Open access Journal SINTA 1
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
- ISSN
- 2443-2555, 2598-6333
- Published
- 2026-10-28
- Publisher
- Universitas Airlangga
- Language
- eng
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
27 authors · 69 co-authorship links
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- IPB University
- Universitas Ahmad Dahlan
- Affiliation unknown
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Karlisa Priandana | IPB University | 3 |
| Arif Imam Suroso | IPB University | 1 |
| Popong Nurhayati | Affiliation unknown | 1 |
| Trukan Sri Bahukeling | Affiliation unknown | 1 |
| Eko Prianto | Universitas Ahmad Dahlan | 1 |
| Zaenal Mustofa | Affiliation unknown | 1 |
| Ilham Ramadan Pandu Setia Negara Siregar | Affiliation unknown | 1 |
| Arwan Nur Ramadhan | Affiliation unknown | 1 |
| Hamdan Djainudin | Affiliation unknown | 1 |
| Andik Asmara | Affiliation unknown | 1 |
| Dzul Fadil Rahman | Affiliation unknown | 1 |
| Yeni Herdiyeni | IPB University | 1 |
| Iskandar Zulkarnaen Siregar | IPB University | 1 |
| Muhammad Rafi Muttaqin | Affiliation unknown | 1 |
| Toto Haryanto | Affiliation unknown | 1 |
| Muhammad Adib Zamzam | Affiliation unknown | 1 |
| Cholid Mawardi | Affiliation unknown | 1 |
| Herianto Herianto | Affiliation unknown | 1 |
| Ida Ayu Made Sri Widiastuti | Affiliation unknown | 1 |
| Katie Weir | Affiliation unknown | 1 |
| Gunadi Harry Sulistyo | Affiliation unknown | 1 |
| Hidayat Hidayat | Affiliation unknown | 1 |
| Sri Wahjuni | IPB University | 1 |
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.