CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

Chairul  Imam; Eka Wahyu Hidayat; Neng Ika Kurniati

doi:10.20884/1.jutif.2021.2.1.37

Chairul Imam Informatika, Fakultas Teknik, Universitas Siliwangi, Indonesia
Eka Wahyu Hidayat Informatika, Fakultas Teknik, Universitas Siliwangi, Indonesia https://orcid.org/0000-0002-2857-5311
Neng Ika Kurniati Informatika, Fakultas Teknik, Universitas Siliwangi, Indonesia https://orcid.org/0000-0001-6635-5275

DOI: https://doi.org/10.20884/1.jutif.2021.2.1.37

Keywords: Artificial Neural Network, Gray Level Co-occurrence Matrix, Image Processing, Meat Consumption, Personal Extreme Programming

Abstract

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.

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