Regression Model Analysis in Forecasting Logistics Transportation Performance at Belawan Port Terminal
DOI:
https://doi.org/10.30743/r85ct584Keywords:
Regression Model; Predictive Modeling; Learning Rate; Stability; Logistics TransportationAbstract
This study analyzes the performance of three regression models: Gradient Boosting Regressor (GBR), AdaBoost Regressor (ABR), and Support Vector Regressor (SVR) in the context of predicting the departure time of logistics vehicles at PT. Pelabuhan Belawan. The results show that GBR is optimal at learning rates between 0.05 and 0.07, with high accuracy but prone to overfitting. ABR shows good stability at higher learning rates, while SVR is the most stable model, consistent across a range of epsilon values. Although GBR excels in accuracy, SVR provides the best balance between performance and generalization ability, with delta metrics that are almost unchanged. The analysis shows that logistics vehicles depart early more often than on time or late, reflecting the sensitivity of the model to aggressive learning rate changes. These findings recommend SVR with a learning rate of 0.1 as the optimal model for prediction applications that require stability.
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