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Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 13 No. 4 (2019) · pp. 409–420

DOI
10.22146/ijccs.46561
ISSN
2460-7258, 1978-1520
Published
2019-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%).

Keywords

Collaboration network of the authors

5 authors · 5 co-authorship links

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  • Universitas Gadjah Mada
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Co-authorOrganisationJoint articles
Yusril IhzaUniversitas Gadjah Mada1
I Made Agus WirawanUniversitas Gadjah Mada1
Retantyo WardoyoUniversitas Gadjah Mada1

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://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Outlier Detection Credit Card Transactions Using Local Outlier Factor Algorithm (LOF)</dc:title>
	<dc:creator>Sugidamayatno, Silvano</dc:creator>
	<dc:creator>Lelono, Danang</dc:creator>
	<dc:subject xml:lang="en-US">Data Mining</dc:subject>
	<dc:subject xml:lang="en-US">credit card; outlier analysis; local outlier factor algorithm</dc:subject>
	<dc:description xml:lang="en-US">Threats or fraud for credit card owners and banks as service providers have been harmed by the actions of perpetrators of credit card thieves. All transaction data are stored in the bank's database, but are limited in information and cannot be used as a knowledge. Knowledge built with credit card transaction data can be used as an early warning by the bank. The outlier analysis method is used to build the knowledge with a local outlier factor algorithm that has high accuracy, recall, and precision results and can be used in multivariate data. Testing uses a matrix sample and confusion method with attributes date, categories, numbers, and countries. The test results using 1803 transaction data from five customers, indicating that the average value accuracy of LOF algorithms (96%), higher than the average accuracy values of the INFLO and AFV algorithms (84% and 77%).</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2019-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/46561</dc:identifier>
	<dc:identifier>10.22146/ijccs.46561</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 13, No 4 (2019): October; 409-420</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/46561/26048</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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