TY - GEN
T1 - Tuna fish classification using decision tree algorithm and image processing method
AU - Khotimah, Wijayanti Nurul
AU - Arifin, Agus Zainal
AU - Yuniarti, Anny
AU - Wijaya, Arya Yudhi
AU - Navastara, Dini Adni
AU - Kalbuadi, Muhammad Akbar
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/8
Y1 - 2016/1/8
N2 - Fishery has contributed a lot to Indonesian economy development such as domestic industries, micro industries, and export industries. Tuna is one of the fishery product. To produce tuna fish product, an industry must separate tuna based on their type. Nowadays, the separation process is still done manually. As consequence, the process was slow and the error rate was high. This research proposed automatic tuna fish classification using decision tree algorithm and image processing method. Eight features, texture feature and shape feature, were extracted from tuna fish image using image processing method. The texture features are contrast, correlation, energy, homogeneity, inverse difference moment, and entropy. While the shape features are the circular rate of tuna's head and the ratio of head area and circular area. These features are then used to create classification model using decision tree. Sixty tuna's image from tree types tuna, Bigeye, Yellowfin, and Skipjack, were used in experiment. From experiment, it shows that the average accuracy of the classification is 88%.
AB - Fishery has contributed a lot to Indonesian economy development such as domestic industries, micro industries, and export industries. Tuna is one of the fishery product. To produce tuna fish product, an industry must separate tuna based on their type. Nowadays, the separation process is still done manually. As consequence, the process was slow and the error rate was high. This research proposed automatic tuna fish classification using decision tree algorithm and image processing method. Eight features, texture feature and shape feature, were extracted from tuna fish image using image processing method. The texture features are contrast, correlation, energy, homogeneity, inverse difference moment, and entropy. While the shape features are the circular rate of tuna's head and the ratio of head area and circular area. These features are then used to create classification model using decision tree. Sixty tuna's image from tree types tuna, Bigeye, Yellowfin, and Skipjack, were used in experiment. From experiment, it shows that the average accuracy of the classification is 88%.
KW - decision tree
KW - shape feature
KW - texture feature
KW - tuna fish
UR - http://www.scopus.com/inward/record.url?scp=84963706345&partnerID=8YFLogxK
U2 - 10.1109/IC3INA.2015.7377759
DO - 10.1109/IC3INA.2015.7377759
M3 - Conference contribution
AN - SCOPUS:84963706345
T3 - Proceeding - 2015 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends in the Era of Internet of Things, IC3INA 2015
SP - 126
EP - 131
BT - Proceeding - 2015 International Conference on Computer, Control, Informatics and Its Applications
A2 - Latifah, Arnida L.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Computer, Control, Informatics and Its Applications, IC3INA 2015
Y2 - 5 October 2015 through 7 October 2015
ER -