Klasifikasi Citra Digital Daun Herbal Menggunakan Support Vector Machine dan Convolutional Neural Network dengan Fitur Fourier Descriptor - Darmawati, Aulia Rezky Rahmadani; Purnawansyah; Herdianti Darwis; Lutfi Budi Ilmawan (LPPM Universitas Potensi Utama, 2024)
Leaves are one component of plants that contain natural properties and are useful for maintaining human health. However, several types of leaves have the same characteristics and characteristics that make it difficult to distinguish. This study aims to classify types of herbal leaves using the SVM method with four kernels (Linear, RBF, Polynomial, Sigmoid) and CNN with Fourier descriptor (FD) feature extraction. The processed dataset is katuk leaf images, and Moringa leaf images of 480 images which are divided into 80% training data and 20% testing data using two scenarios, namely dark and light. From the testing process, it was found that FD + CNN in the light and dark scenarios obtained an accuracy value of 98%. Thus, the FD + SVM algorithm with Linear, RBF, polynomial kernels can be recommended in classifying herbal leaf images to have the best accuracy value of 100%.