<![CDATA[The online ticketing industry faces challenges in managing fluctuating ticket demand. This study aims to develop an online ticket booking prediction system using the Random Forest algorithm to optimize ticket sales. Using historical ticket booking data, a predictive model is built to project future ticket demand based on variables such as ticket price, booking time, event type, and event location. The data used includes 5,000 randomly selected ticket transactions from online ticketing service providers. The results show that the Random Forest model provides more accurate predictions compared to baseline methods (linear regression and single decision tree). The model achieved MAE of 0.142, RMSE of 0.185, and R² of 0.892, showing significant improvement compared to linear regression (MAE: 0.321; RMSE: 0.398; R²: 0.642) and single decision tree (MAE: 0.218; RMSE: 0.285; R²: 0.754). Statistical testing using paired t-test showed significant difference (p-value < 0.001) between Random Forest and baseline models. These findings indicate that a Random Forest-based prediction system can help ticket providers optimize pricing, inventory management, and ticket sales efficiency, and open up opportunities for the model's application in other sectors.]]>
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