With the massive development of investment, cryptocurrency has become one of the world’s most popular and widely traded digital investment instruments today. However, this investment is also affected by high price volatility, making it challenging for investors to make the right decision. Social media platforms like X play a significant role in facilitating investor discussions, where sentiment from the community often influences investment choices. This research analyzes the role of sentiment analysis in influencing Bitcoin price forecasting predictions by integrating the sentiment scores of X tweet data regarding “Bitcoin” and “BTC” with Bitcoin price using the ARIMAX method. Three scenarios were created for exogenous variable input features, including current sentiment score t, sentiment score input from n time steps, and average sentiment score input from n time steps. The results obtained from the ARIMAX(9, 1, 9) model with scenario 2 (sequence of past sentiment scores), which uses sentiment score input from n time steps, is more optimal than other scenarios as an exogenous variable with RMSE evaluation accuracy of 47799.91 and MAE of 40936.45. These results highlight the practical importance of sequential sentiment data in improving prediction accuracy and providing investors with actionable insights into market behaviour. This study recommends adopting sequential sentiment analysis as a key feature in forecasting models to enhance decision-making in cryptocurrency investments.