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IDENTIFIKASI CITRA TANDA TANGAN MENGGUNAKAN LOCAL BINARY PATTERN (LBP) PADA SMARTPHONE BERBASIS ANDROID

SemanTIK: Teknik Informasi · Vol. 5 No. 1 (2019) · pp. 17–24

DOI
10.55679/semantik.v5i1.5200
ISSN
2502-8928, 2460-1446
Published
2019-06-30
Publisher
Informatics Engineering Department of Halu Oleo University
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Signatures are currently used by the majority of the public to authorize a document or as proof of transaction. The signature is a unique writing style of someone's name or as an identification sign of that person. This application uses the Local Binary Pattern (LBP) method as its feature extraction and Manhattan Distance as recognition. The LBP operator is a texture descriptor that uses a comparison of gray values of neighboring pixels. For training, data retrieval is done by collecting data 10 times from 50 participants with the amount of training data of 500 samples. The test is done 5 times each participant with the test results on average by 80.8% and the highest test by 94%.Keywords—Hand Signature, Local Binary Pattern, Manhattan Distance DOI: 10.5281/zenodo.2650530

Keywords

Collaboration network of the authors

40 authors · 96 co-authorship links · 10 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Halu Oleo
  • Universitas Gadjah Mada
  • Indonesia Open University
  • Institut Teknologi Sepuluh Nopember
  • University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Statiswaty StatiswatyUniversitas Halu Oleo5
Adha Mashur SajiahUniversitas Halu Oleo4
Jayanti Yusmah SariUniversitas Halu Oleo3
Muh YaminUniversitas Halu Oleo3
Bambang PramonoUniversitas Halu Oleo3
Rahmat RamadhanUniversitas Halu Oleo3
Sutardi SutardiUniversitas Halu Oleo2
Irmaya Citra HarwendhaniAffiliation unknown2
Wa Ode Rizqanun Karyima BoluUniversitas Halu Oleo2
Dewi HastutiAffiliation unknown2
Agfianto Eko PutraUniversitas Gadjah Mada1
Dian NursantikaIndonesia Open University1
Rabiuldien AmatUniversitas Halu Oleo1
Muh. Abdi FahmiAffiliation unknown1
Azim RamadhanAffiliation unknown1
La Ode ApriadiAffiliation unknown1
Yuwanda Purnamasari PasrunInstitut Teknologi Sepuluh Nopember1
Djafar DjafarAffiliation unknown1
Muhammad FajarUniversity of Indonesia1
La Ode Hasnuddin S. SagalaUniversitas Gadjah Mada1
Maulana MalikAffiliation unknown1
Wa Ode Wahyuni MakmunUniversitas Halu Oleo1
Muhammad Ihsan SaritaUniversitas Halu Oleo1
Rifa’atus ShalihahUniversitas Halu Oleo1
Christine Florence KiswantoAffiliation unknown1
Iqbal TawaqqalAffiliation unknown1
Anita Puspita DewiAffiliation unknown1
Agtria Prilika HerniatyAffiliation unknown1
Muh Farid FachroniUniversitas Halu Oleo1
La Ode Muhammad Golok JayaUniversitas Halu Oleo1
Alif MuqtadirAffiliation unknown1
Muhammad UsganAffiliation unknown1
LM. Taha RamadhanAffiliation unknown1
Muhammad Ichwan UtariAffiliation unknown1
Alfian AlfianAffiliation unknown1
Rizal Adi SaputraUniversitas Halu Oleo1
Roy PaneAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs.uho.ac.id/index.php/semantik/oai, first harvested 29 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">IDENTIFIKASI CITRA TANDA TANGAN MENGGUNAKAN LOCAL BINARY PATTERN (LBP) PADA SMARTPHONE BERBASIS ANDROID</dc:title>
	<dc:creator>Syair, Ayu Sabrina</dc:creator>
	<dc:creator>Sarita, Muh. Ihsan</dc:creator>
	<dc:creator>Ningrum, Ika Purwanti</dc:creator>
	<dc:subject xml:lang="en-US">hand signature, Local Binary Pattern, Manhattan Distance</dc:subject>
	<dc:description xml:lang="en-US">Signatures are currently used by the majority of the public to authorize a document or as proof of transaction. The signature is a unique writing style of someone's name or as an identification sign of that person. This application uses the Local Binary Pattern (LBP) method as its feature extraction and Manhattan Distance as recognition. The LBP operator is a texture descriptor that uses a comparison of gray values of neighboring pixels. For training, data retrieval is done by collecting data 10 times from 50 participants with the amount of training data of 500 samples. The test is done 5 times each participant with the test results on average by 80.8% and the highest test by 94%.Keywords—Hand Signature, Local Binary Pattern, Manhattan Distance DOI : 10.5281/zenodo.2650530</dc:description>
	<dc:publisher xml:lang="en-US">Informatics Engineering Department of Halu Oleo University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-06-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs.uho.ac.id/index.php/semantik/article/view/5200</dc:identifier>
	<dc:identifier>10.55679/semantik.v5i1.5200</dc:identifier>
	<dc:source xml:lang="en-US">SemanTIK : Teknik Informasi; Vol 5, No 1 (2019): semanTIK; 17-24</dc:source>
	<dc:source>2502-8928</dc:source>
	<dc:source>2460-1446</dc:source>
	<dc:source>10.55679/semantik.v5i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs.uho.ac.id/index.php/semantik/article/view/5200/pdf</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 semanTIK</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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