<![CDATA[This study aims to identify and measure the main factors that most significantly affect the Income of Online Motorcycle Taxi Drivers Per Transaction in the gig economy sector. The Machine Learning Random Forest Regressor algorithm was used on driver transaction data. This methodology was chosen for its ability to handle the data's non-linearity and to objectively measure Feature Importance. Traditional linear regression models have limitations in these areas. The main results show the Random Forest model is highly accurate (R2 = 0.9634). It confirms the absolute dominance of distance, which accounts for 94.98% of the total predictive importance of revenue. The Total Transaction Value factor (3.82%) is a secondary predictor. Demographic variables (Age and Gender) and temporal variables (Days and Hours) together had a minimal (less than 1%) influence on fare per trip. This research concludes that the rate per driver transaction is determined almost exclusively by the platform's distance-based pricing policy. It is neutral to the characteristics of the driver. These findings recommend that platforms focus on increasing order volume and optimizing operational costs, rather than modifying base rates.]]>
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