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All Journal ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Education Technology (ITEE)
Al-Kamari, Omar Arif
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Leveraging Data Utilization and Predictive Analytics: Driving Innovation and Enhancing Decision Making through Ethical Governance Br. Karo, Mestiana; Miller, Bella Pertiwi; Al-Kamari, Omar Arif
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.593

Abstract

Advances in information technology have fueled an exponential increase in the volume and diversity of data generated by organizations and individuals. In this era, Data Science has emerged as a crucial discipline for uncovering hidden patterns within data, thereby facilitating smarter decision-making processes. This paper presents a comprehensive and up-to-date overview of the challenges and opportunities in the application of Data Science, with a particular focus on the PLS (Partial Least Squares) analysis method. The PLS method, implemented through the SmartPLS application, synergizes partial path analysis with partial least squares techniques and has gained prominence as a preferred method for analyzing complex structural models within the field of Data Science. This study delves into the practical applications and benefits of PLS in handling diverse and intricate datasets, and also elucidates the potential obstacles encountered during its implementation. By examining the methodological strengths and addressing the challenges associated with PLS, this paper aims to provide valuable insights for researchers and practitioners seeking to leverage this method and the SmartPLS application for enhanced data analysis and informed decision-making.
AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments Abas Sunarya; Sunarjo, Richard Andre; Abbas, Maulana; Al-Kamari, Omar Arif; Sabda Maulana
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i1.950

Abstract

The rapid growth of artificial intelligence in higher education creates new op- portunities to make learning factory environments more adaptive, data-informed, and aligned with industrial practice. This study examines how the integration of educational data analytics and intelligent tutoring systems supports smarter learning factory models that connect theoretical instruction with hands-on indus- trial training. Using a quantitative research design, data were collected from 180 higher education students participating in AI-supported learning factory sessions. Log data on learning interactions, performance metrics, and system- generated feedback were analyzed using statistical modeling to test the effects of AI-driven interventions on learning outcomes. The results show that ed- ucational data analytics significantly increases the adaptability of instructional content, enabling the intelligent tutoring system to personalize learning paths in real time based on individual performance profiles. Students who engaged with AI-based tutoring reported higher learning engagement and achieved better problem-solving scores and stronger retention of practical concepts than those in conventional learning factory settings. These findings indicate that combining educational data analytics with intelligent tutoring systems improves both the efficiency and effectiveness of learning factory models by enabling continuous feedback loops, dynamic adjustment of learning tasks, and learner-centered in- struction. The study concludes that AI-driven, data-informed learning factories can play a strategic role in preparing students with industry-relevant compe- tences and offers practical implications for educational technologists and insti- tutions designing next-generation education technology solutions.
Evaluating the Effectiveness of AI in Developing Digital Marketing Content for Certification Service Firms Sugiyato, Agus; Bangun, Cicilia Sriliasta; Fauzi, Fikri; Mulyati, Mulyati; Al-Kamari, Omar Arif
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.v6i2.1305

Abstract

The implementation of Artificial Intelligence (AI) in digital marketing has become a major driver of business efficiency, yet its strategic implications and the role of human involvement still require in-depth study. This qualitative case study research aims to analyze the effectiveness of adopting generative AI (Gemini AI and Claude AI) in the content creation process at PT. Gaivo Solusi Manajemen, focusing on perceived usefulness, the role of human-in-the-loop, and the perspective of competitive advantage. Data were collected through semi-structured interviews with key informants and analyzed using Thematic Analysis. The findings indicate that AI substantially increases process efficiency, particularly in drafting content and SEO Meta packages, which boosts production volume and speed. However, key findings emphasize that AI is merely a supporting tool and necessitates mandatory supervision by expert staff (human-in-the-loop) to ensure information integrity and quality that complies with professional service industry regulations. Strategically, AI is not considered a source of hardly imitable competitive advantage (it is a commodity), but rather an enabler. The true competitive advantage lies in the staff’s ability in prompt engineering, supported by the company’s relevant internal data. This study provides managerial contributions by recommending a focus on investment in human resource skill development rather than solely on the acquisition of AI tools.
Optimizing Business Workflow Using AI Integrated Blockchain Platforms Djamali, Muhammad Fadheel; Lusiana, Dewi; Parastry, Annisa; Al-Kamari, Omar Arif
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.v7i1.1323

Abstract

In today’s fast evolving digital economy, business workflows often suffer from inefficiencies, data silos, and security vulnerabilities, particularly in environments relying on legacy systems and centralized control. To address these challenges, this study investigates the integration of Artificial Intelligence (AI) and blockchain technologies as a unified platform for enhancing workflow efficiency, transparency, and security across business operations. The primary objective of this research is to analyze how combining AI’s predictive and automation capabilities with blockchain’s decentralized and immutable ledger can optimize key workflow processes such as approval cycles, data validation, and task automation. This study adopts a qualitative case study approach, supported by system modeling and comparative analysis between conventional workflows and AI blockchain enabled systems within a mid sized logistics enterprise. The findings reveal that the integrated platform significantly reduces processing time, enhances traceability, and minimizes errors, especially in interdepartmental transactions and decision making processes. In addition, the use of smart contracts triggered by AI based insights eliminates redundant steps, enabling real time process adaptation. These results confirm that the fusion of AI and blockchain delivers measurable improvements in workflow optimization, offering a scalable and secure foundation for digital transformation. In conclusion, this research demonstrates that AI integrated blockchain platforms not only optimize operational workflows but also provide strategic value for organizations seeking long term agility and resilience in the face of rapid technological and market shifts.
Co-Authors Abas Sunarya Abbas, Maulana Bangun, Cicilia Sriliasta Br. Karo, Mestiana Djamali, Muhammad Fadheel Fauzi, Fikri Lusiana, Dewi Miller, Bella Pertiwi Mulyati Mulyati Parastry, Annisa Sabda Maulana Sugiyato, Agus Sunarjo, Richard Andre