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Public Fuel Filling Stations (SPBU) are important facilities that provide various types of fuel such as gasoline, diesel, and Pertamax to meet the needs of motorized vehicles. The existence of SPBU greatly helps the public in obtaining fuel at a more economical price compared to purchasing retail. However, the transaction system at SPBU generally still uses conventional methods, such as cash payments or the use of debit/credit cards that have not been fully integrated with an efficient digital system. The use of RFID (Radio Frequency Identification) technology has been implemented as a non-cash transaction method at several SPBUs, but this system still has various weaknesses, such as limited device compatibility and delays in transaction processing. This prompted the author to develop the concept of an independent SPBU based on modern technology that is more efficient and secure. The proposed innovation includes the use of contactless smart cards and coin acceptors for the payment system, allowing users to make self-service transactions without operator involvement. In addition, the author also added several supporting components such as proximity sensors, which function to detect the presence of vehicles or people around the SPBU area. These sensors can help in saving electrical energy by activating the system only when needed. Another component is a vibration sensor, which plays a crucial role in detecting excessive vibrations that could potentially cause leaks. If excessive vibration is detected, the system automatically closes the solenoid on the pump to prevent the risk of fire or damage. By integrating this technology, the autonomous gas station system is expected to improve operational efficiency, user convenience, and safety during the automatic refueling process. This development is expected to be an innovative solution for modernizing the gas station system in Indonesia.

The Paiton Steam Power Plant (PLTU) is one of the main sources of electrical energy in East Java, which plays a vital role in maintaining a sustainable electricity supply. The reliability of generator units is a key element in maintaining stable energy distribution. However, the high frequency of sudden generator failures poses serious challenges, such as increased downtime and increased maintenance costs. To address these challenges, this study aims to design a generator maintenance prediction model based on the Naive Bayes algorithm with a predictive maintenance approach. This study uses historical maintenance data and key sensor parameters such as temperature, oil pressure, and vibration as input. The data is analyzed through several stages, namely data preprocessing, selection of relevant features, and labeling generator conditions into three categories: Normal, Warning, and Critical. The Naive Bayes model is trained to classify the data probabilistically to generate predictions of future generator conditions. Model evaluation using accuracy metrics and a confusion matrix shows that the model successfully achieved an accuracy rate of 89% and was able to provide early warnings of potential failures up to 3 days before failure occurs. The implementation of this system is expected to support the shift in maintenance strategies from reactive and scheduled systems to data-driven predictive systems. Implementing failure predictions allows the technical team at the Paiton PLTU to conduct planned maintenance, avoid sudden disruptions, and extend equipment lifespan. Thus, this model has the potential to reduce operational downtime by up to 25%, while providing significant savings in operational and logistics costs. This research also shows that integrating machine learning technology into energy facility management can improve the efficiency and resilience of the overall electric power system.

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in supporting Indonesia's economy, contributing 61.07% to the national Gross Domestic Product (GDP). However, MSMEs still face various challenges, particularly low productivity due to limited access to technology and production equipment. A clear example is Kedai Ndeso, located in Jiyu Village, Kutorejo District, Mojokerto Regency. This small food business faces inefficiencies in the noodle boiling process, which is still done manually using conventional methods, resulting in limited production capacity and longer service times. Through this community service program, the implementation team aimed to develop and apply an appropriate technology innovation in the form of a multi-strainer noodle boiler to help improve MSME productivity. The method used was a participatory approach, actively involving the Kedai Ndeso owner throughout each stage of the program, which was conducted from July 13 to 24, 2025. The developed noodle boiler measures 35x35x35 cm and is made of food-grade stainless steel. It features six separate strainers, allowing six portions of noodles to be cooked simultaneously in the same amount of time.nTesting results demonstrated that this tool significantly increased production efficiency without compromising the quality of the noodles. It also led to operational cost savings, especially in terms of energy use and labor time. Additionally, training and outreach activities were carried out to ensure effective knowledge transfer to the MSME owner. This appropriate technology innovation has proven to be a practical solution for MSMEs in the culinary sector, helping them increase productivity and competitiveness while maintaining consistent product quality and ensuring food safety. Therefore, this initiative provides a tangible contribution to supporting the sustainability and independence of small enterprises in rural areas, empowering them to compete more effectively in the modern economy.

This study aims to analyze the electrical energy consumption of a bench-type drilling machine, RYU RDB 13, by varying machining parameters in the form of spindle speed and feed rate. Electrical energy is a critical aspect of production efficiency in the manufacturing industry, particularly in machining processes that require continuous power consumption throughout the cutting operation. The material used in this study is aluminum 6061, chosen for its lightweight, corrosion resistance, and wide application in the automotive and aerospace industries. The drilling process was carried out using three spindle speed variations: 620 rpm, 920 rpm, and 1280 rpm, along with three feed rate variations: 0.04 mm/rev, 0.08 mm/rev, and 0.1 mm/rev. Current and voltage were measured using a digital wattmeter in real-time, and energy consumption was calculated in wattseconds (Ws) using power calculation formulas. The results indicate that increasing the feed rate and spindle speed leads to higher instantaneous power consumption. However, total energy consumption tends to decrease at higher speed and feed combinations due to shorter machining times. The optimal parameter combination was found at a feed rate of 0.1 mm/rev and a spindle speed of 1280 rpm, which resulted in the lowest energy consumption of 387 Ws and the fastest drilling time. This demonstrates that selecting the right machining parameters not only improves energy efficiency but also maintains or enhances productivity. The observed power consumption pattern typically shows a sharp increase at the beginning of the drilling process, a stable phase during the main cutting stage, and a rapid decrease towards the end of the cut. These findings contribute to a better understanding of the relationship between machining parameters and energy efficiency, serving as a basis for developing sustainable production strategies in the manufacturing sector that prioritize energy savings and cost reduction.

Watering plants is an important activity that cannot be ignored in order to maintain sufficient air intake for optimal growth. Manual watering methods, which are commonly carried out according to a schedule, are often inefficient because they require significant allocation of time and energy. Improper watering techniques are one of the risks of plant growth factor failure, where unmaintained soil moisture can cause plants to wilt and die. This study aims to develop an automatic irrigation system based on the Internet of Things (IoT) that utilizes light energy as its power source, with the aim of being able to save time, lighten human work, and at the same time optimize the use of energy resources. This system is designed to monitor soil conditions in real-time and regulate watering automatically, supported by a sustainable light energy source. The results of this study indicate that the automatic watering system based on the Internet of Things (IoT) developed and powered by light energy is able to overcome the problem of plant drought effectively, while saving time and lightening human work in watering activities. 

This research seeks to explore how the Adiwiyata program is implemented in fostering students' religious character through the Pancasila Student Profile Strengthening Project (P5) at SMA Negeri 1 Tambakboyo.The research employed a qualitative method with a phenomenological approach, focusing on exploring the direct experiences of teachers and students in carrying out the program. The data were obtained using observation, in-depth interviews, and documentation techniques. The findings indicate that the Adiwiyata program not only contributes to raising environmental awareness but also plays a significant role in instilling character values such as responsibility, discipline, cooperation, and socio-ecological awareness. These values are internalized through reflective and participatory daily activities, such as classroom cleaning duties, waste management, composting, and energy-saving campaigns. All of these activities are carried out with active and structured involvement from teachers, the school principal, and the entire school community. Supporting factors include adequate facilities and infrastructure, habituation of positive behaviors, and the integration of religious values into environmental activities. However, some challenges remain, such as the low initial understanding of new students and inconsistent behavior change. The implication of these findings is that environmental programs, when integrated with character education, can serve as effective media for building students’ moral and religious values in a contextual and sustainable manner, in line with national education goals and Islamic principles.

Energy efficiency in air conditioning systems is a critical factor in reducing energy consumption and environmental impact. This study aims to analyze the energy performance of an air conditioning system using the thermodynamic cycle method. The analysis focuses on the refrigeration cycle, particularly the vapor compression cycle commonly used in residential and commercial systems. Key parameters such as coefficient of performance (COP), refrigerant flow rate, and enthalpy at each cycle point were examined to determine overall efficiency. Data were collected through simulations and experimental measurements under standard operating conditions. The results show that optimizing system components, especially the compressor and expansion valve, can significantly improve energy efficiency. The COP increased by 12% when a high-efficiency compressor was utilized. In addition, the selection of eco-friendly refrigerants contributed to better thermal performance and reduced environmental risks. This research highlights the importance of thermodynamic analysis in designing and improving air conditioning systems for sustainable energy usage. Future studies are recommended to incorporate real-time monitoring and adaptive control systems to further enhance system performance and energy savings.

Clean living behavior can be done by maintaining the sustainability of life can be done by reducing waste, saving energy, planting trees, and not littering. Waste sorting can be done by separating organic and inorganic waste into different trash bins. In addition, inorganic waste can also be separated again based on its type, such as plastic, paper, glass, and metal. Facilitates the process of collecting and processing waste for recycling. The Principles of Clean and Healthy Living Behavior (PHBS) recommend the use of waste sorting methods. The daily habits that we do to maintain personal and environmental cleanliness consist of this behavior. . Along with the emergence of various diseases that often attack children aged 6-12 years, PHBS in schools is a collection of behaviors carried out by students, teachers, and school residents based on learning so that they are able to independently prevent disease, improve their health. Implementation of better waste sorting practices can reduce the negative impact of waste on the environment, increase the efficiency of waste management, and provide long-term benefits for school residents. Schools are one of the places that have great potential to educate students about good waste management. SD Negeri 2 Kelusa has problems related to the lack of student knowledge about waste sorting and utilization, especially organic waste. Most students at SD Negeri 2 Kelusa still have limited understanding of the types of waste and how to process it. Most students do not realize and understand that organic waste can be reused, so they tend to throw it away with other waste.

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    

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.

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.

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.

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.

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.

The rapid growth of information technology, including cloud computing, the Internet of Things, and artificial intelligence, is driving increased energy consumption and carbon emissions in the technology sector. A report by the International Energy Agency (IEA) projects that global data center electricity consumption will more than double, reaching approximately 945 TWh by 2030, or about 3% of global electricity consumption. A Deloitte study even estimates that data centers could contribute around 4% by 2030, primarily driven by the growing use of artificial intelligence. Beside physical infrastructure, software also significantly contributes to energy consumption, necessitating Green IT and Green Software approaches to reduce environmental impact. This research aims to identify and classify energy metrics used in the literature, analyze the trade-offs between energy consumption and system performance, and summarize relevant best practices for application in industrial settings. This research uses the Systematic Literature Review (SLR) method, referring to the Kitchenham protocol. The search was conducted on the IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and arXiv databases for articles published between 2021 and 2025 that included empirical data or conceptual models related to the energy efficiency of information systems. From the screening process, a number of articles were obtained and analyzed thematically. The research results show that energy metrics vary from hardware-based measurements (power meter) to estimation models (GSMM, GSMP). A trade-off was also found between energy savings and performance, necessitating a multi-objective optimization approach. This research contributes by providing a framework for classifying energy metrics, visualizing measurement method trends, and offering methodological recommendations that can be integrated into industrial practice. The implications of this research are to support digital sustainability efforts while simultaneously reducing the organization's operational costs.

This research examines how Green Products influence consumer decisions in purchasing energy-saving lighting products. Green Products Refer to products that do not endanger human health and are friendly to the environment, production processes that are efficient in using resources, produce minimal waste, and reducing negative impacts on the environment are the main focus in developing environmentally friendly products. Environmentally friendly products that offer value added without sacrificing quality compared to conventional products tends to attract more consumer attention. The widespread adoption of eco-friendly products can be linked to affordability, environmental harm, and energy efficiency, in addition to the ability to reduce harmful environmental impacts. This phenomenon is explored through a comprehensive understanding of the scientific literature, using a systematic methodology to synthesize the findings. The analysis shows that consumer purchasing behavior is strongly shaped by environmentally friendly product attributes. This study seeks to uncover the factors that drive this relationship, in addition to summarizing and interpreting the results of previous research on how environmentally friendly products influence lighting purchasing decisions.

The use of natural gas to replace liquefied petroleum gas (LPG) is a strategic step to reduce dependence on imported energy and support a sustainable energy transition. The purpose of this study is to analyze the possibilities, challenges, and impacts of using natural gas as a source of household energy in Indonesia. The research method used is a qualitative approach that collects primary data through questionnaires and interviews with household users and secondary data from related magazines. The results of the study indicate that Indonesia has great potential in meeting energy needs because it is rich in natural gas reserves. From an economic perspective, the use of natural gas is cheaper than liquefied gas, especially in the long term. However, the main challenges are limited distribution infrastructure, lack of public awareness of the benefits of natural gas, and the need for regulations to support the implementation of this energy transition. The results are positive impacts such as reduced CO2 emissions, increased energy efficiency and savings in foreign exchange. In summary, the use of natural gas as a substitute for LPG is feasible, but requires strategic steps such as expanding natural gas infrastructure, educating the public, and strengthening the regulatory framework. This study recommends that the government, energy companies, and the community work together to ensure an effective and sustainable energy transition.

This research aims to design and develop a motion detection-based automatic light control system with a light intensity setting feature using IoT technology. The system utilizes a combination of PIR sensor, MOSFET module, and Arduino Uno microcontroller to control lighting based on the presence and position of the user. With an adaptive control algorithm, the lamp can dynamically adjust the lighting intensity to several levels as needed. Test results show that the system successfully reduces energy consumption optimally without reducing user comfort. The implementation of this technology has great potential in supporting energy efficiency in public areas, such as parks, office hallways, and sidewalks.

This research examines the effect of bricks enhancement with phase change material (PCM) consisting of paraffin on their thermal considerations. The experimental methods were aimed at investigating the mechanisms of temperature transfer throughout the thermal rounds, mimicking diurnal fluctuations of temperature. Due to the high amount of latent heat contained in paraffin a portion of it was transferred into regular bricks with temperature probes tracing the heat processes. It is shown that enhancement of bricks with PCM reduced temperature range and controlled the rate of heat transmission more than the ordinary bricks did. It was significant that the PCM-embedded bricks did not drift in heat and were able to stock up excess heat during the day and release it at night which resulted in monumental energy efficiency and comfort in thermal standing. The conversation additionally seeks to bring out the function of paraffin in insulation which does not only save energy consumption but goes forward to maintain comfortable temperatures within the building. The study finally achieved its goal and assert that energy saving with the use of PCM enhanced bricks in construction makes it a preferable material in regards to energy and the fight against climate change.