Improving Support Vector Machine Performance with Hyperparameter Tuning Methods Using a Balanced-Moderate Model for Palm Oil Production Prediction
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Abstract
Prediction is an essential step for anticipating future events, encompassing both accuracy and the rate at which predictions are generated. In palm oil production, which holds strategic economic value in the global economy, particularly for Indonesia, precise forecasting is essential. Machine learning algorithms are indispensable for producing accurate predictions, especially when working with large-scale, long-term datasets. This study proposes a novel Balanced-Moderate hyperparameter tuning strategy for Support Vector Regression (SVR) in forecasting global palm oil production. This strategy represents an innovative approach that integrates gradual parameter adjustment with Bayesian optimization to produce moderate and stable predictions. Unlike previous approaches that prioritize extreme parameter search, the Balanced-Moderate method systematically adjusts the values of C (regularization), gamma, epsilon, and kernel through incremental steps, thereby reducing model complexity while enhancing generalization capability. This method was applied to global palm oil production data spanning 1961–2021, comprising 12,053 records from all producing countries. The baseline SVR model, without hyperparameter tuning, yielded an MSE of 0.5969, RMSE of 0.7726, MAE of 0.2207, and R2 of 0.3759. After applying the Balanced-Moderate strategy combined with Bayesian optimization, performance improved significantly, achieving an MSE of 0.0006, RMSE of 0.0257, MAE of 0.007, and R2 of 0.7937. This result outperformed Grid Search (R2 = 0.556) and Random Search (R2 = 0.512), confirming the superiority of the proposed approach. These findings hold significant relevance for palm oil supply chain planning and plantation policy formulation, given that forecasting accuracy is a crucial factor in ensuring economic sustainability.
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Iwan Rizal Setiawan, Zainul Fanani, A., Suko Basuki, R., & Purwanto. (2026). Improving Support Vector Machine Performance with Hyperparameter Tuning Methods Using a Balanced-Moderate Model for Palm Oil Production Prediction. JITSI : Jurnal Ilmiah Teknologi Sistem Informasi, 7(3), 248 - 261. https://doi.org/10.62527/jitsi.7.3.633
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