Journal of Advanced Engineering and Technology Research (JODETOS)
Journal of Advanced Engineering and Technology Research (JODETOS) is an international, peer-reviewed, and open-access journal that publishes original research, review articles, and applied studies across all branches of engineering and technology. This journal serves as a multidisciplinary platform for researchers, engineers, academicians, and practitioners to exchange innovative ideas, theoretical developments, and practical applications that advance science, technology, and sustainable engineering solutions globally. JODETOS covers a comprehensive range of disciplines including civil, mechanical, electrical, industrial, computer, environmental, chemical, and materials engineering, as well as mechatronics, architecture, robotics, artificial intelligence, transportation, energy, and water resources. The journal also welcomes interdisciplinary research that integrates engineering with management, information systems, environmental sustainability, and technological innovation. With its commitment to academic excellence, scientific integrity, and global collaboration, JODETOS aims to contribute to the dissemination of cutting-edge knowledge and to inspire transformative progress in the fields of engineering and technology for a sustainable future.
Articles
10 Documents
<![CDATA[Thermal And Flow Characterization Of Nanofluid-Based Cooling Systems For High-Performance Mechanical Applications ]]>
<![CDATA[Merin Selva Andela]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 1 No. 1 (2025): JODETOS - November ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v1i1.891
<![CDATA[Nanofluids, engineered suspensions of nanoparticles in base fluids, have emerged as a promising solution for enhancing thermal management in high-performance mechanical systems. This study investigates the thermal and flow characteristics of Al₂O₃-water and CuO-water nanofluid-based cooling systems using both experimental measurements and computational fluid dynamics (CFD) simulations. The effects of nanoparticle concentration, flow rate, and hybrid formulations on convective heat transfer and pressure drop were evaluated. Results indicated that nanofluids significantly improve heat transfer performance, with enhancements up to 28% compared to conventional fluids, while maintaining manageable viscosity levels. Hybrid nanofluids further enhanced thermal performance by leveraging complementary nanoparticle properties. CFD results validated experimental findings, providing detailed insight into nanoparticle transport and local temperature distribution. The study demonstrates that optimized nanofluid-based cooling systems can effectively manage high heat fluxes, offering a practical approach for reliable and efficient thermal management in advanced mechanical applications.]]>
<![CDATA[Performance Assessment Of High-Rise Building Foundations Subjected To Variable Soil And Load Conditions In Coastal Environments ]]>
<![CDATA[Dermawan Zebua]]>;
<![CDATA[Jun Fajar Krisman Giawa]]>;
<![CDATA[Anggerius Loi]]>;
<![CDATA[Cristopher Zebua]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 1 No. 1 (2025): JODETOS - November ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v1i1.892
<![CDATA[High-rise buildings in coastal environments are exposed to complex geotechnical and environmental conditions that significantly affect foundation performance. This study evaluates the behavior of high-rise building foundations under variable soil and load conditions using field investigation, laboratory testing, and numerical modeling. Soil stratigraphy, groundwater fluctuations, and load variability were analyzed to assess settlement, differential tilting, and stress distribution. Results indicate that pile foundations outperform mat and combined mat-pile foundations in controlling differential settlement and maintaining structural serviceability. Groundwater level changes and load variations notably influence foundation performance, emphasizing the need for site-specific, performance-based design. The integrated methodology provides insights for resilient foundation design strategies in challenging coastal environments.]]>
<![CDATA[Hybrid Ai–Iot Framework For Predictive Maintenance In Critical Infrastructure: A Sustainable Approach To Reducing Energy Loss And System Failures ]]>
<![CDATA[Lauzuary]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 1 No. 1 (2025): JODETOS - November ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v1i1.893
<![CDATA[Predictive maintenance has emerged as a critical strategy for enhancing operational reliability, reducing energy losses, and minimizing system failures across modern critical infrastructure. However, conventional monitoring systems often operate with limited real-time analytical capability, resulting in delayed failure detection and suboptimal maintenance decisions. This study proposes a hybrid Artificial Intelligence–Internet of Things (AI–IoT) framework designed to enable real-time condition monitoring, intelligent diagnostics, and energy-efficient maintenance scheduling. The framework integrates edge-based IoT sensor networks with cloud-driven machine learning algorithms—particularly deep learning and anomaly detection models—to capture high-frequency operational data while minimizing latency and computational overhead. A multi-layer architecture is developed, consisting of data acquisition, feature extraction, predictive modeling, and sustainability optimization modules. Experimental validation using a simulated critical infrastructure environment demonstrates that the proposed hybrid framework improves failure prediction accuracy by 18.7%, reduces energy loss by 12.5%, and decreases unplanned downtime by 22.3% compared to traditional maintenance approaches. These findings highlight the potential of the hybrid AI–IoT framework to support sustainable engineering practices, extend asset lifecycles, and enhance the resilience of essential infrastructure systems. The proposed model contributes novel insights into integrating smart sensing technologies with advanced computational intelligence for next-generation maintenance engineering.]]>
<![CDATA[Development Of Self-Healing Eco-Composite Materials Using Nano-Encapsulated Biopolymers For Green Construction Engineering ]]>
<![CDATA[Verawati Itm]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 1 No. 1 (2025): JODETOS - November ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v1i1.894
<![CDATA[The growing demand for sustainable construction materials has accelerated the development of advanced eco-composites with enhanced durability, adaptability, and environmental compatibility. This study presents a novel self-healing eco-composite material engineered through the integration of nano-encapsulated biopolymers designed to autonomously repair microcracks and structural degradation. Utilizing a bio-based polymer matrix reinforced with cellulose nanofibers and embedded healing capsules containing chitosan–lignin nanoemulsions, the material exhibits a dual-function mechanism: crack initiation triggers capsule rupture, while the released bioactive agents polymerize to restore mechanical integrity. Experimental evaluation demonstrates a significant improvement in tensile recovery (up to 87%) and microcrack closure efficiency (92%) compared to conventional composites. Thermal stability and biodegradability assessments further confirm that the nano-encapsulated healing system enhances both performance and ecological compatibility, reducing long-term resource consumption and waste generation. The developed eco-composite shows strong potential for application in green construction engineering, particularly in structures requiring extended life cycles, reduced maintenance cost, and improved resilience against environmental stressors. This research contributes to advancing sustainable materials science by demonstrating a high-performance, self-healing composite built upon renewable biopolymer technology.]]>
<![CDATA[Smart Water Management System Using Edge Computing And Machine Learning For Real-Time Pollution Detection In Urban Waters ]]>
<![CDATA[Taubatuzzumaro]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 1 No. 1 (2025): JODETOS - November ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v1i1.895
<![CDATA[This study proposes a Smart Water Management System grounded in edge computing and machine learning to advance real-time pollution detection in urban aquatic environments. Rapid urbanization has intensified pollutant inflows—ranging from nutrients and heavy metals to organic waste necessitating an adaptive, high-resolution monitoring architecture capable of immediate response. The system integrates distributed edge-based sensor nodes with lightweight analytical models, enabling on-site data processing, reduced latency, and enhanced situational awareness. A hybrid machine learning framework is employed, combining anomaly detection, regression-based water-quality forecasting, and classification of pollutant signatures to strengthen diagnostic accuracy. Field deployment across multiple urban water sites demonstrates that the proposed system reduces data-transmission demands by over 60%, increases detection sensitivity—particularly for turbidity, dissolved oxygen fluctuations, and contaminant spikes—and supports near real-time environmental decision-making. The study further highlights scalability, resilience to intermittent connectivity, and compatibility with existing municipal water-management infrastructures. Overall, the findings underscore that the integration of edge intelligence with adaptive learning algorithms significantly improves pollution monitoring performance, offering a transformative pathway toward smarter, more sustainable urban water governance. This framework provides a replicable model for policymakers, environmental engineers, and urban planners seeking advanced, responsive water-quality protection strategies.]]>
<![CDATA[Utilization Of Indonesian Language-Based Ai Chatbot For Digital Public Services ]]>
<![CDATA[Feberlina Hulu]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 2 No. 1 (2026): JODETOS - Mei ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v2i1.1019
<![CDATA[The rapid development of artificial intelligence (AI) has significantly transformed public service sectors, particularly through the implementation of chatbot technology. This study aims to analyze the utilization of Indonesian-language-based chatbots in improving the efficiency, accessibility, and interactivity of digital public services in Indonesia. A qualitative approach was employed by combining literature review and case analysis of chatbot implementation in government institutions. The results indicate that chatbots equipped with natural language processing (NLP) in the Indonesian language can accelerate service responses, reduce staff workload, and enhance citizen satisfaction. Nevertheless, several challenges remain, including limited understanding of local language context, data security concerns, and the readiness of government digital infrastructure. This study recommends strengthening collaboration among government, academia, and AI developers to build an inclusive and efficient digital public service ecosystem in the future.]]>
<![CDATA[Satellite Image Analysis Using Ai For Deforestation Monitoring In Kalimantan And Sumatera ]]>
<![CDATA[Zacky Anggianto Zebua]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 2 No. 1 (2026): JODETOS - Mei ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v2i1.1020
<![CDATA[Deforestation in Indonesia, particularly in Kalimantan and Sumatra, remains a significant environmental issue with broad impacts on global ecosystems. This study aims to analyze forest cover change using satellite imagery combined with Artificial Intelligence (AI) technology. The data consist of Landsat 8 and Sentinel-2 imagery from 2015 to 2024. The main methods applied are Convolutional Neural Network (CNN) and Random Forest for classifying forest and non-forest areas. The results show that the model achieved an accuracy rate of 92.4% with a Kappa coefficient of 0.89. Central Kalimantan and South Sumatra recorded the highest deforestation rates, mainly driven by oil palm expansion and mining activities. The integration of satellite imagery and AI has proven effective for early warning systems of deforestation and supports evidence-based conservation policy planning.]]>
<![CDATA[Analysis Of Factors Causing Variations In Asphalt Penetration Test Results Among Civil Engineering Students In Highway Engineering Laboratory Practicum ]]>
<![CDATA[Kadek Adi Mahendra]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 2 No. 1 (2026): JODETOS - Mei ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v2i1.1416
<![CDATA[Variations in asphalt penetration test results are a recurring phenomenon in highway engineering laboratory practicums, particularly when tests are conducted by students with differing levels of technical skills and procedural understanding. These variations may compromise the reliability and consistency of laboratory outcomes if not properly analyzed. This study aims to identify and analyze the factors contributing to variations in asphalt penetration test results performed by civil engineering students during highway laboratory practicums. The research employed a quantitative descriptive approach using a survey method. The population consisted of 150 fifth-semester civil engineering students from the 2023 cohort at the University of Lampung, with a sample of 60 respondents selected through simple random sampling. Data were collected using a structured questionnaire based on validated indicators, including compliance with standard operating procedures, time control, temperature control, instrument reading accuracy, needle positioning, and students’ practical experience. Descriptive statistical analysis was applied to evaluate the tendency and distribution of responses. The results indicate that temperature control, time consistency, and instrument reading accuracy are the dominant factors influencing test result variations. The study recommends strengthening procedural supervision, improving laboratory instruction clarity, and integrating simulation-based practicum learning to enhance students’ technical competence and reduce testing variability]]>
<![CDATA[Bridging The Gap Between Civil Engineering Education And Industry Requirements: Evidence From Indonesia ]]>
<![CDATA[Ahmad Haedar Rifqi]]>;
<![CDATA[Bayu Kurniawan]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 2 No. 1 (2026): JODETOS - Mei ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v2i1.1419
<![CDATA[The mismatch between higher education outcomes and industry requirements has become a critical issue, particularly in developing countries such as Indonesia. This study aims to examine the gap between civil engineering education and the competencies required by the construction industry. A mixed methods approach was employed, combining quantitative data from questionnaire surveys and qualitative insights from in-depth interviews with graduates, academics, and industry practitioners. The quantitative analysis utilized gap analysis to identify discrepancies between acquired and required competencies, while thematic analysis was applied to interpret qualitative findings. The results indicate that although civil engineering graduates possess adequate theoretical knowledge, they lack practical skills and essential soft skills such as communication, teamwork, and problem-solving. The largest gaps were identified in non-technical competencies, highlighting the need for a more balanced curriculum. Additionally, factors such as limited industry collaboration, rigid curricula, and insufficient integration of modern technologies contribute significantly to the mismatch. This study emphasizes the importance of strengthening collaboration between universities and industry through internships, project-based learning, and curriculum co-development. Integrating digital tools and soft skills training into academic programs is also essential to enhance graduate employability. The findings provide valuable insights for policymakers, educators, and industry stakeholders in developing more responsive and industry-aligned civil engineering education systems in Indonesia.]]>
<![CDATA[Optimization Of Renewable Energy Microgrids Using Multi-Objective Genetic Algorithms For Rural Electrification And Climate Resilience ]]>
<![CDATA[Indramawan]]>;
<![CDATA[Wahyudin]]>;
<![CDATA[Muhammad Irvan]]>
<![CDATA[ Journal of Advanced Engineering and Technology Research Vol. 2 No. 1 (2026): JODETOS - Mei ]]>
Publisher : <![CDATA[CV. SINAR HOWUHOWU ]]>
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DOI: 10.70134/jodetos.v2i1.1420
<![CDATA[The rapid expansion of rural electrification demands sustainable, resilient, and cost-effective energy solutions capable of withstanding climate-induced disruptions. This study presents an optimized framework for renewable energy microgrids using Multi-Objective Genetic Algorithms (MOGAs) to balance key operational trade-offs: cost minimization, reliability enhancement, emission reduction, and climate-resilience improvement. The proposed model integrates photovoltaic systems, wind turbines, micro-hydro units, and battery energy storage, combined with stochastic simulations of rural demand profiles and climate variability. MOGA-based optimization enables simultaneous exploration of diverse design configurations, producing Pareto-optimal microgrid solutions that adapt to local resource availability and environmental stressors. Results demonstrate that MOGA-optimized microgrids can reduce levelized cost of electricity (LCOE) by up to 37%, improve system reliability by 42%, and enhance resilience metrics under extreme weather scenarios. Comparative analysis with single-objective approaches further reveals substantial performance gains in balancing cost, sustainability, and robustness. This research contributes an advanced computational framework to guide policymakers, rural planners, and energy engineers in designing renewable microgrids that support long-term rural development and climate adaptation. The findings highlight the transformative potential of multi-objective evolutionary optimization in accelerating equitable and climate-resilient rural electrification.]]>
