IDENTIFIKASI SIDIK JARI MENGGUNAKAN JARINGAN SYARAF PROPAGASI BALIK DAN LEARNING VECTOR QUANTIZATION BERBASIS MINUTIAE FITUR BIFURCATION

OLANDA, DERI and Samsuryadi, Samsuryadi and Miraswan, Kanda Januar (2019) IDENTIFIKASI SIDIK JARI MENGGUNAKAN JARINGAN SYARAF PROPAGASI BALIK DAN LEARNING VECTOR QUANTIZATION BERBASIS MINUTIAE FITUR BIFURCATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Fingerprint identification is useful for determining ownership of fingerprints. This determination is based on minutiae. There are several minutiae features and in this study the features used are the bifurcation-feature minutiae. To identify fingerprints based on the bifurcation-feature minutiae, back propagation and learning vector quantization methods are used. The results of this study found that the average back propagation accuracy reached 87.5%, the accuracy of learning vector quantization reached 50%, the average back propagation process time was 0.37378 seconds, and the average learning vector quantization process time was 0.36941 seconds .

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sidik jari, propagasi balik, learning vector quantization, minutiae, bifurcation
Subjects:	Q Science > Q Science (General) > Q350-390 Information theory Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 1675 not found.
Date Deposited:	04 Sep 2019 04:29
Last Modified:	04 Sep 2019 04:29
URI:	http://repository.unsri.ac.id/id/eprint/6196

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