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All Journal IAIC Transactions on Sustainable Digital Innovation (ITSDI) Startupreneur Business Digital (SABDA Journal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi BEAM AI, Innovation, and Resilience for the Environment Journal
Asep Sutarman
University of Muhammadiyah Prof. Dr. HAMKA
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Other

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The Role of Application Programming Interface in Transforming Restaurant Delivery Operations Cicilia Sriliasta Bangun; Solahudin Solahudin; Asep Sutarman; Sipho Dlamini
Startupreneur Business Digital (SABDA Journal) Vol. 5 No. 1 (2026): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sabda.v5i1.1066

Abstract

The rapid growth of online food delivery services has transformed the restaurant industry, requiring efficient and scalable solutions to meet increasing customer demands. However, traditional restaurant management systems often struggle with integration challenges, operational inefficiencies, and customer dissatisfaction due to delays and miscommunication. This study aims to analyze the role of Application Programming Interfaces (API) in optimizing restaurant delivery operations by enhancing order management, inventory tracking, and real-time customer interactions. Using a mixed-method approach, we conducted case studies on multiple restaurant platforms and surveyed industry professionals to assess the effectiveness of API integration. The findings reveal that API-driven systems significantly improve delivery accuracy, reduce processing time, and enhance customer experience by automating order workflows and enabling seamless third-party service connections. The results indicate that restaurants leveraging API can achieve a higher level of efficiency and scalability, minimizing operational bottlenecks while maintaining service quality. This research concludes that API play a crucial role in transforming restaurant delivery operations, providing a competitive advantage in the evolving digital marketplace. Future studies should explore the impact of emerging technologies such as AIpowered API and blockchain integration for further optimization.
Integrating Artificial Intelligence in E-Learning for Organizational Well-Being through Orange Technology Mapping Arthur Simanjuntak; Asep Sutarman; Sheila Aulia Anjani; Asher Nuche
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.v7i1.706

Abstract

This study conducts a bibliometric analysis of Artificial Intelligence (AI) in e-learning, emphasizing its role in organizational well-being and educational transformation. Using Scopus as the primary database and VOSviewer for visualization, 557 articles published between 2020 and 2025 were analyzed across country, organization, source, author, document, and keyword networks. The results reveal that the United States, United Kingdom, and Germany act as central contributors, while India, Saudi Arabia, Singapore, Hong Kong, and Egypt are rapidly growing in influence. Source analysis identifies leading journals that shape the discourse alongside new outlets that diversify the field. Author and document coupling highlight key works that connect immersive learning environments with pedagogy, while keyword analysis identifies three major clusters related to ethics and governance, motivation and technology enhanced learning, and AI tools such as ChatGPT and generative AI. Overall, the findings show that AI in e-learning has evolved from experimental initiatives into a multidimensional, evidence-based domain. The study concludes by emphasizing how Orange Technology and TRAIVIS frameworks can operationalize ethics by design, support adaptive tutoring, and align AI-driven learning ecosystems with sustainable, well-being-centered educational goals.
Sharia-Guided Artificial Intelligence for Ethical Transformation in Modern Education Muchlishina Madani; Harry Agustian; Adam Faturahman; Asep Sutarman; Ramiro Santiago Ikhsan
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.v4i2.975

Abstract

The rapid advancement of digital technologies has accelerated the integration of Artificial Intelligence (AI) into educational systems worldwide. Despite its benefits, concerns about ethics, algorithmic bias, transparency, and value alignment remain significant, particularly in faith-based educational institutions that emphasize moral and spiritual development. This study aims to propose a Sharia-Guided Artificial Intelligence (SGAI) model that supports ethical, accountable, and value-oriented transformation in modern education. The research employs a mixed-method approach by combining qualitative analysis of Islamic educational principles and ethical frameworks with quantitative evaluation of AI-supported governance practices in educational institutions. Data were collected from educators, administrators, and students within Islamic educational environments to assess the feasibility, acceptance, and integrity of AI implementation within a Sharia-compliant framework. The results indicate that AI systems guided by Sharia principles can improve curriculum personalization, administrative efficiency, transparency, and institutional accountability while reducing algorithmic bias and maintaining ethical values in educational processes. Furthermore, the proposed model encourages responsible data governance, ethical decision-making, and inclusive learning environments supported by intelligent technologies. The integration of intellectual, ethical, social, and spiritual dimensions also strengthens holistic educational development. In conclusion, embedding faith-based ethical intelligence into AI-driven educational management can foster sustainable, trustworthy, and socially responsible innovation while providing a practical framework for value-aligned AI adoption in education.
Real-Time Audience Sentiment Analytics for Adaptive and Personalized Media Broadcasting Asep Sutarman; Fandi Ahmad; Royani Royani
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 2 No. 1 November (2026): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/beam.v2i1.194

Abstract

Digital broadcasting platforms increasingly require mechanisms capable of understanding audience reactions while content is being delivered. However, conventional audience measurement relies primarily on delayed ratings, aggregate engagement statistics, or isolated textual sentiment analysis, limiting broadcasters’ ability to adapt content responsively. This study proposes a real-time audience sentiment analytics framework that integrates textual comments, interaction behavior, and temporal engagement signals to support adaptive and personalized media broadcasting. The proposed architecture combines a transformer-based text encoder, a behavioral feature network, temporal attention, and a contextual bandit adaptation engine. A prototype evaluation was conducted using public sentiment resources and a simulated broadcasting stream containing 120,000 audience events. The system classified audience sentiment into positive, neutral, and negative categories and translated aggregated sentiment into controlled broadcasting actions, including recommendation adjustment, segment continuation, notification timing, and presentation-style adaptation. Experimental results indicate that the multimodal model achieved an accuracy of 89.6% and a macro-F1 score of 88.9%, outperforming text-only and conventional machine-learning baselines. The prototype maintained an average end-to-end latency of 184 ms and processed approximately 1,420 events per second. In the simulated adaptive broadcasting experiment, sentiment-aware personalization improved click-through rate by 15.9%, average viewing duration by 12.8%, and audience satisfaction by 10.6% compared with static broadcasting. Fairness constraints, confidence thresholds, human editorial oversight, and privacy-preserving aggregation were incorporated to reduce the risks of emotional manipulation, bias, and unstable content adaptation. The findings demonstrate that real-time sentiment analytics can provide a technically effective foundation for responsive broadcasting when deployed as a decision-support mechanism rather than an autonomous editorial authority.
Decentralized Finance (DeFi) for Enterprise Treasury Management Untung Rahardja; Asep Sutarman; Muhtarom Muhtarom; Berlin Any
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 1 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i1.121

Abstract

This study explores the potential of Decentralized Finance (DeFi) as an innovative approach to enterprise treasury management. The Background of this research is rooted in the growing limitations of traditional treasury systems, which are often constrained by centralized control, limited transparency, high transaction costs, and slow settlement processes. The Object of this paper is to analyze how DeFi mechanisms, including smart contracts, decentralized lending, stablecoins, and blockchain-based liquidity management, can be adopted to improve efficiency, transparency, and financial resilience in corporate treasury operations. The Method employed in this study is a qualitative research approach, combining a systematic literature review, conceptual framework analysis, and case-based evaluation of existing DeFi platforms relevant to enterprise financial management. The Result of the analysis indicates that DeFi offers significant advantages for enterprise treasuries, such as real-time settlement, enhanced auditability, reduced reliance on intermediaries, improved liquidity optimization, and greater flexibility in cross-border transactions. However, the findings also highlight critical challenges, including regulatory uncertainty, smart contract risks, cybersecurity threats, and integration issues with existing enterprise systems. The Conclusion of this research emphasizes that while DeFi is not yet a complete replacement for traditional treasury management frameworks, it represents a promising complementary solution. With appropriate governance structures, regulatory clarity, and risk mitigation strategies, DeFi has the potential to transform enterprise treasury management into a more efficient, transparent, and decentralized financial ecosystem.
Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data Dirvi Surya Abbas; Asep Sutarman; Ryan Davis; Maulana Abbas
AI, Innovation, and Resilience for the Environment (AIR) Vol. 1 No. 2 (2026): AI, Innovation, and Resilience for the Environment (AIR) Journal
Publisher : Sinar Mentari Sundara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68012/air.v1i2.252

Abstract

Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.
Co-Authors Abbas, Dirvi Surya Adam Faturahman Arthur Simanjuntak Asher Nuche Bangun, Cicilia Sriliasta Berlin Any Fandi Ahmad Harry Agustian Maulana Abbas Muchlishina Madani Muhtarom Ramiro Santiago Ikhsan Royani Royani Ryan Davis Sheila Aulia Anjani Sipho Dlamini Solahudin Solahudin Untung Rahardja