KLASIFIKASI CITRA DIGITAL TULISAN TANGAN ANGKA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK
DOI:
https://doi.org/10.56127/juit.v1i1.1718Keywords:
Convolutional Neural Network, Deep Learning, Handwriting, Artificial IntelligenceAbstract
Rapid technological advances have led to the development of computer vision science in various fields. This research aims to detect handwriting using deep learning technology with the Convolutional Neural Network (CNN) method. The research stages are data sample selection, data preprocessing, data training, data testing, and evaluation of results. This research succeeded in detecting handwriting with an accuracy value of 0.9800 and a loss value of 0.0665. There are several classification errors because images with numbers are less clear and almost look like numbers that they should not be. The more training data, the more the network will learn so that the accuracy will be better.
References
O. Cosido et al., “Hybridization of convergent photogrammetry, computer vision, and artificial intelligence for digital documentation of cultural heritage-A case study: The magdalena palace,” Proc. - 2014 Int. Conf. Cyberworlds, CW 2014, pp. 369–376, 2014, doi: 10.1109/CW.2014.58.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/7068349.
K. Fukushima, “Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position,” Biol. Cybern., vol. 36, no. 4, pp. 193–202, 1980, doi: 10.1007/BF00344251.
Y. Le Cun et al., “Handwritten Digit Recognition with a Back-Propagation Network,” 1990.
A. Coates, H. Lee, and A. Y. NG, “An Analysis of Single-Layer Networks in Unsupervised Feature Learning,” 2011.
M. Castelluccio, G. Poggi, C. Sansone, and L. Verdoliva, “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks,” pp. 1–11, 2015, [Online]. Available: http://arxiv.org/abs/1508.00092.
M. B. Bejiga, A. Zeggada, A. Nouffidj, and F. Melgani, “A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery,” Remote Sens., vol. 9, no. 2, 2017, doi: 10.3390/rs9020100.
T. Zhi, L. Y. Duan, Y. Wang, and T. Huang, “Two-stage pooling of deep convolutional features for image retrieval,” Proc. - Int. Conf. Image Process. ICIP, vol. 2016-Augus, pp. 2465–2469, 2016, doi: 10.1109/ICIP.2016.7532802.
S. Hijazi, R. Kumar, and C. Rowen, “Image Recognition Using Convolutional Neural Networks,” Cadane Whitepaper, pp. 1–12, 2015.
S. Albelwi and A. Mahmood, “A framework for designing the architectures of deep Convolutional Neural Networks,” Entropy, vol. 19, no. 6, 2017, doi: 10.3390/e19060242.