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Digital transformation through adoption cloud technology has become catalyst in effort efficiency energy and reduction greenhouse gas emissions glass (GHG). Research This aim for analyze contribution cloud technology against efficiency operational and impact the environment in framework economy green. With use approach studies literature and secondary data analysis from report institution international and journals scientific, research This find that migration to cloud computing can reduce consumption energy up to 84% and emissions carbon up to 88% compared to with traditional IT infrastructure. These results show that cloud computing is not only solution technology, but also important strategy in support development sustainable.

Digital transformation has emerged as a major catalyst for reform in contemporary public administration, reshaping how governments design, deliver, and evaluate public services. This literature review synthesizes key findings from international studies to map the dynamics of technological innovation and bureaucratic adaptation in the era of digital government. The results demonstrate that technologies such as artificial intelligence, blockchain, cloud computing, and the Internet of Things accelerate administrative processes, enhance accuracy, reduce service costs, and strengthen transparency and accountability. However, the review also emphasizes that technological advancement alone is insufficient; the success of digital transformation depends on the capacity of public institutions to reorganize work structures, build digital competencies, and shift bureaucratic culture toward more adaptive and collaborative practices. Furthermore, digital participation platforms have expanded opportunities for citizen engagement, yet persistent digital divides—driven by socio-demographic disparities and unequal access to infrastructure—pose significant challenges to inclusive participation. The literature also reveals recurring barriers related to infrastructure readiness, cybersecurity, resistance to change, and limited digital literacy among public employees. Cross-country evidence from Turkey, Singapore, Italy, Iran, and the UAE shows similar transformation patterns, highlighting bureaucratic adaptation as a mediating factor between technological innovation and governance outcomes. Overall, this review offers an integrated conceptual understanding of digital transformation in public services and underscores the need for holistic strategies that combine technological investment, organizational reform, and inclusive governance to ensure sustainable and equitable digitalization.

The integration of cloud computing and data security systems is vital for the operational success and competitiveness of fintech startups. Cloud computing enables these startups to scale quickly, manage resources efficiently, and reduce infrastructure costs, making it an indispensable tool for businesses in the rapidly evolving fintech sector. However, with the benefits come significant challenges, particularly in data protection and cybersecurity. As fintech services handle sensitive financial data, ensuring robust security measures such as encryption, access controls, and continuous monitoring is crucial to maintaining user trust. Furthermore, regulatory compliance, both local and global, adds complexity to the data protection strategies of fintech companies. This research explores the key factors that drive cloud adoption in fintech, the security challenges associated with cloud environments, and the strategies implemented by startups to address these challenges. Interviews with IT managers from Indonesian fintech startups reveal that while cloud computing offers scalability and cost-effectiveness, issues like compliance with local regulations and the protection of sensitive data remain major concerns. The research suggests that fintech startups should invest in both cloud infrastructure and advanced cybersecurity measures to protect their operations and customer data. Additionally, creating a comprehensive roadmap for regulatory compliance and fostering partnerships with cybersecurity firms will help mitigate risks and ensure long-term success. The findings highlight the importance of integrating cloud computing with effective security strategies to navigate the complex regulatory and security landscape of the fintech industry.

This study aims to analyze the information technology (IT) infrastructure of PT RedEx using SWOT and McFarlan Strategic Grid methods to evaluate internal and external factors affecting IT performance and its strategic positioning. The SWOT analysis identifies strengths, weaknesses, opportunities, and threats, while the McFarlan Grid maps IT systems into four quadrants: Strategic, High Potential, Key Operational, and Support. Data were collected through observation, interviews, and documentation. The results show that PT RedEx’s IT infrastructure supports operational activities but lacks real-time integration across branches. Based on the McFarlan analysis, most systems are in the Key Operational and Support quadrants, indicating a focus on daily operations rather than strategic innovation. Recommendations include system integration through cloud computing, digital transformation, and IT human resource development. The study provides insights into strategic IT planning for logistics companies in the digital era.

This study aims to formulate an information technology development strategy for CV. Situsindo Prima using the VRIO (Value, Rarity, Imitability, Organization) and SWOT (Strengths, Weaknesses, Opportunities, Threats) approaches. CV. Situsindo Prima is a company engaged in Software Development and IT Consulting, focusing on providing technological solutions for both public and private sectors in Purwokerto, Indonesia. The research employs a qualitative descriptive approach by utilizing secondary data obtained from the company profile, literature reviews, and previous studies relevant to strategic information systems management. The results indicate that the company possesses several competitive advantages derived from its competent human resources, strong project reputation, and well-established relationships with public institutions. The VRIO analysis reveals that these resources have significant strategic value and potential for sustainable competitive advantage if supported by an effective organizational system. The SWOT analysis further identifies that internal strengths can be leveraged to capture external opportunities, such as the increasing demand for digital transformation and government support for technological innovation. The formulated strategies include developing a knowledge management system, implementing Service Level Agreements (SLA) for after-sales services, innovating products based on cloud computing and Internet of Things (IoT), and enhancing human resource capabilities through training and certification programs. In conclusion, this research successfully achieved its objectives by producing a comprehensive and applicable IT development strategy for CV. Situsindo Prima. The findings are expected to serve as a reference for strategic planning and strengthening competitive advantage within the digital transformation era.

In the modern computing era, servers face significant challenges in data storage due to hardware failures, cyber attacks, or human errors. The problem highlighted focuses on the impact of file systems on three critical aspects: data integrity (accuracy and consistency of data without corruption), data recovery (the ability to restore data after a failure), and failure resilience (fault tolerance, such as redundancy and journaling to prevent downtime). The main issue is that traditional file systems like FAT32 or NTFS are often susceptible to fragmentation, metadata loss, or long recovery times, which can lead to data loss of up to 20-30% on enterprise servers, especially in high-traffic environments like cloud computing.A simple problem-solving process is conducted through a straightforward comparative analysis approach: (1) A literature review of popular file systems (ext4, ZFS, Btrfs); (2) Failure simulations using tools like fsck and stress testing on virtual servers (e.g., via KVM or Docker); and (3) Measuring performance metrics with benchmarking tools like Bonnie++ for I/O throughput, recovery time, and error rates. This process is designed to be simple, requiring only a virtual lab setup without expensive hardware, and is analyzed quantitatively with descriptive statistics.The solution to the problem indicates that advanced file systems like ZFS or Btrfs provide significant improvements: data integrity is up to 95% more secure through automatic checksums, data recovery is achieved in minutes through snapshots and RAID integration, and failure resilience is higher with copy-on-write features. The main recommendation is to migrate to journaling-based file systems for servers, combined with automated backups, which can reduce the risk of downtime by up to 50%. This research provides practical guidance for system administrators to enhance server reliability without excessive additional costs.

This study aims to analyse the influence of digital products, brand awareness, and service quality on perceived branch performance in a financial institution in Indonesia. The research is motivated by the accelerating digital transformation in the country’s banking industry, which requires conventional financial institutions to innovate to remain competitive amid the growth of fintech and digital banks. The main problem addressed is how these three variables simultaneously and partially affect perceived branch performance. A quantitative approach was employed using survey techniques and questionnaires distributed to customers. Multiple linear regression analysis was used to process the data. The results reveal that digital products, brand awareness, and service quality significantly influence perceived branch performance, both individually and collectively. The coefficient of determination (R²) is 0.652, indicating that the three variables can explain 65.2% of the variance in perceived branch performance. These findings highlight the importance of integrating digital innovation, strong brand presence, and excellent service in improving competitiveness and branch performance in the digital era. The study contributes practical implications for financial institutions in formulating strategic improvements to maintain relevance and competitiveness amid digital disruption.

This study aims to analyze the strategic role of Information Technology (IT) in improving operational efficiency within e-commerce companies in Indonesia. A literature review approach was employed by examining various scholarly sources, including accredited national journals and relevant books. The findings indicate that the implementation of IT such as Enterprise Resource Planning (ERP) systems, Big Data Analytics, and Cloud Computing significantly accelerates business processes, reduces operational costs, and enhances data accuracy and service quality. E-commerce companies that integrate IT into their operations are shown to adapt more effectively to market dynamics and consumer preferences. The study concludes that the use of IT is not merely a supporting tool, but a key factor in creating competitive advantage. The implications of this research offer insights for e-commerce industry players and policymakers to continuously promote digital innovation in pursuit of efficiency and business sustainability.

The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.

The integration of Internet of Things (IoT) with cloud computing has revolutionized healthcare systems, offering scalable and real-time patient monitoring. However, optimizing response times and energy consumption remains crucial for efficient healthcare delivery. This research evaluates various algorithmic approaches for workload migration and resource management within IoT cloud-based healthcare systems. The performance of the implemented algorithm in this research, Hybrid Dynamic Programming and Long Short-Term Memory (Hybrid DP+LSTM), was analyzed against other six key algorithms, namely Gradient Optimization with Back Propagation to Input (GOBI), Deep Reinforcement Learning (DRL), improved GOBI (GOBI2), Predictive Offloading for Network Devices (POND), Mixed Integer Linear Programming (MILP), and Genetic Algorithm (GA) based on their average response time and energy consumption. Hybrid DP+LSTM achieves the lowest response time (82.91ms) with an energy consumption of 2,835,048 joules per container. The outcome of the analysis showed that Hybrid DP+LSTM have significant response times improvement, with percentage increases of 89.3%, 79.0%, 83.8%, 97.0%, 99.8%, and 99.94% against GOBI, GOBI2, DRL, POND, MILP, and GA, respectively. In terms of energy consumption, Hybrid DP+LSTM outperforms other approaches, with GOBI2 (3,664,337 joules) consuming 29.3% more energy, DRL (2,973,238 joules) consuming 4.9% more, GOBI (4,463,010 joules) consuming 57.4% more, POND (3,310,966 joules) consuming 16.8% more, MILP (3,005,498 joules) consuming 6.0% more, and the GA (3,959,935 joules) consuming 39.7% more. The result of ablation of the Hybrid DP+LSTM model achieves a 47.05% improvement over DP-only (156.57ms) and a 70.64% improvement over LSTM-only (282.41ms) in response time. On the energy efficiency side, Hybrid DP+LSTM shows 22.80% improvement over LSTM-only (3,671,51 joules), but 7.34% underperformance compared to DP-only (2,640,93). These research findings indicate that the Hybrid DP+LSTM technique provides the best trade-off between response time and energy efficiency. Future research should further explore hybrid approaches to optimize these metrics in IoT cloud-based healthcare systems.

The waste detection application is a solution to identify and classify waste. With the increasing waste problem, this application aims to assist in classifying waste into organic, inorganic, and unknown categories. This research implements a waste detection application that runs on smartphones with the Android operating system. The application is the result of a capstone project from the Bangkit program in the MSIB batch 6. The development of this application is divided into several parts, namely backend, frontend, and deployment. The author focuses on the deployment process of the application using Google Cloud Platform. The Google Cloud Platform infrastructure was chosen due to its scalability and flexibility. The services utilized include Google Compute Engine (GCE), Google Virtual Private Cloud (VPC), and Google Cloud Storage (GCS). The deployment process involves creating a new project in Google Cloud, configuring virtual machines, and setting up Google Cloud Storage. The server VM configuration includes the installation of SQL Server (MariaDB), deployment of the Machine Learning API, and Backend API. Testing was carried out on the Machine Learning API using Postman and the Backend API through a browser. The results show that Google Cloud Platform can be implemented as a cloud computing infrastructure for waste detection applications. The Machine Learning API is able to read objects in the form of images sent by users.

Dr. Soedirman Kebumen Regional General Hospital has implemented PACS to replace traditional film, but limitations in IT infrastructure, RME integration, and human resource readiness require an integrated implementation model that combines cloud-hybrid, DICOM/HL7 with SSO, continuous training, and managerial support. To evaluate the implementation of the PACS system in the Radiology Department of Dr. Soedirman General Hospital in Kebumen and to analyze the factors that support and hinder the effectiveness of the PACS system in improving the quality of radiology services. The research used a qualitative approach with an interactive model. Data collection was conducted through in-depth interviews, Focus Group Discussions (FGD), direct observation, and documentation. The data obtained were analyzed using ATLAS.ti software to explain the PACS implementation and its impact on the effectiveness of radiology services. The PACS implementation improved the quality of radiology services by accelerating access to medical images and enhancing workflow efficiency. Some challenges, such as system downtime, integration with other systems, and technical limitations, need to be addressed. Integration of artificial intelligence (AI) and telemedicine technologies needs to be enhanced to achieve optimal radiology services. Factors supporting successful implementation include the adoption of advanced technologies (cloud computing and AI), adequate infrastructure, technical support from the IT team, and strong managerial commitment.  Barriers to success include imperfect system integration, power outages, downtime, storage capacity limitations, and a shortage of trained human resources. Proposed implementation models include improving PACS system infrastructure, developing ongoing training for staff, improving PACS system integration with other hospital systems, and improving interdepartmental communication to streamline workflows and reduce obstacles in the diagnostic process.

This research focuses on the development of an Android-based vegetable detection application by utilizing digital image processing technology and data communication through Application Programming Interface (API). This application is designed to make it easier for users to visually recognize different types of vegetables using the device's camera. The detection process is carried out by sending the image to a cloud server, where the image analysis process is carried out to identify the type of vegetable, displaying its name, characteristics, and benefits. The app's implementation includes an intuitive and user-friendly user interface, with key features such as login, registration, and an interactive dashboard. The dashboard displays user information, location, ambient temperature, vegetable detection history, and direct access to the camera for real-time detection processes. The utilization of cloud computing technology not only keeps application performance lightweight and responsive, but also enables high processing efficiency and data scalability. This allows the application to continue to evolve according to the increasing number of users and incoming data. Image processing is done with machine learning algorithms that are trained to recognize the shape, color, and texture of different types of local vegetables. In addition, this system is also equipped with a periodic data update feature to be able to adjust to the development of new vegetable classifications. The test results show that the app is able to recognize different types of vegetables with a high level of accuracy, as well as provide additional relevant information quickly and accurately. Tests are carried out on a variety of lighting and background conditions to ensure the reliability of the system. The success of the development of this application reflects the integration of modern technology in supporting the digital agriculture sector.

The era of the Industrial Revolution 4.0 and Society 5.0 brings significant changes in various aspects of life, including education. Islamic educational institutions, as vital entities in shaping the character and knowledge of younger generations, are required to undergo digital transformation to improve governance and management quality. Management Information Systems (MIS) serve as a primary tool that can integrate administrative processes, data management, and decision-making effectively, efficiently, and accountably. This study discusses the optimization of MIS in strengthening the governance of Islamic educational institutions, focusing on the challenges and opportunities faced in this era of advanced technology. The research method employed is qualitative descriptive, reviewing literature and previous studies concerning MIS and the management of Islamic educational institutions amidst digital technological dynamics. The findings indicate that optimizing MIS demands developing human resource capacities, improving technological infrastructure, and fostering an organizational culture adaptive to digital change. Although challenges such as infrastructure limitations, cultural resistance, and data security issues exist, opportunities arising from technologies like cloud computing and big data pave the way for more modern and competitive institutional management. Strengthening governance through MIS not only enhances efficiency but also supports accountability and transparency in line with Islamic values that emphasize honesty and responsibility. Thus, optimizing MIS is a key strategy to ensure Islamic educational institutions can adapt and thrive in the Industrial Revolution 4.0 and Society 5.0 era.

Digital transformation is one of the essential needs in the development of sharia cooperatives in the Industrial Revolution 4.0 era. Rapidly developing information technology requires microfinance institutions such as cooperatives to adapt, in order to remain competitive, efficient, and relevant in meeting the needs of their members. Two main technologies that play an important role in this transformation are big data and cloud computing. This article aims to examine in depth how the use of big data and cloud computing can support the digital transformation process in sharia cooperatives. Using a literature review approach from various scientific sources, this article analyzes the basic concepts, potential benefits, case studies, and challenges of implementing both technologies in the context of sharia-based cooperatives. The results of the study show that big data can improve data-based decision making, risk management efficiency, and personalization of services to members. Meanwhile, cloud computing provides flexibility, cost efficiency, and accessibility of cooperative information systems widely, even in areas with minimal IT infrastructure. However, the success of implementing this technology is greatly influenced by the readiness of human resources, digital infrastructure, and compliance with sharia principles. This article also provides practical recommendations and a further research agenda as a contribution to the development of technology-based sharia cooperatives.    

Digital transformation in Information Technology (IT) governance has become a crucial aspect in improving the efficiency of public services, particularly within the Department of Community and Village Empowerment, Population, and Civil Registration of North Sumatra Province. This study aims to analyze the implementation of digital transformation in IT governance using the COBIT 2019 framework. The research method includes the analysis of regulations, the role of IT, procurement models, implementation methods, and technology adoption strategies applied by the department. The findings show that IT implementation is predominantly strategic in nature, supporting the digitization of population services and enhancing data transparency. The IT procurement model comprises a combination of outsourcing (30%), cloud computing (30%), and insourcing (40%) to balance efficiency and system control. Agile methodology is the most dominant implementation method (50%), followed by DevOps (35%) for maintenance and traditional approaches (15%) for more structured projects. The department primarily adopts a "follower" technology adoption strategy (75%), reflecting a selective approach to digital innovation. Based on COBIT 2019 evaluation, the BAI (Build, Acquire, and Implement) domain is the main focus, with high scores in solution identification and improvement management (90) and change management (100), indicating the department’s readiness to adopt digital systems. However, challenges remain in information security, inter-agency data integration, and human resource readiness. The digital transformation of IT governance at the department has been systematically implemented, supporting the improvement of population service efficiency. Enhancements in security, infrastructure, and the strengthening of IT governance policies are necessary to optimize and sustain digital transformation implementation.

The advancement of networking and communication technologies has escalated the IoT and edge computing integration by leaps and bounds. This paper provides a review of the current trends, technologies and issues concerning IoT and edge computing. Looking at the key application areas of smart cities, healthcare, industrial IoT and smart grids this paper demonstrates how edge computing solves the problems of cloud computing such as latency, bandwidth and privacy. The survey also reveals present day constraints such as the lack of standards for scalability, integration problems, and lack of strong data protection measures. This work can be considered as a reference source for researchers and practitioners since it reveals the relationship between IoT and edge computing and gives an idea of further advancements.

As industries increasingly integrate cloud-based Industrial Control Systems (ICS), the cyber threat landscape expands. While cloud computing offers scalability, cost efficiency, and remote accessibility, it also introduces security vulnerabilities that adversaries can exploit. This study explores AI-driven threat detection models, encryption techniques, and best practices to enhance ICS resilience. Key security measures, including intrusion detection systems, anomaly detection, and robust encryption mechanisms, are analyzed to mitigate cyber risks. The findings highlight the effectiveness of AI-based security solutions in identifying and preventing attacks, ensuring the reliability and integrity of ICS in cloud environments.  

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

The term "cloud computing" refers to the delivery of numerous services via the Internet. Because of providers that provide cloud services, users can store files and media on faraway servers and subsequently access the items online. Due to the lack of location requirements, this enables anyone to watch them remotely. This design is currently beginning to be implemented in the sphere of education, particularly in universities. In order to determine the factors influencing the adoption of cloud computing in education, previous academics have employed the technology adoption theory, which is discussed in this study. The purpose of this study, which is a systematic literature review, is to identify and assess knowledge gaps about the factors impacting the adoption of cloud computing in education. To fill in any gaps and add to the body of knowledge already in existence, this study will therefore enhance the information by offering a complete examination of the material that is already available.