Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting

  • Kade Bramasta Vikana Putra Udayana University
  • I Putu Agung Bayupati Universitas Udayana
  • Dewa Made Sri Arsa Universitas Udayana
Keywords: Deep learning, Ensemble Learning, Image Recognition, Transfer Learning


Meat is a staple food for some Indonesian people, apart from the taste, meat also contains vitamins and minerals that are good for the human body, however, not all meat can be consumed by the Indonesian people. the texture and color of beef, pork and mutton have similarities and tend to be similar, therefore a system is needed to recognize the three types of meat. In this study, the authors use various types of Deep Learning architecture such as Resnet-50, VGG-16, VGG-19 and Densenet-121 with Hard Voting to improve the performance of Deep Learning in recognizing the three types of meat. The results show that Resnet-50 with Hard Voting can outperform Deep Learning Resnet-50, VGG-16, VGG-19 and Densenet-121- with f1 score 98.88%, precision 98.89% and recall 98.88%. in image classification of pork, beef and mutton.


Download data is not yet available.


Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.

P. Fischer, A. Dosovitskiy, and T. Brox, “Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT.” 2015.

R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, “Self-Taught Learning: Transfer Learning from Unlabeled Data,” in Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 759–766, doi: 10.1145/1273496.1273592.

S. Xia, M. Shao, J. Luo, and Y. Fu, “Understanding kin relationships in a photo,” IEEE Trans. Multimed., vol. 14, no. 4 PART1, pp. 1046–1056, 2012, doi: 10.1109/TMM.2012.2187436.

W. Liu et al., “SSD: Single Shot MultiBox Detector BT - Computer Vision – ECCV 2016,” 2016, pp. 21–37.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.

J. Redmon and A. Farhadi, “YOLO9000: Better, Faster, Stronger,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6517–6525, doi: 10.1109/CVPR.2017.690.

Y. Qian and P. C. Woodland, “Very Deep Convolutional Neural Networks for Robust Speech Recognition.” 2016.

R. A. Asmara et al., “Classification of pork and beef meat images using extraction of color and texture feature by Grey Level Co-Occurrence Matrix method,” IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, 2018, doi: 10.1088/1757-899X/434/1/012072.

N. Neneng, K. Adi, and R. Isnanto, “Support Vector Machine Untuk Klasifikasi Citra Jenis Daging Berdasarkan Tekstur Menggunakan Ekstraksi Ciri Gray Level Co-Occurrence Matrices (GLCM),” J. Sist. Inf. Bisnis, vol. 6, no. 1, p. 1, 2016, doi: 10.21456/vol6iss1pp1-10.

P. Hridayami, I. K. G. D. Putra, and K. S. Wibawa, “Fish species recognition using VGG16 deep convolutional neural network,” J. Comput. Sci. Eng., vol. 13, no. 3, pp. 124–130, 2019, doi: 10.5626/JCSE.2019.13.3.124.

I. M. A. Agastya and A. Setyanto, “Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation,” in 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018, pp. 27–31, doi: 10.1109/ICITISEE.2018.8720990.

R. Rokhana et al., “Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 1, p. 59, 2019, doi: 10.22146/jnteti.v8i1.491.

H. Kaur and G. Kaur, “Voting based classification method for diabetes prediction,” Int. J. Recent Technol. Eng., vol. 8, no. 2 Special Issue 6, pp. 913–918, 2019, doi: 10.35940/ijrte.B1172.0782S619.

A. S. Assiri, S. Nazir, and S. A. Velastin, “Breast Tumor Classification Using an Ensemble Machine Learning Method,” J. Imaging, vol. 6, no. 6, 2020, doi: 10.3390/JIMAGING6060039.

T. G. Dietterich, “Ensemble methods in machine learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1857 LNCS, pp. 1–15, 2000, doi: 10.1007/3-540-45014-9_1.

B. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Cnn实际训练的,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2012.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5455–5516, 2020, doi: 10.1007/s10462-020-09825-6.

R. Systems, “Memory-Based Weighted-Majority Prediction for Recommender Systems.”

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 770–778, 2016, doi: 10.1109/CVPR.2016.90.

How to Cite
Kade Bramasta Vikana Putra, I Putu Agung Bayupati, & Dewa Made Sri Arsa. (2021). Klasifikasi Citra Daging Menggunakan Deep Learning dengan Optimisasi Hard Voting. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 656 - 662. https://doi.org/10.29207/resti.v5i4.3247
Artikel Rekayasa Sistem Informasi