Classification of Coral Images Using Support Vector Machine with Gray Level Co-Occurrence Matrix Feature Extraction

This research developed a coral image classification method using Support Vector Machine (SVM) with Gray Level Co-occurrence Matrix (GLCM) feature extraction to improve the accuracy of coral reef condition monitoring. Coral images were collected in the waters of Sangihe Islands Regency and labelled by experts for healthy, unhealthy, and dead categories. Preprocessing included cropping, background removal, sharpening, and image normalization. GLCM feature extraction was performed with a distance of 1, 2, and 3 pixels and directions of 0°, 45°, 90°, and 135°. SVM uses Linear, Radial Basis Function, and Polynomial kernels with parameters set in a grid. The results indicate that the polynomial kernel with parameters C=10, degree=3, and gamma=1 achieves the highest accuracy, at 91.85%. Oversampling increased the accuracy by 2.17%, while feature selection by boxplot and model-based decreased the accuracy by 0.8% and 0.2%, respectively. On the other hand, feature selection using correlation analysis significantly decreased accuracy by 16.11%. These findings significantly contribute to coral reef conservation by offering a more accurate and efficient classification method. This method enables better and timely monitoring of coral reef conditions, thus supporting more effective conservation interventions. Integrating these research results into IoT systems can improve overall coral reef monitoring and conservation efforts.