Class Balancing and Parameter Tuning of Machine Learning Models for Enhancing Aphrodisiac Herbal Plant Classification - Jayadi, Puguh; Bhagawan, Weka Sidha; Aldida, Jofanza Denis (LPPM Institut Teknologi Telkom Purwokerto, 2025)
Herbal plants with aphrodisiac claims are an important part of traditional medicine that continues to evolve within the modern scientific context. However, the classification process for these plant claims is often done manually and subjectively, necessitating a more objective, data-driven approach. Artificial Intelligence (AI) and its various derivatives, such as Machine Learning, present a reliable solution for several related classification studies. The primary challenge in classification lies in data class imbalance and selecting the optimal model parameters. This study proposes an integrated approach that utilizes machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and XGBoost, combined with SMOTE class balancing techniques and hyperparameter tuning through Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on a dataset of herbal plants with attributes and labels of aphrodisiac claims, and the results were evaluated based on accuracy, precision, recall, and execution time. The findings indicated that the combinatorial approach significantly improved model performance compared to the basic approach. Among the hyperparameter tuning results, the SVM method achieved the best accuracy (0.889) and precision (0.889). This research contributes to the development of an AI-based classification system in the field of ethnopharmacology. It can serve as a reference for creating scientifically validated databases of herbal plants.