IDENTIFIKASI TANDA-TANGAN MENGGUNAKAN JARINGAN SARAF TIRUAN PERAMBATAN-BALIK (BACKPROPAGATION

Abstract

Page 1 IDENTIFIKASI TANDA-TANGAN MENGGUNAKAN JARINGAN SARAF TIRUAN PERAMBATAN-BALIK (BACKPROPAGATION) Achmad Hidayatno, R. Rizal Isnanto, Dian Kurnia Widya Buana Jurusan Teknik Elektro, Fakultas Teknik Universitas Diponegoro, Semarang Jl. Prof. Sudarto, S.H., Tembalang, Semarang 50275 E-mail: rizal_isnanto@yahoo.com; rizal@elektro.ft.undip.ac.id Abstract Human signature identification is a process for identifying and obtaining a person who has the signature. Signature identification technology includes in biometrics system which uses a behavioral human nature characteristics. For the time being, there are many signature forgeries which are generally make a harm for people who have the signatures. Signature forgery occurs easily for which a system which can assist to identify a person’s signature is required. Identification system which will be implemented uses Backpropagation Neural Network model and is supprorted by Delphi programming language. In order to identify a signature, image of signature firstly needs a preprocessing and features extraction. In the preprocessing, there are three stages which have to be performed, there are: converting the grayscaled image, contrasting the image, and edge detection of the image. Features extraction process is performed by segmenting the image in the form of rows and columns which has a purpose to get a significant feature information of the image of signature, as well as to get a data value which will be an input for neural network. Neural network training is performed to get an accurate classification from trained data input of signatures. A signature can be identified when the signature is comprised in one of classes which formed of training process. The research uses 150 images of signatures which consist of 10 responders for database for which it requires 10 data from each responder and 5 responders from outer side of database for which it requires 5 data from each responder. Conclusions of the research are that the application system has a 95% percentage of success level for identifying the signatures from the testing of trained data, while it has only 88% percentage of success level from the testing of outer side of database. Keywords: Signature identification, biometrics, feature extraction, backpropagation neural network