Klasifikasi Citra Game Batu Kertas Gunting Menggunakan Convolutional Neural Network

Mohammad Farid Naufal, Solichul Huda, Aryo Budilaksono, Wisnu Aria Yustisia, Astri Agustina Arius, Fania Alya Miranti, Farrel Arghya Tito Prayoga

Abstract


Permainan batu, gunting, dan kertas sangat populer di seluruh dunia. Permainan ini biasanya dimainkan saat sedang berkumpul untuk mengundi ataupun hanya bermain untuk mengetahui yang menang dan yang kalah. Namun, perkembangan zaman dan teknologi mengakibatkan orang dapat berkumpul secara virtual. Untuk bisa melakukan permainan ini secara virtual, penelitian ini membuat model klasifikasi citra untuk membedakan objek tangan yang menunjuk batu, kertas, dan gunting. Performa metode klasifikasi merupakan hal yang harus diperhatikan dalam kasus ini. Salah satu metode klasifikasi citra yang populer adalah Convolutional Neural Network (CNN). CNN adalah salah satu jenis neural network yang biasa digunakan pada data klasifikasi citra. CNN terinspirasi dari jaringan syaraf manusia. Algoritma ini memiliki 3 tahapan yang dipakai, yaitu convolutional layer, pooling layer, dan fully connected layer. Uji coba 5-Fold cross validation klasifikasi objek tangan yang menunjuk citra batu, kertas, dan gunting menggunakan CNN pada penelitian ini menghasilkan rata-rata akurasi sebesar 97.66%.


Keywords


Permainan batu, gunting, dan kertas, Convolutional neural network (CNN). Klasifikasi Citra

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References


L. Zhu and P. Spachos, “Towards Image Classification with Machine Learning Methodologies for Smartphones,” Mach. Learn. Knowl. Extr., vol. 1, no. 4, pp. 1039–1057, 2019, doi: 10.3390/make1040059.

N. O’Mahony et al., “Deep Learning vs. Traditional Computer Vision,” Adv. Intell. Syst. Comput., vol. 943, no. Cv, pp. 128–144, 2020, doi: 10.1007/978-3-030-17795-9_10.

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” pp. 1–11, 2015, [Online]. Available: http://arxiv.org/abs/1511.08458.

A. A. M. Al-Saffar, H. Tao, and M. A. Talab, “Review of deep convolution neural network in image classification,” Proceeding - 2017 Int. Conf. Radar, Antenna, Microwave, Electron. Telecommun. ICRAMET 2017, vol. 2018-Janua, pp. 26–31, 2017, doi: 10.1109/ICRAMET.2017.8253139.

M. Pak and S. Kim, “A review of deep learning in image recognition,” Proc. 2017 4th Int. Conf. Comput. Appl. Inf. Process. Technol. CAIPT 2017, vol. 2018-Janua, pp. 1–3, 2018, doi: 10.1109/CAIPT.2017.8320684.

“Rock-Paper-Scissors Images | Kaggle.” https://www.kaggle.com/drgfreeman/rockpaperscissors (accessed Dec. 08, 2020).

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, pp. 1–6, 2018, doi: 10.1109/ICEngTechnol.2017.8308186.

A. F. M. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” arXiv, no. 1, pp. 2–8, 2018.

“Keras: the Python deep learning API.” https://keras.io/ (accessed Dec. 08, 2020).

D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.

S. Mannor, B. Peleg, and R. Rubinstein, “The cross entropy method for classification,” ICML 2005 - Proc. 22nd Int. Conf. Mach. Learn., pp. 561–568, 2005, doi: 10.1145/1102351.1102422.

C. Goutte and E. Gaussier, “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation,” Lect. Notes Comput. Sci., vol. 3408, no. April, pp. 345–359, 2005, doi: 10.1007/978-3-540-31865-1_25.

Y. Lecun, L. Bottou, Y. Bengio, and P. Ha, “LeNet,” Proc. IEEE, no. November, pp. 1–46, 1998.

T. F. Gonzalez, “Handbook of approximation algorithms and metaheuristics,” Handb. Approx. Algorithms Metaheuristics, pp. 1–1432, 2007, doi: 10.1201/9781420010749.

M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8689 LNCS, no. PART 1, pp. 818–833, 2014, doi: 10.1007/978-3-319-10590-1_53.

C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

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.

C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, and C. Liu, “A survey on deep transfer learning,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11141 LNCS, pp. 270–279, 2018, doi: 10.1007/978-3-030-01424-7_27.




DOI: https://doi.org/10.33633/tc.v20i1.4273

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