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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Briliant: Jurnal Riset dan Konseptual Journal of Development Research Jurnal Sains dan Informatika at-tamkin: Jurnal Pengabdian kepada Masyarakat Jiko (Jurnal Informatika dan komputer) KOMPUTIKA - Jurnal Sistem Komputer Jurnal Ilmiah Fillia Cendekia Jurnal Linguistik Komputasional ILKOMNIKA: Journal of Computer Science and Applied Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Wahana Peternakan Jurnal Pengabdian dan Pemberdayaan Nusantara (JPPNu) Journal of Science Nusantara Journal Automation Computer Information System (JACIS) Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Computer Engineering, Electronics and Information Technology Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
FATRA NONGGALA PUTRA
Universitas Nahdlatul Ulama Blitar
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Veterinary Other

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Journal : Jiko (Jurnal Informatika dan komputer)

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CLASSIFICATION OF BEEF FRESHNESS LEVELS BASED ON IMAGE USING CONVOLUTIONAL NEURAL NETWORK Anshori, M Subhan; Putra, Fatra Nonggala; Lestariningsih, Lestariningsih
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9519

Abstract

Beef is an essential food commodity with high economic value and a primary source of protein for society. The quality of beef affects consumer preferences, pricing, and market competitiveness. Quality assessment is generally conducted manually through visual inspection and smell, but this method is subjective and time-consuming and requires trained experts. This study aims to design and develop a beef quality classification system using a Convolutional Neural Network (CNN) model based on digital imagery. The dataset used consists of three beef quality categories: Grade 1 (fresh beef), Grade 2 (beef stored at room temperature for 7-14 hours), and Grade 3 (beef stored at room temperature for more than 14 hours). The dataset includes 180 images processed using cropping, resizing, and data augmentation techniques to enhance model variation and accuracy. The CNN architecture employs four convolutional layers with max pooling, followed by dropout and fully connected layers. The model was trained using the Adam optimizer, with a training-to-test data ratio of 80:20. Evaluation results demonstrated the model achieved an accuracy of 97.22%, with precision, recall, and f1-score values of 97.44%, 97.22%, and 97.22%, respectively. These findings suggest that the developed system has the potential to be used as an automatic tool for objective, fast, and accurate beef quality assessment.
CLASSIFICATION OF BEEF FRESHNESS LEVELS BASED ON IMAGE USING CONVOLUTIONAL NEURAL NETWORK Anshori, M Subhan; Putra, Fatra Nonggala; Lestariningsih, Lestariningsih
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 1 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i1.9519

Abstract

Beef is an essential food commodity with high economic value and a primary source of protein for society. The quality of beef affects consumer preferences, pricing, and market competitiveness. Quality assessment is generally conducted manually through visual inspection and smell, but this method is subjective and time-consuming and requires trained experts. This study aims to design and develop a beef quality classification system using a Convolutional Neural Network (CNN) model based on digital imagery. The dataset used consists of three beef quality categories: Grade 1 (fresh beef), Grade 2 (beef stored at room temperature for 7-14 hours), and Grade 3 (beef stored at room temperature for more than 14 hours). The dataset includes 180 images processed using cropping, resizing, and data augmentation techniques to enhance model variation and accuracy. The CNN architecture employs four convolutional layers with max pooling, followed by dropout and fully connected layers. The model was trained using the Adam optimizer, with a training-to-test data ratio of 80:20. Evaluation results demonstrated the model achieved an accuracy of 97.22%, with precision, recall, and f1-score values of 97.44%, 97.22%, and 97.22%, respectively. These findings suggest that the developed system has the potential to be used as an automatic tool for objective, fast, and accurate beef quality assessment.
Co-Authors Abd. Charis Fauzan Agus Zainal Arifin Ahmad Agung Saputra Ahmad Gufron Ahmad Gufron Ahmad Makhi Ahmad Saifudin, Ahmad Anshori, M Subhan Anshori, Subhan Ari Effendi Atallah, Mukhammad Tsafiq Ayu Eviana Bagus Waluyo Binti Aulatul Mufida Chastine Fatichah Dika, Johan Wayan Edoardo Jofan Rifano Egy Nadya Fadhila Nur Hanifah Faizin Choirul Umam Fasta Della Fitrotul Husnayaini Fauzi, Moh Mimbar Gleydis Harwida Hadi Suyono Hakim, Muhammad Helmi Harliana Harliana Herry Santosa Imam Machfud Imam Muchlisin, Mochammad Indradi Wijatmiko Ismu Rini Dwi Ari Iza Arfiana Fauziah Jofan Rifano, Edoardo Kharisma Sabbihatul Mustaghfaroh Lestari, Arum Ayu Lestari, Febbi Sena Lestari, Febbi Senna Lestari, Indah Dewi Lestariningsih Lestariningsih Lestariningsih Lestariningsih Lestariningsih Lestariningsih Lestariningsih Lestariningsih Lestariningsih Maharani, Ayu Pramesti Mashudi Mashudi Maulana, Eka Moch. Agus Choiron Muhammad Lutfi Ashari Naela Nur Choiriyah Nining Haryuni Peni Nohantiya Peni Nohantiya Pipit Lailatul Rahmania Prabowo, Tito Rahmat Yanu Sutrisno Ratika Sekar Ajeng Ananingtyas Riyadus Sholichin Rizqi Darma Rusdian Yusron Rizqi Darma Rusdiyan Yusron Sakti, Ragil Ellang Sekar Ajeng Ananingtyas, Ratika Tricahyo, Vion Age Tyas Alhim Mubarok Vion Age Tricahyo Vion Age Tricahyo Wasimin Wasimin Yeni Ratih Pratiwi Yuniar Alam Zainun Nasikin