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Graph-Based Fuzzy Mamdani Model for Money Laundering Risk Assessment in Mining and Coal Transactions

Journal of Information Systems Engineering and Business Intelligence · Vol. 12 No. 3 (2026) · pp. 596–614

DOI
10.20473/jisebi.12.3.596-614
ISSN
2443-2555, 2598-6333
Published
2026-10-28
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: Financial transactions in the mineral and coal mining sector will become increasingly complex, creating the potential for difficult-to-detect money laundering practices. Therefore, methods are needed to identify the reasonableness of the transaction relationship between the sender and recipient of funds. Objective: This research aims to design and implement a graph-based fuzzy Mamdani model to assess the risk level of transaction relationships that indicate money laundering in the mineral and coal mining sector. Methods: The research employed a quantitative modelling approach. The dataset consists of financial transaction reports obtained from the Indonesian Financial Transaction Reports and Analysis Center (INTRACT). The graph-based Mamdani Fuzzy Inference System (FIS) is applied to assess the risk level of transaction relationships. The model is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). Results: The results show FIS Mamdani model achieved superior performance with an MSE of 0.07, RMSE of 0.26, and R² of 0.93. Testing results with new data whose risk levels are known based on expert assessments and court decisions, show that all risk categories generated by the model are fully aligned with the classifications established based on professional evaluations. Conclusion: These findings confirm that the graph-based Mamdani fuzzy model is an adaptive and accurate tool for assessing transaction relationship risk, which can be used to detect money laundering in the mineral and coal mining sector. This research contributes to the advancement of graph-based financial transaction monitoring systems and expert systems, enhancing their ability to handle complex and dynamic transaction relationship risks. Further research is recommended to explore integration with advanced machine learning techniques such as deep learning. Keywords: Graph-Based Model, Mamdani Fuzzy, Money Laundering Risk, Mining Transactions, Artificial Intelligence

Collaboration network of the authors

17 authors · 28 co-authorship links · 7 across different organisations

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

  • IPB University
  • Universitas Muhammadiyah Parepare
  • Universitas Kristen Wira Wacana Sumba
  • Pakuan University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
IrmansyahAffiliation unknown1
Dhany Nugraha RamdhanyAffiliation unknown1
Aziz KustiyoAffiliation unknown1
Ekowati HandharyaniIPB University1
Mustakim MustakimUniversitas Muhammadiyah Parepare1
Irman HermadiIPB University1
Arini Aha PekuwaliUniversitas Kristen Wira Wacana Sumba1
Wisnu Ananta KusumaIPB University1
Siti RaehanIPB University1
Medria Kusuma Dewi HardhienataAffiliation unknown1
Sri NurdiatiIPB University1
Fajar Delli WihartikoPakuan University1
Edi SantosaIPB University1

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://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Graph-Based Fuzzy Mamdani Model for Money Laundering Risk Assessment in Mining and Coal Transactions</dc:title>
	<dc:creator>Purnomo, Bambang Hadi</dc:creator>
	<dc:creator>Buono , Agus </dc:creator>
	<dc:creator>Sukoco, Heru</dc:creator>
	<dc:creator>Yustiavandana , Ivan </dc:creator>
	<dc:description xml:lang="en-US">Background: Financial transactions in the mineral and coal mining sector will become increasingly complex, creating the potential for difficult-to-detect money laundering practices. Therefore, methods are needed to identify the reasonableness of the transaction relationship between the sender and recipient of funds. 
Objective: This research aims to design and implement a graph-based fuzzy Mamdani model to assess the risk level of transaction relationships that indicate money laundering in the mineral and coal mining sector. 
Methods: The research employed a quantitative modelling approach. The dataset consists of financial transaction reports obtained from the Indonesian Financial Transaction Reports and Analysis Center (INTRACT). The graph-based Mamdani Fuzzy Inference System (FIS) is applied to assess the risk level of transaction relationships. The model is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²). 
Results: The results show FIS Mamdani model achieved superior performance with an MSE of 0.07, RMSE of 0.26, and R² of 0.93. Testing results with new data whose risk levels are known based on expert assessments and court decisions, show that all risk categories generated by the model are fully aligned with the classifications established based on professional evaluations. 
Conclusion: These findings confirm that the graph-based Mamdani fuzzy model is an adaptive and accurate tool for assessing transaction relationship risk, which can be used to detect money laundering in the mineral and coal mining sector. This research contributes to the advancement of graph-based financial transaction monitoring systems and expert systems, enhancing their ability to handle complex and dynamic transaction relationship risks. Further research is recommended to explore integration with advanced machine learning techniques such as deep learning. 
Keywords: Graph-Based Model, Mamdani Fuzzy, Money Laundering Risk, Mining Transactions, Artificial Intelligence </dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2026-10-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/79536</dc:identifier>
	<dc:identifier>10.20473/jisebi.12.3.596-614</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 12 No. 3 (2026): October; 596-614</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/79536/41633</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>