Decapterus Macarellus Rott and Fresh Model

Citation Author(s):
Adri Gabriel
Universitas Katolik Widya Mandira
Submitted by:
Adri Gabriel Sooai
Last updated:
Wed, 02/22/2023 - 22:47
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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.


Dataset contains numeric value of image embedding results 
a total 2048 coloumn and 167 row
It can use directly on classification process using various algorithms
Divided by two classes namely rott and fresh fish of Decapterus Macarellus

Funding Agency: 
LPPM Universitas Katolik Widya Mandira