Deteksi Kualitas Buah Sawo dengan Pendekatan Ekstraksi Fitur GLCM dan Algoritma Support Vector Machine - Fidiya, Karisma Risma; Vikri, Muhammad Jauhar; Kartini, Alif Yuanita (JURNAL RISET KOMPUTER (JURIKOM), 2025)
The quality of sapodilla fruit is a crucial factor in ensuring product standards and consumer satisfaction. This study aims to detect the quality of sapodilla fruit using the Gray Level Co-occurrence Matrix (GLCM) method for texture feature extraction and Support Vector Machine (SVM) as the classification algorithm. A dataset of sapodilla fruit images was collected and processed using data augmentation techniques to enhance image variation. Extracted features, including contrast, homogeneity, energy, and correlation, were used as input for the SVM model. The model was developed using a train-test split approach and evaluated based on accuracy, precision, recall, and F1-score. Experimental results show that the proposed method successfully classifies sapodilla fruit into three categories—raw, ripe, and damaged—with an accuracy of 85%. This model was implemented in a MATLAB-based Graphical User Interface (GUI), enabling users to automatically classify sapodilla quality easily and efficiently.