Physical Quality Identification of Fresh and Packaged Cow's Milk With Computer Vision And Naïve Bayes Continuous

Maulidyah, Nuril Feby (2021) Physical Quality Identification of Fresh and Packaged Cow's Milk With Computer Vision And Naïve Bayes Continuous. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Cow's milk is a nutritious drink that plays an important role in meeting the nutritional needs of the community, but along with increasing knowledge, people are now not only choosing drinks that are highly nutritious but also have an effect on improving health. Ordinary people use a simple way to identify cow's milk, namely by smelling the smell of cow's milk, but this method is still not completely accurate and difficult to identify. The large number of people's need for cow's milk makes some producers ignore the quality of cow's milk which causes reduced nutrition. Factors that affect the taste of milk include odor absorption, animal feed ingredients, livestock conditions, cage conditions, livestock rearing, cow genetics, type of cow, physiological conditions and weather and climate in the area where the farm is made. sun and the addition of foreign materials (Anindita and Soyi, 2017). Several researches in the field of information technology have been widely applied in various fields such as agriculture and animal husbandry. Various algorithms have also been implemented and tested to prove their success rate. As was done in research (Reinhard Immanuel Abraham et al., 2018) to clarify the quality of milk freshness by taking samples of images of cow's milk based on different water mixtures and comparing them to find similarities with the Content Based Image Retrieval (CBIR) and Decision Tree Classification methods. Not paying attention to texture and color. Another way that can be done is based on the visual characteristics of color and texture. Therefore, a new way is needed to identify the quality of cow's milk that is good and suitable for consumption by involving information technology. Based on the description of the research above, it can be concluded that the development of computer vision identification of pure cow's milk and packaging through visual parameters will be a better solution. Therefore, to complete this research, this research was carried out using the Naive Bayes Continous merger with the title "Identification of Pure Cow's Milk and Packaging with Computer Vision and Naive Bayes Continuous". In this study, the classification used is Nave Bayes Continuous. Making a computer vision application to identify pure cow's milk and packaging based on visual characteristics. The visual feature parameters used are RGB color and Glcm texture. Key words :Milk, Color and Texture, Quality, Information Technology

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