DETEKSI PENYAKIT GLAUKOMA MENGGUNAKAN DETEKSI OD DAN SEGMENTASI BV DENGAN ALGORITMA SVM - Yenny Rahmawati; Ilham Fanani; Luthfia Nurma Hapsari; Ahmad Muharya (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Glaucoma is a neurodegenerative disease that causes damage to the optic nerve head and visual field defects. Although the neural aspect is often the main focus, vascular factors play a crucial role but are often overlooked in conventional diagnosis. This study proposes a glaucoma detection approach based on optic disc (OD) detection and blood vessel (BV) segmentation as a more targeted GLCM texture feature extraction stage. The dataset used consists of 550 glaucoma images and 200 non-glaucoma images. The extracted features were then classified using a Support Vector Machine (SVM) with an RBF kernel. The results show that the model achieves an accuracy of 76% with a weighted average F1-score of 0.68. An in-depth evaluation of each class shows significant performance in the glaucoma class with a recall value of 0.99, indicating that the post-segmentation OD and BV texture features are highly informative in capturing structural damage patterns. However, in the non-glaucoma class, a low recall value of 0.12 with a precision of 0.80 was found, indicating a tendency for false positives. The high recall value in the glaucoma class shows that this method is very effective and reliable for early detection (screening) purposes, where minimizing the risk of false negatives is a top priority in clinical diagnosis.