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All Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Yasir Mustafa Kareem
Eesp Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Other

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Inclusive Growth Stimulation in Underserved Communities through Digital Micro Entrepreneurship Programs Maulana Agung Wibowo; Tatik Mariyanti; Mardiana Mardiana; Ester Ananda Natalia; Yasir Mustafa Kareem
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.
Revolutionizing Renewable Energy Systems throughAdvanced Machine Learning Integration Approaches Sri Rahayu; Nanda Septiani; Ramzi Zainum Ikhsan; Yasir Mustafa Kareem; 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.
Data Driven A or B Testing Methodology for Website Effectiveness Qurotul Aini; Aulia Khanza; Vinkan Likita; Steven Harazaki Lase; Yasir Mustafa Kareem
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 Implementation of Digital Storytelling to Enhance Early Childhood Learning Experiences Marviola Hardini; Muhamad Yusup; Yasir Mustafa Kareem; Eka Dian Astuti
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.v4i2.1014

Abstract

This study examines how digital storytelling can enhance early childhood learning experiences in preschool settings, in response to the growing integration of technology in education and the need to ensure its alignment with develop-mentally appropriate practices. The objective of this research is to explore how the implementation of digital storytelling influences children’s cognitive engagement, social interaction, and creative expression. Employing a qualitative de- scriptive approach, the study involves 18 children aged 4-6 years in a preschool setting and utilizes classroom observations, structured interviews with teachers, and documentation of learning activities to analyze the implementation process and its pedagogical impact. The findings indicate a tendency toward improved attention span, vocabulary development, and collaborative behavior, as well as enhanced imagination and emotional expression during digital storytelling ses- sions. Teachers reported that digital tools contributed to more interactive and adaptive learning environments, enabling children to connect personal experi- ences with narrative content in meaningful ways. The study concludes that integrating digital storytelling in early childhood education holds strong poten- tial as a developmentally aligned instructional strategy, supporting both child independence and holistic development. It further highlights the importance of thoughtful technological integration in preschool classrooms as a bridge be- tween modern educational innovation and child-centered learning philosophies.
Co-Authors Aulia Khanza Eka Dian Astuti Ester Ananda Natalia Mardiana Mardiana Marviola Hardini Maulana Agung Wibowo Muhamad Yusup Nanda Septiani Qurotul Aini Ramzi Zainum Ikhsan Sri Rahayu Steven Harazaki Lase Tatik Mariyanti, Tatik Untung Rahardja Vinkan Likita