Analysis of the Naïve Bayes Method in Classifying Formalized Fish Images Using GLCM Feature Extraction

Ayu Pariyandani, Eka Pirdia Wanti, Muhathir Muhathir
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引用次数: 4

Abstract

Fish is one of the foods that are high in protein so that many Indonesians consume fish as protein intake for health. Fish can be found in any waters including Indonesian marine waters, so that some of the Indonesian people work as fishermen. This causes the number of fish catches to increase and the fishermen have to sell the fish quickly in at least one day because the fish will rot easily if not consumed immediately. This has led some traders to cheat by mixing formaldehyde with fish that are not sold out. This action is very detrimental to consumers, so they must be more vigilant in choosing or buying fish on the market. One way for consumers to recognize formaldehyde fish is a technology that can distinguish fresh fish or formalin fish based on the image of the fish, Naive Bayes and GLCM (Gray Level Co-Occurrence Matrix) by using this method the accuracy of this system can reach up to 70%.
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Naïve基于GLCM特征提取的贝叶斯方法对形式化鱼类图像进行分类分析
鱼是富含蛋白质的食物之一,因此许多印尼人食用鱼作为蛋白质摄入以保持健康。在包括印尼海域在内的任何水域都可以找到鱼,因此一些印尼人以渔民为生。这导致鱼的捕鱼量增加,渔民必须在至少一天内迅速出售鱼,因为鱼如果不立即食用很容易腐烂。这导致一些商人将甲醛掺入未售罄的鱼中进行欺骗。这种行为对消费者是非常有害的,所以他们在市场上选择或购买鱼时必须更加警惕。消费者识别甲醛鱼的一种方法是根据鱼的图像、朴素贝叶斯和灰度共生矩阵(GLCM)区分新鲜鱼和甲醛鱼的技术,使用该方法,该系统的准确率可达70%。
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