Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods
(1) Program Magister Teknik Informatika, Universitas Putra Indonesia "YPTK" Padang
(2) Program Magister Teknik Informatika, Universitas Putra Indonesia "YPTK" Padang
(3) Program Magister Teknik Informatika, Universitas Putra Indonesia "YPTK" Padang
(*) Corresponding Author
Abstract
This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.
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Aristantya, R., Santoso, I., & Zahra, A. (n.d.). IDENTIFIKASI TANDA TANGAN MENGGUNAKAN METODE ZONING DAN SVM ( SUPPORT VECTOR MACHINE ).
Dewi, S. P., Nurwati, N., & Rahayu, E. (2022). Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor. Building of Informatics, Technology and Science (BITS), 3(4), 639–648. https://doi.org/10.47065/bits.v3i4.1408
Distance, E. (2016). Pengenalan Citra Tanda Tangan Menggunakan Metode 2D-LDA dan Euclidean Distance. 3(4).
Fatwa, M., Rizki, R., Sriwinarty, P., & Supriyadi, E. (2022). Pengaplikasian Matlab pada Perhitungan Matriks. Papanda Journal of Mathematics and Science Research, 1(2), 81–93. https://doi.org/10.56916/pjmsr.v1i2.260
Helilintar, R. (2023). Implementasi Region of Interest ( ROI ) Untuk Segmentasi Citra Tanda Tangan. 7, 1248–1255.
Kamila, C. (2022). Penerapan Metode Scrum pada Pembuatan Aplikasi Sistem Tanda Tangan Digital dengan QR Code Berbasis Website. Intech, 3(1), 36–41. https://doi.org/10.54895/intech.v3i1.1175
Kanugroho, M. T., Rahman, M. A., & Wihandika, R. C. (2022). Klasifikasi Batik dengan Ekstraksi Fitur Tekstur Local Binary Pattern dan Metode K-Nearest Neighbor. 6(10), 4788–4794. http://j-ptiik.ub.ac.id
Lodong, A. T., Widodo, A. W., & Rahman, M. A. (2023). Penentuan Mutu pada Citra Buah Jeruk Keprok menggunakan Metode Local Binary Pattern ( LBP ). 7(4), 1616–1622.
Nur Cahyo, D., Zulfia Zahro’, H., & Vendyansyah, N. (2023). Pengenalan Ekspresi Mikro Wajah Dengan Ekstraksi Fitur Pada Komponen Wajah Menggunakan Metode Local Binary Pattern Histogram. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 822–829. https://doi.org/10.36040/jati.v7i1.6167
Pengenalan, S., Dokumen, C., & Tangan, T. (2022). Jurnal Energy. 12(2), 54–61.
Putriana, A. D., Canta, D. S., Hadisaputro, E. L., & Wahyuni, N. (2022). Implementasi Backpropagation untuk Identifikasi Tanda Tangan Digital.
Jurnal Informatika Dan Rekayasa Perangkat Lunak, 4(1), 11. https://doi.org/10.36499/jinrpl.v4i1.4996
Sunarya, P. A. (2022). Penerapan Sertifikat pada Sistem Keamanan menggunakan Teknologi Blockchain. Jurnal MENTARI: Manajemen, Pendidikan Dan Teknologi Informasi, 1(1), 58–67. https://doi.org/10.34306/mentari.v1i1.139
DOI: https://doi.org/10.32736/sisfokom.v13i2.2063
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