Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix
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Published:2024-06-12
Issue:3
Volume:7
Page:49
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ISSN:2571-5577
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Container-title:Applied System Innovation
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language:en
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Short-container-title:ASI
Author:
Kiswanto Kiswanto12ORCID, Hadiyanto Hadiyanto3ORCID, Sediyono Eko4
Affiliation:
1. Information Systems Doctoral Program, Graduate School, Diponegoro University, Semarang 50241, Indonesia 2. Department of Information Systems, Atma Luhur Institute of Science and Business, Pangkalpinang 33172, Indonesia 3. Department of Chemical Engineering, Faculty of Chemical Engineering, Diponegoro University, Semarang 50275, Indonesia 4. Department of Computer Science, Faculty of Information and Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia
Abstract
This research aims to examine the use of image processing and texture analysis to find a more reliable and efficient solution for identifying and classifying types of meat, based on their texture. The method used involves the use of feature extraction, Haar wavelet, and gray-level co-occurrence matrix (GLCM) (with angles of 0°, 45°, 90°, and 135°), supported by contrast, correlation, energy, homogeneity, and entropy matrices. The test results showed that the k-NN algorithm excelled at identifying the texture of fresh (99%), frozen (99%), and rotten (96%) meat, with high accuracy. The GLCM provided good results, especially on texture images of fresh (183.21) and rotten meat (115.79). The Haar wavelet results were lower than those of the k-NN algorithm and GLCM, but this method was still useful for identifying texture images of fresh meat (89.96). This research development is expected to significantly increase accuracy and efficiency in identifying and classifying types of meat based on texture in the future, reducing human error and aiding in prompt evaluation.
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