Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Pengembangan Sistem Informasi dan Informatika
Lulus, Lulus
Unknown Affiliation
Computer Science & IT Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Pengembangan Sistem Informasi dan Informatika

 1 
Deteksi Pemalsuan Tanda Tangan Menggunakan Algoritma Convolutional Neural Network (CNN) Lulus, Lulus; Sartika, Dewi; Irfani, Muhammad Hafiz
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 4 No. 2 (2023): Jurnal Pengembangan Sistem Informasi dan Informatika (JPSII)
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v4i2.1745

Abstract

A signature is a person's identity. This makes the existence of a signature necessary, usually taken from the name of a person or other style. Making signatures should not be changed because it will greatly affect the inauthenticity of identity, which is considered to be able to forge an important document and make transactions. Signature verification is mainly done manually, namely by comparing using the sense of sight, which still allows fraud to prevent signature forgery problems. It takes software that can perform original signature recognition to minimize fraud. This research built a signature recognition software that was done offline. The data used in this study were 144 signature images of lecturers at the computer science faculty, Universitas Indo Global Mandiri. The software implements a Deep Learning system by using the Convolutional Neural Network (CNN) algorithm. The CNN model is built with Python programming language and consists of a convolution, pooling, and fully connected layer. The research began with the preprocessing stage, followed by the construction of the CNN model and software testing. The input in this application is a signature image taken using a scanner. The output is in the form of the name of the signature owner and the accuracy of the match between the real and fake signatures. From the research that has been done, it got an accuracy value of 89.65% and a loss of 0.6018
Co-Authors DEWI SARTIKA Irfani, Muhammad Hafiz