<![CDATA[Meat is one of the main food ingredients consumed by humans because it contains a lot of high protein, so it can increase intelligence and increase the stamina that humans need to carry out activities of daily life. Due to the very high level of meat consumption, these meats are often found in the market. The price of pork is cheaper than beef. The price difference between the two meats has led to the emergence of fraudulent practices in the beef trade. To solve this problem, an application to distinguish beef and pork can be designed to help socialize to the public about how to distinguish beef and pork. In this study, color characteristics will be used to distinguish pork and beef, because from previous studies the color characteristics have a higher accuracy. The method that can be used to extract features from meat images is the color moment method. Color feature extraction consists of Mean, Standard Deviation, and Skewness features. Meanwhile, to carry out the process of detecting the type of meat, the Local Binary Pattern Histogram (LBPH) method will be used. LBPH is a technique of the Local Binary Pattern (LBP) method to change the performance of object recognition results. The result of this research is an application that can be used to distinguish beef and pork. The process of recognizing the type of meat using the Color Moment and LBPH methods has a high success rate so that the Color Moment and LBPH methods can be applied to detect the type of meat, with a success rate of 99.33%.]]>
