IDENTIFIKASI TANDA TANGAN DENGAN PENDEKATAN SUPPORT VECTOR MACHINE
ABSTRACT: Signature is one of
the biometric humans that used widely. This system aims to recognize the
signature through feature extraction and image classification method signature
with Support Vector Machine (SVM). Research databases used 15 samples
signatures images from students of Informatics Engineering UNIB with size 300 x
300 pixels. The system is built in the Java programming language with NetBeans
IDE 8.0. The system is divided into 3 stages: preprocessing, feature
extraction, and SVM classification. Preprocessing stages are binerization,
noise Removal, thinning, cropping, and resizing. Feature extraction stage using
Image Centroid Zone (ICZ) and Zone Centroid Zone (ZCZ) methods. Furthermore,
the results of feature vectors ICZ and ZCZ be input training SVM
classification. This research results shows that: (1) the greater the size of
the zone, the higher the identification accuracy; (2) the smaller polynomial degree,
the higher the signatures identification accuracy; (3) The best performance is
obtained for 5x4 size zone and 2 degree polynomial with 97.33% signature
identification accuracy.
Penulis: Endina Putri
Kode Jurnal: jptindustridd150237
