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This study aims to analyze the effect of renewable energy, energy consumption, and Gross Domestic Product (GDP) per capita on carbon dioxide (CO2) emissions in Indonesia for the period 1995-2024. This study uses secondary data over time (time series) with the Ordinary Least Square (OLS) multiple linear regression analysis method corrected using the Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) approach. The results show that renewable energy does not have a significant effect on CO2 emissions, which is caused by the still low share of renewable energy in the national energy mix which only reaches 10.95% in 2024. Energy consumption has a positive and significant effect on CO2 emissions, where every 1% increase in energy consumption increases CO2 emissions by 84.23%. Gross Domestic Product (GDP) per capita has a positive and significant effect on CO2 emissions. Every 1% increase in GDP per capita increases CO2 emissions by 35.03%, indicating that Indonesia remains on the EKC curve. Simultaneously, all three variables have a significant effect, with an adjusted R-squared value of 53.63%. This finding confirms that Indonesia's energy mix, still dominated by fossil fuels, is a major factor in high carbon emissions. Comprehensive energy efficiency policies, accelerated renewable energy transitions, and greener and more sustainable economic growth strategies are needed.

The R05 Community Service Program (KKN), Subgroup 1 in Kalikatir Village, Gondang District, Mojokerto Regency, aims to implement hydram pump technology as an environmentally friendly and energy-efficient irrigation solution. This technology is designed to meet the water needs of 100 hectares of banana plantations, especially during the dry season. The implementation method includes initial surveys, design, manufacture, installation, and testing of the hydram pump, involving the participation of Kalikatir villagers. The results show that the pump is capable of lifting water to a height of 30 meters with 70% efficiency, although it has not yet reached the daily target. This technology has been proven to reduce operational costs and increase agricultural productivity. It is hoped that in the future, this technology can be further developed to improve its efficiency and sustainability.

Gununganyar Reservoir in Tuban Regency is one of the reservoirs used as a source of livelihood for the local community. However, further development and utilization of Gununganyar Reservoir have yet to be undertaken. The development of a small-scale floating Photovoltaic Solar Power Plant (floating solar panels) at Gununganyar Reservoir, aimed at providing an alternative and renewable energy source for operating raw water pumps and for supporting the Gununganyar Nature Tourism (WAG) initiative, constitutes an innovative and strategic effort to enhance energy efficiency and environmental sustainability in rural areas. This study focuses on the design and performance assessment of a floating solar panel system constructed with processed bamboo as an alternative floating material replacing HDPE, while integrating local wisdom and environmental conservation principles to minimize the reservoir’s carbon footprint. The methodology employs a simple quantitative approach combined with a literature review of relevant studies. Based on the analysis, the design of this small-scale floating solar panel system utilizes a 170 Wp off-grid solar module mounted at a tilt angle of approximately 7,1°, capable of generating a peak power output exceeding 2,962 kWp and supported by a battery capacity of  328,33 Ah. The implementation of these floating solar panels is expected to serve as a model for reservoir development by promoting energy self-sufficiency, stimulating local economic growth, and contributing to the achievement of net zero emissions by 2060.

The commercial sector, especially the hospitality industry, is one of the largest consumers of electrical energy, with energy costs often ranking as the second highest operational expense. This study aims to conduct a specific Electrical Energy Audit in the Office Engineering unit of Aston Denpasar Hotel & Convention Center to optimize electricity usage and improve energy efficiency. The research applies a detailed audit approach with a focus on lighting systems and air conditioning (AC), which are major contributors to energy consumption. The initial stage involves calculating the actual Energy Consumption Intensity (IKE) in kWh/m²/month and comparing the results with ASEAN and SNI standards to determine the efficiency classification of the building. Data collection is carried out through direct field measurements as primary data, using instruments such as a Clamp Meter and Lux Meter. The expected outcome of this study is the identification of detailed Energy Saving Opportunities (ESO), along with the estimation of potential monthly energy cost savings and the calculation of the investment Payback Period.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

Edge-Internet of Things (Edge IoT) systems are increasingly integral to applications that require real time signal processing, particularly where low latency and energy efficiency are critical. This paper explores the design and performance evaluation of a heterogeneous microprocessor architecture aimed at optimizing energy consumption and real time performance. The heterogeneous architecture integrates multiple types of cores, such as Central Processing Units (CPUs), Digital Signal Processors (DSPs), and Graphics Processing Units (GPUs), to allocate tasks based on computational demand. The proposed design significantly reduces energy consumption, particularly during high-performance tasks, while maintaining real time processing guarantees. Simulation-based performance evaluation was conducted to assess the energy efficiency, latency, and overall system performance under varying workloads, including real time Digital Signal Processing (DSP) benchmarks. The results showed that the heterogeneous architecture outperformed traditional homogeneous processors, demonstrating up to a 19-fold improvement in energy efficiency. Furthermore, the system reduced latency by up to 45% in real time applications, making it particularly suitable for Edge IoT environments such as industrial automation and smart healthcare, where both performance and energy efficiency are critical. Despite some trade-offs in task scheduling complexity, the heterogeneous design was able to balance power consumption and computational performance effectively. The findings suggest that this architecture can serve as a foundation for future Edge IoT systems, providing significant advantages in terms of energy efficiency, real time processing, and scalability. Future work will focus on further optimization of the architecture and exploring its application across various IoT environments.

The increasing demand for low latency and high-throughput multimedia applications has spurred significant advancements in hardware software co design. This study explores the integration of custom digital signal processing (DSP) hardware accelerators with optimized software frameworks to enhance deep learning accelerated DSP tasks. The proposed co design approach significantly reduces latency and improves throughput compared to traditional software-only DSP implementations. Through the development of custom hardware accelerators built with FPGA technology, the system achieves up to a 1.85x reduction in latency and a 1.5x improvement in throughput for real-time multimedia tasks such as image recognition, video decoding, and audio processing. The combination of hardware and software optimizations allows for better resource utilization, enabling the parallel processing of computationally intensive tasks while the software framework handles less demanding operations. Additionally, the co design system demonstrated improved energy efficiency, making it highly suitable for embedded systems. The results show that the hardware software co design approach offers substantial advantages in performance, latency reduction, and energy efficiency, positioning it as a viable solution for real-time multimedia applications. The findings have important implications for applications requiring fast data processing, such as autonomous driving, healthcare, and disaster management. Future research could explore alternative hardware accelerators, advanced software optimizations, and AI-based resource management to further improve the system’s efficiency and scalability for more complex multimedia tasks.

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

This study aims to examine the impact of green economy initiatives on Indonesia’s GDP growth, with a particular focus on public perceptions of sustainable economic practices. The research employs a descriptive quantitative approach, collecting primary data through an online survey administered to twenty respondents. The survey measured perceptions related to sustainable innovation, environmentally friendly resource management, and energy efficiency as key components of green economy implementation. The findings indicate that respondents hold a generally positive view of green economy practices, as reflected in a high average score of 4.24, suggesting strong agreement regarding their implementation and potential benefits. In addition, perceptions of economic development linked to green initiatives were also rated highly. These results imply that the public perceives a meaningful relationship between the adoption of green economy principles and long-term economic prosperity in Indonesia. Further analysis suggests that green practices can contribute to increased income levels, improved social welfare, and enhanced productivity, while simultaneously supporting environmental sustainability. From a practical perspective, the study highlights the importance of greater community participation, stronger policy commitment from the government, and increased green innovation by corporate entities. Theoretically, the results support existing economic and sustainability theories that emphasize the role of green economy strategies in fostering resilient, inclusive, and equitable economic growth. Overall, this study reinforces the relevance of green economy initiatives as a viable pathway toward sustainable national development.

The development of sustainable tourism facilities is one of the important efforts in increasing the attractiveness of destinations while preserving the environment. This study aims to analyze the implementation of ecological concepts in the development of glamping facilities in the Pearl Beach tourist area. The method used is a descriptive qualitative approach, with data collection through field observations, interviews with managers and tourists, and literature studies related to ecotourism principles and sustainable design. The results of the study show that the application of ecological concepts in glamping facilities in Mutiara Beach includes the use of environmentally friendly materials, integrated waste management, the application of energy efficiency, and designs that integrate the natural landscape without damaging the coastal ecosystem. The application of this concept not only improves the comfort and experience of tourists, but also contributes to increasing environmental awareness and strengthening the positive image of tourist destinations. In addition, this ecologically-conceptual glamping development model is expected to be a reference for the development of sustainable tourism facilities in other coastal areas, which prioritizes nature preservation and the welfare of local communities.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

This research examines the use of blockchain technology to support energy sustainability in urban areas. Blockchain offers transparency, security, and efficiency in recording and distributing energy data, potentially optimizing renewable energy use and reducing carbon emissions. The research method involves literature analysis and simulations of blockchain applications in urban energy systems. The results show that blockchain implementation can increase energy distribution efficiency by up to 20%, reduce data reporting time by up to 99%, and reduce carbon emissions by 50%. In conclusion, blockchain technology can be a strategic innovation in supporting the transition to a sustainable and environmentally friendly energy system.

Digital transformation through adoption cloud technology has become catalyst in effort efficiency energy and reduction greenhouse gas emissions glass (GHG). Research This aim for analyze contribution cloud technology against efficiency operational and impact the environment in framework economy green. With use approach studies literature and secondary data analysis from report institution international and journals scientific, research This find that migration to cloud computing can reduce consumption energy up to 84% and emissions carbon up to 88% compared to with traditional IT infrastructure. These results show that cloud computing is not only solution technology, but also important strategy in support development sustainable.

The oil and gas industry plays a crucial role in meeting global energy needs, with crude oil from production wells being the primary product of upstream operations. Prior to further processing, crude oil requires pretreatment at the production site, one of the key stages being phase separation using a flash separator. This study examines the effect of variations in cooling temperature on the performance of liquid phase separation and energy requirements in the flash separation process of light hydrocarbons. The analysis was conducted through process simulation using Aspen HYSYS version 14.2 with the Peng Robinson property package. The feed stream had a mass rate of 10,000 kg per hour, a temperature of 50°F, and atmospheric pressure, with compositions of ethane, propane, isobutane, and normal butane. The process configuration included compression, cooling, and phase separation in a flash separator at a constant pressure of 50 psia. Variations in cooling temperature were applied at 20, 10, and 0°C. The simulation results indicated a thermodynamic critical point at 10°C. At 20°C, no liquid phase was formed, while at 10°C, significant liquid yield was obtained with moderate energy consumption. Lowering the temperature to 0°C dramatically increases liquid recovery, but the cooling energy requirement also increases sharply. Sensitivity analysis confirms a strong inverse relationship between temperature and condensation yield, as well as a surge in energy consumption at low temperatures. The optimal operating condition is set at 10°C, providing a balance between separation efficiency and energy efficiency in accordance with sustainable manufacturing principles.

This research examines the implementation of green building principles in the design of the Multipurpose Building at SMP–SMA Islam Hidayatullah Semarang, focusing on energy-efficient strategies and spatial comfort based on the GREENSHIP GBCI certification criteria. The study employs a qualitative descriptive method through interviews with the architect, analysis of architectural drawings, interpretation of interior design visualizations produced by Falana Studio, and literature review on sustainable building design. The findings indicate that the building consistently applies passive design strategies, including the optimization of natural lighting through large openings and a central void, the application of cross-ventilation on each floor, and the integration of façade vegetation that reduces surface temperature and improves microclimate performance. Material selection such as GRC panels, HPL, and modular plywood supports long-term durability, while the interior design demonstrates strong visual comfort and ergonomic quality through indirect lighting, neutral color schemes, and activity-based furniture layout. According to the GREENSHIP assessment categories, the building fulfills Energy Efficiency and Conservation (EEC), Indoor Health and Comfort (IHC), Material Resources and Cycle (MRC), and Appropriate Site Development (ASD) criteria. In conclusion, the Multipurpose Building successfully integrates green building principles as an effective approach to energy efficiency and the enhancement of the educational environment.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.

This study aims to analyze the effect of Environmental, Social, and Governance (ESG) on the financial performance of mining companies listed in the Jakarta Islamic Index (JII70) for the 2020–2024 period, with the Debt to Equity Ratio (DER) as a control variable. The findings show that, partially, the Environmental variable has a positive but insignificant effect on ROA, indicating that efforts in energy efficiency, waste management, and emission reduction have not yet been fully reflected in short-term profitability. In contrast, the Social variable has a significant effect on ROA, emphasizing that companies’ engagement in building stakeholder relationships, protecting employee rights, and implementing social responsibility programs contribute substantially to financial performance. The Governance variable also has a significant effect on ROA, highlighting the importance of good governance practices, transparency, and accountability in enhancing profitability. Meanwhile, the control variable DER shows no significant effect on ROA. Simultaneously, ESG performance has a significant effect on ROA, proving that integrated ESG implementation supports the profitability of mining companies. These findings confirm that ESG is not only a compliance measure with sustainability principles but also a long-term business strategy that strengthens companies’ competitiveness and serves as a crucial consideration for investors in making investment decisions.

This study formulates a design concept for the Beach Tourism Resort Area in Botubarani Village by applying tropical architecture that responds to coastal climatic conditions while enhancing the quality of visitor experiences. The research integrates principles of thermal comfort, environmental sustainability, and the ecological potential of coastal environments as the basis for creating climate-adaptive and environmentally responsive tourism areas. The methodology includes site analysis, field observation, climatological assessment, and a literature review related to tropical architecture and coastal tourism design. The collected data were used to determine building orientation, mass layout patterns, visitor capacity, materials, vegetation, and utility systems appropriate for humid tropical climates. The findings show that the site’s position between the sea and the mountains, combined with full-day sun exposure, requires design strategies that maximize cross-ventilation, utilize sloped roofs, provide natural shading, and apply local materials such as wood, bamboo, and red brick. The large spatial needs based on visitor capacity projections are accommodated through a flexible cluster layout that supports visual and functional connectivity between buildings while incorporating green open spaces to enhance microclimate comfort. The utility system is designed using sustainability principles through greywater–blackwater separation, infiltration wells, and recycling-based waste management. This study confirms that the application of tropical architecture in coastal tourism areas can improve thermal comfort, energy efficiency, and environmental sustainability. These findings guide developing tropical tourism area designs that are more adaptive to climate change and more responsive to visitor needs.

This research aims to analyze the alignment of Bank Jago’s digital banking innovations with the Sustainable Development Goals (SDGs) during the 2022–2024 period. The method used is a qualitative descriptive analysis with a document study approach, which includes the Annual Report, Integrated Report, and Sustainability Report of Bank Jago over the past three years. The results of the study indicate that Bank Jago’s digital innovations are aligned with the five main dimensions of the SDGs: economic, social, governance, environmental, and partnership. From the economic perspective, digitalization has enhanced efficiency and financial performance through the growth of third-party funds and the expansion of access to digital services. In the social dimension, financial literacy programs, MSME financing, and digital zakat initiatives have strengthened financial inclusion among communities. The governance and environmental aspects demonstrate a commitment to transparency, energy efficiency, and green banking practices. Meanwhile, collaborations with fintech ecosystems and social institutions have reinforced sustainable partnerships. Overall, Bank Jago has implemented a digital transformation strategy aligned with the principles of Strategic Fit and Value Co-Creation, making digital innovation not only an instrument of economic growth but also a means to achieve sustainable development in Indonesia’s banking sector.

This research focuses on analyzing the performance of a vacuum pan automation sistem using solenoid valves at PT. Duta Sugar International as an effort to improve the efficiency and quality of refined sugar production. The vacuum pan is the main tool in the sugar crystallization process that functions to evaporate the sugar solution under low pressure. Problems faced in the manual sistem are temperature instability and high dependence on operators, which impact time inefficiency and decrease product quality. The purpose of this research is to design and analyze the implementation of an automatic control system based on a Distributed Control Sistem (DCS) with the integration of solenoid valve actuators to optimize temperature stability and cooking process efficiency. The research method was carried out using qualitative and quantitative approaches through direct observation, technical interviews with the automation team, and supporting literature studies. The results showed that the automatic system was able to maintain a stable cooking temperature in the range of 78°C–85°C, lower and more efficient than the manual system which fluctuates between 90°C–100°C. In addition, cooking time was reduced by 10–15 minutes per cycle, and the crystallization process became more uniform with more efficient energy consumption. The results showed that the implementation of DCS-based automatic control with solenoid valves significantly improved operational stability, productivity, and energy efficiency. Thus, this automation sistem proved to be an effective solution for optimizing vacuum pan performance in the modern sugar industry.