PENGENALAN SIDIK JARI MENGGUNAKAN JARINGAN SYARAF TIRUAN BERBASIS SCALED CONJUGATE GRADIENT

Suci Dwijayanti, Puspa Kurniasari

Abstract


Abstrak—Sidik jari merupakan sistem biometrik yang paling banyak digunakan untuk keamanan. Salah satu metode yang sangat baik untuk mengenali sidik jari adalah menggunakan jaringan syaraf tiruan. Penelitian ini membahas tentang pengenalan sidik jari dengan menggunakan algoritma variasi backpropagation, scaled conjugate gradient. Proses pengenalan sidik jari meliputi image acquisition, image pre-processing, feature extraction dan image recognition. Pada proses pre-processing dan feature extraction menggunakan algoritma fast fourier transform untuk memperbaiki kualitas sidik jari yang akan digunakan sebagai input pada proses pengenalan. Proses enrollment menggunakan fingerprint reader. Dari hasil pelatihan, dari 9 sampel sidik jari ada 2 sidik jari yang memiliki error lebih dari 0.05, sedangkan dari data pengujian diperoleh 91% data secara keseluruhan mampu dikenali dengan menggunakan backpropagation berbasis scaled conjugate gradient.
Kata kunci: Jaringan Syaraf Tiruan, Backpropagation, Scaled Conjugate Gradient, Sidik Jari.

Abstract-A fingerprint biometric system is the most widely system used for security. One of the best method to recognize fingerprints is using neural network. This paper describes the fingerprint recognition using scaled conjugate gradient, a variation backpropagation algorithm. The fingerprint recognition procesess include image acquisition, image pre-processing, feature extraction and image recognition. In the pre-processing and feature extraction, Fast Fourier Transform algorithm is used to improve the quality of prints that will be used as input in the recognition process. Enrollment process use the fingerprint reader. From the training results obtained that there are 2 fingerprints have errors more than 0.05 from 9 samples, while test data obtained 91 % of the whole data that could be identified by using backpropagation based on scaled conjugate gradient.
Keywords. Neural Network, Backpropagation, Scaled Conjugate Gradient, Fingerprint


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