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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.  

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

Introduction: 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.

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 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 explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.

The loading and unloading process involves moving goods from one place to another, often requiring specialized equipment such as a wire crane. In maritime operations, unexpected incidents like wire crane breakage can occur. Wire crane breakage refers to damage in the sling wire of a ship's crane, reducing its lifting capacity and causing the wire fibres to unravel. During sea-based fieldwork on the MV. Pacific Bulk, researchers observed a wire crane failure during loading and unloading operations, which caused delays and financial losses. This study aims to identify the causes of wire crane breakage on the MV. Pacific Bulk and examine its technical and operational impacts. Using a qualitative descriptive method and fishbone analysis for data interpretation, the study found several contributing factors: wire corrosion, crane age, lack of standard operating procedures (SOPs) for crane maintenance, insufficient crew, inadequate maintenance, lack of routine inspections, and challenging environmental conditions. The research also highlights the technical and operational consequences of such failures, including delays and economic loss. To address the issue, the study suggests implementing scheduled periodic inspections following SOPs, applying regular lubrication, and maintaining data records of the wire crane’s lifespan. These preventative measures aim to improve operational efficiency and avoid future wire crane failures.

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.

Developing research and community service (P2M) applications is crucial in enhancing efficiency and accuracy in managing related data at higher education institutions. This research aims to design a web-based application that simplifies the data management process for research, community service, and associated activities at Universitas Informatika dan Bisnis Indonesia (UNIBI). The research engaged the Rapid Application Development (RAD) methodology to actively incorporate stakeholders throughout the application development lifecycle, thereby guaranteeing alignment with their requirements. The results showed that the developed Application effectively resolved inaccurate data displays, manual data collection, and inefficient validation processes. Key features include a more accurate dashboard, an automated article validation tool integrated with Google Scholar, and streamlined submission community service activities. The activity submission process enhances operational efficiency and improves transparency and accountability in managing academic data. This research contributes to the broader adoption of digital solutions in educational administration, offering significant improvements in data accuracy and management at UNIBI.

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.

Technological advancements and digital transformation have significantly impacted business management, including the consulting sector. PQM Consultants faces challenges in managing inquiries, which are still manually handled using Microsoft Excel. This manual process results in fragmented data, delays in accessing information, input errors, and difficulties in real-time monitoring of inquiry statuses. These issues affect operational efficiency, data transparency, and timely decision-making.   This research focuses on developing a web-based inquiry management platform through the Design Thinking methodology, aiming to improve the efficiency and effectiveness of inquiry handling at PQM Consultants. Adopting a qualitative case study approach, the study gathers insights through in-depth interviews, direct observations, and document reviews involving consultants and support team members who are directly engaged in the inquiry process. Research participants consist of consultants and support team members directly involved in inquiry management. The study uses a Design Thinking approach, which focuses on solving problems through repeated refinement and actively involving users throughout the development process. This framework supports the development of creative solutions by deeply understanding user needs, defining key challenges, and continuously refining ideas through prototyping and testing (Costich 2021). The Empathize stage focuses on understanding user needs and challenges. The Define stage formulates the core problems specifically. The Ideate stage generates creative ideas as potential solutions. The Prototype stage develops an initial design of the platform, which is then tested during the Test stage to gather feedback for further refinement. The results indicate that the web-based inquiry management platform effectively addresses various challenges in managing inquiry data, such as access delays, input errors, and data fragmentation. The platform enables consultants to monitor inquiry statuses in real-time, access data transparently, and make faster and more accurate decisions.

This study aims to analyze the causes of data duplication in companies, its impact on operational efficiency, and the solutions that can be implemented to prevent and address it. Using a qualitative method with a library research approach, this study examines various literature related to data management. The findings indicate that data duplication is caused by a lack of standardization, input errors, and weak integration systems. The recommended solutions include the implementation of database management technology, regular data audits, and strict data governance policies. With the right strategies, companies can enhance efficiency and ensure data accuracy in business decision-making.  

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.

This study discusses the arrangement of queue lanes in the supervision of weighing freight transportation at UPPKB Singosari to improve service efficiency. Based on the analysis of the Minimum Service Standards (SPM), three main indicators, namely compliance, conformity, and implementation, have reached 100%, while the accuracy indicator has only reached 50% due to the duration of weighing exceeding the set time limit. The main factor causing long queues is the lack of clear lane markings, making it difficult for vehicles to be directed without an officer. In addition, the parking area of 3,330.6 m² has not been utilized optimally. Improvement efforts include the implementation of queue lane markings to reduce dependence on officers, optimization of facilities such as Pos 2 which is parallel to Pos 1, and improvement of human resource management to suit operational needs. The use of Weight in Motion (WIM) technology is also recommended to reduce workload and accelerate the supervision of freight transport vehicles. By implementing this strategy, it is hoped that services at UPPKB Singosari can be more efficient, transparent, and in accordance with applicable standards.

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.

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.

Innovation is a key element in improving business competitiveness and sustainability. This study uses the Systematic Literature Review (SLR) method to analyse the relationship between resources and innovation strategies in entrepreneurship. A total of 22 Scopus-indexed articles were reviewed using the PRISMA approach, focusing on the types of resources (human, financial, technological, informational, and physical) and forms of innovation (product, process, and managerial). The results show that product innovation is largely supported by human resources and technology, where the skills and creativity of the workforce play an important role in the development of new products. Process innovation relies on technology and information, especially in production efficiency and operational digitalisation. Meanwhile, managerial innovation is closely related to human resource management and organisational strategy, which enables companies to be more adaptive to market changes. This study enriches the Resource-Based View (RBV) and Dynamic Capabilities theories, and provides insights for entrepreneurs and policy makers in developing resource-based innovation strategies. In conclusion, successful innovation in entrepreneurship depends on optimal resource utilisation and appropriate policy support to create sustainable innovation.

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.