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Air pollution and greenhouse gas emissions produced by motorized vehicles are one of the main problems in big cities. This encourages the development of more energy efficient and environmentally friendly vehicles, such as electric vehicles. This journal discusses the development of energy-efficient electric vehicles for urban transportation, with a focus on battery technology, vehicle design, and charging infrastructure. This research aims to develop energy-efficient electric vehicles that can meet city transportation needs. This research focuses on several aspects, namely: Lightweight and aerodynamic vehicle design: This design can reduce energy consumption and increase vehicle efficiency. Efficient propulsion system: An efficient propulsion system can increase mileage and reduce exhaust emissions. Advanced battery technology: Advanced battery technology can increase energy density and battery life. It is hoped that the results of this research will produce energy-efficient electric vehicles that are environmentally friendly and can help reduce air pollution in big cities.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

During labor, the mother needs nutrition to produce energy during the labor process, the mother has sufficient energy. Dates are a food source that has energy-producing nutrients because they contain high levels of sugar. The aim of this research is to determine the effect of giving dates on the duration of the second stage of labor in women giving birth at the Friends of Rossy Medika Clinic in 2023. The type of quantitative research is quasy experimental with the research design being non equivalent control group design. The research population was 60 mothers in the second stage of labor. The sample that will be taken is 28 group respondents. The independent variable in this study was consumption of dates, while the dependent variable was the duration of the second stage of labor. The research was carried out at the Rossy Medika Friends Clinic located at Kp. Bojongsari, Sumbersari Village, Pebayuran District, Bekasi Regency, West Java in August 2023 - December 2023. Univariate analysis was used to determine the distribution of respondents' characteristics and the average duration of labor for the control and experimental groups, while bivariate analysis used the independent sample t-test with a sig (2-tailed) value of 0.001<0.005. Giving dates to the duration of the second stage of labor to women giving birth at the Rossy Medika Friends Clinic in 2023 the duration is faster than those who do not consume dates. The conclusion is that there is an effect of giving dates on the duration of the second stage of labor in mothers giving birth at the Rossy Friends Clinic in 2023.

Chronic energy deficiency (CED) in pregnant women can also be interpreted as a situation when pregnant women have insufficient energy intake for a long period of time, causing the pregnant woman to gain less weight and can disrupt the health of the mother and fetus she is carrying. CED in pregnant women can trigger various health problems, such as anemia, respiratory tract infections, metabolic disorders, and increase the risk of complications during childbirth. The aim of this research was to determine the consumption pattern of Ferros (Fe) tablets among pregnant women who experience chronic energy deficiency (KEK) at the Kurra Health Center, Tana Toraja Regency. This research method is quantitative with a cross-sectional research design. The population in this study were 31 pregnant women who experienced Chronic Energy Deficiency (CED) at the Kurra Health Center, Tana Toraja Regency and the sample in this study were 31 pregnant women who experienced Chronic Energy Deficiency (KEK) at the Kurra Health Center, Tana Toraja Regency. pregnant mother. The sampling technique used in this research is total sampling where the researcher will take the entire population. This research was processed using a computerized system with the help of the SPSS program. The data analysis method used is statistical test analysis with a statistical test (chi-square) to determine the consumption pattern of Ferros (Fe) tablets in pregnant women who experience chronic energy deficiency (KEK), where if a p value of 0.000 < 0.05 is obtained, it can be interpreted as significant. Therefore, it is recognized that the pattern of consumption of Ferros (Fe) tablets in pregnant women who experience chronic energy deficiency (KEK)

Renewable energy microgrids integrated with smart control systems are emerging as a sustainable solution for electrifying rural industrial zones, offering substantial improvements in energy efficiency and reductions in carbon emissions. This study explores the implementation of hybrid renewable energy systems, combining solar and wind energy, and the integration of Internet of Things (IoT) sensors to optimize energy consumption in real-time. The findings highlight that the combination of solar and wind energy in microgrids leads to up to a 30% increase in energy efficiency, with a significant reduction in CO₂ emissions, reaching up to 50% compared to traditional grid systems. IoT sensors play a crucial role in load forecasting, optimization, and system stability, enabling real-time monitoring and proactive adjustments to energy distribution. Additionally, the implementation of these systems in rural industrial zones not only provides reliable, clean energy but also reduces reliance on fossil fuels, making them economically viable and environmentally sustainable. However, challenges such as high initial investment costs, integration complexities, and the need for skilled technicians remain. Despite these barriers, the long-term benefits of reduced energy costs, improved energy security, and lower carbon footprints make renewable energy microgrids a promising solution. The study suggests that these systems can be scaled to other rural regions facing similar challenges in energy access and carbon emissions, offering a path to sustainable development. Further research is recommended to explore alternative renewable energy combinations and advancements in IoT applications to improve system scalability and efficiency.

The increasing complexity of modern software systems and the growing demand for real-time data processing have significantly contributed to higher energy consumption in computing infrastructures. This challenge is particularly evident in systems that rely on continuous monitoring, analytics, and adaptive decision-making. Addressing energy efficiency without compromising system performance has therefore become a critical concern in sustainable software engineering. This study proposes an energy-aware software approach that integrates real-time analytics with adaptive feedback mechanisms to optimize energy consumption while maintaining operational performance. The research adopts a design science oriented methodology, encompassing system design, implementation, and experimental evaluation. The proposed system architecture consists of real-time data acquisition, intelligent analytics, and an adaptive control layer based on the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) feedback loop. Experimental evaluations were conducted under dynamic workload scenarios to compare the proposed adaptive system with a baseline non-adaptive system. Key performance indicators included energy consumption, response time, throughput, and adaptation latency. The results demonstrate that the proposed system achieves a substantial reduction in energy consumption while maintaining, and in some cases improving, system performance metrics. The adaptive feedback mechanism enables the system to respond effectively to workload fluctuations, reducing unnecessary energy usage during low-demand periods and ensuring stable performance during peak loads. These findings provide empirical evidence that real-time analytics and adaptive control can effectively support energy-efficient and sustainable software systems. This research contributes to the field of energy-aware software engineering by demonstrating that intelligent real-time adaptation is a viable strategy for achieving sustainability objectives in dynamic and performance-critical environments.

This research explores the potential of local bio-composites made from bamboo and coconut fiber as sustainable building materials in Indonesia. The construction sector in Indonesia faces significant environmental challenges, particularly the high carbon emissions associated with conventional materials like concrete and steel. The primary objective of this research is to evaluate the environmental and economic benefits of substituting these traditional materials with bio-composites, using Life Cycle Assessment (LCA) and energy optimization simulations. The LCA methodology evaluates the environmental impact of bio-composites across their entire life cycle, comparing them with conventional materials in terms of carbon emissions, energy consumption, and waste. Energy optimization simulations focus on assessing the thermal performance and overall building efficiency when using bio-composites in construction. The main findings suggest that bio-composites exhibit a significantly lower carbon footprint and better thermal insulation properties than concrete and steel, contributing to reduced energy consumption in buildings. Additionally, the use of locally sourced materials like bamboo and coconut fiber offers economic advantages, such as lower transportation costs and support for local economies. The research concludes that bio-composites can serve as viable alternatives to traditional materials, providing both environmental and economic benefits. For successful adoption, the research recommends policy support, technological advancements, and educational initiatives to promote the use of bio-composites in Indonesia’s construction industry.

Microenterprises are crucial to the economic landscape, especially in developing countries, as they contribute significantly to GDP and employment. However, these businesses often face challenges in adopting sustainable practices due to financial constraints, limited resources, and market access issues. This research evaluates the impact of energy efficiency on the profitability of Sharia-compliant microenterprises applying green business principles. By integrating energy-saving measures, such as energy-efficient technologies and optimized consumption, microenterprises can reduce operational costs and improve profitability. Sharia-compliant businesses, which adhere to ethical and sustainable practices, tend to be more open to implementing such green practices. The findings show that energy-efficient microenterprises outperform conventional ones in profitability, as energy-saving leads to lower costs and enhanced competitiveness. The study also highlights the importance of green finance in supporting these enterprises by overcoming financial barriers, allowing them to adopt energy-efficient technologies. Despite the clear benefits, challenges such as high initial investment costs and limited access to efficient technologies remain. Policy intervention, including financial incentives and education, is necessary to address these barriers and enable microenterprises to fully capitalize on energy efficiency. In conclusion, energy efficiency is not only a key driver of profitability for Sharia-compliant microenterprises but also a strategy for long-term sustainability and competitiveness.

The integration of Green Internet of Things (Green IoT) systems in agriculture presents a promising solution for addressing critical challenges in water and energy usage. This study investigates the impact of real-time monitoring and data-driven irrigation control on resource optimization in precision agriculture. By incorporating soil moisture sensors, solar-powered IoT devices, and data analytics, the system aims to reduce water and energy consumption, enhancing sustainability in farming practices. The research finds that the Green IoT system can reduce water usage by up to 40% compared to traditional methods, while energy consumption is decreased by approximately 25% through the use of solar energy. The study also explores the advantages of implementing IoT-enabled systems, which ensure precise water delivery, preventing over-watering and under-watering, thereby improving crop yields and reducing waste. Despite these positive outcomes, the research identifies key challenges such as high initial costs, limited infrastructure in rural areas, and concerns related to data security. These barriers hinder the widespread adoption of Green IoT systems, especially in developing agricultural regions. Nonetheless, the findings highlight the potential of Green IoT to foster sustainable agricultural practices by promoting efficient resource use and reducing environmental impact. The study suggests that further research should explore the long-term economic implications of Green IoT adoption and investigate ways to overcome technical and financial challenges. Additionally, expanding the scope of Green IoT to other agricultural sectors, such as livestock farming, could enhance its applicability and overall impact on agricultural sustainability.

As students who tend to be oriented towards in-depth knowledge and understanding, analysis is key in making decisions regarding coffee consumption. In this case, coffee content analysis is the main factor taken into consideration in determining their choice. The findings of this study indicate that students consider various factors in their decisions in choosing the type of coffee based on its ingredients. Influencing factors include caffeine, antioxidants, fat, and sugar content. College students have different preferences regarding the level of caffeine in coffee, which can be influenced by their energy needs and their tolerance level for caffeine. The aim of this research is to analyze the factors that are the main considerations for students in determining the choice of coffee based on its content. This research uses a qualitative and quantitative approach that focuses on questionnaires with a 1-4 Likert scale and bold interviews. The number of participants in this research was 53 people and was reduced to 6 people being interviewed. This qualitative and quantitative approach shows that analysis of factors in selecting coffee based on its content is not a benchmark for coffee consumption. The coffee consumed by several respondents is adjusted to their body condition or personal health. The factors that become a benchmark for choosing coffee consumption are not based on its content but based on needs and health.

Obesity is a result of excessive energy consumption, this excess energy is stored in the body as fat, so that over time the body becomes heavier. This condition can be experienced by everyone, regardless of gender group or group of teenagers to adults. The aim of the research is to determine the effect of nutritional education about obesity using animation media on knowledge and attitudes in obese adolescents. This research was conducted at SMP Negeri 1 Lubuk Pakam. The research was conducted in May 2023 with quantitative methods using a Group Pre and Post Test design. Intervention in the form of counseling using animation media was given 3 times. The population in this study was class VIII, 319 students. The sample in this study was 32 obese students who met the criteria for being a sample. Data were collected using a knowledge and attitude questionnaire using a statistical test, namely the T-dependent test. The research results showed that the average knowledge of teenagers about obesity before counseling was 10.09, after being given counseling was 13.25 with a significant value of p=0.000. The average attitude score before being given counseling was 39.53, after being given counseling was 51.19 with a significant value of p=0.000. The conclusion of this research is that there is an influence of nutrition education about obesity using animation media on the knowledge and attitudes of teenagers at SMP Negeri 1 Lubuk Pakam. It is hoped that the school can play an active and creative role in providing health education about nutrition, especially about obesity in teenagers.    

Along with the current developments, electricity has become a basic need that must be required. With population growth, economic development, and an increase in various activities and the use of facilities that require electricity, the use of electrical energy will continue to increase even in the use of lights as lighting in offices. The expected purpose of this study is to examine the efficiency of energy use as office lighting for one of the fertilizer industries before and after using LED lights, as well as analyze the costs incurred and the economic value obtained from the use of LED lamps. In this study, several data are needed, such as building plan data, inventory of the types of lamps used, electrical energy consumption, and the price of electricity consumption per kWh through the calculation method. The difference in costs incurred for the use of non-LED lamps into LED lamps is IDR 580,931.14 in the 1st-floor office building in one of the fertilizer industries and IDR 319,747.56 in the 2nd-floor office building in one of the fertilizer industries. Based on this, the difference in costs incurred is very large, so the use of LED lamps is the main alternative in saving energy and costs. Therefore, it is important to conduct studies and research on the impact that can be caused by the use of LED lights on energy consumption and electricity cost efficiency in the company.    

The work of farmers is prone to the risk of health problems such as pain in the joints and fatigue. The research problem is that farmers complain of rapid fatigue, so the purpose of the study is to determine the physiological workload on rice farmers when planting rice and determine the best time for farmers. Methods used are Cardiovascular Load (%CVL) and Energy Consumption to determine the weight of the workload of farmers, and rest time to determine the length of rest. Results and discussion, %CVL is 7 people with action no fatigue and 46 people with action needed improvement. Energy consumption is the smallest percentage of 4.14 kcal / minute with a mild classification and the largest percentage of 6.14 kcal / minute with a moderate classification. In the second measurement, the smallest percentage was 4.49 kcal/min with a mild classification, and the largest percentage was 6.90 kcal/min with a moderate classification. In the third measurement, the smallest percentage was 3.96 kcal/min with a mild classification, and the largest percentage was 6.39 kcal/min with a moderate classification. The smallest rest time was 12.65 minutes and the largest rest time was 120.39 minutes. In conclusion, the mean %CVL was 13.21% with no fatigue, 86.79% with improvement needed. Average energy consumption with mild classification with a percentage of 4.21% and moderate classification with a percentage of 6.26%. The smallest rest time is 12.65 minutes while the largest is 120.39 minutes

Street lighting is a safety infrastructure on the highway. However, currently most of the public street lighting lamps do not comply with the provisions of the Indonesian National Standards Agency (BSNI). It is necessary to plan for lamp replacement by using lamps that are appropriate to field conditions, so that the light intensity produced is in accordance with established standards. Jalan Jenggolo Asri - Randu Asri in Sidoarjo is an example of a road that requires planning for lighting changes. This road is a type of secondary collector road with a length of 2500 meters and a road width of 6 meters. The lighting on this road does not meet the standards set by BSNI 7391: 2008 caused by several lights that go out and the power of the lights used is not optimal. Planning for replacing public street lighting is needed on this road by recalculating the maximum light intensity requirements. The method that can be done is to replace the lamp used with an LED lamp. In the current initial conditions, street lighting on this road uses 250W SON and 150W LED lamps. After planning, the lights will be replaced with 100W LED lights. The planning results show that by using a 100W LED lamp, a handlebar ornament angle of 14.06° will be produced, the light intensity is Candela, the lighting intensity is 6.77 Lux, and the power required per day is 26.4 kWh. Comparison with the use of 250 Watt SON lamps shows that the use of 100W LED lamps is more efficient in energy consumption by 51.76%. The results of this planning have met the standard requirements for secondary collector roads. Thus, by replacing the lights according to the plan, it is hoped that street lighting on Jalan Jenggolo Asri - Randu Asri Sidoarjo can meet the standards set by BSNI 7391:2008 and provide optimal light intensity with more efficient energy use.

Background: The burden of malnutrition in Indonesia is quite large, with more than a quarter of the adolescent population aged 16-18 years, 27 percent experiencing stunting and 8 percent being underweight, the prevalence of anemia in young women aged 13-18 years is 22.7 percent. The highest prevalence of chronic energy deficiency (CED) in women of childbearing age is experienced by adolescents aged 15-19 years, reaching 36.3%. Nutritional problems in adolescents have serious implications for the health of young people, affecting the well-being of current and future generations, as well as the economy and health of countries. In particular, the nutritional status of adolescent girls is closely related to pregnancy outcomes and the health and survival of mothers and children. Objective: To identify the diversity of food consumption and nutritional status of young women who live in boarding houses Methods: This study is an analytic observational study with a cross-sectional design that aims to identify the diversity of food consumption and nutritional status of female adolescents living in boarding houses. The sample in this study were young women who had just entered higher education at the age of 18-19 years, who lived in boarding houses, totaling 49 people. The diversity of food consumption was identified using the Semi Food Frequency Questionnaire (FFQ). Nutritional status was assessed based on body mass index and upper arm circumference. Results: The diversity of food consumption of respondents was low at 46.9%, the nutritional status based on BMI was mostly in the normal category, namely 71.42%, the nutritional status based on the LiLA category was lacking, namely 46.93%, there was no relationship between BMI and diversity in food consumption (P Value 0.13, but there is a significant relationship between the diversity of food consumption and LiLA (P Value 0.02). Conclusion: Diversity in food consumption is low and there are nutritional problems in young women living in boarding houses.

Based on the results of measuring PPH consumption for the City of Surakarta in 2020, the PPH score was 90.8 with a total energy consumption of 2,031 Kcal/capita/day. This figure illustrates that the Energy consumption of Surakarta City residents has reached 94.5% of the ideal Energy Adequacy Rate (AKE) of 2,150 Kcal/capita/day. Meanwhile, from the PPH Availability of Surakarta City, a score of 91.9 was obtained with a total energy availability of 2,481 Kcal/capita/day. Judging from these two figures, in terms of quantity and quality in general, the food consumption of Surakarta City residents is close to ideal, so it is expected that the status of the Surakarta City community as a whole is in good condition. Then from the availability side, an overview of the various food ingredients available in Surakarta City has reached 103% of the Availability Energy Adequacy Rate (AKE) of 2,310 Kcal/capita/day.

Green Economy is an economic approach that focuses on sustainable development and utilization of resources, by integrating economic, environmental, and social aspects by involving changes in mindset and lifestyle. With the expectation that people are expected to adopt sustainable behaviors and consumption such as reducing waste, supporting environmentally friendly products, and recycling. The main goals of a green economy are to reduce negative impacts on the environment, including reduction of greenhouse gas emissions, protection of biodiversity, conservation of water resources, and effective waste management.  Green economy not only focuses on environmental protection, but also involves social and economic aspects. This includes improving social welfare and improving quality of life through sustainable job creation, increased access to basic services, and community empowerment in decision-making that impacts the environment. There are several important aspects in realizing a green economy through policies and regulations to encourage the transition to a sustainable economy that considers environmental and social aspects that support the green economy in realizing the transition to a sustainable economy. The second is investment in renewable energy that uses energy such as solar power, wind power, hydroelectricity, biomass and geothermal energy. The third is sustainable infrastructure development, the fourth is community empowerment and the fourth is collaboration between the public and private sectors.

In human life the use of electrical energy is very important and needed, but over time the use of its consumption is increasing. Therefore, it is necessary to save electricity consumption activities. This effort is known as energy conservation, one of which uses the energy audit method. Energy audit is to calculate the level of use or Energy Consumption Intensity (IKE) in the Untirta Integrated Laboratory Building whether it is efficient in the use of electrical energy. Collecting data related to electricity usage, building profiles, and observing lighting loads, air conditioning/air conditioning loads and other loads. The use of the IKE formula as a form of analyzing the data. The results of the study stated that the IKE value in the Untirta Integrated Laboratory Building was 38.019 kWh/m2, from this value it can be seen that the IKE is efficient. This is based on the parameters used by ASEAN-USAID of 240 kWh/m2. There is an addition in saving energy if in the future there are additional burdens such as laboratory equipment or electronic goods. These savings can be made by using LED lamps and replacing TL lamps or using lamps with a lower wattage than the previous lamp and can be done by reducing the duration of usage in consuming electrical energy.