Harjunowibowo, Dewanto and Hartati, Sri and Budianto, Aris (2012) A Counterfeit Paper Currency Recognition System Using LVQ based on UV Light. IJID (International Journal on Informatics for Development), Vol.1 (No.2). pp. 1-5. ISSN 2252-7834
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Text (A Counterfeit Paper Currency Recognition System Using LVQ based on UV Light)
Dewanto Harjunowibowo, dkk - A Counterfeit Paper Currency Recognition System Using LVQ based on UV Light.pdf - Published Version Download (810kB) | Preview |
Abstract
This research is aimed to test a paper currency counterfeit detection system based on Linear Vector Quantization (LVQ) Neural Network. The input image of the system is the dancer object image of paper currency Rp. 50.000,- fluorescent by ultraviolet light. The image of paper currency data was taken from conventional banks. The LVQ method is used to recognize whether the paper currency being tested is counterfeit or not. The coding was carried out using visual programming language. The feature size of the dancer tested object is 114x90 px and the RGBHSI was extracted as the input for LVQ. The experimental results show that the system has an accuracy 100% of detecting 20 real test case data, and 96% of detecting 22 simulated test case data. The simulated case data was generated by varying the brightness of the image data. The real test case data contains of 10 counterfeit paper currency and 10 original paper currency. The simulated case data contains of 11 original paper currency and 11 counterfeit paper currency. The best setting for the system is Learning Rate = 0.01 and MaxEpoh = experimental results show that the system has an accuracy 100% of detecting 20 real test case data, and 96% of detecting 22 simulated test case data. The simulated case data was generated by varying the brightness of the image data. The real test case data contains of 10 counterfeit paper currency and 10 original paper currency. The simulated case data contains of 11 original paper currency and 11 counterfeit paper currency. The best setting for the system is Learning Rate = 0.01 and MaxEpoh = 10.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CRM 2.0; detection system, counterfeit paper currency, neural network, LVQ, digital image processing |
| Subjects: | Komputer > Pemrograman |
| Divisions: | Jurnal > 39. IJID (International Journal On Informatics For Development)) |
| Depositing User: | Sugeng Hariyanto, SIP (sugeng.hariyanto@uin-suka.ac.id) |
| Date Deposited: | 15 Mar 2017 09:05 |
| Last Modified: | 15 Mar 2017 09:05 |
| URI: | http://digilib.uin-suka.ac.id/id/eprint/24368 |
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