Penerapan Algoritma Yolov4-Tiny Dan Efficientnetv2-S Untuk Deteksi Kesegaran Ikan Gurami - Natadjaja, Hans Richard Alim; Wonohadidjojo, Daniel Martomanggolo (STIKOM Tunas Bangsa Pematangsiantar, 2023)
Fish production is one of the largest in Indonesia. Among many fishery products, Gurami fish is one of the most widely processed by society. However, the decrease in the freshness of fishery products is susceptible to occur when they reach the hands of consumers. Several methods for detecting fish freshness have been applied to assist in obtaining fresh fish products. However, some of these methods have a lack of accuracy rate, not all of them were developed to detect gurami freshness. Therefore, a solution is needed to help people detect the freshness of Gurami fish and make it accessible on mobile devices. This solution can be realized by using Deep Learning methods. The methods proposed in this study use the Convolutional Neural Network (CNN) by utilizing the YOLOv4-Tiny algorithm to detect the Region of Interest (ROI) and the EfficientNetV2-S architecture freshness classification on Gurami fish images. The training process using both methods can produce an ROI detection model with a mean average precision of 93,58% and a classification model with an accuracy rate of 93%