Abstract
Indonesia is a maritime country and one of the largest archipelago countries in the world. Indonesian fisheries have many types of fish in stock, this causes difficulties in introducing fish species directly. This study designed a fish species classification system using the You Only Look Once (YOLO) architecture. YOLO is an object detection method using a convolutional network that will only be just once. Unlike the convolutional networks in general that spend thousands of networks to obtain an image with computing that is long enough. The architecture of this work using YOLO9000. The dataset consists of 6 classes, that is banded butterflyfish, blue tang surgeonfish, barred hamlet, black side hawkfish, Arabian Picasso triggerfish, dan black margate grunt. System testing produces an accuracy of 92%, IoS 0.75, and 2.223 FPS using Adam optimizer. The proposed system model has good accuracy and fast detection time.
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