts/.
A. Qin and E. Livni, “China Cracks Down Harder on Cryptocurrency With New Ban,” The New York Times, 2021.
S. Adiguno, Y. Syahra, et al., “Prediksi Peningkatan Omset Penjualan Menggunakan Metode Regresi Linier Berganda,” Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), vol. 1, no. 4, p. 275, 2022, doi: 10.53513/jursi.v1i4.5331.
M. Owen, V. Vincent, R. Br. Ambarita, and E. Indra, “Implementasi Metode Long Short Term Memory Untuk Memprediksi Pergerakan Nilai Harga Emas,” Tekinkom, vol. 5, no. 1, p. 96, Jun. 2022, doi: 10.37600/tekinkom.v5i1.507.
B. Yang, S. Sun, J. Li, X. Lin, and Y. Tian, “Traffic Flow Prediction Using LSTM with Feature Enhancement,”Neurocomputing, vol. 332, pp. 320–327, 2019, doi: 10.1016/j.neucom.2018.12.016.
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, p. 203, 2023, doi: 10.3390/fractalfract7020203.
C. Wen, X. Wu, C. Shen, Z. Huang, and P. Cai, “Bitcoin Price Prediction Based on Sentiment Analysis and LSTM,” Applied and Computational Engineering, vol. 29, pp. 148–159, 2023, doi: 10.54254/2755-2721/29/20230743.
S. A. Alahmadi and K. K. Hossain, “Sentiment Analysis and Cryptocurrency Price Prediction: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 21545–21563, 2022, doi: 10.1109/ACCESS.2022.3151234.
J. V. Critien, A. Gatt, and J. Ellul, "Bitcoin price change and trend prediction through Twitter sentiment and data volume," Financial Innovation, vol. 8, no. 1, p. 45, May 2022, doi: 10.1186/s40854-022-00352-7.
A. Khedr, M. A. El-Hag, and M. A. El-Shafie, “Cryptocurrency Price Prediction Using Machine Learning and Deep Learning Techniques,” IEEE Access, vol. 9, pp. 162245–162259, 2021, doi: 10.1109/ACCESS.2021.3133057.
R. S. Wahyuni, A. P. Windarto, and I. Gunawan, “Penerapan Long Short-Term Memory (LSTM) dalam Prediksi Data Time Series,” Jurnal Media Informatika Budidarma, vol. 5, no. 3, pp. 1175–1182, 2021, doi: 10.30865/mib.v5i3.3112.
CoinGecko, "Cryptocurrency API," [Online]. Available: https://www.coingecko.com/en/api/documentation. [Accessed: 2026]
Alternative.me, "Fear and Greed Index API," [Online]. Available: https://alternative.me/crypto/fear-and-greed-index/. [Accessed: 2026].
K. Cabello-Solorzano, I. Ortigosa de Araujo, M. Peña, L. Correia, and A. J. Tallón-Ballesteros, "The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis," in Lecture Notes in Networks and Systems, vol. 750, Springer, Cham, 2023, doi: 10.1007/978-3-031-42536-3_33.
F. T. Lima and V. M. A. de Souza, "A large comparison of normalization methods on time series," Big Data Research, vol. 34, pp. 100407, 2023, doi: 10.1016/j.bdr.2023.100407.
N. K. Agusmawati, F. Khoiriyah, and A. Tholib, “Prediksi Harga Emas Menggunakan Metode LSTM dan GRU,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 11, no. 3, 2023, doi: 10.23960/jitet.v11i3.3250.
N. P, R. J. Tom, P. Gupta, A. Shanthini, V. M. John, and V. Sharma, “Prediction of Bitcoin Price Using Bi-LSTM Network,” in 2021 International Conference on Computer Communication and Informatics (ICCCI), IEEE, Jan. 2021, pp. 1–5. doi: 10.1109/ICCCI50826.2021.9402427.
Y. Karyadi and H. Santoso, “Prediksi Kualitas Udara Dengan Metoda LSTM, Bidirectional LSTM, dan GRU,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 1, pp. 671–684, Mar. 2022, doi: 10.35957/jatisi.v9i1.1588.
D. M. Nugraha and D. Ariatmanto, “Meningkatkan Akurasi Prediksi Harga Bitcoin dengan Algoritma GRU-LSTM Hibrida,” Jurnal Buffer Informatika, vol. 11, no. 1, Apr. 2025, doi: 10.25134/buffer.v11i1.350.
M. Mroua and A. Lamine, "Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) Network," Humanities and Social Sciences Communications, vol. 10, no. 1, p. 530, Aug. 2023, doi: 10.1057/s41599-023-02042-w.
M. Rafi, S. Hameed, I. Sohail, M. Aliasghar, A. Aziz, and Q. A. K. Mirza, “Enhancing Cryptocurrency Price Forecasting Accuracy: A Feature Selection and Weighting Approach With Bi-Directional LSTM and Trend-Preserving Model Bias Correction,” IEEE Access, vol. 11, pp. 65700–65710, 2023, doi: 10.1109/ACCESS.2023.3287888.
N. A. A. B. S. Bahari et al., "Predicting Sea Level Rise Using Artificial Intelligence: A Review," Archives of Computational Methods in Engineering, vol. 30, no. 7, pp. 4045–4062, 2023, doi: 10.1007/s11831-023-09934-9.
E. H. Kusnadi, R. R. Nasir, and H. Hulwanullah, "Legal Aspects of Crypto Assets on Indonesian Digital Investment Development," Jurnal Hukum Islam, vol. 12, no. 2, pp. 145–162, 2023, doi: 10.14421/sh.v12i2.3168.
N. M. Salih and A. M. Abdulazeez, "Bitcoin Price Prediction Using Hybrid LSTM-GRU Models," Indonesian Journal of Computer Science, vol. 13, no. 1, pp. 94–101, 2024, doi: 10.33022/ijcs.v13i1.3725.
N. Tripathy, S. Hota, and D. Mishra, "Performance Analysis of Bitcoin Forecasting Using Deep Learning Techniques," Indonesian Journal of Electrical Engineering and Computer Science, vol. 31, no. 3, pp. 1515–1522, 2023, doi: 10.11591/ijeecs.v31.i3.pp1515-1522.
A. Noor, M. A. Arifin, and D. P. W. Astuti, "Crypto Assets and Regulation: Taxonomy and Framework Regulatory of Crypto Assets in Indonesia," Jurnal Etika Demokrasi, vol. 8, no. 3, pp. 303–315, 2023, doi: 10.26618/jed.v8i3.10886.
W. Shah, J. Chen, I. Ullah, M. H. Shah, and I. Ullah, "Application of RNN-LSTM in Predicting Drought Patterns in Pakistan: A Pathway to Sustainable Water Resource Management," Water (Switzerland), vol. 16, no. 11, pp. 1–19, 2024, doi: 10.3390/w16111492.
M. R. Pratama, F. A. G. Soerawinata, R. R. Zhafari, Rendy, and H. N. Imanda, "Sentiment Analysis of Beauty Product E-Commerce Using Support Vector Machine Method," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 2, pp. 269–274, 2022, doi: 10.29207/resti.v6i2.3876.
L. Gessie, "Do Bitcoin Returns Move Sentiment? Evidence from the Crypto Fear & Greed Index," Finance Research Letters, 2026, doi: 10.1016/j.finr.2026.100094.
C. P. T. Mendoza and N. G. Tubice, "Analyzing Historical Trends and Predicting Market Sentiment in Digital Currency Using Time Series Decomposition and ARIMA Models on Crypto Fear and Greed Index Data," Journal of Data Mining and Digital Currency, vol. 2, no. 3, 2025, doi: 10.47738/jdmdc.v2i3.37.
I. F. Riziq and A. R. Dzikrillah, “Impelementasi Algoritma LSTM Dan SVR Untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance,” METIK Jurnal, vol. 9, no. 2, 2025, doi: 10.47002/90ypad22.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Kinerja Model LSTM dengan dan tanpa Indeks Fear & Greed dalam Prediksi Harga Bitcoin Berbasis Data Time Series
Pages: 528-537
Copyright (c) 2026 Mada Rabbani Syah, Vinna Rahmayanti Setyaning Nastiti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
