Performance Analysis of Chicken Freshness classification using Naïve Bayes, Decision Tree, and k-NN

Regina Vannya, Arief Hermawan
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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.
基于Naïve贝叶斯、决策树和k-NN的鸡肉新鲜度分类性能分析
鸡肉是人们普遍喜爱的主食之一。为了获得鸡肉的好处,新鲜度的高低成为主要的关键之一。一般来说,鸡肉的新鲜度水平分为两类,即新鲜和不新鲜。从每一类的颜色变化可以看出新鲜度的高低。变质鸡肉(昨天死亡的鸡肉)是非新鲜肉类中的一种。变质鸡肉在公众中广泛销售,这引起了人们对选择适合和不适合食用的鸡肉的怀疑。因此,需要对鸡肉的新鲜度进行分类,以便根据鸡肉的颜色特征进行选择。朴素贝叶斯分类器算法通过计算每个图像通道输入的概率值来对新鲜类和非新鲜类进行分类。本研究比较了基于颜色特征对鸡肉进行分类的朴素贝叶斯、决策树和K-NN算法。研究结果表明,朴素贝叶斯分类器算法优于决策树和K-NN算法,准确率为75%,精密度为79%,召回率为65%。已知在总共36个数据中,27个图像预测正确,9个图像预测错误。本研究使用直方图的目的是利用朴素贝叶斯分类器算法在模型测试过程中将鸡肉图像与非肉类图像区分开来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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