Implementation of the Gradient Boosting Algorithm for Palm Oil Price Prediction

Wilbert Fernando; Hendri; Robby Wijaya

doi:10.59934/jaiea.v5i3.2316

Authors

Wilbert Fernando STMIK TIME
Hendri STMIK TIME
Robby Wijaya STMIK TIME

DOI:

https://doi.org/10.59934/jaiea.v5i3.2316

Keywords:

Price Prediction, Palm Oil, XGBoost Algorithm, Hyperparameter Tuning, Machine Learning

Abstract

The price of palm oil is highly volatile due to the influence of global market dynamics, trade policies, and climate change, creating uncertainty for industry players in decision-making. This research aims to implement the XGBoost (Extreme Gradient Boosting) algorithm, optimized using Grid Search Cross-Validation, to predict palm oil prices. The dataset used is the Palm Oil Futures Historical Data.csv obtained from Kaggle, consisting of nine features. Data preprocessing is performed using StandardScaler for normalization, followed by model training with hyperparameter tuning. The system is built as a web-based application separating the frontend using PHP and Flask as the Backend API. Testing on 105 test data points yielded an MAE of 43.97, RMSE of 65.14, and R² of 91.82%, demonstrating the model’s strong ability to explain palm oil price variation. Based on the results, the XGBoost algorithm is suitable as a decision-support tool for commodity price prediction, achieving high accuracy consistent with standard criteria for commodity price forecasting and capable of handling large datasets.

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