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Indonesia's dependence on fossil energy, which reaches 98%, results in high CO₂ emissions and energy costs. This study aims to design a wind energy system to meet electricity needs at the Arafuru Field of the Indonesian Navy Academy (AAL), which is strategically located near the coast with adequate wind potential. A quantitative research method was used by collecting wind speed data for three months (January-March 2025) at coordinates 7.21755° S, 112.71022° E. The data shows an average wind speed of 2.32 m/s with unpredictable direction. Based on the analysis, the SH-X 10000 vertical wind turbine was selected as the most suitable solution. Calculation results show that one turbine unit can generate power of 104,999 watts, so to meet the total power requirement of 109,120 watts, at least 2 wind turbine units are needed. This system not only reduces dependence on PLN and fossil energy but also has the potential to reduce CO₂ emissions and can be utilized as a learning laboratory for AAL cadets. The implementation of wind energy at AAL's Arafuru Field is expected to be a concrete step in the transition towards renewable energy in the military environment.

This study aims to apply a simple experimental approach titled “Balloon Expands Without Being Blown” to enhance fifth-grade students’ understanding of gas concepts in science learning at MI Malik Ibrahim. The study originates from the observation that students often struggle to grasp the concepts of gases and acid–base reactions, which are typically taught in a purely theoretical manner without engaging hands-on experiences. The research employed a Classroom Action Research (CAR) design following Kurt Lewin’s model, which includes four interconnected stages: planning, acting, observing, and reflecting. The participants consisted of 22 fifth-grade students from MI Malik Ibrahim. The findings reveal a substantial improvement in both learning achievement and classroom participation after the experimental activity was introduced. Students’ average scores increased from 62 to 85, with 82% showing measurable progress. Furthermore, inquiry and discussion activities became more frequent, and most students could correctly explain the formation of carbon dioxide (CO₂) gas. Overall, the results suggest that the balloon expands without being blown experiment effectively fosters scientific conceptual understanding, promotes active engagement, and stimulates motivation toward learning science.

Green Open Space (GOS) is an essential element in urban spatial planning that functions to maintain air quality and ecosystem balance. This research aims to analyze the influence of GOS on the concentration levels of Carbon Dioxide (CO2​) in the urban area of Jayapura City. The study locations were focused on two areas with distinct characteristics: Jalan Sam Ratulangi, which has high vegetation coverage, and Jalan Percetakan, which is dominated by built-up areas and dense commercial activity. The methods used include spatial analysis utilizing Landsat 8 satellite imagery Band 4 (Red) and Band 5 (NIR) to calculate the NDVI (Normalized Difference Vegetation Index), a vegetation time series analysis from 2019–2024, and direct CO2​ concentration measurements taken at three different times (morning, noon, afternoon). The data was processed using ArcGIS with the Kriging Interpolation method to generate spatial distribution maps of CO2​ in both locations. The results show that Jalan Sam Ratulangi has a GOS area of 6.13 ha (94%), while Jalan Percetakan has only 2.95 ha (31%). This difference significantly impacts CO2​ levels, with an average concentration of 484 ppm at Sam Ratulangi, which is lower than Percetakan's average of 567 ppm. The highest CO2​​ value at Percetakan reached 649 ppm during the afternoon, whereas Sam Ratulangi recorded only 488 ppm. This study proves that the wider and healthier the GOS, the lower the CO2​​ concentration detected. These results demonstrate that vegetation plays a significant role in reducing carbon emissions in urban areas, supporting the crucial role of GOS in maintaining air quality and supporting sustainable urban development in Jayapura City.

The purpose of this study is to develop an additional device for vehicles with fuel injection systems to improve fuel consumption efficiency and reduce exhaust emissions, even for older vehicles. The turbocyclone is an auxiliary component installed in the air intake duct before the throttle body to create a swirling airflow entering the combustion chamber. Testing was conducted at engine speeds ranging from 1500 rpm to 6500 rpm, with increments of 1000 rpm. This research aims to minimize fuel consumption and exhaust gas emissions. The experimental method was used, testing exhaust gas contents such as HC, CO, CO₂, and O₂ using a gas analyzer, and measuring fuel consumption based on fuel volume and time. Data processing employed a factorial DOE (Design of Experiments) to analyze results. The dependent variables were exhaust gas emissions and fuel consumption, while the independent variable was the turbocyclone blade angle variations of 20°, 40°, and 60°. The results show that the best reduction in exhaust emissions (HC, CO, CO₂, and O₂) and fuel efficiency occurred at a 40° blade angle, whereas fuel consumption increased at a 60° blade angle.

The increasing demand for electrical energy and the issue of climate change are driving the utilization of renewable energy, one of which is through Solar Power Plants (PLTS). The Province of Bali targets the development of PLTS of 108.2 MW, equivalent to 8.62% of the total solar energy potential of 1,254 MW by 2025. However, land limitations pose an obstacle to the construction of large-scale PLTS. Floating solar power plant design can be a solution to this issue. By utilizing 4,955.32 m² of water surface at the Telaga Tunjung Dam, the Floating Solar Power Plant at Telaga Tunjung Dam has a potential capacity of 141.4 kWp, using 280 Trina Solar modules of type TSM-DEG18MC.20(II) (505 Wp) combined with one SG110CX inverter with a capacity of 110 kW. The annual electricity production of the floating solar power plant, based on Helioscope software, is 201.1 MWh. The economic feasibility parameters used in this study include initial investment cost, Profitability Index (PI), and Payback Period (PP). The analysis results show that the 141.4 kWp floating solar power plant project at Telaga Tunjung Dam has an initial investment value of IDR 1,248,764,582, a PI of 1.26, and a PP of 3.91 years. Therefore, this project is considered economically feasible and contributes positively to reducing CO₂ emissions.

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.

This study analyzes the potential use of solar cells as a renewable energy source on the MV SARI INDAH. With the increasing demand for energy and the negative environmental impacts of fossil fuels, solar cells provide a promising eco-friendly alternative. The research focuses on developing a prototype system to charge batteries using solar power, converting DC power into AC for ship operations. The methodology includes measuring sunlight intensity, current, and output voltage, while also calculating the battery capacity and the ship’s electrical load. Tests were conducted at various tilt angles, with data recorded hourly over two days to assess performance. The results show that a 75° tilt angle yields the highest voltage, current, and power output when compared to other angles. Based on these findings, the study recommends installing solar panels at a 75° angle on the compass deck. The system design utilizes two panels positioned opposite each other, forming a 150° angle to optimize energy capture. This setup is capable of supplying power to key areas on the ship, such as seven lamps in the E/R 2nd Deck, Control Room, Emergency Generator Room, and CO₂ Room. The system is designed to meet the total daily energy demand of approximately 1,400 Wh, which can be efficiently fulfilled by eight solar cells rated at 50 Wp each, providing a sustainable and efficient energy solution for the vessel.

This study aims to analyze the causes of component failure in the Power Train system of unit OHT773E CO2278 at PT. Cipta Kridatama, Samarinda, using the Root Cause Failure Analysis (RCFA) method. The Power Train system is responsible for transferring power from the engine to the final drive and other components, making it critical for the operational success of heavy equipment. Therefore, optimal maintenance is essential to prevent fatal failures that could impact the unit's performance. Based on the analysis, the dominant cause of failure is human factors, particularly technician negligence during component installation. This negligence results from a lack of understanding of the procedures and specifications recommended by the manufacturer, leading to incorrect installation of components. This failure impacts the achievement of the component’s expected lifetime, thus shortening the operational life of the components and increasing the risk of more severe damage. This also leads to higher repair costs and reduced unit productivity, resulting in longer downtime. To address this issue, several preventive measures are recommended, such as regular training for technicians to enhance their understanding of correct procedures and specifications, as well as the importance of following manufacturer guidelines during every maintenance and installation process. Additionally, it is advised to conduct routine discussions between technicians and supervisors to ensure that every maintenance step and installation complies with the established procedures. Increased oversight of the installation and maintenance process is also necessary, along with periodic rejuvenation of components to ensure the optimal performance of the Power Train system. Strengthening Preventive Maintenance (PM) practices is also crucial to minimize future damage potential. Implementing these solutions is expected to enhance the reliability of the Power Train system, extend component lifespan, and reduce failure frequency, ultimately improving the overall efficiency and productivity of the company.

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

This study analyzes the flexural behavior of reinforced geopolymer concrete beams through numerical simulation based on the Finite Element Method (FEM) using ANSYS software, comparing it with conventional reinforced concrete beams. The background of this research focuses on the need for environmentally friendly construction materials, considering the high CO₂ emissions from Portland cement production. The numerical model was developed based on parameters and loading schemes from previous experimental studies, utilizing SOLID65 elements for concrete, LINK180 for steel reinforcement, and SOLID185 for supports. Simulation results show that the flexural behaviour of geopolymer beams is comparable to conventional beams in terms of load-deflection relationships, flexural capacity, and crack patterns, with deviations from experimental data generally below 10%. The patterns and propagation of cracks also exhibited similarities, starting from the tensile zone at mid-span. Furthermore, the analysis demonstrates consistency with the analytical approach based on SNI 2847:2019. This research supports the validity of using geopolymer concrete as a sustainable structural material alternative and shows that the FEM numerical method is effective in evaluating the flexural performance of concrete structural elements.

The issue of carbon emissions in the transportation sector is a major challenge for developing countries like Indonesia, which experiences a sharp increase in vehicle ownership each year. This study aims to compare vehicle ownership policies between Indonesia and Singapore as strategies for reducing carbon dioxide (CO₂) emissions. Using a qualitative method with a comparative study approach, data were obtained through literature analysis on regulations, policies, and secondary emission data. The results indicate that Indonesia focuses more on incentives and emission standards tightening, while Singapore enforces strict vehicle ownership restrictions through the Certificate of Entitlement (COE) system, additional taxes, and vehicle age limitations. The novelty of this study lies in its comparative focus on vehicle ownership regulations as policy instruments for emission control—an aspect rarely examined in the Southeast Asian administrative context. The study concludes that strict and structured regulations, supported by efficient public transport systems, are key to reducing transportation-related carbon emissions.

A reliable electrical grid the power plant scheduling system needs to optimize the plant's performance by considering the economic value and the value of emissions generated by the plant, in addition to the reliability and economic sectors. This system should also take into account the environmental impact, as well as the CO2 and CH4 emissions produced by the plant. This research will use the Dragonfly Algorithm with weighting parameters to schedule hydro and thermal facilities' emissions and economic activities. That the weighting value impacts production costs and emissions is demonstrated by the data acquired from the Dragonfly Algorithm simulation. If economic considerations take precedence in assigning weights, then low-cost generating will result in high-value emissions, and vice versa. The plant's Emission Intensity rating also meets the standards established by the government.

  The current warming trend is a negative externality of human activities since the mid-1800s and is proceeding at a pace unprecedented over the past millennia, such as increased energy consumption leading to an increase in the concentration ofCO2 gas and other gases in the atmosphere. CO2 gas has the largest contribution to global warming at about 56%. Increased energy consumption that causes an increase inCO2 gas because the use of energy consumption in Indonesia is still dominated by fossil energy consumption produced from fossil fuels and often uses technology that is not environmentally friendly so that it can trigger an increase in Greenhouse Gas (GHG) emissions which is the largest contributor to global warming. In response to this warming trend, Indonesia has ratified the Kyoto Protocol in 1997 into Law Number 17 of 2004 and the Paris Agreement into Law Number 16 of 2016 in an effort to reduce the temperature rise limit to 1.5°C above the earth's temperature in pre-industrial times. The objectives of this study are (1) To analyze the effect of electricity consumption and fossil fuel consumption simultaneously on the negative externalities of GHG emissions in Indonesia. (2) To analyze the effect of electricity consumption and fossil fuel consumption partially on the negative externalities of GHG emissions in Indonesia. The data used is secondary data, with multiple linear analysis techniques. The results showed that (1) Electricity consumption and fossil fuel consumption simultaneously affect the negative externalities of GHG emissions in Indonesia. (2) Electricity consumption partially has a positive and significant effect on the negative externalities of GHG emissions in Indonesia. (3) Fossil fuel consumption partially has a positive and insignificant effect on the negative externalities of GHG emissions in Indonesia. Keywords: Greenhouse Gas, Negative Externalities, Energy Consumption, Electricity Consumption, Fossil Fuel Consumption

Background: The global energy transition requires low-carbon solutions that can be integrated into existing thermal systems without drastic infrastructure changes. Hydrogen blending in conventional combustion systems has emerged as a promising pathway to reduce carbon emissions while maintaining operational flexibility. Objective: This study aims to experimentally evaluate the effect of hydrogen blending ratios (0–100% by volume) on thermal efficiency, CO₂ emissions, and NOx emissions, and to determine the optimal blending range based on technical and economic feasibility. Methods: An experimental thermal system prototype was developed and tested under controlled conditions with three repetitions per operating point. Performance parameters included combustion temperature, fuel consumption rate, and thermal efficiency, while emissions of CO₂ and NOx were measured using a calibrated gas analyzer. Data were analyzed using descriptive statistics, one-way ANOVA at a 0.05 significance level, confidence interval estimation, and linear regression to examine the relationship between hydrogen fraction and emission reduction. Results: The findings indicate that increasing hydrogen fraction significantly improves thermal efficiency, reaching 87.5% at 100% hydrogen, while CO₂ emissions decrease linearly to zero. However, NOx emissions increase with higher hydrogen content due to elevated combustion temperatures. Statistical analysis confirms that hydrogen ratio has a significant effect on efficiency and emissions, with a strong linear correlation between hydrogen fraction and CO₂ reduction. A blending range of 40–60% hydrogen provides the most balanced performance in terms of efficiency improvement, emission reduction, and cost feasibility.

Remote islands in Indonesia face significant challenges in achieving sustainable electricity supply. This study analyzes the technical and economic feasibility of implementing a hybrid Solar Power Plant (PLTS) and Wind Power Plant (PLTB) system on Dudepo Island, North Gorontalo Regency. With an average solar radiation of 5.2 kWh/m²/day and wind speed of 4.8 m/s, the hybrid system is designed to supply approximately 97% of local electricity demand with an efficiency of 85%. Simulations using HOMER Pro reveal an optimal configuration of 100 kW PLTS, 60 kW PLTB, and 300 kWh battery storage. Economic analysis indicates a Levelized Cost of Energy (LCOE) of IDR 1,450/kWh, more affordable than conventional diesel generators, with an eight-year payback period. The system’s implementation has enhanced community well-being by providing stable electricity access for education, healthcare, and economic activities. Moreover, it contributes to carbon emission reduction by up to 120 tons of CO₂ annually. Technical challenges and local human resource capacity necessitate sustainable management strategies, including technician training and IoT-based monitoring systems. This study offers recommendations for sustainable renewable energy development on remote islands as a model for environmentally friendly energy transitions.

Global climate change demands immediate technological advancements, particularly in the transport industry that continues to use fossil fuels. One viable solution is to reduce the size of vehicle engines to make them more fuel-efficient and lower carbon emissions. The purpose of this research is to assess the effect of reducing engine size on fuel consumption and CO₂ emissions in low-cost green car hatchbacks in Indonesia. The technique employed is straightforward analytical modeling, employing Pearson correlation analysis and linear regression among three significant variables: engine capacity, fuel economy, and CO₂ emission. The data are obtained from the technical specifications of four hatchback automobile models, all of which have an engine capacity of less than 1,200 cc. Findings indicate that smaller engine capacity is accompanied by greater fuel economy and lower carbon emissions. The lowest engine size of 998 cc is used in the Toyota Agya, which demonstrates the most efficient fuel and lowest emissions. The statistical analysis shows that there is an inverse relationship between engine size and fuel efficiency, but a positive relationship between engine size and CO₂ emissions. The limitation of sample size causes reduced statistical power of the model. In conclusion, engine downsizing can prove to be a productive approach in promoting green schemes, but additional research with a larger data set and other determinants must be undertaken to establish a more advanced and precise model.

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

One of the key processes in the production of APAR cylinders is the welding of cylindrical plates, which is carried out using a Longitudinal Welding Cylinder machine and the CO₂ welding method. However, in practice, operators face several challenges, mainly due to the number of process steps that do not significantly contribute to the quantity or quality of the output, resulting in reduced productivity. This study aims to improve the productivity of the welding process through a series of improvements. The methods used include data analysis and root cause identification through the Overall Equipment Effectiveness (OEE) approach and Lean Six Sigma using the DMAIC stages (Define, Measure, Analyze, Improve, Control). Based on the Define stage, the initial OEE value was 67.49%, which is still far below the world-class standard of 85%. In the Measure stage, Pareto analysis revealed that the largest downtime (2,097 minutes or 40%) was caused by cycle time issues. Further analysis showed that activities such as material setup, additional plate placement, and additional plate cutting (totaling 85 seconds) could still be optimized. During the Improve stage, modifications were made, such as eliminating the additional plate cutting process and adding a stopper to ensure accurate welding alignment. These improvements successfully reduced the cycle time from 180 seconds to 120 seconds, thereby decreasing downtime and increasing the OEE value to 76.12%.

This study aims to determine the composition and structure of vegetation and estimate the potential for carbon dioxide absorption in the Tangale Nature Reserve Area, Gorontalo Regency. The method used in this study is the determination of sampling locations carried out by purposive sampling, making multilevel plots, analyzing vegetation structure, and estimating tree biomass is done non-destructively based on measuring the height and diameter of trees at breast height (DBH), while for lower plants it is done destructively and litter is taken all in the plot area to be weighed wet and dry weight and then estimating biomass, C reserves, and CO2 uptake. The results showed that the vegetation composition consisted of 18 species dominated by the Magnoliopsida class. The highest Important Value Index is found at each observation station. Calculation of biomass value, C reserves, and CO2 uptake at each observation station showed varying results because they were influenced by vegetation type, vegetation density, vegetation composition and structure, and environmental factors. The total value of CO2 uptake by vegetation in the Tangale Nature Reserve Area is 5,304.393 tons of CO2/ha. This research provides a better understanding of carbon sequestration potential in conservation areas to support environmental conservation and climate change mitigation efforts.  

This study evaluates the impact of distributed solar-battery systems on urban electricity resilience and community carbon emissions reduction. As urban areas continue to grow, the demand for electricity has placed considerable strain on traditional centralized grids, resulting in increased vulnerabilities. The integration of decentralized energy resources (DERs), particularly solar photovoltaic (PV) systems paired with battery energy storage systems (BESS), has emerged as a promising solution to enhance grid resilience, reduce carbon emissions, and support the transition to more sustainable energy systems. This research uses a simulation-based approach to model the integration of solar-battery systems into residential blocks, assessing their impact on grid reliability, downtime reduction, and the frequency of power outages. Additionally, the study estimates the reduction in carbon dioxide (CO₂) emissions achieved by shifting from fossil-fuel-based energy generation to renewable sources such as solar PV. The results demonstrate that solar-battery systems significantly improve electricity reliability by providing backup power during outages, while also reducing CO₂ emissions by decreasing reliance on conventional grids. The study also discusses the technical and financial challenges associated with the integration of these systems, such as energy storage capacity, system efficiency, and upfront installation costs. Policy recommendations emphasize the importance of government incentives, grid modernization, and long-term financial benefits to encourage the adoption of decentralized energy solutions. Finally, the study highlights areas for future research, including advanced storage technologies and the integration of electric vehicles with solar-battery systems to further enhance energy resilience and sustainability.