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Bank Ekonomi Rakyat Mangatur Ganda has great potential in improving the performance of Micro, Small, and Medium Enterprises (MSMEs) through effective credit distribution and targeted information delivery, especially for segments that have not been optimally served. Optimizing the role of banking is carried out through various strategies, such as socializing credit programs, providing convenience in the credit application process, implementing outreach strategies, improving service quality, and financing in potential sectors. In addition, the use of information technology is key to expanding service coverage and increasing operational efficiency. Strategic partnerships with the government, other financial institutions, and MSME communities are needed to create an inclusive and sustainable business ecosystem. With management commitment and implementation of targeted strategies, Bank Ekonomi Rakyat Mangatur Ganda has a great opportunity to significantly encourage MSME growth and contribute to improving the welfare of the wider community.

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

In the face of increasingly intense business competition, improving operational performance has become a critical priority for companies. One widely adopted strategy to achieve efficiency is the Just In Time (JIT) system. This study applies a Systematic Literature Review (SLR) method to analyze the impact of JIT implementation on companies' operational performance. The findings reveal that JIT significantly contributes to reducing storage costs, enhancing production efficiency, speeding up distribution processes, and increasing operational flexibility and customer satisfaction. However, the success of JIT implementation largely depends on effective coordination with suppliers, technological readiness, and the competence of human resources.

The rapid growth of e-commerce has driven companies to seek more effective strategies to enhance logistics efficiency and customer satisfaction. This article examines the synergy between artificial intelligence, strategic location determination, and digital marketing in supporting the performance of digital supply chains. This multidimensional approach demonstrates that integrating cutting-edge technologies with precise location strategies and data-driven marketing can create superior customer experiences and more efficient operational costs. This study is based on a literature review of ten recent related studies. Moreover, it highlights consumer behavior shifts due to the digitalization and globalization of supply chains.  

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

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

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

This study explores the integration of AI-driven process optimization in Waste-to-Energy (WtE) systems to enhance urban sustainability. The research focuses on designing a gasification-based WtE system, incorporating AI predictive control to optimize energy conversion processes. The AI system adjusts operational parameters in real-time, improving energy conversion efficiency by 25% and reducing carbon emissions by 40%. Additionally, the system's waste-to-energy conversion rate is projected to increase by 20%, and operational costs are expected to decrease by 30%. Data collection and analysis are carried out using advanced sensors to monitor key parameters such as temperature, gas composition, and energy output, which are then processed by machine learning algorithms for predictive analysis. The results show that the AI optimization significantly enhances system performance, offering a sustainable solution for urban waste management. The study highlights the technical and operational challenges of integrating AI into existing WtE systems, including the need for infrastructure upgrades and scalability considerations. It also discusses the socio-economic impacts, including job creation, reduced energy costs, and improved public health. The findings demonstrate the potential of AI-based WtE systems in reducing waste, generating clean energy, and mitigating climate change, positioning them as a viable solution for sustainable urban development.

This study aims to analyze the suboptimal performance of hydraulic pump system on hatch cover performance on MV. Emerald Indah using USG (Urgency, Seriousness, Growth) method and data collection techniques in the form of observation, documents, and interview. The purpose of this study is to determine the causes, effect and efforts in solving the problems discussed. The results of the study indicate that several factors cause the hydraulic pump system to not function optimally , including dirty filters, leak in the hydraulic pump and pipe, and operation that is not in accordance with procedures. The effect of the suboptimal hydraulic pressure which ultimately becomes the main problem that inhibits the hatch cover opening process. To overcome this problem, improvements are needed such as periodic filter cleaning, replacement of leaking components, and increasing crew understanding of operational procedures. This research is expected to provide insight for related parties in improving the performanceof the hydraulic pump system and the efficiency of the loading and unloading process on ships.

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.

Archives management plays a crucial role in ensuring the accountability of an organization, as proper management of archives can enhance transparency and operational effectiveness. Based on observations made at the Maluku Province Regional Library and Archives Office, several issues were identified within the current archiving system. One of the key problems is the lack of knowledge about proper archives management, which causes significant barriers during the archiving process. The office employs two archivists with high school and diploma (D3) education backgrounds. Although they have attended archives management training, the current system remains ineffective, which in turn affects work efficiency, particularly in the process of searching for archives. In many instances, the required archives are either difficult to locate or entirely missing. This study aims to assess the impact of archives management on work efficiency at the Maluku Province Regional Library and Archives Office. The research employs quantitative analysis with a sample size of 42 employees. Data were collected through a survey method, using a questionnaire to capture relevant information from the participants. Simple linear regression analysis conducted using SPSS 24 revealed that archives management accounts for 35% of the variance in work efficiency, while the remaining 65% is influenced by other factors. The findings suggest that effective archives management has a positive and significant impact on employee work efficiency. Based on the results, several practical implications are suggested: (1) the current archiving system should be maintained and improved for better performance, (2) training and development programs should be implemented to enhance the skills of archivists, (3) archivists should be equipped with specialized skills in archives management to improve overall efficiency, and (4) ongoing coaching and mentorship for archivists should be provided to ensure continuous improvement.

This study aims to evaluate the performance of the Sewage Treatment Plant (STP) at PT PLN Nusantara Power Unit Pembangkitan Gresik in reducing pollutant loads from domestic wastewater. The evaluation involves analyzing water quality parameters including pH, BOD, COD, TSS, oil and grease, total ammonia, and total coliform at both inlet and outlet of the STP. Additionally, the actual daily discharge was observed and compared to the design capacity to assess operational efficiency. The method used was descriptive qualitative, involving field observation, documentation, and laboratory test data analysis during the January–March 2025 period. The results show that all outlet parameters met the effluent standards set by Regulation No. 68/2016 of the Ministry of Environment and Forestry. However, the actual flow rate, which is only 1.6–3.3% of the design capacity, indicates potential inefficiencies in energy use and biological processes. Therefore, operational adjustments and optimization of STP capacity utilization are necessary for more efficient and sustainable system performance.

This research was made to examine the determinants of financial performance of banking companies in Indonesia. There are four independent variables (board of diversity, net interest margin, operational efficiency, and liquidity risk) and a moderating variable (firm size) have been analyzed in this research. Testing the interaction effect of firm size in explaining the influence of these four independent variables on banking financial performance is still very limited. This quantitative research was analyzed by using secondary data from audited annual reports of the company. The purposive sampling technique was used to choose the research’s samples during the observation periods (2018-2022) and obtained 200 observations (40 samples over 5 years of research). Panel data regression with the EViews program was used to test the eight hypotheses which was developed in this research. The results of the Chow test and Hausman test confirm the use of the Random Effect Model in the analysis. The findings from testing the interaction model show that firm size does not moderate the influence of board of diversity and net interest margin on financial performance, while for operational efficiency and liquidity risk variables, the firm size shows a pure moderating role for the both.

This study conducts a systematic literature review (SLR) exploring the Impact of Enterprise Resource Planning (ERP) and Big Data technologies in Improving Business Performance across global sectors. In the context of Industry 4.0, businesses are rapidly adopting the latest systems for operational efficiency. Research shows that ERP significantly improves business performance in various aspects, resulting in significant benefits for companies. Big Data is recommended as a tool for improving business processes; when combined with ERP, it enables effective and efficient production of useful outputs for companies. This SLR brings together a variety of articles from 2019-2025 and highlights a significant positive correlation between ERP, Big Data, and Business Performance of Companies. Although the relevance of these technologies is recognized, discussions on this topic in Indonesia are still limited. This study aims to encourage a deeper understanding and further research on ERP and Big Data.

Pressure vessels are critical components in the energy industry, used to store and process high-pressure fluids. The structural reliability of these vessels plays a pivotal role in ensuring operational safety and system efficiency. This study aims to analyze the design and reliability of pressure vessels using both numerical and experimental approaches to optimize performance and enhance safety factors. The numerical method was conducted through Finite Element Analysis (FEA) using ANSYS software to evaluate stress distribution, stress concentration, and potential failure modes under various operational load scenarios. Meanwhile, the experimental method involved hydrostatic pressure testing, strain measurements using strain gauges, and displacement analysis to validate the numerical simulation results. Data were collected from simulations and laboratory experiments, then analyzed quantitatively by comparing key parameters such as stress distribution, deformation patterns, and safety factors against industry standards. The results indicate that combining numerical and experimental approaches improves the accuracy of pressure vessel behavior predictions, enables more efficient design optimization, and enhances structural reliability. In conclusion, the methods applied in this study can serve as a reference for developing safer, more efficient pressure vessel designs that comply with industrial standards, thereby supporting improved safety and operational efficiency in the energy sector.

process standardization, and system optimization. The study concludes that the HOT-FIT model is This study evaluates the performance of the outpatient registration information system at RSUD Komodo using the Human-Organization-Technology Fit (HOT-FIT) framework. Hospital Information Systems, particularly in the outpatient registration process, are crucial for supporting service efficiency and data accuracy. However, RSUD Komodo has experienced several challenges in the implementation of its SIMRS module, including system slowdowns, sudden monitor failures, and unstable internet connectivity during service hours. These issues hinder operational effectiveness and risk compromising service quality. The objective of this research is to assess system performance comprehensively across human, organizational, and technological dimensions. A qualitative descriptive design was employed, involving in-depth interviews with five key informants: registration staff, IT personnel, coder, head of the medical records unit, and head of the casemix team. The findings show that in the human dimension, users lacked sufficient training and adaptation strategies. In the organizational aspect, weak coordination and the absence of standardized procedures were identified. In the technology dimension, hardware malfunctions and slow system performance significantly disrupted services. These interconnected issues reveal the need for capacity buildingan effective tool for evaluating hospital information systems, offering a structured approach to identifying and resolving performance gaps in outpatient service modules.

Cybersecurity is continuously challenged by increasingly sophisticated and dynamic cyber-attacks, necessitating advanced adaptive defense mechanisms. Deep Reinforcement Learning (DRL) has emerged as a promising approach, offering significant advantages over traditional intrusion detection methods through real-time adaptability and self-learning capabilities. This paper presents an advanced adaptive cybersecurity framework utilizing five prominent DRL algorithms: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Twin Delayed DDPG (TD3), Soft Actor-Critic (SAC), and Asynchronous Advantage Actor-Critic (A3C). The effectiveness of these algorithms is evaluated within complex, realistic simulation environments using live-streaming data, emphasizing key metrics such as accuracy (AUC-ROC), response latency, and network throughput. Experimental results demonstrate that the SAC algorithm consistently achieves superior detection accuracy (95% AUC-ROC) and minimal disruption to network performance compared to other approaches. Additionally, A3C provides the fastest response times suitable for real-time defense scenarios. This comprehensive comparative analysis addresses critical research gaps by integrating both traditional and novel DRL techniques and validates their potential to substantially improve cybersecurity defense strategies in realistic operational settings.

Automatic Identification System (AIS) is a communication technology that plays an important role in improving operational safety and efficiency in the shipping industry. AIS allows ships to exchange real-time data on identity, position, speed, and direction, which helps prevent collisions and facilitates maritime traffic management by port authorities. In addition, AIS functions in search and rescue operations by providing accurate information on the location of ships in trouble. In terms of security, AIS allows monitoring of suspicious ships, thus helping in preventing illegal activities in the waters. This study aims to design and develop a prototype AIS receiver based on LoRa, Arduino, and LCD HMI. The LoRa module was chosen because of its ability to transmit data over long distances with low power consumption, which is suitable for the maritime environment. Arduino is used as the main microcontroller to control the system, while the LCD HMI serves as the display interface for the received data. After the hardware and software design was completed, the system was tested through functional testing and performance measurements using a spectrum analyzer to evaluate the strength of the LoRa signal at various distances. The test results show that the AIS receiver is able to receive data well up to 15 meters on land and 13 meters at sea, with a delay of 100 milliseconds. System performance degrades at longer distances due to environmental interference and signal attenuation. These findings provide insight into the effective limits of LoRa communication in maritime applications and can be used as a reference for frequency testing and optimization of LoRa-based long-range communication systems.

Budget planning in Islamic educational institutions is a crucial factor in ensuring the sustainability and effectiveness of financial management. This article analyzes the strategies for preparing budget plans in Islamic educational institutions from operational and technical perspectives. Proper planning ensures the optimal allocation of resources to support the learning process, facility maintenance, and quality improvement in education. This study highlights the importance of transparency, accountability, and the use of performance-based approaches in budget management. Additionally, government regulations, stakeholder involvement, and the utilization of technology are key factors in enhancing the efficiency and effectiveness of budget planning in Islamic education. The findings of this study are expected to provide insights for Islamic education administrators in developing a more structured and sustainable financial strategy.

This research aims to analyze the impact of the merger between PT Indosat Tbk (ISAT) and PT Hutchison 3 Indonesia (H3I) on both companies. Through a merger analysis approach, we identify key factors that influence the success or failure of the integration between these two entities. The research method is literature review to search a financial information and annual reports. Qualitative and quantitative analyses are used to measure the impact of the merger on the financial, operational, and reputational performance of both companies. The research findings indicate that the merger between ISAT and H3I has a positive impact on operational efficiency and economies of scale. However, challenges related to technology integration, corporate culture, and human resource management are also identified as critical factors that need to be addressed. Furthermore, this research discusses the implications of the merger on the telecommunications market in Indonesia and its impact on industry competition. These findings provide valuable insights for business practitioners, regulators, and academics interested in the study of merger impacts in the telecommunications sector.