Jurnal Krisnadana
Vol 5 No 1 (2025): Jurnal Krisnadana- in Progress September-October 2025

Comparative Time Series Forecasting of Major Cryptocurrencies Using the GRU Deep Neural Network

I Putu Bramasta Priadinata (Faculty of Technology and Informatics, Informatics Study Program, Institut Bisnis dan Teknologi Indonesia, Denpasar, Bali, Indonesia)
I Gede Iwan Sudipa (Faculty of Technology and Informatics, Informatics Study Program, Institut Bisnis dan Teknologi Indonesia, Denpasar, Bali, Indonesia)
Sani Inusa Milala (Faculty of Technology Management and Business, Department of Real Estate and Facilities Management, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia)



Article Info

Publish Date
25 Oct 2025

Abstract

Cryptocurrency investments are increasingly popular due to their potential as digital assets, but high price volatility remains a major challenge in making investment decisions. This study implements the Gated Recurrent Unit (GRU) model to forecast the closing prices of five popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Dogecoin (DOGE), using historical datasets from Yahoo Finance covering the period from November 30, 2019, to November 29, 2024. Performance evaluation was conducted using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results show that the GRU model achieved the best performance for BNB with a MAPE of 2.38% and an RMSE of 17.03, followed by ETH and XRP with MAPE values of 2.51% and 2.64%, respectively. BTC recorded the highest RMSE of 2280.73, reflecting its significant price volatility, while DOGE exhibited the lowest RMSE of 0.01 despite having the highest MAPE of 4.11%. Forecasts for the next six periods indicate that BTC and ETH are likely to experience gradual price increases, XRP and BNB show a flattening trend, and DOGE remains stable with low volatility. This study concludes that the GRU model is effective in forecasting cryptocurrency prices; however, it is recommended to complement the results with fundamental and technical analysis to improve accuracy and support more optimal investment decision-making.

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Journal Info

Abbrev

jkdn

Publisher

Subject

Computer Science & IT Control & Systems Engineering

Description

Jurnal Krisnadana merupakan jurnal yang dapat menjadi wadah bagi civitas akademika dan kalangan profesional dalam mempublikasikan karya ilmiah ataupun hasil penelitiannya dengan tetap mengutamakan orisinalitas karya, pengembangan kelimuan dan kontribusi dalam berbagai bidang. Jurnal Krisnadana ...