Identify The Authenticity of Rupiah Currency Using K Nearest Neighbor (K-NN) Algorithm

Dian Nova Kusuma Hardani, Thomi Luthfianto, Muhammad Taufiq Tamam

Abstract


The rupiah currency is a valid exchange rate used in transactions in the Republic of Indonesia. The Rupiah is often falsified as paper currency. Rupiah paper has a unique texture characteristic so that if processed digitally, it will be easy to distinguish from fake ones.  Designing the authenticity of Rupiah currency system using the K-NN method aims to facilitate the authenticity of the currency and test the accuracy of the method used. The method used in this research is the method of Gray Level Co-occurrence Matrix (GLCM) as a method of feature extraction and K-Nearest Neighbor (K-NN) algorithm used in the identification process. The testing phase uses data for 18 currency images. The results showed an accuracy rate of 100% for the value k = 1, 77.78% for the value k = 3, and 55.56% for the value k = 5. The highest level of accuracy in a currency authenticity identification system occurs when the value of k = 1 is 100%. The value of k on the classification input using the K-NN can determine the level of accuracy of the classification process.

Keywords


Identification; Rupiah; GLCM; K-NN

Full Text:

PDF

References


H. Hassanpour, A. Yaseri, and G. Ardeshiri, “Feature extraction for paper currency recognition,†in 2007 9th International Symposium on Signal Processing and Its Applications, 2007, pp. 1–4, doi: 10.1109/ISSPA.2007.4555366.

S. Omatu, M. Yoshioka, and Y. Kosaka, “Reliable Banknote Classification Using Neural Networks,†in 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences, 2009, pp. 35–40, doi: 10.1109/ADVCOMP.2009.37.

N. Jahangir and A. R. Chowdhury, “Bangladeshi banknote recognition by neural network with axis symmetrical masks,†in 2007 10th international conference on computer and information technology, 2007, pp. 1–5, doi: 10.1109/ICCITECHN.2007.4579423.

C. Chang, T. Yu, and H. Yen, “Paper Currency Verification with Support Vector Machines,†in 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 2007, pp. 860–865, doi: 10.1109/SITIS.2007.146.

J. Qian, D. Qian, and M. Zhang, “A Digit Recognition System for Paper Currency Identification Based on Virtual Instruments,†in 2006 International Conference on Information and Automation, 2006, pp. 228–233, doi: 10.1109/ICINFA.2006.374117.

P. T. Noi and M. Kappas, “Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery,†Sensors, vol. 18, no. 18, pp. 1–20, 2018, doi: 10.3390/s18010018.

V. Bijalwan, V. Kumar, P. Kumari, and J. Pascual, “KNN based Machine Learning Approach for Text and Document Mining,†Int. J. Database Theory Appl., vol. 7, no. 1, pp. 61–70, 2014, available at: Google Scholar.

K. Huang, S. Li, X. Kang, and L. Fang, “Spectral – Spatial Hyperspectral Image Classification Based on KNN,†Sens. Imaging, vol. 17, no. 1, pp. 1–13, 2016, doi: 10.1007/s11220-015-0126-z.

K. Machhale, H. B. Nandpuru, V. Kapur, and L. Kosta, “MRI brain cancer classification using hybrid classifier (SVM-KNN),†in 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 60–65, doi: 10.1109/IIC.2015.7150592.

H. Zhang, A. C. Berg, M. Maire, and J. Malik, “SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition,†in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), 2006, vol. 2, pp. 2126–2136, doi: 10.1109/CVPR.2006.301.

Z. Deng, X. Zhu, D. Cheng, M. Zong, and S. Zhang, “Efficient kNN classification algorithm for big data,†Neurocomputing, vol. 195, pp. 143–148, 2016, doi: 10.1016/j.neucom.2015.08.112.

T. M. Cover and P. E. Hart, “Nearest Neighbor Pattern Classification,†IEEE Trans. Inf. THEORY, vol. IT-13, no. 1, pp. 21–27, 2018, doi: 10.1109/TIT.1967.1053964.

C. H. Wan, L. H. Lee, R. Rajkumar, and D. Isa, “A hybrid text classification approach with low dependency on parameter by integrating K-nearest neighbor and support vector machine,†Expert Syst. Appl., vol. 39, no. 15, pp. 11880–11888, 2012, doi: 10.1016/j.eswa.2012.02.068.

S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN Classification With Different Numbers of Nearest Neighbors,†IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 5, pp. 1774–1785, 2018, doi: 10.1109/TNNLS.2017.2673241.

A. S. R. M. Sinaga, “The Comparison of Signature Verification Result Using 2DPCA Method and SSE Method,†Int. J. Artif. Intelegence Res., vol. 2, no. 1, pp. 18–27, 2018, doi: 10.29099/ijair.v2i1.38.

R. M. Haralick and K. Shanmugam, “Textural Features for Image Classification,†IEEE Trans. Syst. MAN, Cybern., vol. SMC-3, no. 6, pp. 610–621, 1973, doi: 10.1109/TSMC.1973.4309314.

G. Guo, H. Wang, D. Bell, and Y. Bi, “KNN Model-Based Approach in Classification,†Aug. 2004, available at: https://link.springer.com/chapter/10.1007/978-3-540-39964-3_62.




DOI: http://dx.doi.org/10.26555/jiteki.v5i1.13324

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Universitas Ahmad Dahlan

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 
About the JournalJournal PoliciesAuthor Information
 

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
ISSN 2338-3070 (print) | 2338-3062 (online)
Organized by Electrical Engineering Department - Universitas Ahmad Dahlan
Published by Universitas Ahmad Dahlan
Website: http://journal.uad.ac.id/index.php/jiteki
Email 1: jiteki@ee.uad.ac.id
Email 2: alfianmaarif@ee.uad.ac.id
Office Address: Kantor Program Studi Teknik Elektro, Lantai 6 Sayap Barat, Kampus 4 UAD, Jl. Ringroad Selatan, Tamanan, Kec. Banguntapan, Bantul, Daerah Istimewa Yogyakarta 55191, Indonesia