Colors and Texture Feature Extraction Using Learning Vector Quantization 3 Algorithm in Optimization of Beef Identification
Abstract
The Assessment Institute for Foods, Drugs, and Cosmetics of the Indonesian Ulama Council (LPPOM MUI) is responsible for conducting research, evaluations, and determining the halal status of products in accordance with Islamic teachings. In Indonesia, where religious diversity is prevalent, the halal certification process is crucial, particularly due to differences in the halal status of certain foods, such as beef and pork, across religions. One of the challenges in this process lies in ensuring a rapid and accurate determination of various types of meat, including beef, pork, goat, and buffalo, which currently tends to be time-consuming within the LPPOM MUI Halal Center. To address this issue, there is a need for a technological solution that can quickly and accurately identify different types of meat, thereby reducing consumer uncertainty when selecting halal products. This study aims to develop an Android-based application utilizing the Learning Vector Quantization 3 (LVQ3) method to facilitate the classification of meat types by analyzing patterns specific to beef, pork, goat, and buffalo. This system is expected to expedite the halal verification process, thereby supporting more efficient and accurate decision-making in the halal certification sector.
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