Analisis Yuridis Penggunaan Cryptocurrency sebagai Sarana Pendanaan Aksi Terorisme di Indonesia Beserta Tantangan dalam Penegakan Hukum

Authors

  • Rizky Dwi Utami Universitas Wira Buana
  • Ahmad Nafhani Universitas Mulawarman
  • Agung Pratama Law Firm APR and Partners

DOI:

https://doi.org/10.55606/jurrish.v5i3.8643

Keywords:

Criminal Law, Crypto Assets, Cryptocurrency, Law Enforcement, Terrorist Financing

Abstract

The development of financial technology has led to the emergence of cryptocurrency as a decentralized digital instrument that enables fast and cross-border financial transactions. While this technology offers efficiency and flexibility in digital financial activities, it also creates opportunities for misuse in various forms of crime, including terrorist financing. This study aims to analyze the use of cryptocurrency as a means of financing terrorist activities in Indonesia, examine the existing legal framework governing terrorist financing, and identify the challenges faced in law enforcement. This research employs a normative legal method using statutory, conceptual, and case study approaches. The findings indicate that the use of cryptocurrency as a medium for terrorist financing still fulfills the elements of a criminal offense as regulated under Law Number 9 of 2013 concerning the Prevention and Eradication of Terrorism Financing. However, the characteristics of cryptocurrency, such as anonymity, decentralization, and cross-border transactions, create significant challenges in the processes of evidence gathering, transaction tracing, and identification of perpetrators. In addition, there is a regulatory gap between the recognition of crypto assets as economic commodities and the supervision of their potential misuse for terrorist financing. Therefore, stronger regulations are needed to explicitly integrate crypto assets into the terrorist financing prevention regime, along with improving the capacity of law enforcement agencies in blockchain transaction analysis and strengthening international cooperation to enhance the effectiveness of law enforcement in the digital economy era.

Downloads

Download data is not yet available.

References

Adams, S., Scherer, W. T., & Beling, P. A. (2017). Data, insights, models, and decisions: Machine learning in context. In Intuition, trust, and analytics. https://doi.org/10.1201/9781315195551

Alamouti, S. M., Arjomandi, F., Burger, M., & Gün, H. (2026). Device first continuum AI (DFC-AI): Realizing human-like AI. Lecture Notes in Networks and Systems, 1676, 482–495. https://doi.org/10.1007/978-3-032-07989-3_31

Altingovde, I. S., Cambazoglu, B. B., & Tonellotto, N. (2015). LSDS-IR’15: 2015 workshop on large-scale and distributed systems for information retrieval. Proceedings of the International Conference on Information and Knowledge Management, 1947–1948. https://doi.org/10.1145/2806416.2806877

Anand, A. S., Sawant, S., Reinhardt, D. P., & Gros, S. (2025). Predicting what matters: Training AI models for better decisions. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2025.3633573

Barreiro-Gomez, J., Ocampo-Martinez, C., & Quijano, N. (2017). Partitioning for large-scale systems: A sequential distributed MPC design. IFAC-PapersOnLine, 50(1), 8838–8843. https://doi.org/10.1016/j.ifacol.2017.08.1539

Bilal, H., Rehman, A., Aslam, M. S., Ullah, I., Chang, W.-J., Kumar, N., & Almuhaideb, A. M. (2025). Hybrid TrafficAI: A generative AI framework for real-time traffic simulation and adaptive behavior modeling. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2025.3571041

Cabrera, O., Franch, X., & Marco, J. (2017). Ontology-based context modeling in service-oriented computing: A systematic mapping. Data and Knowledge Engineering, 110, 24–53. https://doi.org/10.1016/j.datak.2017.03.008

Cinque, M., Cotroneo, D., Esposito, C., Fiorentino, M., & Russo, S. (2018). Designing resilient and secure large-scale crisis information systems. In Volatiles in the Martian crust. https://doi.org/10.1016/B978-0-12-811373-8.00014-8

Costa, F. S. (2025). Three ways industrial AI enhances traditional control systems. Control Engineering, 72(6), 26–27.

Dai, H., Wang, Y., Kent, K. B., Zeng, L., & Xu, C. (2022). The state of the art of metadata managements in large-scale distributed file systems: Scalability, performance and availability. IEEE Transactions on Parallel and Distributed Systems, 33(12), 3850–3869. https://doi.org/10.1109/TPDS.2022.3170574

De Luca, E. W., Said, A., Crestani, F., & Elsweiler, D. (2015). 5th workshop on context-awareness in retrieval and recommendation. Lecture Notes in Computer Science, 9022, 830–833. https://doi.org/10.1007/978-3-319-16354-3_96

Fujimaki, R., Muraoka, Y., Ito, S., & Yabe, A. (2016). From prediction to decision making: Predictive optimization technology. NEC Technical Journal, 11(1), 62–65.

Haroon, M., Siddiqui, Z. A., Husain, M., Ali, A., & Ahmad, T. (2024). A proactive approach to fault tolerance using predictive machine learning models in distributed systems. International Journal of Experimental Research and Review, 44, 208–220. https://doi.org/10.52756/ijerr.2024.v44spl.018

Karadayi-Usta, S. (2024). Fuzzy rule-based systems: How to construct a FRBS with MATLAB, R, and Python. In Decision-making models: A perspective of fuzzy logic and machine learning. https://doi.org/10.1016/B978-0-443-16147-6.00008-6

Khan, M. T., Durrani, M., Khalid, S., & Aziz, F. (2016). Lifelong aspect extraction from big data: Knowledge engineering. Complex Adaptive Systems Modeling, 4(1). https://doi.org/10.1186/s40294-016-0018-7

Kumar, V. S., Antony, S., Rao, R., Anitha, B., Prasad, B. V. V. S., & Maram, B. (2025). Artificial intelligence and optimization: Perfecting decision-making models. Proceedings of the 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS 2025), 1195–1200. https://doi.org/10.1109/ICSCDS65426.2025.11167208

Legrand, I. C. (2016). Monitoring and control of large-scale distributed systems. Proceedings of the International School of Physics “Enrico Fermi,” 192, 101–151. https://doi.org/10.3254/978-1-61499-643-9-101

Liu, H., Gegov, A., & Cocea, M. (2016). Introduction. Studies in Big Data, 13, 1–9. https://doi.org/10.1007/978-3-319-23696-4_1

Machado, R., Rosa, F., Almeida, R., Primo, T., Pilla, M., Pernas, A., & Yamin, A. (2018). A hybrid architecture to enrich context awareness through data correlation. Proceedings of the ACM Symposium on Applied Computing, 1451–1453. https://doi.org/10.1145/3167132.3167405

Palivela, L. H., Vivekanandan, D., Chinniah, P., Kiranbabu, M. N. V., Panneer Dhas, L., Srivastava, P., & Anantraj, I. (2024). Revolutionizing AI decision-making with hybrid rule-based systems: A novel framework for transparent and accurate outcomes. Communications on Applied Nonlinear Analysis, 31(8S), 115–127. https://doi.org/10.52783/cana.v31.1460

Pierson, J.-M., & Hlavacs, H. (2015). Introduction to energy efficiency in large-scale distributed systems. In Large-scale distributed systems and energy efficiency: A holistic view. https://doi.org/10.1002/9781118981122.ch1

Pushpa, P. V. (2017). Customer context based transactions in mobile commerce business environment. Proceedings of the 13th IEEE International Conference on E-Business Engineering, 208–213. https://doi.org/10.1109/ICEBE.2016.043

Quijano, N., Ocampo-Martinez, C., Barreiro-Gomez, J., Obando, G., Pantoja, A., & Mojica-Nava, E. (2017). The role of population games and evolutionary dynamics in distributed control systems: The advantages of evolutionary game theory. IEEE Control Systems, 37(1), 70–97. https://doi.org/10.1109/MCS.2016.2621479

Rocha, R. R., Oliveira-Lopes, L. C., & Christofides, P. D. (2018). Partitioning for distributed model predictive control of nonlinear processes. Chemical Engineering Research and Design, 139, 116–135. https://doi.org/10.1016/j.cherd.2018.09.003

Savvadelli, E., Kiouvrekis, Y., & Kokkinaki, A. (2026). A literature review on rule-based systems as decision support systems. IFIP Advances in Information and Communication Technology, 761, 376–388. https://doi.org/10.1007/978-3-032-02504-3_26

Segovia, P., Rajaoarisoa, L., Nejjari, F., Duviella, E., & Puig, V. (2019). A communication-based distributed model predictive control approach for large-scale systems. Proceedings of the IEEE Conference on Decision and Control, 8366–8371. https://doi.org/10.1109/CDC40024.2019.9030085

Subbaraj, R., & Venkatraman, N. (2015). A systematic literature review on ontology based context management system. Advances in Intelligent Systems and Computing, 338, 609–619. https://doi.org/10.1007/978-3-319-13731-5_66

Tegicho, B. E., & Graves, C. (2021). Automatic emoji insertion based on environment context signals for the demonstration of pervasive computing features. IEEE SoutheastCon Conference Proceedings. https://doi.org/10.1109/SoutheastCon45413.2021.9401878

Vegega, C., Pytel, P., & Pollo-Cattaneo, M. F. (2019). Application of the requirements elicitation process for the construction of intelligent system-based predictive models in the education area. Communications in Computer and Information Science, 1051, 43–58. https://doi.org/10.1007/978-3-030-32475-9_4

Yamé, J. J., Gabsi, F., Darure, T., Jain, T., Hamelin, F., & Sauer, N. (2019). Optimality condition decomposition approach to distributed model predictive control. Proceedings of the American Control Conference, 742–747. https://doi.org/10.23919/ACC.2019.8814374

Downloads

Published

2026-05-04

How to Cite

Rizky Dwi Utami, Ahmad Nafhani, & Agung Pratama. (2026). Analisis Yuridis Penggunaan Cryptocurrency sebagai Sarana Pendanaan Aksi Terorisme di Indonesia Beserta Tantangan dalam Penegakan Hukum. Jurnal Riset Rumpun Ilmu Sosial, Politik Dan Humaniora, 5(3), 899–918. https://doi.org/10.55606/jurrish.v5i3.8643

Similar Articles

<< < 24 25 26 27 28 29 30 31 > >> 

You may also start an advanced similarity search for this article.