Tuna fish classification using decision tree algorithm and image processing method

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%.