Fingerprints Image Recognition by Using Perceptron Artificial Neural Network
Abstract
Security systems that use passwords or identity cards can be hacked and misused. One of alternative security system is to use biometric identification. The biometric system that is popularly used is fingerprints, because the system is safe and comfortable. Fingerprints have a distinctive pattern for each individual and this makes fingerprints relatively difficult to fake, so the system is safe. Comfortable because the verification process is easily done. The problem that often occurs on the system of fingerprint scanner is found an error and the user has difficulty when accessing. To handle with these problems has developed an artificial intelligence system. One of arificial intelligence in pattern identification is artificial neural networks (ANN). From some of the results of previous research showed that the ANN method is reliable in pattern identification. Based on these facts, the method used in this research is the perceptron ANN method with values learning rate varying. In the research the program conducted by testing 20 samples showed that the performance of the perceptron ANN method is relatively good method in fingerprint image recognition. This can be indicated from the value of accuracy (0.95), precision (0.83), TP rate (1), and FP rate (0.07)). In addition, the location of the point coordinate (FP rate; TP rate) is (0.07; 1) in ROC graphs is located on the upper left (perfect classifier region).
Downloads
References
[2] Santi R.C.N., 2008, Identifikasi Biometrik Sidik Jari dengan Metode Fraktal, Jurnal Teknologi Informasi DINAMIK, Vol. XIII, No.1, pp. 68-72.
[3] Gapar I K.K., Widagda I G.A dan Suarbawa K.N., 2018, Pengenalan Suara Manusia dengan Menggunakan Metode Jaringan Saraf Tiruan Hebb, Buletin Fisika, Vol. 19, No. 1, pp. 16-22.
[4] Yanti N., Rachman F.Z., Jamal N., Purwanto E. dan Fachrurozy, 2018, Jaringan Saraf Tiruan untuk Pengenalan Citra Sidik Jari pada Smart Home, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), Vol. 5, No. 5, pp. 597-604.
[5] Alqurni R.P. dan Muljono, 2016, Pengenalan Tanda Tangan Menggunakan Metode Jaringan Saraf Tiruan Perceptron dan Backpropagation, Techno.COM, Vol. 15, No. 4, pp. 352-363.
[6] Tambunan F., 2015, Pengenalan Aksara Batak dengan Metode Perceptron, IT Jurnal, Vol. 2, No. 2, pp. 1-11.
[7] Pujiyanta A., 2009, Pengenalan Citra Objek Sederhana dengan Jaringan Saraf Tiruan Metode Perceptron, Jurnal Informatika, Vol. 3, No. 1, pp. 268-277.
[8] Husen R, Sutikno T., dan Pujianta A., 2015, Pengenalan Pola Sidik Jari Berbasis Jaringan Saraf Tiruan Perambatan Balik, Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, Vol. 1, No. 1, pp. 1-20.
[9] Ariana A. A. G. B., 2016, Perbandingan Metode SOM/Kohonen dengan Adaptive Resonance Theory 2 pada Data Mining Perusahaan Retail, Magister Teknik Elektro Fakultas Teknik Universitas Udayana, Denpasar, pp. 20-24.
[10] Siang J.J, 2005, Jaringan Saraf Tiruan dan Pemrogamannya Menggunakan Matlab, Penerbit Andi, Yogyakarta, pp. 2-60.
[11] Fausett L., 1994, Fundamentals of Neural Networks Architectures, Algoritms, and Application, Prentice Hall.Inc., Upper Saddle River, pp. 59-80.
[12] Widagda I G.A. dan Suyanto H., 2019, Klasifikasi Pola Berbentuk Primitif dengan Menggunakan Metode Principal Component Analysis (PCA), Buletin Fisika, Vol. 20, No. 2, pp. 12-21.
