Tuna Meat Grade Classification Using Color Histogram And Grey Level Co-occurrence Matrix

Abstrak

Abstract As one out of the biggest country that produce Tuna in the world, Indonesia supply more than 16% of total production from 7.7 million metric tons of tuna. Exported tuna must have a good quality, to know the quality of tuna it must be tested by trained panelist that required approved certification. Tuna quality can be tested by using Organoleptic that checked color, texture, smell, and appearance. In this thesis, the feature of color and texture of the tuna will be extracted using Color Histogram and Grey Level coOccurrence Matrix. The result of the classification by using Support Vector Machine as the classifier is 81.6% with 36 data contain 13 grade A data, 11 grade B data, and 12 grade D data. Keywords: Tuna, RGB, HSV, Grey Level co-Occurrence Matrix (GLCM), Support Vector Machine (SVM)