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All Journal Jurnal Simantec Jurnal Ilmiah Kursor Jurnal Pekommas REKAYASA ELINVO (Electronics, Informatics, and Vocational Education) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Network Engineering Research Operation [NERO] JSiI (Jurnal Sistem Informasi) Multitek Indonesia : Jurnal Ilmiah JATI (Jurnal Mahasiswa Teknik Informatika) Pancaran Pendidikan Jurnal Pengabdian dan Pemberdayaan Banyuwangi
Rochman, Eka Mala Sari
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Trunojoyo
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Physics Social Sciences Other

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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 Solihin, Firdaus; Syarief, Muhammad; Rochman, Eka Mala Sari; Rachmad, Aeri
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 -, Husni Aeri Rachmad Alfa Faridh Suni Bain Khusnul Khotimah Dyah S Rahayu Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Fitri Damayanti Husni - Huzain Azis Ifan Pratama Ika Oktavia Suzanti Imamah Imamah Kamil, Fajrul Ihsan Khozaimi, Ach. Mohammad Syarief, Mohammad Muhammad Syarief, Muhammad Nabil Dzul Afkar, Ahmad Pramudita, Yoga Dwitya Pratama, Ifan Putro, Sigit Susanto Ratna Nur Tiara Shanty Rima Tri Wahyuningrum Syakur, Muhammad Ali Wahyudi Setiawan Yudha Dwi Putra Negara, Yudha