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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Computer Science and Technology Application BEAM
Dewi Immaniar Desrianti
University of Raharja
Computer Science & IT Other

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Big Data Analytics for Smart Cities: Optimizing Urban Traffic Management Using Real-Time Data Processing Mohammad Miftah; Dewi Immaniar Desrianti; Nanda Septiani; Ahmad Yadi Fauzi; Cole Williams
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.56

Abstract

Smart cities require efficient traffic management to address congestion and optimize urban mobility. With increasing urban populations and vehicle vol- umes, traditional traffic control systems struggle to meet growing demands, ne- cessitating advanced technological interventions. This study aims to explore the integration of big data analytics and real-time data processing in optimizing urban traffic management. By leveraging machine learning algorithms, sensor data, and predictive models, this research seeks to enhance traffic flow and improve overall transportation efficiency. The methodology involves col- lecting data from traffic sensors, GPS-equipped vehicles, and surveillance cameras, which are then analyzed using Apache Hadoop and Apache Spark to derive meaningful insights. Real-time data processing techniques ensure im- mediate responses to traffic conditions, dynamically adjusting signal timings and rerouting vehicles to mitigate congestion. The results indicate a 15-25% reduc- tion in travel times in high-traffic areas where real-time adaptive signal control is implemented. Furthermore, the analysis highlights distinct traffic patterns, congestion hotspots, and travel time optimization opportunities that can sig- nificantly enhance urban transportation efficiency. This research confirms that big data-driven traffic management can lead to better decision-making, im- proved commuter experiences, and reduced environmental impact through lower emissions. Future studies should focus on advanced predictive algo- rithms, connected vehicle technology, and AI-driven automation to further refine urban traffic solutions. By implementing real-time analytics, smart cities can develop sustainable, efficient, and adaptive traffic management systems that improve mobility and quality of life for urban residents.
Enhancing Audience Engagement through Interactive AIDriven Narrative Structures Michael Surya Gunawan; Untung Rahardja; Annisa Ardien; John Edwards; Dewi Immaniar Desrianti
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 2 May (2026): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid evolution of digital media has increased the demand for adaptive storytelling approaches capable of sustaining audience engagement beyond conventional linear narratives. This study investigates the integration of Large Language Models (LLMs) into interactive non-linear storytelling to enhance audience immersion and narrative personalization. A conceptual AI-driven narrative framework is proposed by incorporating adaptive weighting mechanisms inspired by modified gravity theory to dynamically adjust narrative progression according to user interactions and contextual preferences. The proposed framework is evaluated through a comparative analysis between conventional branching narratives and AI-driven adaptive narrative structures using engagement-oriented performance indicators. The findings indicate that adaptive AI-based storytelling improves narrative flexibility, emotional engagement, and user agency compared with traditional fixed-choice approaches. Rather than treating narrative progression as a static sequence, the proposed framework dynamically modifies story development based on real-time interaction, enabling a more personalized storytelling experience. The study contributes to the advancement of intelligent media broadcasting by introducing an interdisciplinary framework that combines artificial intelligence with adaptive narrative modeling. These findings provide practical implications for interactive entertainment, digital education, and AI-assisted content creation while offering a foundation for future empirical research on adaptive storytelling systems.
Co-Authors Ahmad Yadi Fauzi Annisa Ardien Cole Williams John Edwards Michael Surya Gunawan Mohammad Miftah Nanda Septiani Untung Rahardja