Identification of beef and pork using gray level co-occurrence matrix and probabilistic neural network
Abstract
Objective: Identify images of beef and pork using texture feature extraction Gray Level Co-Occurrence Matrix and Probabilistic Neural Network classification algorithm.
Design/method/approach: Apply texture feature extraction to Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier to perform classification.
Results: From the test results with k-fold cross-validation and confusion matrix, it shows that feature extraction of Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier get an average accuracy of 87%, precision 83%, and recall 90%.
Authenticity/state of the art: In this study, several scenarios were tested, namely the effect of using resize, brightness, and rotate values. Using a resize value of 256 x 256 pixels from the test results got the best accuracy of 87%. The brightness test of 20% affects the accuracy rate of 86% on increasing brightness and 90% on reducing brightness. In contrast, the test on the rotated image does not affect the accuracy results. The average accuracy obtained is 87%. The data in this study were obtained by collecting primary data on images of beef and pork in several markets in Denpasar.
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DOI: https://doi.org/10.31315/cip.v1i1.6126
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