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All Journal Aptisi Transactions on Management ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Education Technology (ITEE) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application JOT BEAM
John Edwards
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences Other

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Blockchain Integration for Secure Data Provenance and Interoperable Database Management Terra Saptina Maulani; Dwi Cahyono; Yansa Sendi Fadillah; Maulidya Reva Aprianti; John Edwards
Blockchain Frontier Technology Vol. 6 No. 1 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v6i1.1025

Abstract

The rapid advancement of digital technologies has led to a significant increase in data volume and complexity, while traditional database systems continue to face challenges in ensuring data security, integrity, transparency, and interoperability across platforms, resulting in higher risks of data tampering, limited audit trails, and the formation of data silos. This study aims to examine and develop a blockchain integration model with conventional database systems to strengthen secure data provenance and enhance interoperability among heterogeneous databases. This research proposes a hybrid architecture that combines on data recording using a permissioned blockchain with off data storage through Relational Database Management System (RDBMS) or Not Only SQL (NoSQL) databases, where blockchain functions as a trust layer that records data hashes, metadata, and immutable change histories, while system evaluation is conducted through security testing, data integrity assessment, auditability analysis, latency measurement, throughput evaluation, data consistency analysis, and cross-platform interoperability testing. The experimental results demonstrate that blockchain integration significantly improves data security and traceability by providing transparent and tamper-resistant audit trails, while enabling secure and consistent data exchange across systems through integration modules and API gateways, despite introducing additional performance overhead compared to conventional database systems. This study concludes that integrating blockchain with conventional database systems is an effective approach for ensuring secure data provenance and interoperable database management, offering a balanced trade-off between security, transparency, and system efficiency, and presenting strong potential for further development in large-scale distributed data environments.
A Comparative Analysis of Traditional and Decentralized Storage Systems in the Digital Age Po Abbas Sunarya; Desy Apriani; Ruli Supriati; Danang Surya Budi; John Edwards
APTISI Transactions on Management (ATM) Vol 10 No 1 (2026): ATM (APTISI Transactions on Management: January)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i1.2531

Abstract

The background of this research originates from the critical role of data storage in the advancement of modern digital technology, where centralized traditional cloud storage models have become dominant due to their accessibility and ease of data management. However, the challenges faced by these models include limited scalability, high operational costs, and vulnerabilities related to data security and privacy. In response to these limitations, the InterPlanetary File System (IPFS) has emerged as a decentralized storage solution that offers an alternative approach to data storage and distribution. The objective of this study is to compare IPFS and traditional cloud storage based on four key aspects: scalability, security, cost, and performance. The methodology involves a literature review and case studies of IPFS implementation in various practical scenarios. The findings reveal that while IPFS offers superior decentralization and resistance to data censorship, it still suffers from inefficiencies in data re- trieval and challenges in large-scale adoption. The results show that traditional cloud storage demonstrates advantages in access speed and integration capabilities but remains constrained by higher costs and the risks associated with data centralization. The conclusion emphasizes the need for continued research to enhance the efficiency of IPFS and to explore hybrid storage models that integrate the benefits of both decentralized and centralized technologies.
Integrating AI and Big Data to Enhance Performance andSustainability in Hospitality Hasrul Azwar Hasibuan; Syaifuddin; Rusiadi; John Edwards
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.118

Abstract

This paper explores the impact of Big Data and Artificial Intelligence (AI) on Employee Performance and Sustainability in the hospitality industry. The paper further explains how integrating Big Data and AI can optimize operations, enhance employee efficiency, and promote sustainable practices. The research uses SmartPLS to analyze the relationships between these variables, with a focus on how Big Data and AI influence Employee Performance, which in turn contributes to Sustainability efforts. The findings, show that both Big Data and AI have significant positive effects on Employee Performance, with Big Data demonstrating a stronger impact. Moreover, Employee Performance mediates the relationship between Big Data, AI, and Sustainability, indicating that improvements in employee performance lead to better sustainability outcomes, such as resource optimization and waste reduction. The study findings align with SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production), highlighting the potential of technology to drive both economic and environmental sustainability in the hospitality sector This research contributes to understanding how the application of Big Data and AI can help hospitality businesses achieve long-term success through improved operational efficiency and sustainable practices.
Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks Aswadi Jaya; Suca Rusdian; Fitra Putri Oganda; Tuti Nurhaeni; John Edwards
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kz2w4z84

Abstract

The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.
Technology Innovation Adoption for Business Performance in the Digital Economy Mustam Mustam; Agung Rizky; John Edwards
APTISI Transactions on Management (ATM) Vol 10 No 3 (2026): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v10i3.2644

Abstract

The rapid development of the digital economy has encouraged businesses to adopt digital technologies such as digital payment systems, e-commerce platforms, cloud-based management systems, and digital marketing tools. However, many businesses, particularly small and medium-sized enterprises, still face adoption challenges due to limited resources, digital skills, and implementation costs. This study aims to explore how technology innovation adoption influences business performance by examining the experiences, strategies, and challenges of business actors. A descriptive qualitative approach was employed through interviews, observations, and documentation involving business owners, managers, and technology stakeholders. Data were analyzed using data reduction, data display, thematic analysis, and conclusion drawing. The novelty of this study lies in its integrated qualitative perspective that views technology adoption as a multidimensional transformation process. Unlike previous studies that often focus on individual technologies or quantitative relationships, this research examines the combined adoption of multiple digital technologies within a unified framework. The findings show that technology adoption improves operational efficiency, productivity, market expansion, customer engagement, and competitiveness, while organizational readiness and digital capability remain key challenges. The study concludes that successful digital transformation requires alignment between technological readiness, organizational capability, and strategic implementation, providing contextual insights into how integrated digital technologies create business value.
Digital Transformation and Branding for Empowering the Creative Economy Based on Local Wisdom: Transformasi Digital dan Branding untuk Pemberdayaan Ekonomi Kreatif Berbasis Kearifan Lokal Muhamad Yusup; Muhammad Faris Ariq; Erni Juliana Al Hasanah Nasution; Aulianda Zahrina Fahreza; John Edwards
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 1 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i1.1228

Abstract

Digital transformation and strengthening local brand identity are key to encouraging creative economic empowerment based on local wisdom. This study aims to analyze the influence of creative communication strategies, brand personality, digital marketing engagement, user-generated content, and social media platform interactivity on the effectiveness of village community empowerment programs. Using a quantitative approach and Structural Equation Modelling (SEM) methods through SmartPLS software, data were collected from 300 respondents involved in a local potential-based digital entrepreneurship program. The results show that creative communication strategies and interactive social media platforms significantly increase digital participation and community emotional connection to local products. Brand personality and user-generated content also contribute positively to creative business image and market appeal. This study emphasizes the importance of integrating local values into digital branding narratives to create differentiation and consumer loyalty. These findings provide practical implications for stakeholders in designing empowerment policies and programs that are technology based yet rooted in local culture. Thus, this study contributes to the development of a sustainable and inclusive village creative economy ecosystem in the digital era.
Sentiment Aware Chatbots as Companions for Reducing Loneliness through Positive Computing Heni Nurhaeni; Sandy Kosasi; Made Bunga Thalia; Elisa Ananda Natalia; John Edwards
Journal of Orange Technology Vol. 1 No. 1 (2024): October
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/jot.v1i1.10

Abstract

Loneliness has increasingly emerged as a global mental health concern, particularly among vulnerable populations such as the elderly, students in remote learning environments, and individuals experiencing social isolation in urban societies. Advances in affective and positive computing offer promising solutions for addressing these challenges by creating empathetic digital companions capable of responding to human emotions in real time. This study aims to evaluate the effectiveness of sentiment-aware chatbots as digital companions in reducing loneliness and enhancing emotional well-being through positive computing principles. A quantitative experimental approach was employed, integrating sentiment analysis algorithms with Natural Language Processing (NLP) to detect emotional cues from user input and generate empathetic responses. The chatbot system was tested with 150 participants over a six-week period using standardized psychometric instruments, including the UCLA Loneliness Scale and the WHO-5 Well-Being Index. Statistical analysis using paired-sample t-tests, ANOVA, and Structural Equation Modeling (SEM) revealed significant improvements in loneliness reduction and psychological well-being among participants interacting with the sentiment-aware chatbot. Furthermore, perceived empathy and user satisfaction were found to mediate these effects, highlighting the emotional quality of human AI interaction as a crucial determinant of positive outcomes. Findings provide empirical evidence that sentiment-aware chatbots can function as effective digital companions, reducing loneliness and fostering psychological resilience. By integrating affective and positive computing principles, this study contributes to the advancement of compassionate AI systems designed to promote human well-being and support broader societal goals.
Understanding Data Driven Decision Making Practices in Learning Factory Environments John Edwards; Reem Ahli; Mohd Faiz Hilmi
International Transactions on Education Technology (ITEE) Vol. 4 No. 2 (2026): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i2.1096

Abstract

The increasing integration of data science and learning analytics in learning factory environments has created new opportunities to enhance training effectiveness and align educational processes with real industrial needs, yet understanding how decision making practices are enacted in these contexts remains limited. This study aims to explore how stakeholders interpret and utilize data to support instructional, operational, and strategic decisions that influence skill development and adaptive training in learning factories. A qualitative research design was employed through multiple case studies involving semi structured interviews, direct observations, and analysis of institutional documents to capture in depth insights into practices, challenges, and contextual dynamics surrounding data driven decision making. The findings indicate that successful implementation is shaped by factors such as organizational culture, data literacy levels, leadership support, and the availability of integrated information systems, while common challenges include fragmented data sources, limited analytical competencies, and resistance to data informed change; participants reported that collaborative reflection and continuous feedback loops significantly improved training relevance and learner engagement. The study concludes that strengthening governance structures, investing in capacity building, and promoting a culture that values evidence based decision making can enhance both learning outcomes and operational performance in learning factory settings, providing meaningful implications for educators, industry partners, and policymakers seeking to advance sustainable and technology enhanced workforce development.
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

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

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 Agung Rizky Annisa Ardien Aswadi Jaya Aulianda Zahrina Fahreza Danang Surya Budi Desy Apriani Dewi Immaniar Desrianti Dwi Cahyono Elisa Ananda Natalia Erni Juliana Al Hasanah Nasution Fitra Putri Oganda Hasrul Azwar Hasibuan Heni Nurhaeni Made Bunga Thalia Maulidya Reva Aprianti Michael Surya Gunawan Mohd Faiz Hilmi Muhamad Yusup Muhammad Faris Ariq Mustam Mustam Po Abbas Sunarya Reem Ahli Ruli Supriati, Ruli Sandy Kosasi Suca Rusdian Syaifuddin Terra Saptina Maulani Tuti Nurhaeni Untung Rahardja Yansa Sendi Fadillah