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BEAM
Published by Sinar Mentari Sundara
ISSN : 31638015     EISSN : 31638031     DOI : https://doi.org/10.68012/beam
Core Subject :
Bridging of Emerging AI and Media Broadcasting (BEAM) is an international, peer-reviewed, open-access journal dedicated to the intersection of artificial intelligence, telecommunications engineering, and digital media studies. The journal provides a high-impact platform for researchers, engineers, and media theorists to disseminate transformative discoveries that redefine how content is created, transmitted, and consumed in a decentralized, AI-driven ecosystem. The scope of BEAM is categorized into five primary pillars of research: 1. AI-Native Content & Generative Media This pillar focuses on the "Orange Tech" philosophy—using AI to enhance rather than replace human emotional resonance. Algorithmic Creativity: Automated scriptwriting, AI-assisted music composition, and synthetic voice generation for broadcasting. Virtual Personalities: The development and ethics of AI "DJs," automated news anchors, and digital humans. Real-time Synthesis: Generative video for live broadcasting and interactive storytelling. 2. Smart Transmission & Next-Gen Infrastructure Focusing on the technical "synchronization" of the media landscape, ensuring seamless global "signal" delivery. Network Evolution: 5G/6G applications in high-definition broadcasting and ultra-low latency streaming. Cloud & Edge Computing: Decentralized streaming architectures and the transition from terrestrial towers to cloud-based broadcasting. Signal Processing: Advanced encoding, error correction, and optimization for VR/AR and immersive media events. 3. Interactive Audience Analytics & Behavioral Modeling Researching the shift from unidirectional broadcasting to an interactive, AI-enhanced dialogue. Sentiment Analysis: Machine learning models to decode viewer/listener engagement and cultural trends in real-time. Personalization Engines: Hyper-local content delivery and algorithmic recommendation systems. Cognitive Media: The impact of media consumption on human psychology and social behavior. 4. Ethics, Policy, and Journalistic Integrity Addressing the "Signal-to-Noise" challenge by ensuring academic and ethical rigor in the AI era. Synthetic Authenticity: Regulating deepfakes, provenance watermarking, and AI-labeling in news reporting. Data Sovereignty: Privacy frameworks for streaming audiences and algorithmic transparency. Media Governance: Legislative responses to decentralized media and global broadcasting standards. 5. Sustainability & Green Broadcasting Ensuring the future of media is resilient and environmentally responsible. Energy-Efficient AI: Reducing the computational and carbon footprint of generative models. Sustainable Infrastructure: Green data centers and energy-optimized transmission hardware. Circular Media Economy: Strategies for reducing electronic waste in the broadcasting industry.
Arjuna Subject : -
Articles 12 Documents
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Integrating Broadcasting Data Mining and Visualizationfor Effective Big Data Decision Support Diego Abbas; Arthur Simanjuntak; Thomas Sumarsan Goh
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 2 May (2026): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the era of big data and digital broadcasting, organizations face increasing challenges in transforming large-scale broadcasting datasets into actionable insights for effective decision-making. This study addresses the need for an integrated decision support framework that combines broadcasting data mining and interactive visualization to improve the interpretation of complex data patterns. The objective of this research is to develop an integrated approach that applies data mining techniques to broadcasting-related data, such as audience behavior, content performance, engagement patterns, and multi-source media data, supported by visual dashboards for strategic analysis. The method employed includes clustering, classification, and association rule mining to identify meaningful patterns, audience segments, trends, and anomalies within broadcasting datasets. These analytical results are then presented through interactive visualization dashboards that enable stakeholders to explore insights more efficiently and make data-driven decisions. The results show that integrating broadcasting data mining with visualization improves the speed, accuracy, and clarity of insight extraction compared to conventional analytics approaches. User evaluation also indicates that visualized analytical outputs enhance stakeholder understanding of complex broadcasting data and support more accurate strategic decisions. The conclusion of this study confirms that the integration of broadcast- ing data mining and visualization within a big data decision support framework can bridge the gap between raw media data and practical decision-making. This research contributes to the development of more adaptive, efficient, and human centered decision support systems for broadcasting industries and digital media organizations.
Real-Time Audience Sentiment Analytics for Adaptive and Personalized Media Broadcasting Asep Sutarman; Fandi Ahmad; Royani Royani
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 2 No. 1 November (2026): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/beam.v2i1.194

Abstract

Digital broadcasting platforms increasingly require mechanisms capable of understanding audience reactions while content is being delivered. However, conventional audience measurement relies primarily on delayed ratings, aggregate engagement statistics, or isolated textual sentiment analysis, limiting broadcasters’ ability to adapt content responsively. This study proposes a real-time audience sentiment analytics framework that integrates textual comments, interaction behavior, and temporal engagement signals to support adaptive and personalized media broadcasting. The proposed architecture combines a transformer-based text encoder, a behavioral feature network, temporal attention, and a contextual bandit adaptation engine. A prototype evaluation was conducted using public sentiment resources and a simulated broadcasting stream containing 120,000 audience events. The system classified audience sentiment into positive, neutral, and negative categories and translated aggregated sentiment into controlled broadcasting actions, including recommendation adjustment, segment continuation, notification timing, and presentation-style adaptation. Experimental results indicate that the multimodal model achieved an accuracy of 89.6% and a macro-F1 score of 88.9%, outperforming text-only and conventional machine-learning baselines. The prototype maintained an average end-to-end latency of 184 ms and processed approximately 1,420 events per second. In the simulated adaptive broadcasting experiment, sentiment-aware personalization improved click-through rate by 15.9%, average viewing duration by 12.8%, and audience satisfaction by 10.6% compared with static broadcasting. Fairness constraints, confidence thresholds, human editorial oversight, and privacy-preserving aggregation were incorporated to reduce the risks of emotional manipulation, bias, and unstable content adaptation. The findings demonstrate that real-time sentiment analytics can provide a technically effective foundation for responsive broadcasting when deployed as a decision-support mechanism rather than an autonomous editorial authority.

Page 2 of 2 | Total Record : 12

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