DOI: https://doi.org/10.14710/jtsiskom.8.2.2020.150-156

Unjuk kerja k-nearest neighbor untuk alihaksara citra aksara Nusantara

K-nearest neighbor performance for Nusantara scripts image transliteration

Department of Informatics, Universitas Sanata Dharma, Indonesia

Received: 23 Aug 2019; Revised: 28 Feb 2020; Accepted: 13 Mar 2020; Available online: 20 Mar 2020; Published: 30 Apr 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
The concept of classification using the k-nearest neighbor (KNN) method is simple, easy to understand, and easy to be implemented in the system. The main challenge in classification with KNN is determining the proximity measure of an object and how to make a compact reference class. This paper studied the implementation of the KNN for the automatic transliteration of Javanese, Sundanese, and Bataknese script images into Roman script. The study used the KNN algorithm with the number k set to 1, 3, 5, 7, and 9. Tests used the image dataset of 2520 data. With the 3-fold and 10-fold cross-validation, the results exposed the accuracy differences if the area of the extracted image, the number of neighbors in the classification, and the number of data training were different.
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Keywords: classification; KNN algorithm; transliteration; Nusantara script image
Funding: Universitas Sanata Dharma under contract 042/LPPM USD/V/2019

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Section: Original Research Articles
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  1. T. M. Cover and P. E. Hart, "Nearest neighbor pattern classification" IEEE Transactions on Information Theory, vol. 13, pp. 21-27, 1967. doi: 10.1109/TIT.1967.1053964
  2. A. Kataria and M. D. Singh, "A review of data classification using k-nearest neighbour algorithm," International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 6, pp. 354-360, 2013
  3. N. Bhatia and A. Vandana, "Survey of nearest neighbor techniques," International Journal of Computer Science and Information Security, vol. 8, no. 2, pp. 302-305, 2010
  4. S. Roy, dan M. Saravanan, "Handwritten character recognition using k-nn classification algorithm," International Journal of Advance Research and Innovative Ideas in Education, vol 3, no. 5, pp. 1245-1250, 2017
  5. W. Wahyono, I. N. P. Trisna, S. L. Sariwening, M. Fajar, and D. Wijayanto, "Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual," Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 1, pp. 54-48, 2020. doi: 10.14710/jtsiskom.8.1.2020.54-58
  6. C. Premavathi and P. Thangaraj, "Efficient hand-dorsa vein pattern recognition using knn classification with completed histogram cb in tp feature descriptor," International Journal of Recent Technology and Engineering, vol. 7, no. 4, pp. 50-55, 2018
  7. V. Ong and D. Suhartono, "Using k-nearest neighbor in optical character recognition," ComTech: Computer, Mathematics, and Engineering Applications, vol. 7, no. 1, pp. 53-65, 2016. doi: 10.21512/comtech.v7i1.2223
  8. J. F. Ajao, D. O. Olawuyi, and O. O. Odejobi, "Yoruba handwritten character recognition using freeman chain code and k-nearest neighbor classifier," Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 2, pp. 129-134, Oct. 2018. doi: 10.14710/jtsiskom.6.4.2018.129-134
  9. Z. Al-asady and A. Al-amery, "Human action recognition using a corners and blob detector with different classification methods," IOP Conference Series: Materials Science and Engineering, vol. 518, no. 5, pp. 1-9, 2019. doi: 10.1088/1757-899X/518/5/052008
  10. Y. Hamamoto, S. Uchimura and S. Tomita, "A bootstrap technique for nearest neighbor classifier design," IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 19, no. 1, pp. 73-79, 1997. doi: 10.1109/34.566814
  11. E. Alpaydin, "Voting over multiple condensed nearest neighbors," Artificial Intelligence Review, vol. 11, pp. 115-132, 1997. doi: 10.1007/978-94-017-2053-3_4
  12. T. Kim, W. Ko, and J. Kim, "Analysis and impact evaluation of missing data imputation in day-ahead pv generation forecasting," Applied Sciences, vol. 9, no. 1, pp. 1-18, 2019. doi: 10.3390/app9010204
  13. K. T. Do et al., "Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies," Metabolomics, vol. 128, pp. 1-18, 2018. doi: 10.1007/s11306-018-1420-2
  14. A. B. Hassanat, M. A. Abbadi, G. A. Altarawneh, and A.A. Alhasanat, "Solving the problem of the k parameter in the knn classifier using an ensemble learning approach," International Journal of Computer Science and Information Security, vol. 12, no. 8, pp. 33-39, 2014
  15. S. Mirah and A. R. Widiarti, “Automatic recognition of the NIK in electronic KTP,” in 1st International Conference on Science and Technology for an Internet of Things, Yogyakarta, Indonesia, Oct. 2018, pp. 1-11. doi: 10.4108/eai.19-10-2018.2282544

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