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All Journal DISTANCE: Journal of Data Science, Technology, and Computer Science
Zein Fai'z Al Hidayah
STMIK Pelita Nusantara
Computer Science & IT

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Image Identification Of Formaline Chicken Meat Using Naive Bayes And GLCM (Gray Level Co-Occurrence Matrix) Methods Zein Fai'z Al Hidayah; Miftahul Jannah
DISTANCE: Journal of Data Science, Technology, and Computer Science Vol 1 No 1 (2021): December : 2021
Publisher : Putaka Timur Publisher

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Abstract

Rapid technological developments gave birth to many systems and applications that help complete human work. One of the applications of information technology is image processing theory. Image processing theory is the development of computer computing that uses image data in processing data. There are many algorithms used in image processing applications such as Naïve Bayes, GLCM, LVA, and others. The protein content contained in chicken meat is certainly very high and is needed by everyone. Because people really like chicken meat, the demand for chicken meat increases. thus making broiler meat traders flooded with orders from the public, of course, chicken traders will not be able to fulfill orders every day, therefore think of dirty ways from traders such as mixing chicken meat with formalin so that the appearance of chicken meat will change color, texture, and taste. The use of computer technology can be applied to assist in identifying formalin and non-formalin meats by using image processing technology. For the testing data above, it is assumed that the result of the test is formulated chicken because, from the probability value obtained from the Naive Bayes calculation process, the probability value of formulated chicken is higher than the probability value of fresh chicken. The provisions of the decision taken are If the Entropy Result is greater than or equal to 3.4 then the status is formalized, otherwise it is fresh. From the 20 test images used, the decision is that the chicken meat is formalin with an entropy value of 3.4215.
Co-Authors Miftahul Jannah