Klasifikasi Penyakit Daun Tomat Menggunakan Algoritma K-NN Berdasarkan Ekstraksi Fitur GLCM dan LBP

Abstract

Tomatoes are widely cultivated in Indonesia and are one of the crops with high economic value. According to the Central Bureau of Statistics, the Indonesian nation has the capacity to produce up to 1. 17 million tons of tomatoes in 2020. Tomatoes contain ingredients that the body needs. Additionally, grapes can also be consumed in different forms. However, tomato production in Indonesia decreased from 2013 to 2015 due to the spread of pests. Therefore, we conducted a study to classify tomato leaf diseases using the K-nearest neighbor method based on the grayscale coexistence matrix and the extraction of local binary pattern features. The data used was 9157 data obtained from the Plant Village database and classified into 6 classes (healthy, spot fungus, late blight, leaf mold, mosaic virus, spider mite, and target spot). The testing process was performed using the K-fold cross-validation technique, followed by performance calculations using the confusion matrix method. The highest accuracy was obtained at 86.8% when classifying using K9 and K10 with a precision value 40.6% and recall 49.2% when classifying using a value of K = 9, and precision 49.7% and recall of 49.3% when classifying using a value of K = 10.