OPTIMIZED IMAGE CLASSIFICATION FOR MORINGA DRY LEAVES BASED ON DECISION TREE ALGORITHM - Sugiartawan, Putu; Wardani, Ni Wayan; Anggara Putra, I Wayan Kintara (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This study explores the application of the Decision Tree algorithm to classify Moringa dry leaves based on numerical features such as average color values and histogram data. The research aims to optimize the classification process for agricultural applications, particularly for the automation of Moringa leaf quality assessment. A systematic methodology was employed, including data preprocessing, feature scaling, and hyperparameter tuning, to improve the model's performance. The Decision Tree model achieved a classification accuracy of 67.78%, with notable performance for certain classes, such as Class F (precision: 0.83, recall: 0.88). However, challenges were observed in handling overlapping features and class imbalances, especially for Class E (precision: 0.48, recall: 0.52). These results highlight the potential of Decision Tree algorithms for agricultural datasets while emphasizing the need for further optimization. Future improvements could include ensemble methods and advanced feature engineering to enhance robustness and generalization. This research contributes to the development of automated systems for agricultural quality control and the broader adoption of machine learning techniques in agriculture.