Application of Support Vector Machine and Image Segmentation Methods to Classify Types of Medicinal Leaves in Betel Leaves
Abstract
Indonesia has a rich biodiversity, including medicinal plants. Leaves are one of the plant parts widely used as traditional medicine. However, people often find it difficult to identify types of medicinal leaves. This study aims to classify types of medicinal leaves using the Support Vector Machine (SVM) method and digital image segmentation. The data used are images of betel leaves, which are processed through several stages: color transformation from RGB to HSV, image segmentation using the thresholding method, and texture and color feature extraction. The results show that the SVM method and image segmentation can classify leaf types with high accuracy. Therefore, this method can help people identify medicinal leaves more easily and accurately.
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Indonesian Journal of Applied Technology, Computer and Science [IJATCos]
Universitas Muhammadiyah Sumatera Utara
Kampus Utama
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