Penerapan Metode Segmentasi Gabor Filter Dan Algoritma Support Vector Machine Untuk Pendeteksian Penyakit Daun Tomat

Abstract

This research discusses about processing a formulation that we can give to diseased tomato leaves. Gabor Filter is a method used to detect textures using frequency and orientation parameters. The Support Vector Machine (SVM) algorithm is an algorithm that can be used classifying tomato leaf diseases. The purpose of this research is to determine the accuracy of the Gabor Filter segmentation and the Support Vector Machine Algorithm for detecting tomato leaf disease to facilitate farmers in analyzing diseases on tomato leaves. The input will go through pre-processing of RGB pixels to Greyscale ones before being processed using Gabor Filter. This Gabor Filter process segments the image to produce a magnitude value. The results of the image magnitude values here will be seen and will enter the classification process using SVM. The SVM algorithm aims to find the best hyperlane on tomato leaves that have been segmented to separate classes in the input space. The application of the SVM method with class classification of tomato leaves by calculating the energy value and entropy of the extraction results, assisted by 12 features, namely: CiriR, Feature G, FeatureB, Standard DeviationR, Standard DeviationG, Standard DeviationB, SkewnessR, SkewnessG, SkewnessB, Mean, Energy, Entropy are used to the simplity classification process with a high degree of accuracy. The process of classification of tomato leaf disease with test data of 600 images managed to get an accuracy value of 74.1667%. In order to facilitate the performance of farmers in predicting tomato leaf disease.