Charmelia Yunizar Jerandu, Alicia Herlin Mondolang, Corazon Olivia Sianturi, Shine Crossifixio Sianturi, Jenita Felixia Ximenes, Agustinus Bimo Gumelar, Alfry Aristo Jansen Sinlae, Patrisius Batarius, Umi Laili Yuhana, Adri Gabriel Sooai
Pelagic fish such as mackerel are a source of protein in Indonesia. However, there is no decapterus macarellus as an open dataset for image processing using various classification algorithms. Where its use includes the sensor-assisted sorting process in checking fresh fish and rotten fish. For this reason, this study aims to provide a classification model for pelagic fish and their primary datasets which is available for free on the IEEE data port. Artificial intelligence is used in the process of guided classification with the help of ground truth for the preparation of fish classes. The dataset used is a primary dataset consisting of fish images, arranged in two classes, namely 71 fresh fish and 96 rotten fish. The methods used are k-NN classifiers, naive bayes and ridge regression. Experiments were drawn up to classify rotten fish and fresh fish. The preprocessing was assisted by InceptionV3 as a feature extraction method. Furthermore, the image data is trained in a ratio of 60:40 for training and testing data. Validation was performed using 2-fold cross validation with the results obtained being 99.4%, 94% and 100% accuracy using the classifiers namely k-NN, naive bayes and ridge regression, respectively. © 2022 IEEE.
Universitas Katolik Widya Mandira, Dept. of Computer Science, Kupang, Indonesia; Universitas Negeri Malang, Dept. of Mathematics, Malang, Indonesia; Universitas Sanata Dharma, Dept. of Mathematics, Yogyakarta, Indonesia; Universitas Sanata Dharma, Dept. of Computer Science, Yogyakarta, Indonesia; Institut Teknologi Sepuluh Nopember, Dept. of Electrical Engineering, Surabaya, Indonesia; Institut Teknologi Sepuluh Nopember, Dept. of Informatics Engineering, Surabaya, Indonesia
