MEATCHECK: DETEKSI KUALITAS DAGING SAPI BERBASIS MOBILE DEEP LEARNING
Institut Sains dan Bisnis Atma Luhur
Indonesia
Universitas Budi Luhur
Indonesia
Article Submitted: 23 April 2025
Article Published: 14 June 2025
Abstract
Beef is an important foodstuff that affects consumer satisfaction and market value in the meat industry. The purpose of this research is to develop a model to classify beef quality using the transfer learning method. The data collection method is carried out through taking pictures of beef, which are then labeled based on their quality. Classification uses a transfer learning architecture that can improve the performance of the machine learning model generated for the classification of fresh and rotten meat. The model was tested by looking at accuracy, precision, recall, and f1-score. The results showed an accuracy of 61%, precision of 60.78%, recall of 61%, and an f1-score of 60.89%, which was achieved with a learning rate of 0.1, 10 epoch, and batch size of 8. Conclusion, the model developed with the transfer learning algorithm MobileNetV2 was able to classify the quality of beef with a good level of accuracy. The prototype of the developed system can provide real-time predictions, help consumers choose quality meat, and increase market value. Next, it is recommended to increase accuracy and develop models by increasing the size of the dataset and exploring other, more complex architectures.
Keywords
References
Dewan Teknologi Informasi dan Komunikasi Nasional, “Transformasi Digital untuk Pelayanan Publik,” Buletin Wantiknas, vol. 1, 2020.
A. Heatubun and M. J. Matatula, “Produksi daging sapi di indonesia dan skenario peningkatan: sebuah analisis dampak untuk pengambilan kebijakan,” Agrinimal Jurnal Ilmu Ternak dan Tanaman, vol. 11, no. 2, pp. 92–100, Oct. 2023, doi: 10.30598/ajitt.2023.11.2.92-100.
M. Lorens and N. Rachmat, “Identifikasi Kesegaran Daging Sapi Berdasarkan Jarak Potret Menggunakan Metode Jaringan,” 2ND MDP STUDENT CONFERENCE (MSC) 2023, 2023.
S. Kukreja, R. Sahoo, D. Jain, and V. Arora, “Image Processing,” in Computational Science and Its Applications, 2024. doi: 10.1201/9781003347484-5.
R. G. Wardhana, G. Wang, and F. Sibuea, “PENERAPAN MACHINE LEARNING DALAM PREDIKSI TINGKAT KASUS PENYAKIT DI INDONESIA,” Journal of Information System Management (JOISM), vol. 5, no. 1, 2023, doi: 10.24076/joism.2023v5i1.1136.
N. Nufus et al., “Sistem Pendeteksi Pejalan Kaki Di Lingkungan Terbatas Berbasis SSD MobileNet V2 Dengan Menggunakan Gambar 360° Ternormalisasi,” Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO), vol. 3, pp. 123–134, Dec. 2021, doi: 10.54706/senastindo.v3.2021.123.
H. H. Tseng, M. Der Yang, R. Saminathan, Y. C. Hsu, C. Y. Yang, and D. H. Wu, “Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning,” Remote Sens (Basel), vol. 14, no. 12, 2022, doi: 10.3390/rs14122837.
A. Imanuel and D. H. Setiabudi, “Penerapan Convolutional Neural Network dengan Pre-Trained Model Xception untuk Meningkatkan Akurasi dalam Mengidentifikasi Jenis Ras Kucing,” Jurnal INFRA, vol. 10, no. 2, 2022.
Putra Ronald, Rizal Achmad, and Cahyadi Willy, “Rancang bangun sistem pendeteksi kesegaran daging berdasarkan sensor bau dan warna design of meat freshness detection system based on odor and color,” Feb. 2021.
K. Śmiecińska and T. Daszkiewicz, “Lipid oxidation and color changes in beef stored under different modified atmospheres,” J Food Process Preserv, vol. 45, no. 3, 2021, doi: 10.1111/jfpp.15263.
Al-Jabbar Habib, H. Fitriyah, and R. Maulana, “Sistem Klasifikasi Kesegaran Daging Sapi Berdasarkan Citra Menggunakan Metode Naïve Bayes Berbasis Raspberry Pi,” 2021. [Online]. Available: http://j-ptiik.ub.ac.id
Pratama Adi, Juwita Ayu, and Mudzakir Tohirin, “Klasifikasi Daging Sapi Berdasarkan Ciri Warna Dengan Metode Otsu dan K-Nearest Neighbor,” Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 6, no. 1, 2021, doi: 10.36805/technoxplore.v6i1.1239.
J. Huang et al., “BM-Net: CNN-Based MobileNet-V3 and Bilinear Structure for Breast Cancer Detection in Whole Slide Images,” Bioengineering, vol. 9, no. 6, Jun. 2022, doi: 10.3390/bioengineering9060261.
S. Aras, A. Setyanto, and Rismayani, “Deep Learning Untuk Klasifikasi Motif Batik Papua Menggunakan EfficientNet dan Trasnfer Learning,” Insect (Informatics and Security): Jurnal Teknik Informatika, vol. 8, no. 1, 2022, doi: 10.33506/insect.v8i1.1865.
J. A. Firdaus, E. Setiawan, and D. Syauqy, “Sistem Pengukur Kesegaran Daging Sapi menggunakan Metode K-Nearest Neighbor (K-NN) dengan Fitur Penambahan Data Latih berbasis EEPROM,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 4, no. 5, 2020.
A. H. Artya, J. Jasril, S. Sanjaya, F. Syafria, and E. Budianita, “Implementasi Convolutional Neural Network Untuk Klasifikasi Daging Menggunakan Fitur Ekstraksi Tekstur dan Arsitektur AlexNet,” JURIKOM (Jurnal Riset Komputer), vol. 9, no. 3, 2022, doi: 10.30865/jurikom.v9i3.4177.
M. F. Martias, J. Jasril, S. Sanjaya, L. Handayani, and F. Yanto, “Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 4, 2023, doi: 10.30865/json.v4i4.6195.
T. Yulianti, M. Telaumbanua, H. D. Septama, H. Fitriawan, and A. Yudamson, “PENGARUH SELEKSI FITUR CITRA TERHADAP KLASIFIKASI TINGKAT KESEGARAN DAGING SAPI LOKAL,” Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering), vol. 10, no. 1, 2021, doi: 10.23960/jtep-l.v10i1.85-95.
L. Fimawahib and E. Rouza, “Penerapan K-Means Clustering pada Penentuan Jenis Pembelajaran di Universitas Pasir Pengaraian,” INOVTEK Polbeng - Seri Informatika, vol. 6, no. 2, 2021, doi: 10.35314/isi.v6i2.2096.
F. S. Wahyuni, H. Z. Zah’ro, A. P. Sasmito, and M. Z. Musyafa, “Penerapan Metode K-Nearest Neighbor Untuk Klasifikasi Tutupan Lahan,” Prosiding SENIATI, vol. 7, no. 1, 2023, doi: 10.36040/seniati.v7i1.7936.
