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All Journal Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Kamal Arif Al-Farouqi
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Computer Science & IT Control & Systems Engineering Education Library & Information Science Other

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Optimizing Blockchain Based IoT Integration for Sustainable Mobility in Smart Cities Ria Sari Pamungkas; Aan Kanivia; Ariesya Aprillia; Kamal Arif Al-Farouqi
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.v4i2.710

Abstract

This research highlights the critical role of Internet of Things (IoT) technology and data analytics in fostering sustainable mobility within the Smart City concept. The primary objective is to examine how IoT sensors and real-time data analysis can optimize transportation efficiency while promoting environmental sustainability. The proposed Method involves integrating IoT sensors across urban infrastructure to collect realtime data on traffic patterns, air quality, and travel behavior, which is then analyzed using advanced data analytics techniques. The gap addressed in this study lies in the limited empirical evidence regarding the practical implementation of IoT and data analytics in improving urban mobility and environmental outcomes. The novelty of this research is in developing a predictive model that leverages IoT data to optimize public transportation routes, reduce congestion, and lower carbon emissions. Preliminary results suggest significant benefits, including a 25% reduction in emissions and a 40% increase in travel efficiency, demonstrating the potential of IoT-driven analytics in transforming urban mobility. The findings of this study contribute to a deeper understanding of sustainable transportation solutions within smart cities, offering a data driven approach to enhance public transportation networks and minimize environmental impact, ultimately paving the way for a more efficient and eco friendly urban ecosystem
Efficient Machine Learning Acceleration with Randomized Linear Algebra for Big Data Cahyono, Dwi; Sijabat, Apriani; Kamal Arif Al-Farouqi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/rw6c2y67

Abstract

The rapid growth of big data has significantly increased the computational complexity of machine learning models, particularly due to intensive linear algebra operations that limit scalability and efficiency. This study aims to investigate the effectiveness of Randomized Linear Algebra (RLA) as an acceleration strategy for machine learning in large scale data environments. The research adopts an experimental methodology by integrating randomized techniques such as matrix sketching and random projection into standard machine learning pipelines and evaluating their performance against deterministic baseline approaches. Experiments are conducted on large dimensional datasets using multiple machine learning models, with performance assessed in terms of computational time, memory usage, model accuracy, and scalability. The results demonstrate that the proposed RLA based approach substantially reduces computational cost and memory consumption while maintaining comparable predictive accuracy to conventional methods. These findings indicate that randomized techniques provide an effective trade off between efficiency and accuracy, enabling scalable machine learning for big data applications. In conclusion, this study contributes to the advancement of efficient Artificial Intelligence (AI) systems by demonstrating that RLA can serve as a practical and scalable solution for accelerating machine learning computations in big data contexts, aligning with the growing demand for resource efficient and high performance AI infrastructures.
Co-Authors Aan Kanivia Apriani Sijabat Ariesya Aprillia DWI CAHYONO Ria Sari Pamungkas