Implementasi Metode K-Nearest Neighbor Untuk Klasifikasi Penyakit Tanaman Mentimun Pada Citra Daun

Abstract

Cucumber is a vegetable that is widely consumed by Indonesian people. However, cucumber plants are susceptible to disease attack which causes substantial yield loss. Examples of disease in cucumber plants are downy mildew, powdery mildew, and cucumber mozaic virus. This disease can be recognized visually because it has a characteristic color and texture. Through an image, information can be learned about the cucumber plant disease. This study aims to build a disease classification system on cucumber leaf images so that it can provide information on the type of disease. The application of the system consisting of pre-processing, feature extraction, classification, and evaluation stages. The pre-processing stages resizes the RGB image and then converts it to Grayscale. The feature extraction stage uses the GLCM (Gray Level Co-Occurence) method. The classification stage uses the K-NN (K-Nearest Neighbor) algorithm. Evaluation stage is a confusion matrix. The results of the cucumber leaf disease classification test used the K-Nearest Neighbor algorithm, produced the best accuracy value by using the neighborhood value k=1 reaching 90%.