Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
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This Author published in this journals
All Journal International Journal of Social Service and Research Novatio : Journal of Management Technology and Innovation Logistica : Journal of Logistic and Transportation
Widayat, Tri Agung
Unknown Affiliation
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Transportation

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Journal : Logistica : Journal of Logistic and Transportation

 1 
Barriers and Opportunities in Circular Logistics: A Global Comparative Narrative Review Widayat, Tri Agung; Mintje, Quirina Ariantji Patrisia; Yosepha, Sri Yanthy
Logistica : Journal of Logistic and Transportation Vol. 2 No. 3 (2024): July 2024
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v2i3.1055

Abstract

This study reviews and synthesizes current knowledge on eco-efficient transport models within the frameworks of green logistics and the circular economy. The aim is to evaluate how technological, regulatory, and economic factors influence adoption and implementation. Literature was systematically gathered from major databases such as Scopus, Web of Science, and Google Scholar, using targeted keywords and Boolean search strategies. Inclusion criteria prioritized peer-reviewed articles published between 2018 and 2025 that addressed sustainable logistics, circular supply chains, and digital innovations. The review identified four major themes: drivers, barriers, case studies, and regional comparisons. Findings reveal that digital technologies, including artificial intelligence, blockchain, and the Internet of Things, enhance transparency, traceability, and efficiency. Regulatory frameworks, particularly in Europe, accelerate adoption, while economic incentives strengthen competitiveness. However, barriers persist, especially high initial costs, infrastructural deficits, and weak enforcement in developing economies. Case studies confirm measurable benefits, such as emission reductions and cost savings, while comparative analyses show significant regional disparities. The discussion emphasizes the importance of systemic alignment across policy, markets, and organizational culture to overcome these challenges. Future research is recommended to expand empirical evidence, develop standardized evaluation tools, and examine underrepresented regions. Overall, the review highlights the urgent need for integrated strategies that combine technology, regulation, and collaboration to advance sustainable logistics.
Enhancing Driver Stress Detection through Multimodal Integration of Eye Tracking and Physiological Signals Widayat, Tri Agung; Mintje, Quirina Ariantji Patrisia; Yosepha, Sri Yanthy
Logistica : Journal of Logistic and Transportation Vol. 3 No. 3 (2025): July 2025
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/logistica.v3i3.1147

Abstract

Driver stress poses significant risks to traffic safety, impairing attention, decision-making, and reaction time. Traditional monitoring methods often lack sensitivity. This study proposes and validates a novel multimodal framework that integrates synchronized eye-tracking and physiological data to significantly enhance the sensitivity and real-time accuracy of driver stress detection, addressing limitations of earlier unimodal approaches. Thirty licensed drivers participated in simulated driving tasks under baseline and stress-induced conditions. Eye-tracking metrics (pupil diameter, fixation duration, blink rate) and physiological signals (heart rate, skin conductance, heart rate variability) were collected. Data were synchronized and analyzed using Linear Discriminant Analysis (LDA) and other machine learning models to classify stress conditions. Under stress, pupil dilation increased by 20%, blink rate rose by 35%, and gaze spread narrowed, indicating visual tunneling. Physiologically, heart rate increased by 17%, skin conductance by 31%, and HRV decreased by 19%. The combined multimodal model achieved 91.4% classification accuracy, outperforming unimodal approaches. These results align with previous research showing that multimodal systems provide more reliable stress detection by integrating visual and autonomic markers. The findings highlight the system’s potential for real-time applications in Driver Monitoring Systems (DMS). Multimodal integration of eye-tracking and physiological signals enhances the sensitivity and reliability of driver stress detection. This approach offers a foundation for intelligent, adaptive DMS capable of improving road safety. Future work should focus on real-world validation and ethical implementation strategies. These findings demonstrate that multimodal integration provides a more comprehensive understanding of driver stress through complementary visual and autonomic indicators. The proposed framework forms a foundation for intelligent, adaptive Driver Monitoring Systems (DMS) capable of real-time stress recognition and proactive safety intervention.
Co-Authors Andrianto, Deny Tomy Daniel Happy Putra Marthalia, Lia Mintje, Quirina Ariantji Patrisia Sarfilianty Anggiani, Sarfilianty Toja, Andi Batari Willy Arafah, Willy Yosepha, Sri Yanthy