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Green Engineering: Journal of Engineering and Applied Science
Published by International Forum of Researchers and Lecturers
ISSN : 30636841     EISSN : 30636833     DOI : 10.70062
Core Subject : Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering
(Green Engineering: Journal of Engineering and Applied Science) [e-ISSN : 3063-6833, p-ISSN : 3063-6841] is an open access Journal published by the IFREL ( Forum of Researchers and Lecturers). Green Engineering accepts manuscripts based on empirical research results, new scientific literature review, and comments/ criticism of scientific papers published by Green Engineering. This journal is a means of publication and a place to share research and development work in the field of Engineering and Applied Science. Articles published in Green Engineering are processed fully online. Submitted articles will go through peer review by a qualified international Reviewers. Complete information for article submission and other instructions are available in each issue. Green Engineering publishes 4 (four) issues a year in January, April, July and October, however articles that have been declared accepted will be queued in the In-Press issue before published in the determined time.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 32 Documents
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Feature Extraction Using Discrete Wavelet Transform and Zero Sequence Current for Multi-Layer Perceptron Based Fault Classification Khoirudin, Irfan; Sri Arttini Dwi Prasetyowati
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 4 (2025): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i4.239

Abstract

Application of Multi-Layer Perceptron neural network to fault classification in high-voltage transmission lines is demonstrated in this paper. Different fault types on protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of Discrete Wavelet Transform energy features combined with zero sequence current magnitude as input features for neural network classifier. The proposed method uses eight extracted features to learn hidden relationship in fault signal patterns. Using proposed approach, fault detection and classification of all 11 fault types could be achieved with high accuracy. Improved performance is experienced once the neural network is trained sufficiently with 1188 fault samples, thus performing correctly when faced with different system conditions. Results of performance studies show that proposed neural network-based classifier achieves 96.18% average accuracy, which demonstrates that it can improve the performance of conventional fault classification algorithms, which in turn can provide more efficient solutions in the management and protection of high voltage electrical systems.
Development of Predictive Maintenance Framework Using IoT-Enabled Sensor Networks to Minimize Energy Losses in Manufacturing Plants Agus Suwarno; Wiyanto Wiyanto; Agung Nugroho
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 4 (2024): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i4.252

Abstract

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.
Low-Carbon Concrete Development through the Integration of Industrial Waste and Carbon Capture Materials Mohammad Burhan Hanif; Handini Arga Damar Rani; Surono Surono
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 2 (2024): April: 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.v1i1.254

Abstract

This study explores the development of low-carbon concrete by integrating industrial waste materials and CO₂-absorbing minerals to reduce carbon emissions in the construction industry. The research investigates various mix ratios involving fly ash, slag, and CO₂-absorbing minerals, aiming to optimize both performance and sustainability. Experimental methods included compressive strength testing, carbon emission measurement, and durability evaluation. The results indicate that low-carbon concrete formulations achieve up to 45% reduction in carbon emissions compared to conventional concrete, which uses Portland cement. Despite a slight decrease in compressive strength, the optimal mix reached 34 MPa, which is sufficient for structural applications. Durability testing revealed that concrete with CO₂-absorbing minerals showed improved long-term performance compared to other formulations. The findings highlight the potential of using industrial waste materials and carbon capture technologies to create more environmentally sustainable concrete while maintaining necessary structural properties. This study contributes to the growing demand for eco-friendly construction materials and supports the implementation of low-carbon concrete in large-scale industrial applications. Further optimization of mix ratios and long-term performance studies are recommended for broader adoption in the construction industry.
Multi-Objective Optimization of Green Building Retrofit Strategies Considering Thermal Comfort, Energy Efficiency, and Indoor Air Quality in Tropical Climate Zones Efvy Zamidra Zam; Wahyu Caesarendra; Nopriadi Nopriadi
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 4 (2024): October: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i4.255

Abstract

This study investigates optimal retrofit strategies for buildings in tropical climates, focusing on energy efficiency, thermal comfort, and indoor air quality (IAQ). Given the unique challenges of high temperatures, humidity, and energy demands in tropical regions, traditional retrofitting methods often fall short of achieving a balance between these critical factors. By employing a multi-objective optimization approach, this research identifies the most effective combination of retrofit solutions, including insulation, natural ventilation, and high-performance window treatments. The results show that the proposed retrofit strategy significantly reduces cooling energy consumption, while maintaining or improving occupant comfort and IAQ. Insulation, particularly external insulation, proved to be the most effective in reducing heat transfer, while natural ventilation strategies and advanced materials further contributed to improving thermal regulation. The study demonstrates that integrating passive and active retrofit measures, tailored specifically to tropical climates, leads to optimal building performance. The multi-objective optimization algorithm (NSGA-II) allowed for the generation of Pareto-optimal solutions, offering a set of trade-offs between energy efficiency, thermal comfort, and IAQ. These findings are particularly relevant for policymakers and building professionals seeking sustainable retrofit solutions in tropical regions. The study also highlights the importance of integrating energy efficiency and IAQ considerations in retrofit strategies to avoid compromising occupant health. Further research is recommended to explore the integration of advanced materials, such as phase change materials (PCMs), and to enhance IAQ management in retrofitted buildings, ensuring long-term sustainability and occupant well-being in tropical environments.
Comparative Analysis of Carbon-Neutral District Heating Solutions Incorporating Waste Heat Recovery and Geothermal Exchange in Tropical Urban Settings Rahardian Luthfi Prasetyo; Isra' Nuur Darmawan; Kholistianingsih
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 1 (2025): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i1.258

Abstract

This research investigates the potential of integrating waste heat recovery and geothermal exchange within district heating systems as a carbon-neutral energy solution tailored to tropical urban settings. Tropical climates present unique challenges for heating, ventilation, and air conditioning (HVAC) systems, including high humidity, temperature fluctuations, and increasing energy demands, particularly for cooling. This study aims to address these challenges by proposing a hybrid district heating system that combines renewable energy sources to improve energy efficiency and reduce carbon emissions. The methodology includes thermal and hydraulic modeling, performance simulation of the hybrid system under tropical climate conditions, and emission and energy efficiency analysis. The results indicate that the hybrid system significantly reduces energy consumption and carbon emissions compared to traditional heating systems, with waste heat recovery optimizing energy use and geothermal exchange enhancing system efficiency. The comparison with conventional systems and other environmentally friendly alternatives reveals that the hybrid system offers a cost-effective, sustainable solution for tropical urban areas. The study concludes that integrating waste heat recovery and geothermal exchange is feasible and can contribute to the creation of carbon-neutral cities. Future research should focus on optimizing geothermal systems in tropical climates and exploring further integration with other renewable energy sources to enhance system performance and sustainability.
Life Cycle Assessment and Optimization of Local Bio-Composite Materials for Sustainable Building Components in Indonesia Turahyo Turahyo; Arfittariah Arfittariah; Osman Fuad Gargari
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 1 (2024): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i1.259

Abstract

This research explores the potential of local bio-composites made from bamboo and coconut fiber as sustainable building materials in Indonesia. The construction sector in Indonesia faces significant environmental challenges, particularly the high carbon emissions associated with conventional materials like concrete and steel. The primary objective of this research is to evaluate the environmental and economic benefits of substituting these traditional materials with bio-composites, using Life Cycle Assessment (LCA) and energy optimization simulations. The LCA methodology evaluates the environmental impact of bio-composites across their entire life cycle, comparing them with conventional materials in terms of carbon emissions, energy consumption, and waste. Energy optimization simulations focus on assessing the thermal performance and overall building efficiency when using bio-composites in construction. The main findings suggest that bio-composites exhibit a significantly lower carbon footprint and better thermal insulation properties than concrete and steel, contributing to reduced energy consumption in buildings. Additionally, the use of locally sourced materials like bamboo and coconut fiber offers economic advantages, such as lower transportation costs and support for local economies. The research concludes that bio-composites can serve as viable alternatives to traditional materials, providing both environmental and economic benefits. For successful adoption, the research recommends policy support, technological advancements, and educational initiatives to promote the use of bio-composites in Indonesia’s construction industry.
Development of a Decision Support System for Optimizing Urban Green Infrastructure Placement to Maximize Stormwater Infiltration and Reduce Flood Risk under Climate Change Scenarios Yuventius Tyas Catur Pramudi; Raden Arief Nugroho; Edy Mulyanto; Muljono Muljono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 1 (2025): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i1.260

Abstract

Urban flooding is increasingly concerning due to climate change and rapid urbanization. Factors such as intensified rainfall, urban sprawl, and reduced permeable surfaces heighten flood risks, making efficient stormwater management crucial. This study focuses on developing a Decision Support System (DSS) to optimize green infrastructure (GI) placement in urban areas, aiming to enhance stormwater infiltration and reduce flood risks under climate change scenarios. The research reviews current strategies for GI planning and DSS in urban flood management. By integrating GIS tools, hydrological models, and climate data, the DSS identifies ideal locations for GI measures like rain gardens, bioswales, and permeable pavements, promoting effective stormwater management while addressing climate change. Hydrological models simulate stormwater behavior under varying rainfall conditions, and GIS maps potential GI sites within urban areas. Simulations of future extreme rainfall events assess GI performance under changing climate conditions. Results show significant reductions in stormwater runoff and flood risks, particularly in areas with high impervious surfaces. Challenges such as space constraints in dense urban areas, scalability of GI solutions, and long-term maintenance are discussed. The study concludes that integrating GI with traditional stormwater systems offers a comprehensive approach to urban flood mitigation, with the DSS serving as a key tool for urban planners and policymakers.
Application of Green Hydrogen Technology for Industrial Decarbonization: Techno-Economic and Environmental Assessment Amiq Fahmi; Raden Arief Nugroho; Muljono Muljono; Noorsidi Aizuddin Bin Mat Noor
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 2 (2024): April: 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.v1i2.261

Abstract

This study explores the application of green hydrogen technology for industrial decarbonization, focusing on its techno-economic and environmental feasibility. A quantitative approach was used, incorporating system modeling of a solar-based hydrogen production system combined with electrolyzers. The techno-economic assessment involved calculating the Levelized Cost of Hydrogen (LCOH), estimating capital and operational expenditures (CAPEX and OPEX), and evaluating the system's energy efficiency and hydrogen output. The environmental impact was analyzed using Life Cycle Assessment (LCA), comparing the carbon footprint of green hydrogen with fossil-based hydrogen. The results reveal that green hydrogen can reduce carbon emissions by up to 60% compared to fossil hydrogen, primarily due to the use of renewable energy for production. Additionally, the study found significant improvements in energy efficiency as electrolyzer performance and solar capacity increased. The LCOH is expected to decrease steadily as solar panel and electrolyzer prices continue to fall, enhancing the competitiveness of green hydrogen in the energy market. The findings also highlight the potential for heavy industries, such as cement and steel production, to transition from fossil fuels to green hydrogen, contributing to a cleaner industrial energy mix. This transition presents both environmental and economic benefits, with long-term savings from reduced fossil fuel dependency and lower production costs.
Development of Terminal and Ship Operational Integration System for Docking and Berthing Time Optimization Based on Historical Data Irfan Faozun; Larsen Barasa; Natanael Suranta; Ronald Simanjuntak; Imam Fachruddin
Green Engineering: International Journal of Engineering and Applied Science Vol. 3 No. 1 (2026): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v3i1.262

Abstract

This research investigates the development of integrated operational systems connecting terminal and ship operations for docking and berthing time optimization through systematic analysis of historical data. Port efficiency depends critically on minimizing vessel turnaround time, with berth allocation, docking procedures, and cargo operations coordination determining overall port productivity and competitiveness. Through qualitative analysis involving port operators, terminal managers, ship agents, harbor masters, and operations research specialists, this study examines how historical operational data can inform intelligent coordination systems improving berthing efficiency. Results demonstrate that data-driven integration systems incorporating predictive analytics, automated scheduling, and coordinated workflows can reduce average berth turnaround time by 15-30%, improve berth utilization by 20-35%, and decrease operational conflicts by 40-60% through optimized allocation and proactive coordination. Key implementation challenges include data quality and availability, system integration complexity, organizational coordination barriers, and resistance to automated decision support. Findings reveal that historical data-based optimization represents transformative advancement from experience-based scheduling to evidence-driven operational planning supporting port efficiency enhancement, capacity maximization, and service reliability improvement. This research contributes to port operations literature by providing practical frameworks for data-driven berthing optimization applicable to diverse port operational contexts.
Development of IT-Based AIS Data Surveillance Model in Supporting Maritime Safety Pargaulan Dwikora Simanjuntak; R. Herlan Guntoro
Green Engineering: International Journal of Engineering and Applied Science Vol. 3 No. 1 (2026): January: Green Engineering: International Journal of Engineering and Applied Sc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v3i1.263

Abstract

This research investigates the development of IT-based Automatic Identification System (AIS) data surveillance models supporting maritime safety through integration of advanced information technology, maritime engineering principles, and human factors optimization. AIS technology generates vast real-time vessel movement data creating unprecedented opportunities for safety enhancement through systematic surveillance, collision risk detection, traffic pattern analysis, and incident prevention, yet effectiveness depends critically on intelligent data processing algorithms, reliable IT infrastructure, and competent personnel capable of interpreting surveillance outputs and taking appropriate actions. Through qualitative analysis involving maritime safety authorities, vessel traffic service (VTS) operators, port authorities, marine engineers, IT specialists, data scientists, and maritime training institutions, this study examines how IT-based surveillance models incorporating pattern recognition, anomaly detection, predictive analytics, and crew-centered interfaces can transform maritime safety management from reactive incident response toward proactive risk prevention. Results demonstrate that intelligent AIS surveillance can identify 75-90% of high-risk situations 15-45 minutes before critical events, reduce collision risks by 60-80%, improve traffic management efficiency by 35-55%, and enhance crew situational awareness by 45-65% when integrated with appropriate training programs developing personnel competencies in data interpretation, system operation, and coordinated response. Key implementation challenges include data quality and completeness issues, computational infrastructure requirements, algorithm development complexity, personnel competency gaps requiring substantial training investments, organizational coordination barriers, and privacy/security concerns. Findings reveal that successful AIS surveillance implementation requires holistic sociotechnical approaches integrating IT systems engineering, maritime domain expertise, and human capability development through coordinated design, deployment, and training strategies. This research contributes to maritime safety literature by providing integrated frameworks for IT-based surveillance systems incorporating technical capabilities, operational requirements, and human factors supporting evidence-based safety management.

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