CLASSIFICATION OF BEEF FRESHNESS LEVELS BASED ON IMAGE USING CONVOLUTIONAL NEURAL NETWORK

M Subhan Anshori, Fatra Nonggala Putra, Lestariningsih Lestariningsih

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 tends to be subjective, 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 employed features 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 91.67%, with precision, recall, and f1-score values of 93.33%, 91.67%, and 91.82%, respectively. These findings suggest the developed system has the potential to be used as an automatic tool for objective, fast, and accurate beef quality assessment.

References


P. D. dan S. I. Pertanian, “Analisis Kinerja Perdagangan Daging Sapi,” vol. 14, no. 1G, pp. 1–67, 2024.

M. A. Nasrudin, PENGARUH FASILITAS, KUALITAS DAN HARGA TERHADAP MINAT BELI ULANG (Studi Kasus Pada Konsumen Daging Sapi Rumah Potong Hewan Semarang). 2022.

P. B. Asmoro and A. Solichin, “Penerapan Metode Convolutional Neural Network Untuk Klasifkasi Kualitas Daging Sapi Pada Aplikasi Berbasis Android,” Fakt. Exacta, vol. 16, no. 4, pp. 286–298, 2024, doi: 10.30998/faktorexacta.v16i4.19564.

A. Rizky pratama, “Klasifikasi Daging Sapi Berdasarkan Ciri Warna Dengan Metode Otsu dan K-Nearest Neighbor,” Techno Xplore J. Ilmu Komput. dan Teknol. Inf., vol. 6, no. 1, pp. 9–18, 2021, doi: 10.36805/technoxplore.v6i1.1239.

S. Bagas Valentino, “Klasifikasi Kualitas Daging Marmer Berdasarkan Citra Warna Daging Menggunakan Metode Convolutional Neural Network,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 125–129, 2023, doi: 10.36040/jati.v7i1.6128.

T. Ekamila, F. Rahayu, A. Zuchriadi, and A. Octa Indarso, “Penerapan Deep Learning Untuk Klasifikasi Kesegaran Daging Sapi Berbasis Mobile Apps,” Edu Komputika J., vol. 10, no. 1, pp. 10–16, 2023, doi: 10.15294/edukomputika.v10i1.68478.

A. Antoni, T. Rohana, and A. R. Pratama, “Implementasi Algoritma Convolutional Neural Network Untuk Klasifikasi Citra Kemasan Kardus Defect dan No Defect,” Build. Informatics, Technol. Sci., vol. 4, no. 4, pp. 1941–1950, 2023, doi: 10.47065/bits.v4i4.3270.

R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” J. Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.

L. H. Ganda and H. Bunyamin, “Penggunaan Augmentasi Data pada Klasifikasi Jenis Kanker Payudara dengan Model Resnet-34,” J. Strateg., vol. 3, no. 1, pp. 187–193, 2021.

R. W. Wiratama, “Implementasi dan Klasifikasi Jenis-Jenis Batik Menggunakan Algoritma Convolutional Neural Network (CNN) dengan Model Arsitektur Resnet,” Politeknik Negeri Malang, 2023.

M. F. Naufal and S. F. Kusuma, “Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 6, pp. 1293–1300, 2021, doi: 10.25126/jtiik.2021865201.

R. Shinta, “Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN Dengan Arsitektur VGG-19,” Universitas Islam Negeri Sultan Syarif Kasim Riau, 2023.

M. Toyib, T. D. K. Pratama, and I. Aqil, “Penerapan Algoritma CNN untuk Mendeteksi Tulisan Tangan Angka Romawi dengan Augmentasi Data,” J. Mat. Ilmu Pengetah. Alam, Kebumian dan Angkasa, vol. 2, no. 3, pp. 108–120, 2024.

G. P. H. P. Gusti, E. Haerani, F. Syafria, F. Yanto, and S. K. Gusti, “Implementasi Algoritma Convolutional Neural Network (Resnet-50) untuk Klasifikasi Kanker Kulit Benign dan Malignant,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 3, pp. 984–992, 2024, doi: 10.57152/malcom.v4i3.1398.




DOI: https://doi.org/10.33387/jiko.v8i1.9519

Refbacks

  • There are currently no refbacks.