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All Journal Jurnal Bina Manajemen Intelektual : Jurnal Penelitian Dosen Akuntansi Blockchain Frontier Technology (BFRONT)
Tessa Handra Tessa Handra
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : Blockchain Frontier Technology (BFRONT)

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Application of IoT and AI Based on ESP32CAM to Support Sustainable Mobility in Smart Cities Mohammad Annas; Ramahdan, Fikri Arsla; Tessa Handra; Achmad Hidayat Dwi Saputra; Henrik Jensen
Blockchain Frontier Technology Vol. 4 No. 2 (2025): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v4i2.707

Abstract

Sustainable mobility is a crucial component of smart city development, aiming to create an efficient, ecofriendly, and high quality urban environment. The rapid urbanization and increasing vehicle numbers have led to traffic congestion, high carbon emissions, and inefficient public transportation systems. To address these challenges, integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies, particularly through the ESP32CAM platform, offers innovative solutions for realtime monitoring, analysis, and management of transportation systems. This study aims to develop an intelligent mobility system that leverages ESP32CAM as the primary device for capturing visual data, including traffic density, vehicle tracking, and parking area occupancy. The collected data is processed using AI algorithms to generate insights, such as traffic pattern predictions and alternative route recommendations. The gap in existing smart mobility solutions lies in their high implementation costs and limited adaptability to developing regions. this study introduces an affordable and flexible system that optimizes transportation efficiency by up to 30%, reducing travel time and carbon emissions while enhancing public transportation management. The results indicate that implementing this system significantly contributes to sustainable urban development by improving mobility, reducing environmental impact, and accelerating the transformation towards smart cities, especially in developing countries where cost-effective solutions are essential.
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 : IAIC Bangun Bangsa

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 Achmad Hidayat Dwi Saputra Ariesya Aprillia Faye Maya Dewi Fhia Amelia Fitra Putri Oganda Henrik Jensen Lod Sulivyo Mohammad Annas Ninda Lutfiani Noah Rangi Ramahdan, fikri Arsla Subandriyo Vincent Vincent