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All Journal ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Journal of Computer Science and Technology Application
Kareem, Yasir Mustafa
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science Other

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Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Septiani, Nanda; Ramzi Zainum Ikhsan; Kareem, Yasir Mustafa; Untung Rahardja
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.v2i2.115

Abstract

The increasing global emphasis on sustainability has accelerated investments in renewable energy technologies, positioning sources like solar, wind, and hydroelectric power as vital alternatives to fossil fuels. Despite significant progress, integrating renewable energy into existing grids remains challenging due to variability in energy output, grid instability, and inefficiencies in energy storage systems. This study investigates the potential of machine learning (ML) to revolutionize the renewable energy sector by enhancing energy forecasting, grid management, and energy storage optimization. Using a combination of supervised learning, deep learning, and reinforcement learning techniques, we developed predictive and optimization models based on historical and real-time datasets. Additionally, structural equation modeling (SEM) with SmartPLS was employed to analyze the relationships between key variables, such as machine learning algorithms, renewable energy sources, sustainability performance, and operational efficiency. The results indicate that machine learning significantly improves energy forecasting accuracy, grid reliability, and storage efficiency, with R-squared values of 0.685 for operational efficiency and 0.588 for sustainability performance. These findings highlight the transformative role of ML in optimizing renewable energy systems and achieving sustainable energy goals. While ML offers promising solutions for renewable energy challenges, further research is needed to address real-time data integration, model scalability, and economic feasibility. This study provides a foundation for future innovations, emphasizing the importance of intelligent, data-driven strategies in advancing global energy sustainability.
Inclusive Growth Stimulation in Underserved Communities through Digital Micro Entrepreneurship Programs Wibowo, Maulana Agung; Mariyanti, Tatik; Mardiana, Mardiana; Natalia, Ester Ananda; Kareem, Yasir Mustafa
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1354

Abstract

The rapid development of the digital economy has intensified consumer interactions with businesses through digital platforms. This condition highlights the urgency of implementing digital business ethics, including transparency in privacy policies, honesty in marketing communication, data protection, and fair algorithmic practices, as key factors in building consumer trust. This paper examines the relationship between digital business ethical practices and consumer trust levels through a review of empirical and conceptual literature (2021–2025). The synthesis indicates that clear ethical practices (transparency, data security, information accuracy, and AI accountability) enhance both emotional and cognitive consumer trust, which in turn strengthens repurchase intention and loyalty. Practical implications and future research directions are provided.
Decentralized Decision Intelligence Using AI and Blockchain in Modern Enterprises Ayubi, M. Nizar; Anggoro, Sigit; Kareem, Yasir Mustafa
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1320

Abstract

In the era of digital transformation, enterprises face increasing pressure to enhance transparency, operational efficiency, and trust in their decision making processes, especially in complex, data-intensive environments. While prior studies have separately explored the roles of Artificial Intelligence (AI) and Blockchain, few have examined their combined impact in creating decentralized and intelligent decision systems within real enterprise contexts. This study introduces a novel conceptual integration model that merges AI-driven analytics with blockchain-based validation mechanisms to enable transparent, traceable, and autonomous decision-making. By synthesizing AI predictive and analytical capabilities with blockchain immutable and distributed architecture, this research extends recent studies (2021-2025) by demonstrating how such convergence can eliminate central dependencies, enhance digital trust, and support data governance across departments. A qualitative case study approach was used to analyze organizations adopting AI blockchain frameworks, and the findings reveal new insights on interoperability, adaptive governance, and smart contract-driven autonomy. The study originality lies in its emphasis on the AI Blockchain synergy as a unified decision-intelligence infrastructure, contributing to the growing discourse on ethical and resilient enterprise systems.
Co-Authors Anggoro, Sigit Ayubi, M. Nizar Mardiana Mardiana Natalia, Ester Ananda Ramzi Zainum Ikhsan Septiani, Nanda SRI RAHAYU Tatik Mariyanti, Tatik Untung Rahardja Wibowo, Maulana Agung