Abstract— Chicken is one of the staple foods that is widely
enjoyed by all. To obtain the benefits of chicken meat, the level of
freshness becomes one of the main keys. In general, the level of
freshness of chicken meat is divided into two classes, namely fresh
and non-fresh. The difference in the level of freshness can be seen
from the color changes of each class. Spoiled chicken (chicken died
yesterday) is one type of meat in the non-fresh group. The
widespread sale of spoiled chicken meat among the public raises
doubts about choosing chicken that is suitable and unsuitable for
consumption. Therefore, chicken meat freshness classification is
needed to facilitate the selection of chicken meat based on color
characteristics. The use of Naive Bayes Classifier algorithm in
categorizing fresh and non-fresh classes is done by calculating the
probability value of each image channel input. This research was
conducted to compare the Naive Bayes, decision tree, and K-NN
algorithms in classifying chicken meat based on color
characteristics. The results of the study showed that the Naive

Bayes classifier algorithm was superior to the decision tree and K-
NN algorithms with an accuracy rate of 75%, precision of 79%,

and recall of 65%. It is known that 27 images were predicted
correctly and 9 images were predicted incorrectly out of a total 36
data. The use of a histogram in this study aims to differentiate
chicken meat images from non-meat during the testing process of
the model using the Naive Bayes classifier algorithm.