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All Journal Teknika: Jurnal Sains dan Teknologi Jurnal Pendidikan Biologi Indonesia ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Sistemasi: Jurnal Sistem Informasi Jurnal Teknika Biosaintifika: Journal of Biology & Biology Education Jurnal Listrik, Instrumentasi, dan Elektronika Terapan Makara Journal of Technology Enigma in Education
Irma Saraswati
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Other

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Classification of Beef, Goat, and Pork using GLCM Texture-Based Backpropagation Neural Network Saraswati, Irma; Fahrizal, Rian; Fauzan, Anugrah Nuur; Yudono, Muchtar Ali Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4715

Abstract

Identifying different types of meat is crucial for preventing fraudulent activities and improving food safety. This research aims to create a classification system for various meat types (beef, goat, and pork) using the Gray Level Co-occurrence Matrix (GLCM) for extracting texture features, followed by classification through a Backpropagation Neural Network (BPNN). The methodology utilizes 60 images of beef, goat, and pork, achieving a remarkable accuracy of 100% in the training phase, which highlights the model's capability to effectively recognize patterns. However, when tested with new data, the system exhibits a sensitivity of 90% and a specificity of 95%, with some misclassifications occurring between goat and beef due to their similar textures. The findings of this study suggest that GLCM is an effective tool for deriving relevant statistical parameters necessary for classification. This research makes a significant contribution to developing a meat identification system that safeguards consumers and promotes awareness of food safety issues. The results are anticipated to provide a solid foundation for advancing meat type recognition and practical applications in the marketplace, ultimately boosting public trust in the meat products they purchase.
Co-Authors Achmad Abdur Rozaq Ahendyarti, Ceri Alimuddin, A. Elhadj Dogheche Fachry Abda El Rahman Fauzan, Anugrah Nuur Hamzah Hamzah Heri Kusharyanto Hermawan, Cecep Maman Imamul Muttakin Kevin Dilly Junior Luh Putu Ratna Sundari Lukman Nul Hakim Lukman Nul Hakim Mega Tjaraka, Mega Muchtar Ali Setyo Yudono Nji Raden Poespawati OKTA ROSFIANI Prasart Nuangchalerm, Prasart Rahmah, Aqila Azizah Retno Wigajatri Purnamaningsih Ri Munarto Ri Munarto, Ri Rian Fahrizal Rida Oktorida Khastini RIYANDIKA, RIZKY Rocky Alfanz, Rocky Rohmadi Rohmadi Syah Alam Teguh Firmansyah Toto Supriyanto Wahyuni, Indria Wahyuni Wildan, Muh.