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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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Real Time Audience Analytics Using Machine Learning to Measure Listener and Viewer Cultural Engagement Asep Sutarman; Felix Sutisna; Dimas Aditya Prabowo; Kgomotso Moyo
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid evolution of digital media has transformed audience interaction, yet traditional metrics like views and likes fail to capture the nuanced emotional and cultural dynamics of broadcast content. This study develops a real-time audience analytics framework using machine learning to measure deep cultural engagement and emotional resonance within digital media environments. Adopting a hybrid methodological approach, the research integrates Natural Language Processing (NLP) with qualitative interpretation. The system processes live interaction data, employing sentiment analysis and pattern recognition to categorize audience responses into complex emotional and cultural engagement tiers beyond simple polarity. Findings demonstrate that the machine learning model effectively identifies real-time shifts in audience sentiment, revealing how specific cultural cues trigger heightened engagement and collective emotional responses. This research advances audience analytics by bridging the gap between computational speed and qualitative depth, offering a scalable model for broadcasters and researchers to understand the cultural impact of digital content as it happens.
Artificial Intelligence Driven Audience Sentiment Analytics for Interactive Digital Broadcasting Platforms Richard Andre Sunarjo; Tessa Handra; Rifqa Nabila Muti; Kamal Arif Al-Farouqi
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid growth of interactive digital broadcasting platforms has significantly transformed the way audiences engage with media content through live chats, comments, and social media interactions. However, the massive volume of usergenerated feedback creates challenges for broadcasters in understanding audience sentiment efficiently. This study aims to analyze audience sentiment using Artificial Intelligence (AI) driven analytics to improve the understanding of audience engagement in interactive digital broadcasting platforms. The research applies a quantitative approach using AI based Natural Language Processing (NLP) techniques to process and analyze audience feedback data collected from comments, live chat interactions, and social media responses related to digital broadcast content. The analytical process includes data preprocessing, sentiment classification, and machine learning based modeling to identify patterns of audience emotional responses and engagement. The findings indicate that AI driven sentiment analytics can effectively classify audience opinions and detect real time sentiment trends associated with broadcasted content. The results also demonstrate that AI-based analysis enables broadcasters to gain deeper insights into audience preferences, evaluate content performance, and optimize broadcasting strategies more efficiently compared with conventional manual analysis methods. In conclusion, the integration of AI in audience sentiment analytics offers a valuable approach for enhancing audience understanding and supporting data-driven decision-making in modern digital broadcasting ecosystems while promoting more responsive and personalized media experiences.
Algorithmic Scriptwriting and the Preservation of Human Emotional Resonance in Narrative Broadcasting Lusyani Sunarya; Mohamad Rakhmansyah; Lukita Pasha; Chua Toh Hua
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid advancement of artificial intelligence in media production has introduced algorithmic scriptwriting as a transformative approach in narrative broadcasting, raising concerns about whether automated content generation can maintain the depth of human emotional resonance traditionally embedded in storytelling. This study aims to examine how algorithmic scriptwriting systems influence the preservation of emotional authenticity and narrative engagement within broadcasting content while exploring the balance between computational efficiency and human-centered storytelling values. To achieve this, the research employs a mixed-method approach that combines qualitative narrative analysis and quantitative audience perception evaluation, involving comparative assessment between algorithm-generated scripts and human-written scripts within selected broadcasting scenarios. The findings indicate that algorithmic scriptwriting can effectively structure narrative flow and optimize production efficiency, however, scripts developed through purely algorithmic processes tend to exhibit limitations in conveying nuanced emotional layers, empathy, and contextual cultural expression. Nevertheless, the integration of human editorial intervention and emotional modeling techniques significantly enhances the perceived authenticity and emotional engagement of algorithm-assisted narratives. Therefore, this study concludes that algorithmic scriptwriting should not be positioned as a replacement for human creativity but rather as a collaborative tool that augments the creative process, where human oversight remains essential to preserve emotional resonance and narrative depth in broadcasting practices.
Artificial Intelligence Driven Broadcasting with Virtual News Anchors and Automated Script Generation Desy Apriani; Galih Putra Cesna; Muhtarom Muhtarom; Lod Sulivyo; Yasir Mustafa Kareem
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid advancement of artificial intelligence technologies has significantly transformed the digital media ecosystem. In contemporary broadcasting environments, artificial intelligence systems are increasingly capable of generating and delivering media content autonomously. The emergence of AI driven radio hosts, virtual news anchors, and automated script generation systems reflects the evolution of algorithmic media production. This study aims to examine the technological architecture and operational impact of artificial intelligence in automated broadcasting environments. The research adopts an empirical qualitative approach supported by comparative analysis of documented AI broadcasting platforms and secondary quantitative indicators from industry reports. The analysis focuses on three components of algorithmic media production: AI radio hosts, virtual news anchors, and automated scriptwriting systems. The findings indicate that AI driven broadcasting significantly improves production efficiency, enables continuous media delivery, and enhances the scalability of digital media services. However, several challenges remain regarding editorial transparency, audience trust, and governance of automated information systems. The study concludes that algorithmic media production can contribute to the development of sustainable digital communication infrastructure when supported by responsible policy frameworks and human editorial oversight.
Decentralized Edge Computing Frameworks for Global Cloud Based Broadcasting Systems Dwi Andayani; Novena Ulita; Mardhalia Saitakela; Abdullah Arif Kamal
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 growth of global cloud based broadcasting systems has increased the demand for efficient data processing and low latency content delivery. However, many community based broadcasting initiatives still rely on centralized cloud infrastructures, which often cause latency issues, limited scalability, and high bandwidth consumption. In the context of community digital empowerment, integrating decentralized edge computing frameworks offers an opportunity to improve broadcasting performance while strengthening community technological capacity. This community service research aims to design and implement a decentralized edge computing framework to support global cloud-based broadcasting systems while enhancing community understanding and utilization of distributed computing technologies. Program applied a participatory community service approach consisting of needs assessment, system design, training workshops, and implementation. Community participants and local digital media practitioners were introduced to decentralized edge computing concepts, followed by hands on training in deploying edge nodes and integrating them with cloud broadcasting platforms. Implementation demonstrated improved broadcasting efficiency with reduced latency and optimized bandwidth utilization. Participants also showed increased technical competence in managing decentralized infrastructure and operating cloud assisted broadcasting services. Decentralized edge computing framework effectively supports scalable broadcasting systems while empowering communities with practical digital infrastructure skills. This approach contributes to sustainable digital transformation and pro- vides a replicable model for community-based broadcasting development.
Ethical and Regulatory Governance of Deepfakes in AI-Driven Media Broadcasting Solahudin Solahudin; Qurotul Aini; Tatik Maryanti; Yunita Wulansari; Marta Rodriguez
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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The rise of artificial intelligence has significantly transformed media broadcast- ing, particularly with the proliferation of deepfakes, raising critical concerns regarding ethical standards, misinformation, and regulatory oversight. This study investigates the challenges and policy approaches necessary to address the growing threat of deepfakes in the broadcasting sector, emphasizing the im- portance of maintaining journalistic integrity while protecting audiences from manipulative content. Employing a Broad Oversight and Monitoring frame- work for Regulatory Compliance (BOMRC), the research analyzes existing legislation, industry guidelines, and technological interventions across multiple jurisdictions to evaluate their effectiveness in mitigating risks associated with synthetic media. The study utilizes a mixed-methods approach, combining qualitative policy analysis with case studies of recent deepfake incidents to high- light gaps in enforcement, ethical standards, and public awareness. Findings in- dicate that while some regulatory frameworks offer preliminary safeguards, they often lack clarity in implementation, cross-border coordination, and alignment with digital ethics principles. The study further explores how the integration of BOMRC mechanisms within broadcasting organizations can enhance proactive detection, accountability, and transparency, ultimately fostering public trust in media content. By providing a comprehensive assessment of both policy chal- lenges and practical solutions, this research contributes to the ongoing discourse on AI governance, digital ethics, and media regulation. It offers actionable in- sights for policymakers, regulators, and media practitioners aiming to balance innovation with responsibility in an era where artificial intelligence increasingly shapes public perception and information integrity.
Human-Centered Generative AI for Ethical andSustainable Media Broadcasting Asher Nuche; Rifqi Fahrudin; Royani Royani
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 expansion of generative artificial intelligence has transformed media broadcasting by enabling automated content creation, synthetic personalities, real-time production, and personalized audience interaction within decentralized digital ecosystems. However, this transformation also raises critical concerns regarding ethical accountability, audience trust, algorithmic transparency, energy consumption, and the sustainability of AI-driven broadcasting infrastructure. This study aims to examine how human-centered generative AI can support ethical and sustainable media broadcasting while maintaining creativity, journalistic integrity, audience engagement, and environmental responsibility. The method research employs a qualitative conceptual approach based on a structured literature review of recent studies on generative AI, digital media broadcasting, decentralized content systems, AI ethics, and green computing. The analysis is organized around key dimensions, including human-centered design, synthetic media governance, audience personalization, decentralized infrastructure, and energy-efficient AI implementation. The Result findings indicate that human-centered generative AI can enhance broadcasting innovation by improving content efficiency, adaptive storytelling, audience relevance, and interactive media experiences. At the same time, ethical safeguards such as AI labeling, provenance tracking, privacy protection, editorial oversight, and transparent algorithmic governance are essential to prevent misinformation, manipulation, and audience distrust. The study also highlights that sustainable broadcasting re- quires optimized AI models, green data centers, and responsible infrastructure management. This paper concludes that the integration of human-centered, ethical, and sustainable principles is necessary to ensure that generative AI strengthens media broadcasting ecosystems without replacing human values, creativity, and social responsibility.
Machine Learning and Blockchain Integration for RealTime Sentiment Analysis and Digital Rupiah Ecosystem Ninda Lutfiani; Umi Rusilowati; Steven Harazaki Lase; Kristina Vaher
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid growth of digital streaming platforms and global online communities has significantly increased the volume of user generated content, making it difficult for organizations to understand viewer engagement trends in real time. This study develops and evaluates machine learning models for real time sentiment analysis to identify global viewer engagement patterns across large scale digital data streams. Analytical framework is employed by collecting viewer comments and interaction data from multiple online platforms, followed by preprocessing techniques including text normalization, tokenization, and feature extraction. Several machine learning algorithms, including supervised classification models and natural language processing techniques, are trained and evaluated to detect positive, negative, and neutral sentiments in real time. Model performance is assessed using accuracy, precision, recall, and F1 score to determine the most effective approach for large scale sentiment monitoring. The findings demonstrate that optimized machine learning models significantly improve the accuracy and responsiveness of real time sentiment detection, enabling more reliable identification of global viewer engagement trends and behavioral patterns. The integration of automated sentiment analysis also enhances the capability of organizations to process large volumes of streaming textual data efficiently. This research highlights the importance of machine learning driven sentiment analysis systems as strategic tools for understanding global audience engagement, supporting data driven decision making, and improving adaptive content strategies in rapidly evolving digital media environments.
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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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.
Semantic Edge Orchestration for 6G-Enabled Immersive Media Broadcasting Lunantari Sanbella; Chandra Lukita; Anandha Fitriani; Ihda Nur Fathiyah
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.V2i1.191

Abstract

The rapid evolution of 6G networks is expected to transform immersive media broadcasting by enabling ultra-low-latency communication, intelligent edge computing, and high-capacity transmission for extended reality, holographic content, and interactive multimedia. However, conventional edge resource management remains limited in understanding the semantic importance of media content, resulting in inefficient computation and transmission when network resources fluctuate. This study aims to develop a Semantic Edge Orchestration framework for 6G-enabled immersive media broadcasting that dynamically prioritizes computational, communication, and caching resources according to the semantic relevance of media streams and real-time network conditions. The proposed framework integrates semantic feature extraction, edge intelligence, and adaptive resource orchestration to classify content importance and coordinate processing across distributed edge nodes. Its performance is evaluated through simulation under different traffic loads and network conditions using end-to-end latency, bandwidth utilization, edge resource efficiency, and immersive media quality as evaluation metrics. The experimental results demonstrate that the proposed approach reduces end-to-end latency by 27.8%, decreases bandwidth consumption by 22.4%, and improves edge resource utilization by 18.6% compared with conventional resource aware orchestration while maintaining consistent perceptual quality during high network loads. These findings indicate that semantic aware edge orchestration can provide an effective architecture for intelligent 6G broadcasting by allocating network resources according to both contextual content importance and infrastructure conditions, thereby supporting scalable, responsive, and resource-efficient delivery of next- generation immersive media services.

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