Tuna Eye Image Classification for Freshness Detection Using PCA and SVM - I Made Dwi Putra Asana; I Wayan Aldinata; Made Leo Radhitya; Ni Putu Suci Meinarni; Ida Bagus Gede Anandita (Yayasan Sinergi Widya Nusantara (Sidyanusa), 2026)
Freshness assessment of tuna (Auxis thazard) in traditional markets still relies mainly on subjective visual inspection, which introduces inconsistency and food-safety risk. This study proposes an objective, low-cost classification pipeline for tuna freshness based on eye images. Six hundred forty fish-eye images were acquired at Kedonganan Fish Market and labeled through a 30-panelist organoleptic test based on SNI 2729:2013. The pipeline segments the eye Region of Interest using U-Net, extracts HSV color features from the segmented eye, reduces dimensionality using Principal Component Analysis (PCA), and classifies freshness with a Support Vector Machine (SVM). A Group K-Fold (k=8) validation scheme and per-fold standardization are used to prevent data leakage across acquisition groups. Grid search over target cumulative variance (50%-95%) and SVM regularization (C=0.1, 1, 10) yields a best configuration at cumulative variance of 55% and C=1, achieving 96.72% accuracy, 97.76% precision, 96.72% recall, and 96.51% F1-score. Compared with SVM without PCA (95.47% accuracy, 215.82 s), the PCA-SVM model reaches equivalent or higher accuracy with 47% lower classification time, supporting deployment on resource-constrained devices.