Penggunaan Metode Ekstraksi Fitur Tekstur Gray Level Co-ocurrrence Matrix dan K-Nearest Neighbor untuk Identifikasi Jenis Penyakit Tanaman Apel - Muhammad Iqbal Mustofa; Muhammad Tanzil Furqon; Dian Eka Ratnawati (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2022)
Indonesia is a country with a tropical climate that makes it easy for apple plants to grow, even the apple crop farming industry is one of the fields that is widely cultivated in Indonesia. Malang is one of the largest apple producing areas in Indonesia. It was also explained in the data from the Central Statistics Agency (BPS) in 2019 that Malang Regency could produce 1,406,173 quintals of apples. In apple cultivation, pest and disease control is one of the important factors in the development of apple plants because it can affect the yield and quality of apples. There are several main diseases that attack plants such as Apple Scab caused by the fungus Venturia inaequalis, Black Rot caused by the fungus Botryosphaeria obtusa and Cedar-Apple Rust caused by the fungus Gymnosporangium juniperi-virginianae. Information technology is needed to speed up the process of identifying apple plant diseases. This study utilizes the results of texture feature extraction of Gray Level Co-occurrence Matrix (GLCM) and the K-Nearest Neighbor classification method. In this study, the data used were 1943 leaf images with 4 classes including Apple Scab, Black Rot, Cedar Apple Rust and Healthy. The GLCM features used in this research are Variance, Homogeneity, Energy and Correlation. In the evaluation, the K-fold cross validation method was used to eliminate bias in the data with k=10. Of all the tests carried out, the highest average accuracy was 84.56% at an angle of 90Â° with a value of d=2 and at a value of k=3 with the Euclidean Distance calculation method.