Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal ADI Journal on Recent Innovation (AJRI) IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Agustian, Harry
Unknown Affiliation
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
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.
Sharia-Guided Artificial Intelligence for Ethical Transformation in Modern Education Madani, Muchlishina; Agustian, Harry; Faturahman, Adam; Sutarman, Asep; Ikhsan, Ramiro Santiago
Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Vol 4 No 2 (2026): March
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/mentari.v4i2.975

Abstract

The rapid advancement of digital technologies has accelerated the integration of Artificial Intelligence (AI) into educational systems worldwide. Despite its benefits, concerns about ethics, algorithmic bias, transparency, and value alignment remain significant, particularly in faith-based educational institutions that emphasize moral and spiritual development. This study aims to propose a Sharia-Guided Artificial Intelligence (SGAI) model that supports ethical, accountable, and value-oriented transformation in modern education. The research employs a mixed-method approach by combining qualitative analysis of Islamic educational principles and ethical frameworks with quantitative evaluation of AI-supported governance practices in educational institutions. Data were collected from educators, administrators, and students within Islamic educational environments to assess the feasibility, acceptance, and integrity of AI implementation within a Sharia-compliant framework. The results indicate that AI systems guided by Sharia principles can improve curriculum personalization, administrative efficiency, transparency, and institutional accountability while reducing algorithmic bias and maintaining ethical values in educational processes. Furthermore, the proposed model encourages responsible data governance, ethical decision-making, and inclusive learning environments supported by intelligent technologies. The integration of intellectual, ethical, social, and spiritual dimensions also strengthens holistic educational development. In conclusion, embedding faith-based ethical intelligence into AI-driven educational management can foster sustainable, trustworthy, and socially responsible innovation while providing a practical framework for value-aligned AI adoption in education.
Analysis of Inorganic Waste Classification Orange Box Based on TensorFlow Lite using Raspberry Pi 5 Aini, Qurotul; Faturahman, Adam; Agustian, Harry; Aritonang, Frengky Jonathan; Zainarthur, Henry
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1428

Abstract

While Smart City initiatives are evolving, waste management infrastructure remains a critical bottleneck, often hindered by high energy dependency and latency issues associated with cloud computing. Traditional automated solutions lack the autonomy required for scalable, outdoor deployment. This research introduces Orange Box a self-sustaining Edge-AI waste classifier designed to bridge the gap between high-performance computing and energy efficiency. The primary goal is to demonstrate that complex Deep Learning tasks can be executed locally on renewable energy without sacrificing classification precision. The system orchestrates a MobileNetV2 architecture on the Raspberry Pi 5, utilizing TensorFlow Lite (TFLite) quantization to drastically reduce computational load. Uniquely, this Green IoT node is fully decoupled from the power grid, driven by a custom power management system utilizing a 100Wp monocrystalline solar panel to sustain both the neural processing unit and robotic actuators. Experimental benchmarks reveal a robust 92% classification accuracy with an inference latency of just 45ms, significantly outperforming previous edge-device generations. Crucially, energy analysis validates operational autonomy for up to 72 hours without sunlight, confirming the system’s reliability for continuous urban deployment. This study demonstrates that the convergence of quantized Edge AI and solar harvesting is not merely theoretical but a deployable standard for the next generation of Smart City infrastructure, directly advancing the Sustainable Development Goals (SDGs) for sustainable urbanization.
Co-Authors Aritonang, Frengky Jonathan Dwi Andayani, Dwi Evans, Richard Faturahman, Adam Henry, Henry Ikhsan, Ramiro Santiago Lachlan, Nicholas Lutfiyah, Konita Madani, Muchlishina Qurotul Aini Ratih, Arista Rizki Indrawan Septiani, Nanda Sutarman, Asep Wei Ming, LI Zainarthur, Henry