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Ferdinan, Dani
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Journal : Nuansa Informatika

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Predicting Basic Shipping Tariff Using Machine Learning: Prediksi Tarif Dasar Pengiriman Menggunakan Machine Learning Harani, Nisa Hanum; Setyawan, M. Yusril Helmi; Ferdinan, Dani
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.388

Abstract

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.
Co-Authors Harani, Nisa Hanum Setyawan, M. Yusril Helmi