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This research aims to analyze the influence of the application of information technology on increasing the efficiency of business processes in the banking sector. In an increasingly competitive and dynamic business environment, operational efficiency is one of the keys to success for banks. The application of information technology through various digital systems, such as Internet Banking, Mobile Banking, and cloud computing-based platforms, has been proven to speed up transactions and reduce operational costs. Apart from that, automating business processes through ERP and CRM also increases accuracy and makes data-based decision making easier. However, the implementation of this technology faces challenges, such as limited technical skills and organizational cultural resistance. However, proper use of IT can increase productivity, customer satisfaction and strengthen the bank's competitive position. This research provides insight into the importance of IT investment in supporting digital transformation and operational efficiency in the banking sector.

Digital transformation in village governance has become a primary strategy for enhancing efficiency and effectiveness in human resource (HR) management. This study aims to analyze the implementation of digital technology in village government HR management and its impact on productivity and administrative efficiency. Employing a systematic literature review method, this research examines implementation trends, success factors, challenges, and optimal strategies for digitalization in HR management at the village level. The findings indicate that the adoption of technologies such as electronic Human Resource Management (e-HRM), cloud computing, and village information systems contributes to faster administration, increased accountability, and optimized performance of village officials. However, challenges remain in technology adoption, including low digital literacy among village officials, limited information and communication technology (ICT) infrastructure, as well as regulatory and policy constraints. To overcome these hurdles, a comprehensive strategy is required, encompassing capacity building through training, strengthening digital infrastructure, and fostering collaboration among central government, village governments, and the private sector. With appropriate policy implementation, the integration of digital technology in village-level HR management can improve public service quality and support more modern and responsive governance.

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Optimizing Windows-based operating systems for databases in Cloud and Edge Computing environments is very important to increase data management efficiency and performance. In this study, we discuss ways to optimize Windows in both environments. In the Cloud environment, the main focus is on data management using services such as Google Drive, while in the Edge environment, optimization is carried out using Windows IoT Core and Windows IoT Enterprise. Of these two, we optimize Windows IoT Enterprise more because it is more directed towards Edge computing. Our research results show that by implementing the right optimization and efficient performance results, database performance can be significantly improved, which provides great benefits for individual users and society

Technological developments in the digital world have now begun to increase the use of operating systems such as Android which are often used on electronic devices such as smartphones and tablets, penetrating various fields of human life. The use of these devices is essential in facilitating various tasks, especially digital ordering and transactions. This integration provides better transaction protection, more effective usage, and benefits and convenience. Cloud computing makes it easy and efficient to integrate digital ordering applications into payment systems. Observations show that these integrations provide user convenience and purchase trends, assist marketing strategies, and generate more accurate business decisions. If carefully developed and implemented, this integration has great potential to change the way business is done in the digital age.    

This paper introduces a real-time air quality monitoring system based on the Internet of Things (IoT) integrated with advanced data analytics. The system uses low-cost sensors to gather data on air pollutants, which are then processed using cloud computing and visualized through a web application. Field tests conducted in urban and suburban areas demonstrate the system’s effectiveness in providing accurate, real-time air quality information, enabling timely responses to pollution events.  

Industrial revolution 4.0 is a continuation of the industrial development process that is taking place. Progress in this era is marked by the integration of Cyber ​​Physical Systems (CFS), industrial wireless networks, and other supporting technologies. Utilization of artificial intelligence, robotics and the Internet of Things (IoT) to create interconnected intelligent systems. With the existence of intelligent systems and smart machines that are connected to each other, automation, blood exchange in the production process, combining physical connection networks with cloud computing through increasingly sophisticated internet connectivity has implications for people's daily lives. Advances in information technology have brought many changes to society's daily life. The use of information technology has become a digital culture in today's society. The aim of this research is to answer the research question, namely to find out the application of digital culture in several fields including organizations, the social sector and the educational sector. And how to implement digital culture in this field, whether using applications, social media and information technology devices. This research method uses a Systematic Literature Review, by looking for journal articles and proceedings published between 2020 and 2024. The mapping results show that digital culture is widely applied in the organizational, social and educational fields by implementing applications, social media and technological devices. information.  

This journal is based on For many years, the manufacturing industry has been one of the industries that contributes to the value of the Indonesian economy, with a stable growth rate. In fact, according to a 2017 World Bank report, Indonesia was able to rank highest in ASEAN for the contribution of the manufacturing sector to the world economy with a contribution of 20.5%, The method used in this writing uses a causality quantitative research method, especially using literature materials as a source of research data or also called (Library research).the results of this study conclude that digital transformation has become a necessity for manufacturing to be able to compete in the digital era. By adopting technologies such as IoT, robotics, analytics, and cloud computing, manufacturers can improve the efficiency, productivity, and quality of their products.

This article explores how cloud computing solutions enhance operational efficiency, scalability, and cost-effectiveness in small and medium enterprises (SMEs). By examining case studies from various industries, the research highlights the transformative role of cloud platforms in driving innovation, improving collaboration, and expanding market reach. The findings underscore the potential of cloud technologies in accelerating SME growth, while also addressing challenges such as adoption costs, technical expertise, and data security concerns.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.

This paper explores the relationship between digital transformation and entrepreneurial innovation within emerging economies. As the world becomes increasingly digitized, entrepreneurs are leveraging new technologies to innovate and create disruptive solutions. The study examines how digital tools, such as artificial intelligence, blockchain, and cloud computing, are being utilized by entrepreneurs to enhance productivity, streamline operations, and develop new business models. The research highlights key success factors, challenges, and opportunities faced by entrepreneurs in emerging markets and provides recommendations for integrating digital transformation into entrepreneurial strategies.