TY - GEN
T1 - Image Classification of Decapterus Macarellus Using Ridge Regression
AU - Jerandu, Charmelia Yunizar
AU - Mondolang, Alicia Herlin
AU - Sianturi, Corazon Olivia
AU - Sianturi, Shine Crossifixio
AU - Ximenes, Jenita Felixia
AU - Gumelar, Agustinus Bimo
AU - Sinlae, Alfry Aristo Jansen
AU - Batarius, Patrisius
AU - Yuhana, Umi Laili
AU - Sooai, Adri Gabriel
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - image classification
KW - k-NN
KW - naïve bayes
KW - pelagic fish
KW - ridge regression
UR - https://www.scopus.com/pages/publications/85148068880
U2 - 10.1109/ICET56879.2022.9990820
DO - 10.1109/ICET56879.2022.9990820
M3 - Conference contribution
AN - SCOPUS:85148068880
T3 - Proceedings - International Conference on Education and Technology, ICET
SP - 81
EP - 86
BT - Proceedings - 2022 8th International Conference on Education and Technology
PB - Institute of Electrical and Electronics Engineers
T2 - 8th International Conference on Education and Technology, ICET 2022
Y2 - 15 October 2022 through 16 October 2022
ER -