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In organizing the MotoGP Event at Zainuddin Abdul Madjid International Airport, Lombok, a significant increase in flight traffic affects operational capacity, especially in the Apron area. The Apron Movement Control (AMC) Unit plays a crucial role in ensuring the smooth and safe movement of aircraft, vehicles, and personnel. During the event, the surge in passenger numbers and aircraft movements puts additional pressure on the AMC, making it essential to maintain operational safety and efficiency in the apron area. This study aims to determine how the handling of increased flight traffic affects the efficiency of apron space usage during the 2024 MotoGP Event and what strategies and mechanisms are implemented by the Apron Movement Control (AMC) Unit in increasing the efficiency and safety of flight operations during the 2024 MotoGP Event at Zainuddin Abdul Madjid International Airport. This study uses a qualitative method. The data used in this study are primary data and secondary data. Data collection techniques used in this study are observation, interviews and documentation. Based on the research, it can be concluded that the role of Apron Movement Control (AMC) at Zainuddin Abdul Madjid Lombok International Airport during the 2024 MotoGP Event is very vital for the smooth running of flight operations, especially in managing increased traffic. AMC is tasked with following established procedures, including aircraft parking arrangements and efficient use of apron space. To overcome the spike in traffic, AMC added personnel, increased supervision in the airside area, and coordinated with relevant stakeholders, including Airnav Indonesia. In addition, airport infrastructure was expanded and operations were improved to meet the needs during the event, ensuring that all aircraft movements were safe and orderly.

The development of the digital economy in Indonesia drives the need for a transparent and efficient transaction system. Blockchain technology is present as a creative answer to this challenge, through distributed and unmodifiable transaction recording. This study uses a literature study method by reviewing various scientific literature related to the role of blockchain in increasing transparency and security of digital transactions. The results of the study state that blockchain can strengthen the digital economy ecosystem by increasing public trust, data protection, and operational efficiency, especially in the financial and MSME sectors. However, the implementation of this technology still faces obstacles such as limited regulations, infrastructure, and digital literacy. Therefore, this study recommends a strategy for developing a blockchain-based digital economy platform through a decentralized architecture approach, smart contract integration, digital identity protection, regulatory compliance, and public education.

Artificial Intelligence (AI) has become an essential tool in the world of management for decision-making. This article examines the ways in which AI can be used to improve the quality and speed of decision-making, and how AI can improve the operational efficiency of companies. In addition, this article also examines the challenges and opportunities that companies face in adopting AI.In the rapidly evolving digital era, AI has become an essential component of modern business strategies. Today's managers are often faced with the challenge of analyzing very large and complex volumes of data. To make good and timely decisions, AI offers a potential solution with fast and precise data analysis capabilities.The use of AI in decision-making involves machine learning algorithms and models to efficiently process and analyze large amounts of data. This helps managers gain deeper and more accurate insights, enabling more effective decision-making.

Management of medical Hazardous and Toxic (B3) waste is a major challenge in the health and environmental fields. PT Ubersari Kertalangu, as a company operating in the waste and environmental management sector, requires a structured company architecture system to support B3 medical waste management activities safely, efficiently and in accordance with regulations. This research designs a TOGAF-based corporate architecture planning method that is integrated with the ISO 31000 risk management approach. The results show that this integrative model is able to increase waste tracking accuracy, mitigate environmental and legal risks, as well as company operational efficiency..    

This study aims to analyze the impact of the transition from manual to electronic (e-Filing) annual tax return (SPT) reporting on the efficiency of tax administration in Indonesia. Indonesia adopts a self-assessment system, granting taxpayers full trust to report and calculate their own tax obligations. However, manual SPT reporting often leads to challenges such as slow processes, human error risks, and high administrative burdens. To address these issues, the government has implemented an electronic filing system (e-Filing) as part of its tax service modernization efforts. This research employs a qualitative library research method by reviewing literature, regulations, and previous relevant studies. The findings indicate that e-Filing significantly enhances administrative efficiency, accelerates processes, reduces operational costs, and improves data accuracy and security. The implications of this research highlight the importance of optimizing socialization, training, and further development of the e-Filing system to encourage more taxpayers to shift to electronic reporting, thereby fostering a more modern, transparent, and accountable tax administration.

The Occupational Safety and Health Management System (SMK3) has an important role in creating a safe and productive work environment, especially in the manufacturing industry which has high risk potential. This research uses a literature study approach to evaluate the concept, implementation, and impact of SMK3 on operational performance. The results show that consistent implementation of SMK3 can reduce the number of work accidents, improve the efficiency of the production process, and encourage the creation of a positive safety culture. However, several obstacles are still faced, such as low awareness and compliance with safety procedures and weak supervision. Therefore, the active involvement of all parties, from management to employees, as well as regulatory support from the government is needed to ensure the effectiveness of SMK3 in the long term and improve industrial competitiveness.

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.

This study aims to optimize sales profit at the MSME Batagor Bandung Tarakan by applying the Simplex method in linear programming, supported by the POM-QM for Windows software. Profit optimization plays a crucial role in strategic decision-making, particularly in determining the optimal daily production quantities to enhance operational efficiency, maximize the use of raw materials, and meet market demand effectively. The Simplex method was chosen for its ability to solve linear optimization problems involving multiple constraints and variables, providing accurate mathematical solutions to achieve maximum profit. This research utilizes daily production data as the basis for analysis, which is then processed using the Simplex method in POM-QM. The results show that implementing the Simplex method can increase daily profits from IDR 634,000 to IDR 831,408 through a more efficient production combination. The study concludes that the Simplex method, when applied via the POM-QM software, is an effective tool for daily production decision-making and can support MSMEs like Batagor Bandung Tarakan in improving their competitiveness and business sustainability.

This research aims to develop and implement the design and front-end transition of a web-based admin dashboard to facilitate information access and support digitalization at PT Kalbe Farma Tbk, particularly in managing the weekly production schedule. The main issue faced is that the preparation of weekly production schedules at PT Kalbe Farma Tbk is still conducted manually using paper and spreadsheets, making it prone to errors and inefficiency. Established in 1966, PT Kalbe Farma Tbk has become a leader in Indonesia's pharmaceutical industry, with a strong commitment to innovation and quality. In addressing modernization challenges, the company seeks to enhance operational efficiency through advanced technology implementation. The platform was developed using the Laravel framework and Tailwind CSS, with PostgreSQL as the database. Key features of the platform include production scheduling, approval submissions, real-time schedule adjustments, and informative data visualization. This research focuses on implementing interface design, transitions, and page routing to improve employee productivity, expedite schedule approvals by production managers, and simplify schedule adjustments by warehouse staff, while reducing errors and improving information accuracy. The methodology used in this study is Rapid Application Development (RAD), which allows for rapid and flexible development with user involvement at every stage. The research also addresses system architecture, implementation processes, and the integration of key features, as well as testing to ensure the platform is accessible across various devices and browsers. The results of this research are expected to significantly contribute to improving operational efficiency and the competitiveness of PT Kalbe Farma Tbk in the pharmaceutical industry.

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.

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.  

 The purpose of this study is to analyze the implementation of Database Management System (DBMS) in improving the operational efficiency of companies. This study uses a qualitative research method through a literature review, where various academic sources and literature are analyzed to identify the benefits, challenges, and impacts of DBMS implementation on operational efficiency. The results indicate that DBMS helps in structuring data management, enhancing data security, and accelerating decision-making processes. Future research is suggested to conduct empirical studies with a quantitative approach to measure the impact of DBMS implementation more specifically across various industrial sectors.

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 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.