Classification of mango plants based on leaf shape using GLCM and K-nearest neighbor methods
Abstract
Objective: Apply the GLCM method to select mango leaf feature extraction and determine the accuracy level obtained from the K-Nearest Neighbor classification results.
Design/method/approach: Using GLCM and K-Nearest Neighbor(KNN) methods. System development using the Prototype method.
Results: The test results have been carried out using as many as 60 mango leaves compared to training data and 80:20 test data, with different accuracy. The highest accuracy is at K = 3 by 81% using 6 features, K = 6 by 78% using 5 features, and K = 7 by 74% using 4 features.
Authenticity/state of the art: The difference between this research and previous research is the pre-processing method, the type of features used, and the classification method. In this method, the mango leaf image is converted to grayscale, and a feature extraction process is carried out. Then the results of feature extraction will be classified using the K-Nearest Neighbor method. The output of this system is the result of the image classification of mango leaves, such as Kweni, Lalijowo, and Madu.
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DOI: https://doi.org/10.31315/cip.v1i1.6124
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