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Fuzzy MADM Approach for Rating of Process-Based Fraud

Journal of ICT Research and Applications (JICTRA) · Vol. 9 No. 2 (2015) · pp. 111–128

DOI
10.5614/itbj.ict.res.appl.2015.9.2.1
ISSN
2338-5499, 2337-5787
Published
2015-11-30
Publisher
DRI - ITB
Language
eng

Abstract

Process-Based Fraud (PBF) is fraud enabled by process deviations that occur in business processes. Several studies have proposed PBF detection methods; however, false decisions are still often made because of cases with low deviation. Low deviation is caused by ambiguity in determining fraud attribute values and low frequency of occurrence. This paper proposes a method of detecting PBF with low deviation in order to correctly detect fraudulent cases. Firstly, the fraudulence attributes are established, then a fuzzy approach is utilized to weigh the importance of the fraud attributes. Further, multi-attribute decision making (MADM) is employed to obtain a PBF rating according to attribute values and attribute importance weights. Finally, a decision is made whether the deviation is fraudulent or not, based on the PBF rating. Experimental validation showed that the accuracy and false discovery rate of the method were 0.92 and 0.33, respectively.

Collaboration network of the authors

40 authors · 76 co-authorship links · 23 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.

  • Institut Teknologi Sepuluh Nopember
  • Universitas Dian Nuswantoro
  • Politeknik Negeri Malang
  • Universitas Internasional Semen Indonesia
  • University of Brawijaya
  • Universiti Malaysia Terengganu
  • Mercu Buana University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Royyana Muslim IjtihadieInstitut Teknologi Sepuluh Nopember4
Kelly Rossa SungkonoInstitut Teknologi Sepuluh Nopember4
Hendro Eko PrabowoInstitut Teknologi Sepuluh Nopember2
Chastine FatichahInstitut Teknologi Sepuluh Nopember2
Imam GhozaliInstitut Teknologi Sepuluh Nopember2
Rachmad AbdullahInstitut Teknologi Sepuluh Nopember2
Faisal RahutomoPoliteknik Negeri Malang1
Rully SoelaimanInstitut Teknologi Sepuluh Nopember1
Esther HanajaInstitut Teknologi Sepuluh Nopember1
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Margo PudjiantaraInstitut Teknologi Sepuluh Nopember1
Muhammad HolilAffiliation unknown1
Yohanes I. RiskajayaInstitut Teknologi Sepuluh Nopember1
Doni Setio PambudiUniversitas Internasional Semen Indonesia1
Arief PrasetyoInstitut Teknologi Sepuluh Nopember1
Bayu PriyambadhaUniversity of Brawijaya1
Yogi KurniawanInstitut Teknologi Sepuluh Nopember1
Nurul Fajrin AriyaniInstitut Teknologi Sepuluh Nopember1
Sarwosri SarwosriInstitut Teknologi Sepuluh Nopember1
Alief Yoga PriyantoInstitut Teknologi Sepuluh Nopember1
Vynska Amalia PermadiAffiliation unknown1
Bagus Jati SantosoInstitut Teknologi Sepuluh Nopember1
Muhammad Fattahilah RangkutyInstitut Teknologi Sepuluh Nopember1
Dino Budi PrakosoInstitut Teknologi Sepuluh Nopember1
Akbar Pandu SegaraInstitut Teknologi Sepuluh Nopember1
I Gusti Agung Chintya Prema DewiInstitut Teknologi Sepuluh Nopember1
Muhammad Suzuri HitamUniversiti Malaysia Terengganu1
Fika Hastarita RachmanInstitut Teknologi Sepuluh Nopember1
Maurice NtahobariInstitut Teknologi Sepuluh Nopember1
Siti RochimahInstitut Teknologi Sepuluh Nopember1
Bambang JokonowoMercu Buana University1
Jan ClaesGhent University1
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Anto Satriyo NugrohoBadan Pengkajian dan Penerapan Teknologi1
Umi SalamahSebelas Maret University1
Ismail Eko Prayitno RoziAffiliation unknown1
Puji Budi Setia AsihEijkman Institute for Molecular Biology1

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://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Fuzzy MADM Approach for Rating of Process-Based Fraud</dc:title>
	<dc:creator>Huda, Solichul</dc:creator>
	<dc:creator>Sarno, Riyanarto</dc:creator>
	<dc:creator>Ahmad, Tohari</dc:creator>
	<dc:description xml:lang="en-US">Process-Based Fraud (PBF) is fraud enabled by process deviations that occur in business processes. Several studies have proposed PBF detection methods; however, false decisions are still often made because of cases with low deviation. Low deviation is caused by ambiguity in determining fraud attribute values and low frequency of occurrence. This paper proposes a method of detecting PBF with low deviation in order to correctly detect fraudulent cases. Firstly, the fraudulence attributes are established, then a fuzzy approach is utilized to weigh the importance of the fraud attributes. Further, multi-attribute decision making (MADM) is employed to obtain a PBF rating according to attribute values and attribute importance weights. Finally, a decision is made whether the deviation is fraudulent or not, based on the PBF rating. Experimental validation showed that the accuracy and false discovery rate of the method were 0.92 and 0.33, respectively.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2015-11-30</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://journals.itb.ac.id/index.php/jictra/article/view/944</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2015.9.2.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 9 No. 2 (2015); 111-128</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/944/1056</dc:relation>
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