Pemodelan Dataset On-chain pada BiLSTM untuk Prediksi Harga Bitcoin

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DOI:

https://doi.org/10.37859/jf.v16i1.11275
Keywords: bitcoin, price prediction, on-chain data, deep learning, BiLSTM

Abstract

Bitcoin is a crypto asset for investment. It can give high profit, but it also has high risk because the price changes very fast and is not stable. To reduce the risk of loss, we need a prediction system that can read price changes well. This research aims to model and predict the closing price of Bitcoin using network activity data (on-chain metrics). The method used is Deep Learning with the BiLSTM algorithm. This method is chosen because it can process data in two directions (forward and backward), so it can learn patterns better than standard LSTM. The dataset is taken from the public Blockchain network using BigQuery, from August 18, 2011, to February 6, 2026, with 5,287 daily data. The model uses the main input active_spending_addresses and two volatility indicators: Percent of Top Range (PTR) and Percent Low Range (PLR). Before modeling, the data is processed using a sliding window of 60 days, with 90% training data and 10% testing data. The results show that the BiLSTM model has high accuracy, with MAE 2.958, RMSE 3.905, and MAPE 3.22%. The comparison shows that BiLSTM is better than other models. LSTM has MAPE 29.06%, and MLP has MAPE 4.01%. In conclusion, BiLSTM can handle extreme crypto market changes very well, so it gives stable and accurate Bitcoin price predictions.

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References

P. Zhu, X. Zhang, Y. Wu, H. Zheng, and Y. Zhang, “Investor attention and cryptocurrency: Evidence from the Bitcoin market,” PLoS One, vol. 16, no. 2, p. e0246331, 2021.

S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” Available at SSRN 3440802, 2008.

D. O. Milando, R. Rahim, and F. Adrianto, “Analisis Pengaruh World Commodity Price terhadap Harga Bitcoin dengan Indeks Dolar sebagai Variabel Moderasi,” Jurnal Informatika Ekonomi Bisnis, pp. 1107–1114, 2023.

P. H. Padhila, I. Cholissodin, and P. P. Adikara, “Prediksi Harga Bitcoin berdasarkan Data Historis Harian dan Google Trend Index menggunakan Algoritme Extreme Learning Machine,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 7, pp. 3515–3524, 2022.

S. Singh, A. Pise, and B. Yoon, “Prediction of bitcoin stock price using feature subset optimization,” Heliyon, vol. 10, no. 7, p. e28415, 2024, doi: https://doi.org/10.1016/j.heliyon.2024.e28415.

S. M. Raju and A. M. Tarif, “Real-time prediction of BITCOIN price using machine learning techniques and public sentiment analysis,” arXiv preprint arXiv:2006.14473, 2020.

R. Dubey and D. Enke, “Bitcoin price direction prediction using on-chain data and feature selection,” Machine Learning with Applications, vol. 20, p. 100674, 2025, doi: https://doi.org/10.1016/j.mlwa.2025.100674.

A. Brauneis, R. Mestel, R. Riordan, and E. Theissen, “Bitcoin unchained: Determinants of cryptocurrency exchange liquidity,” J. Empir. Finance, vol. 69, pp. 106–122, 2022, doi: https://doi.org/10.1016/j.jempfin.2022.08.004.

K. Alabi, “Digital blockchain networks appear to be following Metcalfe’s Law,” Electron. Commer. Res. Appl., vol. 24, pp. 23–29, 2017.

H. Utama, “Pendekatan Deep Learning Menggunakan Metode Lstm Untuk Prediksi Harga Bitcoin,” The Indonesian Journal of Computer Science Research, vol. 2, no. 2, pp. 43–50, 2023.

F. Febriansyah, A. Sujjada, and F. Sembiring, “Prediksi Harga Bitcoin Menggunakan Algoritma Long Short Term Memory (LSTM),” INOVTEK Polbeng-Seri Informatika, vol. 9, no. 1, 2024.

A. F. Hanif, T. B. Sasongko, and A. D. Laksito, “Perbandingan Kinerja LSTM, Bi-LSTM, dan GRU pada Klasifikasi Judul Berita Clickbait,” The Indonesian Journal of Computer Science, vol. 12, no. 4, 2023.

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, 2023.

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

R. Parlika, R. R. Isnanto, and B. Rahmat, “Prediction of ROI achievements and potential maximum profit on spot Bitcoin Rupiah trading using K-means clustering and patterned dataset model,” JOIV: International Journal on Informatics Visualization, vol. 8, no. 3–2, pp. 1987–2001, 2024.

I. B. N. A. P. Wiryawan, “Perbandingan Time Step pada Model Prediksi State of Health Baterai Lithium-ion Berbasis BiLSTM,” in Seminar Nasional Riset Inovatif, 2024.

E. N. Waroi, A. Arief, and K. Khusnawi, “Prediksi harga laptop menggunakan algoritma GRU dan BILSTM,” Jurnal Sosial Teknologi, vol. 4, no. 7, pp. 408–424, 2024.

Z. Hameed and B. Garcia-Zapirain, “Sentiment classification using a single-layered BiLSTM model,” Ieee Access, vol. 8, pp. 73992–74001, 2020.

N. Jagannath et al., “An on-chain analysis-based approach to predict ethereum prices,” IEEE Access, vol. 9, pp. 167972–167989, 2021.

P. Jay, V. Kalariya, P. Parmar, S. Tanwar, N. Kumar, and M. Alazab, “Stochastic neural networks for cryptocurrency price prediction,” Ieee access, vol. 8, pp. 82804–82818, 2020.

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Published

2026-05-03

Issue

Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)

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