IMPLEMENTATION OF LOCAL BINARY PATTERN FOR TOMATO LEAF TEXTURE CLASSIFICATION WITH SUPPORT VECTOR MACHINE METHOD
Abstract
Purpose: Implementing the Support Vector Machine method with Local Binary Pattern feature extraction to determine the performance of the method in classifying diseases on tomato leaves. Design/method/approach: The texture feature extraction method used is the Local Binary Pattern and the classification method used is the Support Vector Machine. The system development method used is the prototype method. Findings/Results: Based on the test results with the confusion ma- trix, it shows that the performance of the polynomial kernel produces an accuracy of 89%, precision of 89%, and recall of 88%. Judging from the results of accuracy, precision, and recall, it shows that the use of polynomial kernels in the Support Vector Machine method works well in classifying diseases on tomato leaves. Originality/value/ state of the art: This study was made to classify tomato leaf diseases using the Local Binary Pattern and Support Vector Machine methods with tomato leaf data obtained from the kaggle site.
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