Predicting Basic Shipping Tariff Using Machine Learning
Prediksi Tarif Dasar Pengiriman Menggunakan Machine Learning
DOI:
https://doi.org/10.25134/ilkom.v19i2.388Keywords:
Web-Based Attendance, GPS Validation, Laravel Framework, Black Box TestingAbstract
This study explores the application of machine learning algorithms in predicting the Basic Shipping Tariff for logistics, focusing on variables such as Item Price, Shipment Weight, and Distance (KM). Random Forest Regressor and Linear Regression models were used as comparison methods. Experimental results show that the Random Forest Regressor outperforms Linear Regression, achieving an R² value of 0.915 and RMSE of 0.154, while Linear Regression reached an R² value of 0.706 and RMSE of 0.113. Additionally, the Random Forest model achieved lower error values with MSE of 0.000 and MAE of 0.003, compared to Linear Regression with MSE of 0.001 and MAE of 0.007. These error metrics further highlight the superiority of the Random Forest model. In-depth analysis reveals significant relationships between these variables and the Basic Shipping Tariff, showcasing the model's potential application in dynamic pricing strategies within the Indonesian logistics industry. This study aims to contribute to operational efficiency and improve pricing accuracy in the logistics business in Indonesia.
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