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Indonesia, which is located on the equator, has great potential to utilize solar energy, with an average solar radiation reaching 4.80 kWh/m² per day. This provides an advantage in the development of Solar Power Plants (SPP), one of which is through the installation of rooftop on the roof of the building as a source of electrical energy. This study aims to analyze the feasibility of SPP development by considering the techno-economic aspects of the Pondok Juice cafe. The total real electrical energy consumption required by the Pondok Juice cafe is 32,548 Wh/day. Assuming a system loss of 15%, the total electrical energy requirement that must be supplied by the SPP is 37,430.2 Wh/day. The initial investment projection is IDR 204,265,197, with operational and maintenance costs for 25 years of IDR 105,760,028. The investment feasibility analysis shows NPV of IDR 441,523,820.10, BCR 4.11, DPP ±5 years, and IRR 15.55%, which proves that this SPP planning is feasible to implement.

The scarcity of fuel that has an impact on the high selling price of fuel, then there needs to be an effort to save fuel in motor vehicles. The solution to this problem is to use renewable fuel, one of the renewable fuels is hydrogen gas. With the addition of renewable energy in the form of HHO gas (Hydrogen Hydrogen Oxygen) from water electrolysis. The purpose of this study was to determine the effect of the application of an electrolyzer on saving fuel consumption in motor vehicles. By using a quantitative method with an experimental research design to test the effect of two variables whether there is a change in the form of fuel savings before and after the installation of the electrolyzer tube. Based on the trials that the researcher has conducted, the results of the study were obtained, namely that the addition of an electrolyzer has an effect on the fuel consumption of 4-stroke motorcycles, both in static and dynamic tests. So with the addition of an electrolyzer, it will be able to save fuel consumption in motorcycles when compared to without using an electrolyzer    

This study discusses the application of Internet of Things (IoT) technology in the monitoring and control system of smokehouse machines at PT Serena Harsa Utama. IoT enables the integration of sensors to monitor critical parameters such as temperature and humidity in real-time, thereby enhancing production efficiency and product quality. An experimental method was employed to test the effectiveness of the IoT system, with data collection through integrated sensors. The results indicate that IoT implementation can increase production efficiency by up to 30%, reduce human error, and optimize energy consumption. Despite challenges related to initial investment and operator training, the long-term benefits of IoT adoption, such as improved product quality and reduced downtime, make it a promising solution in the food processing industry. This research emphasizes the importance of adopting IoT technology to enhance competitiveness in the modern industry.

The rapid development of e-commerce in Indonesia has a significant impact on the growth of the digital economy, but also presents major challenges related to environmental sustainability. This study aims to evaluate the environmental impacts caused by e-commerce activities, especially in terms of increasing plastic waste from single-use packaging, carbon emissions in logistics activities, and energy consumption from data centers. Through a literature study and descriptive analysis approach, this study highlights the large contribution of the e-commerce sector to the volume of domestic waste and the need for effective mitigation strategies. In addition to identifying existing challenges, this study also explores various sustainable practices, such as the use of environmentally friendly packaging, consumer education, and the development of low-emission logistics systems. The results of the study indicate that sustainability in e-commerce can only be achieved through collaboration between the government, business actors, and the community. Strict regulations, technological innovation, and increased consumer awareness are needed so that e-commerce can develop in line with the principles of environmental sustainability.    

Research aims to design and develop a boiler system for heating Marine Fuel Oil (MFO) 180 CST by integrating Internet of Things (IoT) technology to enhance efficiency and operational monitoring. The methods used include boiler system design, selection of types and materials according to international standards, and the implementation of an optimal combustion system. IoT sensors are strategically placed to monitor key parameters such as temperature, pressure, and fluid flow in real-time. The collected data is transmitted to a cloud platform, enabling remote monitoring and automated performance analysis through a web or mobile-based application.. The research results indicate that IoT integration in the boiler system improves fuel heating efficiency, optimizes energy consumption, and facilitates easier monitoring and process control. Testing includes pressure tests, combustion efficiency, steam capacity, and material durability, with real-time monitoring to support performance analysis and early problem detection. Operational data evaluation allows for design adjustments or system settings to further enhance energy efficiency. With this innovation, the boiler system can operate more optimally, support energy efficiency, and facilitate predictive maintenance for sustainable industrial operations. The implementation of IoT in this system is expected to improve safety, effectiveness, and automation in boiler management, making it a more reliable and modern solution.

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: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

Aquaculture is a vital sector in global food production, providing essential protein sources. However, the industry faces significant challenges, including high energy consumption and environmental impact. The integration of renewable energy, particularly solar power, with automation and IoT systems offers a promising solution to enhance energy efficiency, sustainability, and productivity in aquaculture operations. This study aims to evaluate the effectiveness of solar powered autonomous systems in reducing energy usage, improving operational efficiency, and promoting environmental sustainability in aquaculture. Literature Review: Recent research has explored various technologies, such as Digital Twins (DTs) and Precision Fish Farming (PFF), which integrate IoT sensors for real time monitoring and optimization of fish farming operations. The combination of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, has further advanced the industry by enabling automated decision making and predictive analytics. Solar power integration with IoT systems has been shown to significantly reduce operational costs, minimize carbon emissions, and enhance the sustainability of aquaculture practices. These advancements have the potential to address the challenges of energy consumption and environmental degradation in the industry. Materials and Method: This research utilizes a hybrid solar powered IoT system for aquaculture, integrating solar panels, IoT sensors, and automated control systems. The system monitors key water quality parameters, such as pH, dissolved oxygen, turbidity, and temperature, to maintain optimal conditions for aquatic life. Data is collected through IoT sensors and analyzed through a cloud-based platform. A pilot study is conducted on a small scale aquaculture farm to evaluate the system's performance, including energy consumption, water quality management, and fish health. Energy savings, operational efficiency, and environmental impact are assessed. Results and Discussion: The integration of solar powered IoT systems significantly reduced energy consumption compared to traditional systems, with a notable decrease in grid electricity reliance. The system successfully maintained optimal water quality conditions, enhancing fish health and growth. Solar powered systems proved reliable, even in regions with variable sunlight, and demonstrated improvements in operational efficiency through automation. The environmental benefits were evident, with a reduction in carbon emissions and lower operational costs. The study highlights the feasibility of solar powered IoT systems as a sustainable solution for modern aquaculture operations.

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.

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

The development of electric propulsion systems has become a major focus in efforts to provide energy-efficient and environmentally friendly air propulsion technology. One emerging innovation is the electric motor-based turbojet fan, which is expected to replace conventional fossil-fueled systems. As the need for energy efficiency increases, studies on electrical power consumption and airflow performance are crucial in supporting the development of new-generation propulsion systems. This study aims to evaluate the relationship between nozzle angle and the characteristics of electrical power consumption and airflow velocity in a double-spool turbojet fan. The method used is an experimental test with an ESP32-based control system. The duty cycle is set at 80% to maintain operational stability. Research data is obtained through measurements of electrical current, voltage, and airflow velocity. The nozzle angle variations tested include 13°, 19°, and 25°. The test results show a significant difference between nozzle angle variations on electrical power consumption and wind speed performance. The 13° nozzle angle produces the highest electrical power consumption, indicating a greater energy requirement to maintain airflow. Conversely, the optimal wind speed was found at an angle of 19°, indicating a balance between energy efficiency and aerodynamic performance. Meanwhile, an angle of 25° showed a decrease in performance in terms of both power and speed, making it less effective. In conclusion, the nozzle configuration has a direct influence on energy consumption and fluid dynamics in electric turbojet fan systems. This research provides an important contribution to the design of electric-based propulsion systems by emphasizing efficiency and performance aspects, while supporting the transition to environmentally friendly technologies.

The prevalence of Chronic Energy Deficiency (CED) in the Riau Islands is a concerning public health issue, with 18.7% of non-pregnant women reported to be affected. In Batam City specifically, 95 cases of undernutrition were recorded in 2024. This study aimed to analyze the relationship between food intake and the nutritional status of women of reproductive age experiencing CED. A descriptive correlational research design with a cross-sectional approach was employed. The study used total sampling, involving 31 women from the Batu Aji KUA in Batam. Data on food intake were collected and compared to recommended dietary standards. The findings indicated that a majority of women had food intake levels below the recommended daily allowance. Despite this, not all of the participants were found to be suffering from CED. Statistical analysis using the Chi-square test revealed a significant association between food intake and nutritional status (p = 0.025, where α = 0.05), suggesting that food intake plays an influential role in determining nutritional outcomes. Furthermore, women whose food intake was lower than the recommended levels were found to have a 0.611 times greater risk of experiencing chronic energy deficiency. These findings emphasize the crucial role of adequate energy and nutrient intake, especially among women of reproductive age, in preventing CED and improving overall health. Improving food consumption patterns in this demographic is essential not only for individual well-being but also as a preventive measure to reduce the risk of stunting in future generations. The study underscores the need for targeted educational interventions to increase awareness about proper nutrition and energy intake among women, particularly those in reproductive age, as a strategy to address undernutrition and its broader public health consequences.

The SQ-FFQ is a commonly used food consumption assessment method because it is quick, relatively inexpensive, simple, and can be self-administered by respondents. Each SQ-FFQ instrument developed should be validated for the target population to avoid overestimation of food consumption measurements. This study aims to determine the validity of the SQ-FFQ instrument as a tool for assessing food consumption. The 3 Days Food Record is used as the gold standard for comparison in the validity of the SQ-FFQ. Sampling was conducted using accidental sampling, with 30 student respondents. The Nutrisurvey application was used to determine the intake of energy, macronutrients, and micronutrients. Meanwhile, for the diversity score, it was based on the IDDS (Individual Dietary Diversity Score) with a score scale of 0-9. Data analysis was performed using SPSS software with Pearson Correlation tests to examine the relationship between the two methods. The correlation coefficient values for all nutrients showed a significance level of <0.005, with the highest correlation coefficient found for energy intake (r=0.708, p<0.001) and the lowest for iron intake (r=0.412, p=0.024). The correlation test for consumption diversity showed a p-value of <0.001 with a correlation coefficient of 0.706, indicating a strong relationship between the SQ-FFQ method and the 3 Days EFR. The results of this study indicate that the SQ-FFQ method is a valid tool for measuring food intake and diversity among university students in Surabaya.

The reliability of a data center is highly dependent on its air conditioning and cooling system. This research evaluates the existing cooling system of Universitas Krisnadwipayana’s data center using the PPDIOO Network Life-Cycle approach. The study finds that the current cooling system, which relies on AC Split, fails to meet TIA-942 standards, posing significant overheating risks and increasing downtime probability. Observational analysis shows that the cooling distribution is inefficient due to inadequate airflow and the absence of a structured cooling layout. To address these issues, this research proposes an optimized cooling system design that incorporates Computer Room Air Conditioning (CRAC), hot aisle-cold aisle arrangement, and raised floor implementation. The recommended improvements also include installing temperature and humidity sensors for real-time environmental monitoring and implementing N+1 redundancy for enhanced system reliability. These solutions are expected to improve cooling efficiency, reduce energy consumption, and mitigate downtime risks. Future research should focus on evaluating the practical impact of this design by conducting real-world trials and exploring liquid cooling technology as a potential alternative for further efficiency improvements.

Muffin is a wheat flour-based snack with gluten content and high glycemic index, so substitution is needed to reduce the impact of excess consumption on health. The ingredients used are purple sweet potato and red bean which have high content of energy, carbohydrates, protein and low GI. The study aimed to determine the effect of substituting wheat flour with sweet potato flour and red bean flour on the nutritional value of muffins. This was an experimental study with a single-factor completely randomized design (CRD) with one control group and three treatment groups with the proportion of wheat flour: purple sweet potato flour: red bean flour namely 100:0:0 (P0), 30:20:50 (P1), 30:35:35 (P2), dan 30:50:20 (P3). They were repeated 3 times.  The results showed that there was an effect of wheat flour substitution with sweet potato flour and red bean flour on the nutritional value of muffins (p<0.001). The highest average nutritional value of each treatment is water content P3 (34.45%), ash content P3 (3.06%), protein P1 (9.92%), fat P1 (9.39%), carbohydrate P3 (44.67%), and energy P1 (294.23 kcal). The nutritional value of 50g muffins is in accordance with the quality standards according to SNI and the standard requirements for children, but the fat content is above the standard if it is consumed as snacks for DM patients, therefore it needs to reformulate to fulfill the standard.

This study discusses the comparison of efficiency between Pulse Width Modulation (PWM) and Variable Frequency Drive (VFD) techniques in electric motor control. The background of this research is based on the importance of energy efficiency in industrial applications using electric motors. The aim of this study is to identify the advantages and disadvantages of each method in the context of energy savings and motor performance. The method used is a quasi-experimental design, where both techniques are tested on the same three-phase induction motor to compare power consumption and operational efficiency. The findings indicate that the use of VFD is significantly more efficient in reducing power consumption compared to PWM and provides better torque stability in the motor. The implications of this research suggest that the application of VFD can enhance energy efficiency and reduce operational costs, which is crucial for industries. This study is expected to serve as a reference for further development in electric motor control technology.

Energy audit is a technique used to obtain the efficiency of a building with a certain method. Continuous research implementation is expected to be able to identify electrical efficiency and the purpose of data analysis is to obtain electrical energy efficiency to calculate the Energy Consumption Intensity (IKE) value in the building. Energy audits can also be carried out at any time or according to a predetermined schedule. Regular monitoring of energy usage is a must to find out the amount of energy used in each part of the operation during a certain period of time. Thus, savings efforts can be made.

This study aims to identify and analyze the factors that influence economic growth in Singapore. The research method used is a qualitative method with descriptive analysis techniques using a literature review approach such as scientific journals and digital books. The results showed that the factors that influence the increase in Singapore's economic growth are foreign direct investment, progressive economic policies, export sector and international trade, energy consumption and infrastructure, human resources and education, finance and banking sector, innovation and technology and strategic geographical location and research issues discussed.  

This research investigates the potential of integrating waste heat recovery and geothermal exchange within district heating systems as a carbon-neutral energy solution tailored to tropical urban settings. Tropical climates present unique challenges for heating, ventilation, and air conditioning (HVAC) systems, including high humidity, temperature fluctuations, and increasing energy demands, particularly for cooling. This study aims to address these challenges by proposing a hybrid district heating system that combines renewable energy sources to improve energy efficiency and reduce carbon emissions. The methodology includes thermal and hydraulic modeling, performance simulation of the hybrid system under tropical climate conditions, and emission and energy efficiency analysis. The results indicate that the hybrid system significantly reduces energy consumption and carbon emissions compared to traditional heating systems, with waste heat recovery optimizing energy use and geothermal exchange enhancing system efficiency. The comparison with conventional systems and other environmentally friendly alternatives reveals that the hybrid system offers a cost-effective, sustainable solution for tropical urban areas. The study concludes that integrating waste heat recovery and geothermal exchange is feasible and can contribute to the creation of carbon-neutral cities. Future research should focus on optimizing geothermal systems in tropical climates and exploring further integration with other renewable energy sources to enhance system performance and sustainability.

This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.