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p-Index From 2021 - 2026
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This Author published in this journals
All Journal ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Startupreneur Business Digital (SABDA Journal) 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.
Data Driven A or B Testing Methodology for Website Effectiveness Qurotul Aini; Aulia Khanza; Vinkan Likita; Lase, Steven Harazaki; Kareem, Yasir Mustafa
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/t04mab20

Abstract

Website design and optimization decisions are often driven by subjective opinions, internal organizational preferences, or prevailing industry trends rather than empirical evidence derived from large-scale user interaction data, resulting in suboptimal performance and inconsistent user experiences. In digital environments characterized by high data volume and velocity, the absence of a structured experimentation methodology limits organizations’ ability to effectively leverage Big Data for continuous website improvement. This paper presents a comprehensive and systematic methodological guide to A or B testing as a data-driven approach for enhancing website effectiveness in data-intensive contexts. Unlike existing A or B testing guides that focus mainly on tools or isolated experimental outcomes, this study proposes an end-to-end framework integrating hypothesis formulation, scalable experimental design, statistical rigor, iterative learning, and practical decision-making into a unified and replicable process. The methodology outlines the complete A or B testing lifecycle, including alignment of business objectives with measurable data signals, development of testable hypotheses, controlled experiment implementation, large-scale data collection, and statistical analysis to ensure validity and significance of findings. The results demonstrate that a disciplined and continuous A or B testing program supported by Big Data analytics enables incremental yet compounding improvements in website performance. Through illustrative case examples, the study shows that relatively small, data-informed changes to website elements such as headlines, calls-to-action, images, and layout structures can lead to statistically significant gains in conversion rates, user engagement, and overall user experience. The paper concludes that A or B testing serves as a strategic Big Data analytics mechanism that supports evidence-based website optimization decisions grounded in empirical user behavior rather than intuition.
The Dynamics of Adaptation and Innovation of MSMEs in Facing Changes in Consumer Behavior Post Pandemic Lukita, Chandra; Kareem, Yasir Mustafa; Rawat, Bhupesh
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 1 (2026): Startupreneur Business Digital (SABDA)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i1.1015

Abstract

The COVID-19 pandemic has significantly reshaped consumer behavior, particularly in terms of digital engagement, purchasing preferences, and expectations toward products and services. These changes have compelled Micro, Small, and Medium Enterprises (MSMEs) in Indonesia to adopt adaptive and innovative strategies to sustain their businesses in the post pandemic era. This study aims to explore how Indonesian MSMEs adapt and innovate in response to post pandemic changes in consumer behavior. Using a qualitative research approach, data were collected through in depth interviews and observations involving MSME owners from various sectors, including food and beverage, fashion, and creative industries. The findings reveal that MSMEs implemented innovation not only in product development but also in digital marketing practices, customer relationship management, and technology based distribution channels. Post pandemic consumers were perceived as more health conscious, digitally oriented, and value driven, prompting MSMEs to reconfigure their business models and operational routines. From a theoretical perspective, this study contributes to the literature by demonstrating how Dynamic Capabilities and Innovation Diffusion theories explain MSMEs sustained innovation beyond crisis response, particularly in the post pandemic context of an emerging economy. Practically, the findings offer insights for policymakers and support institutions in designing programs that strengthen digital literacy, managerial flexibility, and innovation capacity among MSMEs to support sustainable post pandemic recovery.
Co-Authors Anggoro, Sigit Aulia Khanza Ayubi, M. Nizar Lase, Steven Harazaki Mardiana Mardiana Natalia, Ester Ananda Qurotul Aini Ramzi Zainum Ikhsan Rawat, Bhupesh Septiani, Nanda SRI RAHAYU Tatik Mariyanti, Tatik Untung Rahardja Vinkan Likita Wibowo, Maulana Agung