Prediksi Harga Bitcoin Menggunakan Support Vector Regression (SVR) Berbasis Particle Swarm Optimization (PSO)
DOI:
https://doi.org/10.37859/jf.v16i2.11583
Abstract
Bitcoin prices exhibit high volatility and complex non-linear patterns, making accurate price predictions challenging in the context of cryptocurrency market research. In this study, an optimized SVR-PSO predictive modeling approach is developed to help predict Bitcoin prices, and its performance is compared against a Grid Search-optimized SVR baseline. The SVR-PSO modeling approach developed in this study uses daily historical Bitcoin records obtained from Yahoo Finance between March 10, 2019, and May 11, 2026. The data consists of open, high, low, close, and volume values as attributes, and the closed value as the target. Data preprocessing consists of chronologically organizing the data, removing blank rows, min-max scaling, and splitting the data into 80:20 for training and testing. PSO is used to identify the optimal SVR parameters C, ε, and γ with the aim of reducing MAPE. The optimal parameters for the SVR model are C = 501, ε = 0.001, and γ = 0.001. On the test dataset, the SVR-PSO model achieved an MAE of 1,521.47, an RMSE of 2,101.70, a MAPE of 1.65%, and an R² of 0.9813, outperforming the SVR-GridSearch baseline (MAE 1,559.87; RMSE 2,128.52; MAPE 1.69%; R² 0.9809). These results suggest that SVR-based Bitcoin price prediction is more accurate when optimized using PSO than using a discrete Grid Search.
Downloads
References
Listiani, A., and Al Mahkya, D., 2022. Peramalan Cryptocurrency dengan Autoregressive Integrated Moving Average (ARIMA) dan Risiko Kerugian dengan Value at Risk (VaR). Journal of Science and Applicative Technology, 6(2), pp.85–91. https://doi.org/10.35472/jsat.v6i2.904
Melina, M., Sukono, S., Napitupulu, H., Mohamed, N., Chrisnanto, Y. H., Hadiana, A. I., and Kusumaningtyas, V. A., 2025. Cryptocurrency Time Series Forecasting Model Using GRU Algorithm Based on Machine Learning. BAREKENG: Journal of Mathematics and Its Applications, 19(2), pp.1317–1328. https://doi.org/10.30598/barekengvol19iss2pp1317-1328
Ramadhan, F. A., and Nathasia, N. D., 2025. Prediksi Harga dan Kinerja Aset Bitcoin Menggunakan Algoritma Long Short-Term Memory. Jurnal Fasilkom, 15(1), pp.68–76.
Wang, Y., Andreeva, G., and Martin-Barragan, B., 2023. Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants. International Review of Financial Analysis, 90, pp.1–21. https://doi.org/10.1016/j.irfa.2023.102914
Brini, A., and Lenz, J., 2024. A comparison of cryptocurrency volatility-benchmarking new and mature asset classes. Financial Innovation, 10(1). https://doi.org/10.1186/s40854-024-00646-y
Kukacka, J., and Kristoufek, L., 2023. Fundamental and speculative components of the cryptocurrency pricing dynamics. Financial Innovation, 9(1), pp.1–23. https://doi.org/10.1186/s40854-023-00465-7.
Nguyen, L. T. M., and Nguyen, P. T., 2024. Determinants of cryptocurrency and decentralized finance adoption - A configurational exploration. Technological Forecasting and Social Change, 201, pp.1–19. https://doi.org/10.1016/j.techfore.2024.123244
T. Bagh, M. A. Khan, and K. Iftikhar, “Investor sentiment and cryptocurrency market dynamics: A Vector Autoregressive and Granger causality approach,” Social Sciences and Humanities Open, vol. 12, Jan. 2025, doi: 10.1016/j.ssaho.2025.102128.
Bagh, T., Khan, M. A., and Iftikhar, K., 2025. Investor sentiment and cryptocurrency market dynamics: A Vector Autoregressive and Granger causality approach. Social Sciences & Humanities Open, 12, pp.1–12. https://doi.org/10.1016/j.ssaho.2025.102128
Kuncara, T., and Anugrah, K. P., 2023. Analisis Volatilitas Cryptocurrency pada Sebelum Pandemi dan pada Saat Pandemi COVID-19 dengan Metode Return pada Bitcoin dan Ethereum. KEUNIS, 11(1), pp.86–94. https://doi.org/10.32497/keunis.v11i1.3981
No, J. K. S. D., Ahmed, O. M., and Abdulazeez, A. M., 2025. Comparative Analysis of Machine Learning and Deep Learning Models for Bitcoin Price Prediction. Indonesian Journal of Computer Science Attribution, 13(1), pp.407–414.
Riziq, I. F., and Dzikrillah, A. R., 2024. Implementasi Algoritma LSTM dan SVR untuk Prediksi Harga Bitcoin Menggunakan Data Yahoo Finance. METIK Jurnal, 9(2), pp.2025–2032. https://doi.org/10.47002/metik.v9i2.1077
Rahmah, M., Ramadanti, K., and Aulia, I. F., 2026. Perbandingan Algoritma Regresi dalam Memprediksi Penjualan Berdasarkan Indikator Sosial Ekonomi Kabupaten Cirebon (2010–2023). Jurnal Fasilkom, 16(1), pp.1–9.
Ruliana, R., Rais, Z., Marni, M., and Ahmar, A. S., 2024. Implementation of the Support Vector Regression (SVR) Method in Inflation Prediction in Makassar City. ARRUS Journal of Mathematics and Applied Science, 4(1), pp.28–35. https://doi.org/10.35877/mathscience2608
Zulmawati, W. S., Ramadanti, K., Syafriandi, S., and Aulia, A. S., 2023. Bitcoin Price Prediction Using Support Vector Regression. UNP Journal of Statistics and Data Science, 1(5), pp.488–495. https://doi.org/10.24036/ujsds/vol1-iss5/121
Wu, J., and Wang, Y. G., 2023. A working likelihood approach to support vector regression with a data-driven insensitivity parameter. International Journal of Machine Learning and Cybernetics, 14(3), pp.929–945. https://doi.org/10.1007/s13042-022-01672-x
Gananta, I. M., Purnama, I. N., Fredlina, K. Q., and Salma, A., 2023. Optimasi Prediksi Harga Emas dengan Metode Support Vector Regression (SVR) Menggunakan Algoritma Grid Search. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), pp.3160–3165.
Won, J., Lee, H. S., and Lee, J. W., 2025. A review on multi-fidelity hyperparameter optimization in machine learning. ICT Express, 11(2), pp.245–257. https://doi.org/10.1016/j.icte.2025.02.008
Purwinarko, A., and Langgundi, F. A., 2023. Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods. Journal of Soft Computing Exploration, 4(2), pp.99–106. https://doi.org/10.52465/joscex.v4i2.159
Ula, M., Ilhadi, V., and Sidek, Z. M., 2024. Comparing Long Short-Term Memory and Random Forest Accuracy for Bitcoin Price Forecasting. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 23(2), pp.259–272. https://doi.org/10.30812/matrik.v23i2.3267
Kong, X., et al., 2025. Deep learning for time series forecasting: a survey. International Journal of Machine Learning and Cybernetics, 16(7–8), pp.5079–5112. https://doi.org/10.1007/s13042-025-02560-w
Casolaro, A., Capone, V., Iannuzzo, G., and Camastra, F., 2023. Deep Learning for Time Series Forecasting: Advances and Open Problems. Information, 14(11), p.598. https://doi.org/10.3390/info14110598
Kamisan, N. A. B., Lee, M. H., and Sulandari, W., 2025. Optimizing Train-Test Splits for LSTM and MLP Models in Bitcoin Price Forecasting Accuracy. Statistika, 25(2). https://doi.org/10.29313/statistika.v25i2.6989
Grimm, F., Bitsch, G., and van Dinther, C., 2026. The Influence of Preprocessing on Time Series Prediction. Procedia Computer Science, 277, pp.739–748. https://doi.org/10.1016/j.procs.2026.02.115
Mulyani, A. S., Mustafid, M., and Widiharih, T., 2025. Implementasi Metode Support Vector Regression untuk Prediksi Harga Saham PT. Adaro Energy Tbk. Jurnal Gaussian, 14(2), pp.457–468. https://doi.org/10.14710/j.gauss.14.2.457-468
Ferdinand, F., Anthony, R., Winata, T. J., Sutanto, J., Souwiko, R., and Fernando, C., 2025. Comparison of Support Vector Regression and Extreme Learning Machine Methods for Predicting Bitcoin Prices. Journal of Applied Informatics and Computing, 9(5), pp.2121–2130. https://doi.org/10.30871/jaic.v9i5.10983
Arsi, P., Wahyudi, R., and Waluyo, R., 2021. Optimasi SVM Berbasis PSO pada Analisis Sentimen Wacana Pindah Ibu Kota Indonesia. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(2), pp.231–237. https://doi.org/10.29207/resti.v5i2.2698
Su, X., Jiang, H., Qin, T., and Lin, G., 2024. Particle Swarm Optimization–Support Vector Regression (PSO-SVR)-Based Rapid Prediction Method for Radiant Heat Transfer for a Spacecraft Vacuum Thermal Test. Applied Sciences, 14(20), p.9407. https://doi.org/10.3390/app14209407
Windy, W., and Prathivi, R., 2025. Komparasi Algoritma Random Forest dan XGBoost dalam Prediksi Premi Asuransi Kesehatan. Jurnal Transformatika, 23(1), pp.118–125. https://doi.org/10.26623/transformatika.v23i1.11914
Oprea, S. V., and Bâra, A., 2026. Regime-Aware Adaptive Forecasting Framework for Bitcoin Prices Using Probabilistic Generative Models. Computational Economics. https://doi.org/10.1007/s10614-026-11338-3
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rizky Parlika, Wahyudi Wahyudi, Ahmad Budi Trisnawan, Akhlis Munazilin, Prasasti Karunia Farista Ananto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright Notice
An author who publishes in the Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is 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 the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her 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 the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.
