Fishku Apps: Fishes Freshness Detection Using CNN With MobilenetV2 - Muthia Farah Hanifa; Anugrah Tri Ramadhan; Ni’Matul Husna; Nabila Apriliana Widiyono; Rhamdan Syahrul Mubarak; Adisti Anjani Putri; Sigit Priyanta (IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., 2023)
Marine fish are one of the most promising economic commodities for the Indonesian economy. Marine fish will decrease in protein content along with the decreasing level of freshness of the fish that will be consumed. There are still many people who do not know about the classification of fresh and unfresh fish, so we need a system that can classify which fish are fresh and which are not. Previous studies have succeeded in classifying tuna using a convolutional neural network (CNN) algorithm with an accuracy of 90%. In the preprocessing stage of this research, segmentation is carried out, which aims to separate the object to be studied and the background image, then feature extraction is carried out using a color moment, which aims to get the value of the object to be studied. This research was conducted to increase the accuracy value in the freshness classification of tuna and also to add some fish for freshness detection, such as mackerel and milkfish, using the MobilenetV2. The results were able to produce accuracy of 97%, 94%, and 93% for each fish. The freshness detection method in this study has been implemented in the Fishku mobile-based application.