Classification of Guarantee Types Using Leaf Feature Extraction with Minutiae and GLCM Using K-NN Method - Muhammad Haris Zuhri; Adhe Irham Thoriq; Abdul Syukur; Affandy Affandy; Muslih Muslih; Moch Arief Soeleman (Journal of Development Research, 2022)
Indonesia is a fertile area that has a sub-tropical climate that makes plants grow well in various parts of Indonesia. There are various variants of guava in Indonesia. Of the several types have differences including the structure of the fruit, tree and leaves. The focus of this research is to classify guava species based on leaf bone image using GLCM feature extraction, minutiae and shape extraction using the K-NN method. In this study using a dataset of 4 types of guava as many as 300 images, where each type of as many as 75 images. In the extraction process to get the leaf bone image in this study, there are several processes, namely preprocessing, grayscale image, binary image and morphology then only get the leaf bone image. After getting the extracted value, then the data is processed using the K-NN method. The highest accuracy in the K-NN method is at k1 = 92.42% with a standard deviation of 6.05% (micro average: 92.45%). Thus GLCM feature extraction, minutiae and shape extraction can potentially increase the level of accuracy in guava classification based on leaf bone images.