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All Journal Technomedia Journal Aptisi Transactions on Management ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Transactions on Education Technology (ITEE) Blockchain Frontier Technology (BFRONT) International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application JOT
Noah Rangi
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Adaptive Workflow Management with Decentralized AI in Blockchain Based Distributed Ledger Systems Tessa Handra; Ninda Lutfiani; Ariesya Aprillia; Fitra Putri Oganda; Fhia Amelia; Noah Rangi
Blockchain Frontier Technology Vol. 5 No. 1 (2025): Blockchain Frontier Technology
Publisher : Indonesian Association of Higher Education in Informatics and Computing (APTIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i1.804

Abstract

Blockchain technology has rapidly evolved as a decentralized solution offering high security and transparency; however, several challenges still hinder the effective management of workflows within blockchain based environments. This study aims to develop an adaptive workflow management model that utilizes decentralized artificial intelligence (AI) and distributed ledger technology (DLT) to enhance the performance, security, and flexibility of processes in blockchainn networks. A mixed method approach combining simulation and experimentation on a dedicated blockchain platform was employed. The adaptive workflow model consists of a realtime process monitoring module, a decentralized AI module for adaptive decision making, and a DLT component that ensures data consistency and security. Statistical methods and system performance evaluations were used to analyze the experimental data. Results show that the proposed model can reduce workflow response times by up to 25% and increase the successful execution rate of smart contracts to 98%. Moreover, the integration of decentralized AI optimizes workload distribution across nodes, enabling network scalability improvements of up to 150% without significant performance degradation. The findings demonstrate that the adaptive workflow model combining AI and DLT enhances the flexibility and governance of blockchain networks through AI’s predictive capabilities and DLT’s security. Nevertheless, challenges such as high computational resource demands and technical complexities must be addressed. This research opens opportunities for further development to expand the scope of complex and dynamic blockchain applications and supports their integration with technologies like the Internet of Things (IoT).
Co-Authors Adam Faturahman Agung Rizky Ardivan Avandi Ariesya Aprillia Ayu Ekasari Bahtiar Usman Cicilia Sriliasta Dwi Nur Ramadhan Eka Dian Astuti Farisha Andi Baso Felix Sutisna Fhia Amelia Fitra Putri Oganda Fitra Putri Oganda Galih Putra Cesna Hutabarat, Zoel Kanon Mommsen Wongkar Lukita Pasha Maulana Arif Komara Meriyana Sunengsih Michael Surya Gunawan Migunani Migunani Mitra Trima Dessincer Putri Muhammad Rifat Syauqy Nesti Anggraini Santoso Ninda Lutfiani Nugroho Prihantoni Nuke Puji Lestari Santoso Pranata, Sudadi Rifqa Nabila Muti Sandy Setiawan Susy Alestriani Sibagariang Tatik Mariyanti, Tatik Terra Saptina Maulani Tessa Handra Tuti Nurhaeni Zubair Ahmad