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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.

This research investigates intelligent cooling system design for main ship engines operating in tropical waters, integrating advanced machinery engineering with human factors to address thermal management challenges affecting engine performance, reliability, and crew operational effectiveness. Tropical maritime environments impose severe cooling demands through elevated seawater temperatures (28-32°C), high ambient conditions (28-35°C), and accelerated biofouling, reducing conventional cooling system effectiveness by 15-25% while increasing maintenance burdens and operational risks. Through qualitative analysis involving marine engineers, chief engineers with tropical operational experience, cooling system manufacturers, naval architects, automation specialists, and maritime training institutions, this study examines how intelligent cooling systems incorporating variable-speed pumps, adaptive control algorithms, predictive maintenance, and crew-centered interfaces can optimize thermal management while supporting effective human-machine collaboration. Results demonstrate that intelligent systems can reduce cooling energy consumption by 20-35%, improve temperature stability by 50-65%, extend maintenance intervals by 40-80%, and enhance crew situational awareness through intuitive monitoring interfaces, while requiring comprehensive training programs developing technical understanding and operational competencies. Key implementation challenges include control system complexity, sensor reliability in harsh marine environments, integration with existing engine management platforms, crew competency development requirements, and lifecycle cost justification. Findings reveal that successful intelligent cooling system implementation requires holistic sociotechnical approach addressing machinery engineering optimization, automation technology deployment, and human capability development through coordinated design and training strategies. This research contributes to marine engineering literature by providing integrated frameworks for intelligent system design incorporating machinery performance, automation capabilities, and human factors supporting operational excellence in tropical maritime operations.

This study examines the extent to which West Java Province has achieved Sustainable Development Goal (SDG) 7 on clean and affordable energy in the electricity sector. The study uses a qualitative method with a case study approach to evaluate policies and achievements in terms of energy access, renewable energy use, energy efficiency, and the dynamics of cooperation between government agencies. The results show that the electrification rate in West Java has almost reached 100% thanks to government policies such as the free electricity program for underprivileged communities. However, the share of renewable energy in the province was still around 15% in 2022, which has not yet reached the target of 17% by 2025. Furthermore, energy efficiency is also an important issue because primary energy consumption in West Java increased in 2022. This study emphasizes the need to enhance inter-agency cooperation, innovation in local policies, and political commitment to achieve SDG 7 targets in line with national directives.

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.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

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.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

The increasing demand for electrical energy, particularly in offices and commercial buildings, has made energy efficiency a critical aspect of sustainable development. Among various building components, lighting systems are recognized as one of the major consumers of energy. This study investigates the potential for energy savings through the adoption of a smart lighting system incorporating IoT-based sensors, motion detectors, and dimming controls. Employing a quantitative descriptive approach, the research was conducted at the workspace of Indie Light, comparing energy consumption before and after the implementation of the system. Data were collected using direct observation, light and power meters, and real-time monitoring devices to ensure accurate measurement. The results demonstrate that smart lighting systems can substantially reduce energy use without compromising lighting quality or comfort. By integrating intelligent sensors and adaptive control algorithms, the system not only optimizes energy efficiency but also aligns with national policies on energy conservation, supporting broader environmental sustainability efforts. These findings suggest that smart lighting solutions can play a significant role in promoting energy-efficient practices in commercial spaces while contributing to sustainable development goals.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

The digital economy and e-commerce are rapidly transforming global markets, driving efficiency, inclusivity, and innovation. However, these developments also produce unintended consequences, particularly regarding environmental sustainability. This study aims to examine the relationship between digital transformation, the expansion of e-commerce, and their impact on carbon emissions and socio-economic outcomes. Using bibliometric analysis and VOS Viewer to map and analyze research trends from leading academic databases, this paper identifies key themes, knowledge clusters, and research gaps in the intersection of digital economy, logistics, and sustainability. The findings indicate that technological advances foster economic growth and greater accessibility but simultaneously contribute to rising energy consumption, logistics intensity, and carbon footprints. These results highlight the dual nature of digitalization as both a catalyst for inclusive development and a driver of environmental pressures. The study argues that an integrated policy framework is crucial to leverage the benefits of digital transformation while mitigating its environmental costs. It emphasizes the importance of green innovation, sustainable infrastructure investment, and inclusive e-commerce practices as key strategies for ensuring long-term socio-economic resilience. Ultimately, the paper contributes to the policy discourse by positioning innovation, inclusivity, and environmental stewardship as complementary rather than competing forces, thereby offering a pathway for future digital economy development that is both equitable and sustainable.

This study discusses the phenomenon of digital entrepreneurship in the era of global climate awareness, focusing on the integration of artificial intelligence (AI) ethics, sustainable technology, and green innovation. The main issues raised are the fragmentation of analysis between digital business ethics, green economic opportunities, and technological challenges such as greenwashing, high AI energy consumption, and the digital divide. The purpose of this study is to formulate an interdisciplinary framework that combines ethical, technological, and sustainability dimensions to strengthen the role of digital entrepreneurs in achieving low-carbon development. The methods used include critical literature analysis, bibliometrics of 200 publications (2018-2025) using VOSviewer, and fuzzy logic-based simulations using the UNESCO AI ethics framework (2021) and the sustainable business model of Bocken et al. (2014). The results show four main research clusters: AI for Sustainable Innovation, Ethical Digital Business, Blockchain for Green Supply Chain, and Circular Digital Economy. The application of AI ethics increases the efficiency of green business decisions by up to 20%, consumer trust by 17%, and MSME participation by 14%. The synthesis of findings confirms that AI ethics acts as a conceptual mediator that strengthens the link between technological innovation and sustainability. In conclusion, ethical digital entrepreneurship has great potential as a driving force for Indonesia's green economy, but it requires digital ethics audit policies and the adoption of low-carbon technologies to address ethical and environmental risks in the AI era.

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.

Given the great benefits of electrical energy, the availability of electrical energy sources is limited. Currently, the availability of electrical energy sources is not able to meet the increasing demand for electricity in Indonesia. The high use of electrical energy in daily life will have a negative impact on the environment. Therefore, to maintain the sustainability of energy sources, it is necessary to pursue strategic steps that can support the provision of electrical energy optimally and affordably, This study aims to find out how much total energy is used by the Energy Consumption Index (IKE) on electrical energy from the influence of electrical power and the length of time of use of electrical energy at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. This study aims to determine the value of energy consumption used or Energy Consumption Index (IKE) and energy saving opportunities at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. The results of this study are for the IKE value of the first floor which is 1.71 kWh/m2, for the IKE value of the second floor which is 0.03 kWh/m2, for the IKE value of one building, which is with a value of 1.74 kWh/m2, for the annual IKE of 0.022 kWh/m2/year and for the value of energy-saving opportunities of IDR 651 646/month IDR 7 819 755/year.

This experiment investigates the heat transfer characteristics of an ice bag gel phase change material (PCM) incorporated within bricks. The study seeks to investigate the performance of ice bag gel as PCM in improving thermal behavior of building material. The experiment consisted of subjecting brick samples with and without ice bag gel PCM to thermal cycles in a semi-automated laboratory setup. The results indicate that ice bag gel PCM incorporated in bricks exhibited minimal changes and better heat transfer as compared to the dry bricks. It was observed that the ice bag gel PCM registered lower peak temperature and slower rates of temperature drop which means their heat storage and release characteristics were efficient. Furthermore, the ice bag gel system produced a steady radiation flux, indicating that it was able to minimize the effects of temperature variations. These results imply that ice bag gel PCM has the potential to be a green and economical option for enhancing thermal comfort and decrease energy consumption in buildings.

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.

Energy audits are an essential step in supporting the efficiency of electricity utilization, particularly in large-scale commercial buildings such as shopping malls. This study was conducted to measure and analyze electricity consumption at Pollux Malls Paragon Semarang using a direct measurement approach combined with historical monitoring of energy consumption. The main focus of the audit was on the lighting system and the Heating, Ventilation, and Air Conditioning (HVAC) system from the Basement to the 6th Floor. Measurement results showed that the Energy Consumption Intensity (ECI) ranged between 39.94–45.20 kWh/m²/month, far above the national efficiency standard (maximum 18.5 kWh/m²/month), indicating a highly wasteful energy usage condition. The two main systems contributing to the largest share of consumption were HVAC and lighting, with a combined estimated share exceeding 60% of the total monthly energy use. Based on the analysis, several energy-saving opportunities were identified, including the replacement of energy-efficient lighting (LED), installation of automatic control systems (light, temperature, and timer sensors), and regular maintenance of HVAC systems. With the implementation of technical, managerial, and operational efficiency strategies, it is estimated that energy consumption savings could reach 20–30%, or around 60,000 kWh per month, without compromising visitor comfort.

The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.

The DH Building of the Electrical Engineering Study Program at Udayana University faces significant challenges in energy efficiency, as it still relies on conventional electrical systems. User negligence, such as forgetting to switch off lights and air conditioners (AC) after use, often results in unnecessary energy waste and increased operational costs. This issue highlights the urgent need for smart solutions capable of automating energy management, reducing waste caused by human error, and supporting the creation of a more efficient and sustainable campus environment. To address this problem, this study designs and implements a smart building system based on the Internet of Things (IoT). The system employs a NodeMCU ESP32 microcontroller as the main processing unit, integrated with a series of sensors including a DHT22 sensor for monitoring temperature and humidity, an MQ2 sensor for smoke detection, a PIR sensor for motion detection, and a PZEM-004T sensor for monitoring energy consumption. Control of electronic devices such as lights and AC units is carried out both automatically and manually through relay modules connected to the system. All sensor data and control functions are accessed via a web interface developed using the Laravel framework and a MySQL database. The testing results indicate that the designed system was successfully implemented and functions as expected. Sensor testing demonstrated high accuracy compared to standard measuring instruments, while the electronic device control system achieved an average response time of approximately 3.6 seconds, proving its reliability. Overall, the system provides a comprehensive solution for energy consumption monitoring and control, while also enhancing comfort and safety in the DH Building, in line with the goals of energy efficiency and facility modernization.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.