<![CDATA[Bitcoin is a cryptocurrency asset characterized by high volatility and non-linear movement patterns that trigger sudden market risks. Considering that the majority of previous studies have solely focused on mathematical metric evaluations without providing practical solutions, this study aims to compare the performance of the conventional statistical model Autoregressive Integrated Moving Average (ARIMA) with Deep Learning architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and to implement the best-performing model into an interactive dashboard using Streamlit. The dataset used encompasses daily Bitcoin closing prices for the 2020–2026 period. The methodology includes data preprocessing, MinMaxScaler normalization, sequential data generation using a 30-day sliding window technique, chronological data splitting (80:20), and evaluation using MAE, RMSE, MAPE, Accuracy, and R² Score. Experimental results prove that ARIMA fails to adapt to non-stationary data, whereas GRU outperforms LSTM in terms of architectural efficiency and precision. The GRU model achieved the best performance with an MAE of IDR 34,773,098.88, an RMSE of IDR 46,684,832.94, a MAPE of 2.26%, an Accuracy of 97.74%, and an R² Score of 0.9661. The GRU model was then successfully implemented into a Streamlit web dashboard that facilitates the real-time visualization of historical and predicted prices. In conclusion, the GRU architecture is the most effective and efficient approach for Bitcoin price forecasting, successfully bridging the gap between theoretical analysis and the availability of practical applications.]]>
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