Classification of Herbal Leaves Using Support Vector Machine (SVM)

Yakub Takandiwa Takandiwa (Universitas Kristen Wira Wacana Sumba)
Pingky Alfa Ray Leo Lede (Universitas Kristen Wira Wacana Sumba)

Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Vol. 5 No. 3 (2026): June 2026

Indonesia is a country with high biodiversity, including various types of herbal leaves with potential use as traditional medicine. Manual identification of herbal leaves often encounters challenges due to morphological similarities among species and the limited availability of experts, thereby necessitating a fast and accurate technology-based classification method. This study aims to classify 10 types of herbal leaves using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel. The dataset consists of 3,500 leaf images (350 images per class), from which color features (HSV), texture features (Gray Level Co-occurrence Matrix/GLCM), and shape features (area, perimeter, and aspect ratio) were extracted. The research process includes preprocessing, feature extraction, data splitting into training and testing sets, model training, and performance evaluation. Evaluation was conducted using a confusion matrix, with accuracy as the primary metric due to the balanced class distribution. Precision, recall, and F1-score were employed as supporting evaluation metrics. The results indicate that the SVM model with an RBF kernel successfully classified the 10 types of herbal leaves with an accuracy of 81.29%. Based on per-class analysis, the highest performance was achieved in the Papaya class with an F1-score of 90.00%, followed by Jambu Biji (89.36%) and Pandan (87.14%). In contrast, the lowest performance was observed in the Aloe Vera class with an F1-score of 65.71% and Lime with 70.00%. The model achieved an average precision of 81.16%, recall of 80.73%, and F1-score of 80.94%. Misclassifications primarily occurred among classes with high morphological similarity, such as Aloe Vera, which was frequently misclassified as Pandan (9 cases) and Basil (5 cases). The system has been implemented as a Graphical User Interface (GUI) application that allows users to upload leaf images and obtain classification results along with information regarding their herbal benefits within 1–2 seconds.