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Sistem Verifikasi Tanda Tangan Off-Line Berdasar Ciri Histogram Of Oriented Gradient (HOG) Dan Histogram Of Curvature (HoC) Agus Wahyu Widodo; Agus Harjoko
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2, No 1: April 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (334.075 KB) | DOI: 10.25126/jtiik.201521121

Abstract

Abstrak Tanda tangan dengan sifat uniknya merupakan salah satu dari sekian banyak atribut personal yang diterima secara luas untuk verifikasi indentitas seseorang, alat pembuktian kepemilikan berbagai transaksi atau dokumen di dalam masyarakat. Keberhasilan penggunaan ciri gradien dan curvature dalam bidang-bidang penelitian pengenalan pola dan bahwa tanda tangan dapat dikatakan merupakan hasil tulisan tangan yang tersusun atas beragam garis dan lengkungan (curve) yang memiliki arah atau orientasi merupakan alasan bahwa kedua ciri tersebut digunakan sebagai metoda verifikasi tanda tangan offline di penelitian ini. Berbagai implementasi dari pre-processing, ekstraksi dan representasi ciri, dan pembelajaran SVM serta usaha perbaikan yang telah dilakukan dalam penelitian ini menunjukkan hasil bahwa ciri HOG dan HoC mampu dimanfaatkan dalam proses verifikasi tanda tangan secara offline.  Pada basis data GPDS960Signature, HOG dan HoC yang dihitung pada ukuran sel 30 x 30 piksel memberikan dengan nilai %FRR terbaik 26,90 dan %FAR 37,56.  Sedangkan pada basis data FUM-PHSDB, HOG dn HoC yang dihitung pada ukuran 60 x 60 piksel memberikan nilai %FRR terbaik 4 dan %FAR 57. Kata kunci: verifikasi tanda tangan, curvature, orientation, gradient, histogram of curvature (HoC), histogram of oriented gradient (HOG) Abstract Signature with unique properties is one of the many personal attributes that are widely accepted to verify a person's identity, proof of ownership transactions instrument or document in the community. The successful use of gradient and curvature feature in the research fields of pattern recognition is the reason that both of these features are used as an offline signature verification method in this study. Various implementations of preprocessing, feature extraction and representation, and SVM learning has been done in the study showed results that HOG and HoC feature can be utilized in the process of offline signature verification.  HOG and HOC calculated on a combination of two different cell sizes at a time.  Improvement effort has been made and showed the expected results, although of little value. HOG and HOC calculated on a such cell sizes at a time. In database GPDS960Signature, best cell size is in 30 with the value 26.90% FRR and FAR 37.56%. While the database FUM-PHSDB, the best cell size is 60 with a value of 4% FRR and FAR 57%. Keywords: signature verification, curvature, orientation, gradient, a histogram of curvature (HOC), a histogram of oriented gradient (HOG)
PENERAPAN ALGORITMA GENETIKA UNTUK KOMPRESI CITRA FRAKTAL Putu Indah Ciptayani; Wayan Firdaus Mahmudy; Agus Wahyu Widodo
Jurnal Ilmu Komputer Vol. 2, No. 1 April 2009
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (116.392 KB)

Abstract

Fractal image compression is one of compression techniques which produce a high compression ratio with good quality of result image. But this method has weakness is the time to compress image is too long because checking domain and range block is done by brute force method. Because of it, necessary to get approach with genetic algorithm which genetic algorithm is an appropriate approach for complex combinatorial problem. Genetic algorithm play role in searching the matching domain and range block. Experiment is done by use three crossover and mutation method, the size of range block is 4, mutation probability is 0.1, crossover probability is 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0. Maximal size of generations are 500. The best result of compression image has ratio 75.01% with compression time is 10.7 second and MSE is 0.158839.