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All Journal JDM (Jurnal Dinamika Manajemen) International Journal of Artificial Intelligence Research Aptisi Transactions on Technopreneurship (ATT) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics IAIC Transactions on Sustainable Digital Innovation (ITSDI) Universal Raharja Community (URNITY Journal) RJOCS (Riau Journal of Computer Science) International Transactions on Education Technology (ITEE) Journal of Computer Science and Technology Application
Maulana, Sabda
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Mapping the Nexus of Strategic Management and Sustainable Development Goals 12 with a Focus on Innovation: A Bibliometric Analysis Erika, Erika; Riza, Bob Subhan; Maulana, Sabda; Rahagi, Shakeel
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.v7i1.704

Abstract

This study aims to map the research landscape at the intersection of Strategic Management and Sustainable Development Goal (SDGs) 12: Responsible Consumption and Production, using bibliometric methods. A total of 94 articles published between 2015 and 2025 were analyzed using VOSviewer and Bibliometrix to identify key authors, countries, institutions, and thematic clusters. The results revealed four dominant keyword clusters: growth, strategy, innovation, and stakeholder integration. Notably, India emerged as a central player in global collaborations related to SDGs 12. Theoretically, this study extends the understanding of how technological and green innovations intersect within strategic management frameworks to foster sustainable consumption and production practices. Managerially, the findings suggest that organizations should invest in digital infrastructure, particularly in AI, IoT, and data analytics, and adopt cross-sector governance mechanisms to drive sustainability goals. Furthermore, embedding sustainability metrics into key performance indicators (KPIs) can create a competitive advantage while aligning with global sustainability agendas. This paper provides actionable insights for both researchers and managers seeking to integrate strategic innovation with SDGs 12.
AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments Sunarya, Po Abas; Sunarjo, Richard Andre; Abbas, Maulana; Al-Kamari, Omar Arif; Maulana, Sabda
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.
Co-Authors Abbas, Maulana Adyanata Lubis Al-Kamari, Omar Arif Amsyar, Izwan Anggy Giri Prawiyogi Anjani, Sheila Aulia Anwar, Aang Solahudin Apriliasari, Dwi Bist, Ankur Singh Bob Subhan Riza, Bob Subhan Christopher, Ethan Dithi, Arusyi Elyandri Prasiwinigrum, Elyandri Erika Erika Faturahman, Adam Fitriani, Gaby ISKANDAR Khan, Amar Najiv Marviola Hardini Maulana Yusuf Millah, Shofiyul Mochamad Heru Riza Chakim Muhamad Yusup Permadi, Nur Ihsan Po Abas Sunarya Qurotul Aini Rahagi, Shakeel Rosdiana Rosdiana S, Sondang Visiana Sanjaya, Yulia Putri Ayu Santoso, Nuke Puji Lestari Sithole, Precious Sunarjo, Richard Andre Untung Rahardja