Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Green Engineering: Journal of Engineering and Applied Science

Exploring Research Landscapes in Blockchain Logistics within Islamic Economics: A Bibliometric Study of Trends and Collaboration Networks Uswatun Kasanah, Yulinda; Miftahol Arifin
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 3 (2025): July : Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i3.199

Abstract

Blockchain logistics represents the integration of blockchain technology into the logistics sector, aiming to enhance efficiency, transparency, and security across supply chain processes. From an Islamic economics perspective, digital transformation must align with core values such as justice, transparency, and honesty to support the development of fair and sustainable logistics systems. The decentralized nature of blockchain offers promising solutions for building supply chains rooted in Islamic ethical principles. This study conducts a bibliometric analysis to examine the development and research trends of blockchain logistics within the context of Islamic economics. Using VOSviewer software, relevant scientific publications were analyzed based on bibliographic data sourced from reputable academic databases. Bibliometric parameters—such as the maximum number of authors per document and the minimum number of documents per author—were applied to identify key contributors and dominant research themes. The bibliometric mapping reveals the growth trajectory of blockchain logistics research framed by Islamic values. The visualization highlights research clusters, prominent authors, co-authorship networks, and publication trends that illustrate the evolution and scholarly interest in this interdisciplinary area. Emerging themes suggest a convergence between blockchain-driven logistics innovation and ethical economic practices advocated in Islamic teachings. The findings provide a comprehensive overview of the current landscape and collaboration opportunities in blockchain logistics research through an Islamic lens. This study contributes to the strategic positioning of future research by identifying gaps, potential synergies, and critical areas for development. Ultimately, it offers a foundational reference for scholars seeking to explore the integration of Islamic ethical principles within the advancement of blockchain-enabled logistics systems.
Classification of Fatigue Levels of Tofu Industrial Workers Based on MOQS and Cardiovascular Load Variables Using Decision Tree Algorithm Intan Berlianty; Miftahol Arifin
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 3 (2025): July : Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i3.220

Abstract

Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments.
A Quantitative Well-to-Wheel Analysis of the Effect of Electric Vehicle Adoption on CO₂ Emissions in Indonesian Urban Logistics Miftahol Arifin; Dinda Natasya Artaviana
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 3 (2025): July : Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i3.223

Abstract

Urban logistics is a significant source of carbon emissions in Indonesia, and effective decarbonization strategies are required. EVs offer a promising solution, but their impact requires quantitative evaluation within the local context. This study analyzes the effect of adopting an electric van fleet on total well-to-wheel carbon emissions within an urban distribution network in Indonesia. This study employs a comparative case study method. A baseline scenario consisting of 25 diesel vans is compared to an intervention scenario where electric vans replace the entire fleet. The emission analysis was conducted using the WtW framework, utilizing specific emission factors for diesel fuel from the IPCC (2006) and the Java-Madura-Bali (JAMALI) grid emission factor from IESR (2023) to ensure contextual relevance. The transition to an electric fleet successfully reduces the total well-to-wheel carbon footprint by 13.63%. This reduction is equivalent to an absolute CO2 emission decrease of nearly 3 tons of CO₂ per month. Nevertheless, indirect emissions from electricity generation still contribute a significant carbon footprint, indicating that the national energy mix is highly dependent on the environmental benefits of EVs. This study concludes that fleet electrification is a viable and effective decarbonization strategy for Indonesia’s logistics sector, even with the current state of the electricity grid. However, fleet decarbonization efforts must run in parallel with policies for a national transition toward renewable energy to maximize the emission reduction potential of electric mobility. Future research should include the total cost of ownership (TCO) and life cycle assessment (LCA) for a more holistic evaluation.