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A Beef and Pork Meat Image Identification System Using Hybrid Feature Extraction and Support Vector Machine

SemanTIK: Teknik Informasi · Vol. 12 No. 1 (2026) · pp. 181–188

DOI
10.55679/semantik.v12i1.241
ISSN
2502-8928, 2460-1446
Published
2026-06-25
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Beef and pork differentiation is an important issue in halal food authentication, particularly in Muslim-majority countries such as Indonesia, where meat adulteration can reduce consumer trust and violate religious dietary requirements. Since raw beef and pork often exhibit similar visual characteristics, manual inspection is frequently unreliable. This study proposes an automated classification system for beef (halal) and pork (non-halal) using Support Vector Machine (SVM) with hybrid features that combine statistical color descriptors (mean, variance, skewness, kurtosis) and texture descriptors (hausdorff dimensions). The results demonstrated that both the Linear and Polynomial kernels achieved the highest accuracy of 98,08%, while the RBF kernel achieved 90,38%. Further comparison with K-Nearest Neighbor and Naïve Bayes classifiers showed that SVM with a Linear kernel outperformed the other methods, with KNN and Naïve Bayes achieving 90,38% and 96,15% accuracy, respectively. These findings indicate that SVM with a Linear kernel is highly effective for differentiating beef and pork, offering a reliable tool to support halal authentication and consumer protection.

Keywords

Collaboration network of the authors

8 authors · 14 co-authorship links · 1 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Universitas Sembilanbelas November Kolaka
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nurfitria NingsiUniversitas Sembilanbelas November Kolaka1
SarimuddinAffiliation unknown1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Jayanti Yusmah SariInstitut Teknologi Sepuluh Nopember1
Mohammad ZarkasiInstitut Teknologi Sepuluh Nopember1
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Nanik SuciatiInstitut Teknologi Sepuluh Nopember1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://semantik.uho.ac.id/index.php/journal/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Beef and Pork Meat Image Identification System Using Hybrid Feature Extraction and Support Vector Machine</dc:title>
	<dc:creator>Muchtar, Mutmainnah</dc:creator>
	<dc:subject xml:lang="en-US">beef</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">halal</dc:subject>
	<dc:subject xml:lang="en-US">pork</dc:subject>
	<dc:subject xml:lang="en-US">Algoritma Support Vector Machine</dc:subject>
	<dc:description xml:lang="en-US">Beef and pork differentiation is an important issue in halal food authentication, particularly in Muslim-majority countries such as Indonesia, where meat adulteration can reduce consumer trust and violate religious dietary requirements. Since raw beef and pork often exhibit similar visual characteristics, manual inspection is frequently unreliable. This study proposes an automated classification system for beef (halal) and pork (non-halal) using Support Vector Machine (SVM) with hybrid features that combine statistical color descriptors (mean, variance, skewness, kurtosis) and texture descriptors (hausdorff dimensions). The results demonstrated that both the Linear and Polynomial kernels achieved the highest accuracy of 98,08%, while the RBF kernel achieved 90,38%. Further comparison with K-Nearest Neighbor and Naïve Bayes classifiers showed that SVM with a Linear kernel outperformed the other methods, with KNN and Naïve Bayes achieving 90,38% and 96,15% accuracy, respectively. These findings indicate that SVM with a Linear kernel is highly effective for differentiating beef and pork, offering a reliable tool to support halal authentication and consumer protection.</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:date>2026-06-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://semantik.uho.ac.id/index.php/journal/article/view/241</dc:identifier>
	<dc:identifier>10.55679/semantik.v12i1.241</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol. 12 No. 1 (2026): Vol. 12 No. 1 (2026): SemanTIK Teknik Informasi; 181-188</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://semantik.uho.ac.id/index.php/journal/article/view/241/72</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Mutmainnah Muchtar</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>