Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Support Vector Machine dengan Fitur Fourier Descriptor

Putri Regina Prayoga (Program Studi Teknik Informatika, Universitas Muslim Indonesia)
Purnawansyah Purnawansyah (Program Studi Teknik Informatika, Universitas Muslim Indonesia)
Tasrif Hasanuddin (Program Studi Teknik Informatika, Universitas Muslim Indonesia)
Herdianti Darwis (Program Studi Teknik Informatika, Universitas Muslim Indonesia)

EDUMATIC: Jurnal Pendidikan Informatika
Vol 7 No 1 (2023): Edumatic: Jurnal Pendidikan Informatika

Indonesia is a rich country in herbal plants that can be used as traditional medicine. Leaves are one of the main components of herbal plants that are difficult to distinguish in texture and shape. This study aims to classify two types of herbal leaves, namely Sauropus androgynus and Moringa leaves using the K-nearest neighbor (KNN) and Support vector machine (SVM) with fourier descriptor (FD) feature extraction on texture and shape features. The research uses primary data collected through a smartphone camera as much as 480 image data with light and dark scenarios which are then divided into 80:20 training and testing data. Based on the research that has been done, it is found that the KNN for light scenario data and dark scenarios get 92% and 94% accuracy respectively. The test results using SVM with FD feature extraction obtain an accuracy of 96% for light and dark scenarios. Thus, SVM is more recommended in the classification of herbal leaf images.