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All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Agustian, Harry
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Library & Information Science Materials Science & Nanotechnology Other

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Optimizing Digital Marketing Strategies through Big Data and Machine Learning: Insights and Applications Andayani, Dwi; Madani, Muchlishina; Agustian, Harry; Septiani, Nanda; Wei Ming, LI
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.29

Abstract

In the dynamic realm of digital marketing, the convergence of Big Data and machine learning has ushered in transformative changes, reshaping strategies through advanced data analytics and predictive modeling. This paper examines the pivotal role of these technologies in enhancing marketing practices, focusing on their impact on consumer targeting, engagement, and overall campaign effectiveness. By harnessing vast datasets and applying sophisticated machine learning algorithms, marketers can now predict consumer behavior with unprecedented accuracy, personalize marketing messages, and optimize operational strategies to maximize engagement and return on investment. Despite the profound advantages, the integration of these technologies raises substantial challenges, including data privacy concerns and the need for specialized skills. Through a mixed-methods approach combining quantitative data analysis and qualitative interviews, this study not only demonstrates the improved predictive accuracy and segmentation capabilities afforded by these technologies but also discusses the barriers to their full potential realization. The findings highlight a clear trajectory towards more data-driven, responsive marketing paradigms, suggesting a future where digital marketing strategies are increasingly informed by insights derived from Big Data and machine learning. This paper aims to provide a comprehensive overview of the current landscape and future potential of these transformative technologies in digital marketing.
Assessing the Environmental and Economic Impact of Smart Grid Integration in Renewable Energy Management Henry, Henry; Lutfiyah, Konita; Agustian, Harry; Lachlan, Nicholas
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 7 No 1 (2025): October
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v7i1.708

Abstract

The global transition to renewable energy aims to reduce environmental impacts and combat climate change, yet challenges arise due to the intermittent nature of renewable sources, complicating their integration into traditional power grids and requiring advanced management solutions. Smart grid technology presents promising capabilities to optimize renewable energy management, promoting both environmental sustainability and economic efficiency. This study evaluates the environmental and economic impacts of smart grid integration, fo- cusing on carbon emission reductions, enhanced energy efficiency, and cost savings for energy providers and consumers. Using Structural Equation Modeling via SmartPLS, data were collected and analyzed from various stakeholders engaged in renewable energy and smart grid applications, allowing a detailed assessment of the relationships between smart grid integration, environmental outcomes, and economic benefits. Results indicate that smart grid integration significantly reduces carbon emissions and improves energy efficiency by over 30% while economically, it yields substantial cost savings, cutting operational expenses by up to 25% over time. The SmartPLS analysis confirms a positive relationship between smart grid deployment and both environmental and economic outcomes, highlighting that smart grids not only support emission reductions but also deliver considerable financial benefits in renewable energy management. These findings offer important insights for policymakers and industry stakeholders, emphasizing the role of smart grids in advancing sustainable and economically viable global energy systems.
Governance Models for Blockchain Integrated IoT Ecosystems Indrawan, Rizki; Ratih, Arista; Agustian, Harry; Evans, Richard
Blockchain Frontier Technology Vol. 5 No. 2 (2026): Blockchain Frontier Technology
Publisher : IAIC Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/bfront.v5i2.974

Abstract

The rapid advancement of the Internet of Things (IoT) has led to the creation of large-scale interconnected networks of smart devices capable of autonomously collecting, processing, and exchanging data in real time across diverse application domains. While this development offers significant benefits, it also introduces critical challenges related to data security, privacy protection, interoperability, and the increasingly complex governance of distributed IoT systems. Traditional centralized governance approaches often fail to address these issues effectively due to single points of failure, limited transparency, and insufficient trust mechanisms. The integration of blockchain technology into IoT ecosystems provides a promising alternative by leveraging decentralized architecture, immutable ledgers, transparency, and tamper-resistant features that enhance accountability and trust. This study aims to identify and design an appropriate governance model for blockchain-integrated IoT systems that balances security, operational efficiency, and decentralization. The research adopts a conceptual and qualitative approach through a systematic literature analysis and the synthesis of existing governance, blockchain, and IoT frameworks to develop a structured governance model. The proposed framework defines institutional roles, policy structures, decision-making processes, and control mechanisms among participating entities. The results demonstrate that a blockchain-based governance model enhances system security, operational efficiency, and inter-organizational trust by reducing reliance on centralized authorities and improving data integrity. In addition, the use of smart contracts enables automated policy enforcement, transparent coordination, and sustainable system operations, supporting scalable and resilient governance for future blockchain IoT ecosystems.
Co-Authors Dwi Andayani, Dwi Evans, Richard Henry, Henry Lachlan, Nicholas Lutfiyah, Konita Madani, Muchlishina Ratih, Arista Rizki Indrawan Septiani, Nanda Wei Ming, LI