<![CDATA[Bitcoin is a cryptocurrency aset with extreme volatility, necessitating precise forecasting models for investment risk mitigation. This study aims to analyze and forecast Bitcoin price volatility using an integrated Autoregressive Integrated Moving Average - Generalized Autoregressive Conditional Heteroskedasticity (ARIMA-GARCH) approach and compare its performance with a Deep Learning method, specifically Long Short-Term Memory (LSTM). The data used is the daily closing price of Bitcoin for the period 2018 to 2025. The results indicate that the ARIMA(1,1,1)-GARCH(1,1) model effectively captures the volatility clustering phenomenon, with a significant beta parameter value of 0.8691, indicating long-term volatility persistence. However, in terms of price prediction accuracy, the LSTM model significantly outperforms the conventional statistical model. Based on the testing, the ARIMA-GARCH model produced a Mean Absolute Percentage Error (MAPE) of 18.11%, which falls into the "good forecasting" category. In contrast, the LSTM model achieved a MAPE of 3.09%, categorized as "highly accurate forecasting." The significant difference in Root Mean Square Error (RMSE) values also reinforces that the LSTM architecture is more adaptive in processing non-linear data patterns and complex Bitcoin price fluctuations. This study concludes that while ARIMA-GARCH excels in risk structure analysis, the LSTM model provides more reliable price projection results for crypto market participants.]]>
