Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit

CNN LeNet Model for Year Digit Recognition on Relic Inscriptions of Majapahit Kingdom

Tri Septianto -  Sekolah Tinggi Teknik Surabaya, Indonesia
Endang Setyati -  Sekolah Tinggi Teknik Surabaya, Indonesia
*Joan Santoso -  Sekolah Tinggi Teknik Surabaya, Indonesia
Received: 4 Jun 2018; Published: 31 Jul 2018.
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
Citation Format:
Article Info
Section: Articles
Language: ID
Full Text:
In Volume 6, Issue 3, Year 2018 (July 2018)
Statistics: 469 173
Abstract
The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.
Keywords
LeNet recognition performance; inscription digit recognition; CNN performance comparison; VGG recognition performance

Article Metrics:

  1. L. Shang, Q. Yang, J. Wang, S. Li, and W. Lei, "Detection of Rail Surface Defects Based on CNN Image Recognition and Classification," in 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon-si Gangwon-do, Korea, Feb. 2018, pp. 45–51.
  2. X. Zhao, X. Shi, and S. Zhang, "Facial Expression Recognition via Deep Learning," IETE Technical Review, vol. 32, no. 5, pp. 347–355, 2015.
  3. Y. Yang, D. Li, and Z. Duan, "Chinese Vehicle License Plate Recognition using Kernel-based Extreme Learning Machine with Deep Convolutional Features," IET Intelligent Transport Systems, vol. 12, no. 3, pp. 213–219, 2017.
  4. H. D. Nguyen and M. Nakagawa, "Deep Neural Networks for Online Handwritten Mathematical Characters," in 18th Meeting on Image Recognition and Understanding, 2015, pp. 1–2.
  5. S. Lee, S. J. Son, J. Oh, and N. Kwak, "Handwritten Music Symbol Classification Using Deep Convolutional Neural Networks," in 2016 International Conference on Information Science and Security, Pattaya, Thailand, Dec. 2016, pp. 1-5.
  6. S. Nagaraj, B. Muthiyan, S. Ravi, V. Menezes, K. Kapoor, and H. Jeon, "Edge-based Street Object Detection," in 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Jun. 2017, pp. 1–4.
  7. A. R. F. Quiros et al., "A kNN-based Approach for the Machine Vision of Character Recognition of License Plate Numbers," in 2017 IEEE Regional 10 Conference (TENCON 2017), Nov. 2017, pp. 1081–1086.
  8. D. Singh, M. A. Khan, A. Bansal, and N. Bansal, "An Application of SVM in Character Recognition with Chain Code," in 2015 Communication, Control, and Intelligent Systems (CCIS), Mathura, India, Nov. 2015, pp. 167–171.
  9. M. Y. W. Teow, "Understanding Convolutional Neural Networks Using A Minimal Model for Handwritten Digit Recognition," in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), Kota Kinibalu, Malaysia, Oct. 2017, pp. 167–172.
  10. S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a Convolutional Neural Network," in 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, Aug. 2017, pp. 1–6.
  11. P. P. Nair, A. James, and C. Saranavan, "Malayalam Handwritten Character Recognition using Convolutional Neural Network," in 2017 International Conference on Inventive Communication and Computational Technologise (ICICCT), Coimbatore, India, Mar. 2017, pp. 278–281.
  12. Z. Shokoohi, A. M. Hormat, F. Mahmoudi, and H. Badalabadi, "Persian Handwritten Numeral Recognition using Complex Neural Network and Non-linear Feature Extraction," in 2013 1st Iranian Conference on Pattern Recognition and Image Analysis (PRIA). Birjand, Iran, Mar. 2013.
  13. M. A. H. Akhand, M. M. Rahman, P. C. Shill, S. Islam, and M. M. H. Rahman, "Bangla Handwritten Numeral Recognition using Convolutional Neural Network," in 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2015, pp. 1–5.
  14. M. A. Wibowo, M. Soleh, W. Pradani, A. N. Hidayanto, and A. M. Arymurthy, "Handwritten Javanese Character Recognition using Descriminative Deep Learning Technique," in 2017 2nd International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, Nov. 2017, pp. 324–329.
  15. M. He, S. Zhang, H. Mao, and L. Jin, "Recognition Confidence Analysis of Handwritten Chinese Character with CNN," in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, pp. 61–65.
  16. T. Datta, B. Purkaystha, and M. S. Islam, "Bengali Handwritten Character Recognition Using Deep Convolutional Neural Network," in 2017 20th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, Dec. 2017, pp. 22–24.
  17. M. D. Zeiler, "ADADELTA: An Adaptive Learning Rate Method," arXiv: 1212.5701 [cs.LG], Dec. 2012.
  18. K. Simonyan, and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv: 1409.1556 [cs.CV], Sept. 2014.

Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer License URL: https://creativecommons.org/licenses/by-sa/4.0

The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro as the publisher of the journal. Copyright encompasses rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations. 

JTSiskom journal and Department of Computer Engineering, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in JTSiskom journal are the sole and exclusive responsibility of their respective authors and advertisers.

The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form JTSiskom]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :  

Eko Didik Widianto (Editor-in-Chief)
Editorial Office of Jurnal Teknologi dan Sistem Komputer
Department of Computer Engineering, Universitas Diponegoro
Jl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275
Telp./Fax: +62-24-76480609
Email: jtsiskom@ce.undip.ac.id