RECOGNITION OF FINGERPRINT PATTERNS WITH LOCAL BINARY PATTERN METHOD AND LEARNING VECTOR QUANTIZATION

J-Icon : Jurnal Komputer dan Informatika

View Publication Info
 
 
Field Value
 
Title RECOGNITION OF FINGERPRINT PATTERNS WITH LOCAL BINARY PATTERN METHOD AND LEARNING VECTOR QUANTIZATION
PENGENALAN POLA SIDIK JARI DENGAN METODE LOCAL BINARY PATTERN DAN LEARNING VECTOR QUANTIZATION
 
Creator Fanggidae, Adriana
Sihotang, Dony M
Rihi Pati, Adnan Putra
 
Description Fingerprint is the generic structure in the form of a very detailed pattern and a sign that inherent in human beings. Many biometric systems using fingerprint as input data, because the nature of each individual is different although identical twins and do not change unless got a accident. The method used in this research is image segmentation using Otsu thresholding algorithm, feature extraction using Local Binary Pattern (LBP) algorithm and the learning method using Learning Vector Quantization (LVQ) algorithm. The used data is grayscale fingerprint image with 200x300 pixel and *.jpg extension format. The fingerprint image is composed of 25 people, each person has 6 training data and 2 test data. Experiment of training data and test data conducted for four systems, namely the system with characteristics of LBP = 8, 64, 128 and 256 and their respective uses 2 pieces of data set where data set 1 amounted to 15 people and data set 2 amounted to 25 people. The fourth experiment results show that the system is a system with a number of LBP characteristics = 128 is a system with the best combination of high system accuracy and fast learning time.
Sidik jari merupakan strukur genetika dalam bentuk pola yang sangat detail dan tanda yang melekat pada diri manusia. Banyak sistem biometrika yang menggunakan sidik jari sebagai data masukan, karena sifat dari sidik jari setiap individu berbeda meskipun kembar identik dan tidak berubah kecuali mendapat kecelakaan. Metode yang digunakan dalam penelitian ini yaitu segmentasi dengan algoritma Otsu thresholding, ekstraksi ciri dengan algoritma Local Binary Pattern (LBP), dan pembelajaran dengan algoritma Learning Vector Quantization (LVQ). Data yang digunakan adalah citra sidik jari jempol berukuran 200 x 300 piksel, berjenis keabuan dan berformat *.jpg. Citra sidik jari terdiri dari 25 orang, masing-masing orang memiliki 6 data latih dan 2 data uji. Pengujian data latih dan data uji dilakukan kepada empat sistem yaitu sistem dengan jumlah ciri LBP = 8, 64, 128 dan 256 dan menggunakan masing-masing 2 buah data set dimana data set 1 berjumlah 15 orang dan data set 2 berjumlah 25 orang. Hasil pengujian keempat sistem menunjukkan bahwa sistem dengan jumlah ciri LBP = 128 merupakan sistem yang terbaik dengan kombinasi akurasi sistem yang tinggi dan juga waktu pembelajaran yang cepat.
 
Publisher Universitas Nusa Cendana
 
Date 2019-11-27
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ejurnal.undana.ac.id/jicon/article/view/1635
10.35508/jicon.v7i2.1635
 
Source J-Icon : Jurnal Komputer dan Informatika; Vol 7 No 2 (2019): Oktober 2019; 148-156
2654-4091
2337-7631
10.35508/jicon.v7i2
 
Language eng
 
Relation http://ejurnal.undana.ac.id/jicon/article/view/1635/1292
 
Rights Copyright (c) 2019 Jurnal Komputer dan Informatika (JICON)
http://creativecommons.org/licenses/by-nd/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library