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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.    

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.

Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. This research aims to classify satellite image land cover in the Trenggalek Regency area, East Java. The level of accuracy in this study uses a confusion matrix. The accuracy test results show a value of 90.23%.

Background: The rapid advancement of Industry 4.0 has accelerated the integration of digital technologies such as the Industrial Internet of Things (IIoT), edge computing, and Digital Twin systems in smart manufacturing environments. However, many existing implementations remain fragmented and heavily dependent on centralized cloud infrastructures, resulting in latency constraints, limited scalability, and suboptimal real-time decision making. Objective: This study aims to develop and validate an integrated edge based Digital Twin optimization framework that combines IIoT sensing, hybrid edge cloud architecture, and reinforcement learning based adaptive control. Methods: The research adopts a multi phase design consisting of framework development, simulation based validation, and industrial pilot implementation. The proposed system integrates real time data acquisition, localized edge processing, Digital Twin synchronization, and intelligent optimization mechanisms to enhance operational efficiency. Results: The findings demonstrate significant performance improvements compared to conventional cloud based systems, including substantial latency reduction, increased production throughput, reduced downtime, and improved energy efficiency. Scalability and robustness testing further confirm that distributed edge intelligence enhances system resilience under increased workloads and network disruptions. These results indicate that integrating edge computing with Digital Twin modeling and reinforcement learning provides a scalable, responsive, and energy efficient solution for next-generation smart factories.

In this paper, a variety of data integration, cloud computing, and web services-related topics were discussed. The conversation covered the advantages of using cloud-based methods for cloud-based data integration, such as enhanced data accuracy and completeness, as well as the challenges and considerations that must be addressed. In addition, the significance of document integrity and the use of auto-enhance document tools to ensure data accuracy were emphasized. The paper also provided a broad overview of the subject, touching on a variety of aspects and providing insights into the potential of cloud-based data integration methods in the cloud industry.

Technology has undergone significant and rapid change with the transition from self-managed data centers to visualized infrastructure. Data archiving has a very important role because it can make it easier to process data quickly and accurately. Web-based information systems have become an integral part of digital transformation in various industrial sectors. By providing centralized access to computing resources, data storage, and applications over the internet, the cloud provides significant freedom and flexibility. Web-based information systems bring great benefits to organizations that successfully adopt them. While challenges such as security and regulatory compliance need to be overcome, the opportunities for innovation and operational efficiency make cloud-based information systems a strategic choice in a company's digital transformation roadmap. Several institutional companies and individuals have used web-based archiving but it has not been effective. For this reason, a good archiving system is needed that makes it easier to search and process data. Web-Based Archiving Management Information System which will provide convenience in processing data to be more effective and accurate. Apart from that, it can also save time and better manage reports and decision making within the Company.

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.

Perkembangan ekonomi modern yang pesat telah didorong oleh inovasi digital yang terus berkembang, terutama dalam konteks e-bisnis dan ekosistem Ekonomi 5.0. Dengan menggunakan analisis literatur dan studi kasus untuk mengeksplorasi peran inovasi digital dalam membentuk paradigma baru dalam ekonomi saat ini. Inovasi digital, seperti kecerdasan buatan, Internet of Things, analisis big data, dan cloud computing, telah memungkinkan terciptanya ekosistem bisnis yang lebih terhubung, adaptif, dan responsif. Melalui penggunaan teknologi ini, perusahaan dapat meningkatkan efisiensi operasional, memperluas pasar, dan meningkatkan pengalaman pelanggan. Namun, seiring dengan manfaatnya, inovasi digital juga menghadirkan sejumlah tantangan, termasuk ketidakpastian teknologi, masalah keamanan data, dan kesenjangan keterampilan karyawan. Dalam konteks Ekonomi 5.0, inovasi digital bukan hanya tentang meningkatkan produktivitas dan profitabilitas, tetapi juga tentang meningkatkan kesejahteraan manusia secara keseluruhan. Oleh karena itu, untuk memperkuat ekosistem Ekonomi 5.0, perusahaan harus terus mendorong inovasi digital, berinvestasi dalam pengembangan keterampilan digital karyawan, dan menjaga keamanan data sebagai prioritas utama.

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.

The rapid growth of cross-platform applications has significantly increased the volume and diversity of sensitive user data processed across heterogeneous and distributed environments. Personally identifiable information, device identifiers, behavioral data, and financial information are routinely collected to support personalization, analytics, and service optimization. While these practices enhance application functionality and user experience, they also introduce substantial privacy risks, including unauthorized data access, device fingerprint–based re-identification, cross-user data leakage, and large-scale data breaches. These risks are further amplified by distributed processing architectures and extensive third-party library integrations commonly used in modern cross-platform systems. This study aims to systematically analyze privacy issues in cross-platform applications by examining the types of sensitive data involved, identifying dominant privacy threats, and reviewing state-of-the-art privacy-preserving mitigation strategies. A systematic literature-based methodology was employed, focusing on recent Scopus-indexed journal articles, conference papers, and book chapters. The analysis synthesizes findings using thematic categorization and a conceptual research framework that maps sensitive data sources to privacy threats and corresponding mitigation mechanisms. The results indicate that privacy risks in cross-platform applications originate not only from external attacks but also from internal architectural weaknesses, such as flawed authorization logic and excessive data sharing across system components. Privacy-preserving techniques including differential privacy, federated learning, blockchain-based data governance, secure multi-party computation, and fine-grained access control mechanisms are shown to provide stronger privacy guarantees compared to conventional centralized approaches. However, these techniques also present trade-offs related to system complexity and performance. Overall, the study highlights the importance of adopting a multi-layered, privacy-by-design approach to ensure sustainable, trustworthy, and regulation-compliant cross-platform application development.

Cloud computing has revolutionized data storage and management; however, security and privacy concerns remain critical challenges. This paper explores the integration of blockchain technology to enhance data security in cloud environments. A decentralized approach ensures data integrity, authentication, and tamper resistance. The proposed framework leverages smart contracts to enforce access control policies efficiently. Simulation results indicate that our model significantly improves data security without compromising system performance.

Cloud computing has revolutionized data storage and management; however, security and privacy concerns remain critical challenges. This paper explores the integration of blockchain technology to enhance data security in cloud environments. A decentralized approach ensures data integrity, authentication, and tamper resistance. The proposed framework leverages smart contracts to enforce access control policies efficiently. Simulation results indicate that our model significantly improves data security without compromising system performance.

This webinar discusses the importance of technological innovation in accounting information systems to enhance the efficiency and effectiveness of financial management among Micro, Small, and Medium Enterprises (MSMEs). By leveraging technologies such as cloud computing and integrated accounting information systems, MSMEs can improve transparency, accuracy, and speed in financial reporting, ultimately supporting better strategic planning and decision-making. The findings from this webinar reveal that many MSMEs still rely on traditional methods to assess financial health, which are inadequate to face the current economic challenges. The speakers emphasized the importance of good risk management and effective branding strategies to achieve sustainable business growth. By adopting new technologies and implementing prudent financial management, MSMEs can maintain their competitiveness in the dynamic global market.

Dalam mengatasi masalah rendahnya kesadaran masyarakat dalam pemeriksaan kesehatan gigi dan mulut, machine learning dapat menjadi solusi praktis untuk mengatasi masalah tersebut. Solusi yang dapat diberikan berupa aplikasi yang dapat mengklasifikasikan dan mendeteksi secara dini gambaran penyakit gigi dan mulut. Ada banyak jenis pendekatan yang dapat digunakan untuk melakukan klasifikasi dan deteksi, namun yang paling banyak diteliti dan diterapkan adalah metode convolutional neural network yang merupakan salah satu dari beberapa jenis metode dari algoritma deep learning. Komputasi tanpa server dapat dilakukan dalam lingkungan yang terisolasi, ini dapat digunakan untuk menguji beberapa hipotesis secara paralel yang menguntungkan bagi pengembang, tetapi tantangan tetap ada pada komputasi tanpa server seperti jangka waktu dan kapasitas memori yang perlu ditangani, tetapi untuk saat ini komputasi tanpa server tampaknya telah menjadi alternatif yang layak untuk model proses pelatihan.

Designing and Implementing Cloud Computing Based Information System Services Software As A Service for MSMEs in Central Java. Able to create and apply Cloud Computing Based Information Services Software As A Service to MSMEs in Central Java. Design and Implementation of Information System Services Based on Cloud Computing Software as a Service for MSMEs in Central Java. The use of cloud computing software as a service for MSMEs can work according to its objectives because users do not need to make significant investments and only pay according to their needs.