Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal

Abstract

Plants are vital to human life on earth, and the leaves and their whole parts have many benefits. These parts of the plant can help distinguish between different species. The leaf identification can be performed at any time, while the other parts of the plants can only be identified at a certain time. The study aims to classify two types of herbs i.e. saur-opus androgynous and moringa oleifera, implementing the Fourier Descriptor method to extract the shape and texture features. In the process of classification using the Naïve Bayes method with three types of nuclei (Gaussian, Bernoulli, and Multinomial) and a Convolutional Neural Network. The testing process was carried out using two scenarios, dark and light, where each scenario consisted of 240 images for a total of 480 images divided into 20% of the data testing and 80% of the training data. The Fourier Descriptor-Bernoulli Naive Bayes method gives the lowest accuracy in both light and dark scenarios, at 46% and 52%, respectively. As for the classification of herbal leaves using a combination of the Fourier Descriptor-Convolutional Neural Network method, it is recommended to be used in light image scenarios and Fourier Descriptor-Gaussian Naive Bayes in the dark scenarios because it is able to detect herbal leaf types with 100% accuracy.