Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Dahur , Arnoldus Janssen
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Detection of Corn Leaf Blight Disease Based on GLCM and HSV Feature Extraction Using Support Vector Machine Chen, Sami'un, Defitroh; Dahur , Arnoldus Janssen; Manalu, Mamed; Arif, M Aidil; Jaya, Dery Yuswanto
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 06 (2026): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i06.2270

Abstract

Corn leaf blight is a major disease that reduces crop productivity, making early detection essential. This study proposes an image-based detection method using Gray Level Co-occurrence Matrix (GLCM) and HSV feature extraction with Support Vector Machine (SVM) classification. The dataset, obtained from Kaggle, consists of 2308 corn leaf images categorized into healthy and blight classes. The method includes preprocessing, segmentation, feature extraction, and classification. Preprocessing involves resizing, grayscale conversion, noise reduction, and normalization. Segmentation is performed using Otsu thresholding and K-Means clustering to isolate leaf regions and highlight disease areas. Feature extraction combines four GLCM texture features and three HSV color features to represent each image. The SVM model, evaluated using an 80:20 data split, achieved an accuracy of 94.8% with balanced precision, recall, and F1-score values of approximately 0.95. These results indicate that the proposed method is effective for detecting corn leaf blight and has potential for practical agricultural applications
Co-Authors Arif, M Aidil Chen, Sami'un, Defitroh Manalu, Mamed Yuswanto Jaya, Dery