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This study aims to design and develop a pipeline leakage detection system based on the Internet of Things (IoT) that provides real-time notifications and determines the location of leaks with high accuracy. Pipeline leakage is a serious issue, as it can lead to water wastage, environmental damage, and high maintenance costs. Therefore, a system that can detect leaks quickly and accurately is crucial for improving the efficiency of pipeline infrastructure management. The system developed in this study uses an ESP32 microcontroller, Waterflow sensor, and GPS module. The ESP32 microcontroller serves as the central processing unit that processes the data received from the Waterflow sensor and the GPS module. The Waterflow sensor detects changes in water flow that indicate a leak in the pipeline. When an abnormal reduction in flow is detected, the sensor sends a signal to the microcontroller. The GPS module then provides location coordinates, pinpointing the exact location of the leak, allowing the maintenance team to quickly address the issue. Additionally, the system is integrated with the Blynk application, which enables remote monitoring through a mobile device. The Blynk application provides a user interface that facilitates the monitoring of pipeline status and delivers notifications whenever a leak is detected. Testing results show that the IoT-based leakage detection system is capable of identifying leaks and sending real-time information with good accuracy. With this system, the process of identifying and addressing pipeline leaks can be done faster and more efficiently, ultimately reducing the losses caused by leakage. The system also offers a more effective solution for pipeline maintenance, improving the reliability of water distribution systems and reducing water resource wastage.

This study examines the role of environmental cost management in improving operational efficiency in manufacturing companies. Using a descriptive qualitative approach through a case study in Indonesia, the findings show that effective environmental cost management reduces waste and enhances production efficiency. Separate reporting of environmental costs also improves transparency and accountability.

Climate change and environmental degradation have negatively impacted mankind's sustainable development. The concept of green entrepreneurship has gradually been accepted by society, including ecoprint batik. Through the application of zero waste, ecoprint batik production offers a strategy that is in line with the principles of green entrepreneurship. This study aims to analyze the application of zero waste to the efficiency of eco-print batik production in the context of green entrepreneurship. The method used in this study is a qualitative approach with secondary data collection on batik business actors in Sragen. The data obtained were then analyzed using descriptive analysis to identify the analysis of the application of zero waste in batik innovation towards green entrepreneurship efforts. The results of the study indicate that the application of zero waste in eco-print batik production has a significant impact on production efficiency, especially in reducing waste and optimizing the use of raw materials. However, there are several obstacles such as higher production costs, limited supply of natural raw materials, and low consumer awareness of the added value of zero waste-based batik. Policy support and incentive programs from the Government are also very important to encourage the sustainability of eco-print batik businesses based on green entrepreneurship.

This study aimed to evaluate the implementation of green accounting at Polrestabes Palembang and its impact on sustainable environmental management. A descriptive qualitative approach was employed, utilizing observation, interviews, and documentation for data collection. The findings revealed that green accounting practices at Polrestabes Palembang were not yet fully integrated into the institution’s financial accounting system. Environmental costs were still recorded under general operational expenses without specific classifications, and there was no systematic measurement or disclosure in accordance with environmental accounting principles. However, several positive initiatives existed, such as energy efficiency, greening efforts, waste management, and food security programs. The study recommends strengthening environmental cost recording systems, integrating data across departments, and providing training to enhance understanding of green accounting. Effective implementation of green accounting is expected to improve transparency, accountability, and institutional legitimacy in supporting sustainable development.

This study aims to analyze the economic and social impacts within the environmental context on the sustainability of catfish farming in Basanah Village. Environmental issues such as water quality, land availability, and land-use changes have significantly affected aquaculture practices. Moreover, the social dynamics of the surrounding community, including patterns of cooperation, public perception of catfish farming, and economic impacts on farmers’ income and welfare, are central to the investigation. This research employs a descriptive qualitative approach through field observations, in-depth interviews, and documentation involving local farmers, community leaders, and relevant stakeholders. The findings reveal that economic aspects, such as production cost efficiency and income improvement, are highly dependent on stable environmental conditions and strong social support. Conversely, environmental degradation and weak social participation hinder business development. Therefore, participatory-based policies integrating sustainable environmental management with the strengthening of local socioeconomic capacities are urgently needed. This study offers a significant contribution as a foundation for formulating empowerment strategies for environmentally and socially-based small-scale aquaculture enterprises at the village level.

This study investigates the effect of environmental costs on the level of environmental information transparency in manufacturing companies in the basic materials subsector, while providing empirical evidence regarding the relationship. Environmental costs are calculated through the ratio of corporate social responsibility (CSR) burden to the organization's net profit. Meanwhile, the company's environmental performance is evaluated using the PROPER rating on an ordinal scale. The level of environmental coverage is measured comprehensively through the Clarkson index which covers various dimensions of existing reporting. A quantitative approach with multiple linear regression analysis is applied to test the relationship between variables. In selecting the sample, a purposive sampling technique was used by considering the completeness of the data and certain sector criteria, resulting in 35 companies as the final sample that met the analysis requirements. The results of the study revealed that an increase in environmental costs is directly proportional to a significant increase in environmental coverage. This indicates that company investment in environmental programs encourages more transparent reporting practices. In addition, environmental performance as reflected in the PROPER rating is also proven to have a positive and significant effect on the extent of environmental coverage.

Ground-mounted solar panels are directly exposed to environmental conditions, particularly wind, making structural design essential. While wind tunnel experiments have been a primary research method, they are costly and time-consuming. Computational Fluid Dynamics (CFD) simulations provide an efficient alternative by accurately modeling fluid-structure interactions, allowing for detailed aerodynamic analysis without physical testing. This study examines a solar panel configuration of 2×18 (1700×1200 mm panels) under a wind speed of 30 m/s at a height of 10 m. Wind direction variations (0°, 30°, 60°, 90°, 120°, 150°, 180°) significantly impact aerodynamic behavior. Drag Force analysis reveals that frontal wind (0°) generates the highest drag, while lateral wind (90°) has the least effect. Surface Pressure and Surface Coefficient (Cp) analysis indicate maximum pressure at 0°, where Cp values are highest, highlighting a pronounced pressure differential. At 90°, pressure distribution is more uniform, reducing aerodynamic impact. At 180°, rear-side pressure remains influential but lower than frontal exposure.

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

This study explores the transformative potential of sustainable business models (SBMs) in driving social and environmental value creation across industries. Employing a mixed-method approach, the research integrates qualitative interviews with 30 key informants and quantitative analysis of industry-specific data to provide comprehensive insights. The findings reveal that implementing SBMs leads to significant reductions in carbon emissions, improved resource efficiency, and enhanced stakeholder engagement. Key enablers include the adoption of circular economy principles and collaborative stakeholder strategies, while challenges such as regulatory inconsistencies and high initial investment costs persist. The results align with theoretical frameworks like the Triple Bottom Line and Resource-Based View, emphasizing the need for balanced economic, social, and environmental outcomes. This study concludes by offering actionable recommendations for policymakers and business leaders, including fostering inclusivity, enhancing policy clarity, and promoting innovation to overcome barriers. The findings contribute to advancing knowledge on sustainable practices and supporting global sustainability initiatives.

This study intends to evaluate the effect of Green Accounting, Company Size, and Leverage on Financial Performance. Companies engaged in manufacturing have an important role in the Indonesian economy and are one of the largest contributors to non-oil and gas GDP. The population in this study is from manufacturing companies engaged in food and beverages listed on the IDX for the 2019-2023 period. Green accounting focuses on environmental costs, company size uses the natural lagorithm of total assets and leverage using DER, while financial performance uses ROA. The research method applied is quantitative. The sampling procedure is “purposive sampling”, which is the selection of ten companies based on predetermined criteria. The analysis methods used in this study include descriptive statistical tests, classical assumption tests, and multiple linear regression. This study uses secondary data sourced from financial statements. The results show green accounting affects the company's financial performance with a significant value of 0.012 and a regression coefficient of -88.769, company size does not affect the company's financial performance with a significant value of 0.471 and a regression coefficient of 0.378, and leverage has an influence on the company's financial performance with a significant value of 0.034 and a regression coefficient of -4.273. Green accounting, company size, and leverage simultaneously affect the company's financial performance with a significant value of 0.001.

Outdoor lighting, such as in community parks, plays a crucial role in supporting community activities at night while enhancing safety and comfort. Unfortunately, traditional electricity usage is often inefficient and has negative environmental impacts due to carbon emissions from fossil fuel-based power plants. This study aims to implement solar panel technology as an environmentally friendly lighting solution in Taman RukunWarga, located in Kelurahan Bendung, Kecamatan Kasemen, Kota Serang. The methods applied in this study include site surveys, assembly of the solar-powered lighting system, and performance analysis after installation. The lighting system consists of a 30 Wp solar panel, a battery (accumulator), a solar charge controller, a 12V 20W DC LED light, and other supporting components. The results show that this system can provide adequate and even lighting for the park area while being energy-efficient. One of the main advantages of this system is its ability to reduce operational costs by using renewable solar energy and requiring minimal maintenance. Additionally, the implementation of this solar-powered lighting system has increased public awareness of the importance of utilizing renewable energy and reducing carbon emissions in the surrounding environment. Overall, the installation of this solar-powered lighting system has proven to be a sustainable, energy-efficient, and environmentally friendly alternative for meeting lighting needs in community parks. This system not only reduces dependence on fossil fuels but also contributes to efforts to mitigate negative environmental impacts. By utilizing solar energy, this system serves as a positive example of the application of renewable energy technology in daily life, while helping to create a cleaner and healthier environment for the community.

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

This study aims to provide empirical evidence of the influence of state capital participation, company size, environmental costs, and company ownership structure on the financial performance of state-owned enterprises. This test uses a statistical tool, namely IBM SPSS version 25. The population in this study were all state-owned companies or BUMN, namely 113 companies and the sample selection used the purposive sampling method so that 31 companies were selected. The analysis methods used were descriptive statistical analysis, classical assumption test, multiple linear regression analysis, coefficient of determination, F test, and t test. The coefficient of determination of 0.58 indicates the ability of the independent variable to explain the dependent variable by 58%.

This research aims to analyze the impact of green accounting and environmental costs on the profitability of mining companies listed on the Indonesia Stock Exchange (BEI) during the period from 2020 to 2023. A quantitative descriptive method with an associative quantitative approach was employed, relying on secondary data obtained from the companies' annual reports available on the official BEI website. The study sample consisted of 11 companies, and data analysis was conducted using IBM SPSS version 29. The findings reveal that the implementation of green accounting significantly and positively contributes to the profitability of these companies. Conversely, expenditures on environmental costs did not demonstrate a significant impact on profitability. Nevertheless, when examined together, both variables collectively influence an increase in the companies' profitability.

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.

Adoption of melon (Cucumis Melo L.) cultivation innovation plays an important role in increasing productivity and efficiency. This study discusses the application of innovation adoption in melon plants with greenhouse technology, hydroponics, modern irrigation technology, post-harvest processing mechanisms for breeding. This literature review uses the Systematic Literature Review (SLR) method to compare National Journals and International Journals that are relevant to Innovation Adoption. The use of the Internet of Things (IoT) allows automatic monitoring of environmental conditions. Technology in melon cultivation has a significant impact on agricultural productivity and efficiency. In the hydroponic system of soilless cultivation in limited land conditions, each innovation has challenges in its implementation, such as high investment costs, the need for training for farmers, and limited access to certain technologies  

This research is based on the fact that in the process of making a Driving License, the Manggarai Timur Community is faced with serious challenges, considering that until now the Manggarai Timur Police have not provided this service, forcing the Manggarai Timur community to travel to Ruteng, Polres Manggarai to take care of their SIM. Long trips not only take a lot of time but also cost a lot of money for the community, especially for those who live in remote areas in the Manggarai Timur area. The type of research used is empirical legal research. Data Collection Techniques interviews, observations and documentation. The results of this study indicate that the aspects of the SIM C making service for the Manggarai Timur community at the Manggarai Polres Traffic Unit, include; Accessibility of SIM C making services at the Manggarai Polres, Speed ​​​​in the SIM C Making Process, and the Level of Satisfaction and Perception of the Manggarai Timur Community Regarding the Services Provided Driving License services at the Manggarai Polres Traffic Unit are quite adequate, they only need to be improved. Meanwhile, the factors that influence the service for making Class C Driving Licenses for the people of East Manggarai at the Manggarai Police Traffic Unit include the availability of facilities and infrastructure, geographical conditions or environmental factors, the existence of complex and complicated requirements that must be met in the application and making of Class C Driving Licenses, the number of officers available to serve Class C Driving License applications, and the potential for corrupt practices or illegal levies that can hinder the process of making Class C Driving Licenses efficiently and transparently.

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.

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.