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All Journal ELINVO (Electronics, Informatics, and Vocational Education)
Aeri Rachmad
Departemen of Informatics, Faculty of Engineering, University of Trunojoyo, Madura, Bangkalan, Indonesia
Computer Science & IT Education Electrical & Electronics Engineering

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Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction Firdaus Solihin; Muhammad Syarief; Eka Mala Sari Rochman; Aeri Rachmad
Elinvo (Electronics, Informatics, and Vocational Education) Vol 8, No 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.55759

Abstract

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.
Co-Authors Eka Mala Sari Rochman Firdaus Solihin Muhammad Syarief