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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) AGRIVITA, Journal of Agricultural Science
Aminuddin, Raihah
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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Semi-supervised spectral clustering using shared nearest neighbor for data with different shape and density YouSheng, Gao; Abdul Rahim, Siti Khatijah Nor; Hamzah, Raseeda; Ang, Li; Aminuddin, Raihah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2283-2290

Abstract

In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a semisupervised spectral clustering algorithm based on shared nearest neighbor (SNN). The proposed algorithm combines the idea of semi-supervised clustering, adding SNN to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.
Support Vector Machine (SVM) for Tomato Leaf Disease Detection Ibrahim, Shafaf; Mohd Fuad, Nur Afiqah; Md Ghani, Nor Azura; Aminuddin, Raihah; Sunarko, Budi
AGRIVITA Journal of Agricultural Science Vol 47, No 2 (2025)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v47i2.3746

Abstract

Tomatoes rank among the top five most globally demanded crops and serve as a key ingredient in numerous dishes. However, productivity may decline due to challenges such as diseases, pest infestations, and climate change. Therefore, automatic disease detection is essential to identify early signs of illness during the growth period. This study proposes a method for detecting tomato leaf diseases using image processing techniques. The approach involves image enhancement, feature extraction, and classification. Initially, leaf disease images were enhanced using the Contrast Adjustment technique. Subsequently, color and texture features were extracted using Color Moments and the Gray-Level Co-occurrence Matrix (GLCM), respectively. Disease detection was carried out using a Support Vector Machine (SVM). The method was tested on 50 images each for healthy leaves and four types of tomato leaf diseases: Bacterial Spot, Yellow Leaf Curl Virus, Early Blight, and Late Blight. The performance of the disease detection system was evaluated using a confusion matrix, achieving an overall accuracy, sensitivity, and specificity of 96%, 90%, and 97.5%, respectively. These results demonstrate the effectiveness of the proposed SVM-based approach for tomato leaf disease detection.
Co-Authors Abdul Rahim, Siti Khatijah Nor Budi Sunarko Hamzah, Raseeda Ibrahim, Shafaf Li Ang, Li Md Ghani, Nor Azura Mohd Fuad, Nur Afiqah YouSheng, Gao