Metode SURF dan FLANN untuk Identifikasi Nominal Uang Kertas Rupiah Tahun Emisi 2016 pada Variasi Rotasi

Identification of Rupiah Paper Currency Denomination using SURF and FLANN Methods at Rotation Variation

*Adri Priadana -  Department of Informatics, Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
Aris Wahyu Murdiyanto -  Department of Information System, Universitas Jenderal Achmad Yani Yogyakarta, Indonesia
Received: 31 Dec 2018; Revised: 31 Jan 2019; Accepted: 31 Jan 2019; Published: 31 Jan 2019; Available online: 31 Mar 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Abstract

In December 2016, Bank Indonesia (BI) officially launched the 2016 Year Emission Rupiah. With the development of technology, the process of buying and selling are not only possible between humans and humans, but humans with a machine. In addition, the machine must also be able to read and recognize the nominal banknotes in various variations of face and rotation. This is because humans can put money in machines with various variations of face and rotation. This study aims to apply and analyze the level of accuracy of nominal rupiah banknotes identification with the SURF and FLANN methods for rotation variation. Testing for identification of nominal rupiah banknotes is carried out with different rotation variations, namely 0o, 90o, 180o, and 270o. The proposed identification method provides 100% of accuracy.

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Keywords
identification of nominal banknotes; feature extraction; SURF; feature matching; FLANN

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  1. S. Haware and A. Barhatte, “Retina Based Biometric Identification using SURF and ORB Feature Descriptors,” in 2017 International Conference on Microelectronic Devices, Circuits And Systems (ICMDCS), Vellore, India, Aug. 2017, pp. 1-6.
  2. J. Farooq, “Object Detection and Identification using SURF and BoW Model,” in 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan, Apr. 2016, pp. 318–323.
  3. S. Ahmed, T. Gaber, A. Tharwat, A. E. Hassanien, and V. Snael, “Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach,” in 2015 International Conference on Intelligent Networking and Collaborative Systems, Taipei, Taiwan, Sept. 2015, pp. 99–104.
  4. R. A. Yunmar and A. Harjoko, “Sistem Identifikasi Relief pada Situs Bersejarah Menggunakan Perangkat Mobile Android (Studi Kasus Candi Borobudur),” IJCCS (Indonesian Journal of Computin and Cybernetics Systems), vol. 8, no. 2, pp. 133–144, Jul. 2014.
  5. M. B. Ariel, R. D. Atmaja, and A. Azizah, “Implementasi Metode Speed Up Robust Feature dan Scale Invariant Feature Transform untuk Identifikasi Telapak Kaki Individu,” Jurnal Al-Azhar Indonesesia, vol. 3, no. 4, pp. 178–186, Dec. 2017.
  6. F. F. Adi, M. Ichwan, and Y. Miftahuddin, “Implementasi Algoritma Speeded Up Robust Features (SURF) pada Pengenalan Rambu-Rambu Lalu Lintas,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 3, no. 3, Dec. 2017.
  7. H. Hikmayanti and O. Komarudin, “Penggunaan Algoritma Backpropagation Levenberg Marquardt dan Teknik Pengolahan Citra Digital untuk Identifikasi Nominal Uang Kertas,” Jurnal Ilmiah Solusi, vol. 1, no. 2, pp. 16-33, Jul. 2015.
  8. I. G. Saputra, E. Susanto, and R. Nugraha, “Implementasi Metode Jaringan Saraf Tiruan (JST) pada Alat Deteksi Nilai Nominal Uang,” eProceedings of Engineering, vol. 3, no. 1, pp. 65-71, Apr. 2016.
  9. R. Izah, “Klasifikasi Nominal Uang Kertas Rupiah Tahun Emisi 2017 dengan Algoritma Convolutional Neural Network Menggunakan MXNET,” Skripsi, Universitas Islam Indonesia, 2018.
  10. Z. Hamizan and R. Sumiharto, “Sistem Pentautan Citra Udara Menggunakan Algoritme SURF dan Metode Reduksi Data,” IJEIS (Indonesian Journal of Electronics and Instrumentations Systems, vol. 7, no. 2, pp. 127-138, Oct. 2017.
  11. L. Arsy, O. D. Nurhayati, and K. T. Martono, “Aplikasi Pengolahan Citra Digital Meat Detection dengan Metode Segmentasi K-Mean Clustering Berbasis OpenCV dan Eclipse,” Jurnal Teknologi dan Sistem Komputer, vol. 4, no. 2, p. 322-332, Apr. 2016.
  12. A. Priadana, “Analisis Pengaruh Ukuran Citra Hasil Resizing terhadap Jumlah Keypoint Hasil Ekstraksi Ciri pada Metode SIFT dan SURF,” Teknomatika, vol. 11, no. 1, pp. 9–18, Dec. 2018.

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