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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.

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.

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.

The construction industry faces major challenges in managing material inventory, particularly among start-up companies that still rely on paper-based manual records. Such practices often lead to data inconsistencies, delays in decision-making, and project inefficiencies. This study aims to analyze the implementation of a cloud-based and real-time inventory management system utilizing spreadsheets at PT X, a start-up contractor located in Karanganyar, Central Java. The research employs a qualitative case study approach, with data collected through direct observation, semi-structured interviews with finance staff, logistics administration staff, and the project manager, as well as documentation of material inflows and outflows. The findings reveal that the use of cloud-based spreadsheets enhances data transparency, facilitates real-time monitoring between field and office, and accelerates stock opname validation. The system also supports more responsive decision-making regarding material reordering and request postponements. Nevertheless, several obstacles remain, including limited digital literacy among staff, potential input errors, and reliance on internet connectivity. Theoretically, this research contributes to the literature on accounting information systems and inventory management in small-scale construction sectors. Practically, it demonstrates that low-cost cloud solutions can improve operational efficiency and serve as a foundation for developing more integrated systems in the future.

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.

       The rapid development of information technology has brought significant changes in the management of administrative processes in the field of education, including the process of student learning assessment. This study aims to design and develop a cloud-based report card application to assist Inpres Bhoanawa 1 Elementary School in managing academic data more effectively and securely. The system was developed using the PHP Native programming language and MySQL database, with a waterfall system development approach. The application has three main levels of access: school administrators, homeroom teachers, and subject teachers. School administrators are responsible for entering student data and granting access to homeroom teachers; homeroom teachers are responsible for entering learning objectives, student grades, and printing report cards; while subject teachers are responsible for entering formative and summative assessments as well as learning objectives. Cloud-based storage was chosen to ensure data security and availability, allowing access anytime and from any device connected to the internet.       The test results show that the system functions properly according to the design, features a simple interface, and is easy to operate even by users with limited technological skills. The implementation of this system can speed up the report card creation process, reduce the risk of data loss, and minimize paper usage, making it more environmentally friendly. In conclusion, this cloud-based report card application serves as a practical solution for elementary schools in managing student assessments while improving the efficiency of educational administration. Keywords: report card application, cloud computing, PHP, MySQL

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.

This study conducts a systematic qualitative literature review to examine the transformation of banking from basic digitisation to full digital banking through the multilevel integration of Industry 4.0 technologies. Synthesising peer-reviewed research across information systems, banking, and innovation studies, the review analyses how technologies such as artificial intelligence, big data analytics, cloud computing, blockchain, and platform architectures reshape banking at the micro (customer), meso (organisational), and macro (industry ecosystem) levels. The findings show that digital banking transformation is a non-linear, socio-technical process in which technological adoption generates value only when complemented by dynamic organisational capabilities, governance mechanisms, and supportive regulatory frameworks. The review further highlights the growing importance of platformisation, open banking, and fintech collaboration in redefining competitive boundaries and value creation in the banking sector. By integrating multilevel perspectives and established conceptual frameworks, this study advances theoretical understanding of digital banking transformation and offers insights for managers and policymakers navigating the transition toward full digital banking.

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.

Digital image processing is a rapidly developing branch of computer science and has many applications in everyday life. One of the fields that most often utilizes this technique is object detection and color identification in images and videos. This study specifically aims to implement the thresholding method in the HSV (Hue, Saturation, Value) color space to detect three basic colors, namely red, green, and blue, in digital images. The research process begins with uploading images using the Google Colab platform, a cloud-based computing environment that makes it easy for users to run Python programs without requiring additional software installation. After the image is uploaded, the next step is to convert it from the RGB (Red, Green, Blue) color space to the HSV color space. This conversion is important because the HSV color space is more suitable for use in the color segmentation process. The Hue value represents the type of color, Saturation shows the level of saturation, while Value describes the level of brightness. Once the image is in the HSV color space, the next step is to determine the HSV value range for each basic color. This range is determined based on experimental results and references from related literature. Using this range, masking is performed to extract the appropriate pixels so that only the red, green, or blue portions of the image are visible, while the other colors are reduced. The results show that the thresholding method in the HSV color space is capable of detecting primary colors with a good level of visual accuracy, especially in simple images with contrasting backgrounds. The implementation of this program is relatively lightweight, easy to run directly in Google Colab, and does not require high-spec hardware. Therefore, this method is very suitable for use as basic learning material for digital image processing, both for students and novice researchers.

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.

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.

Growth rapid computing cloud, especially on academic, government, and service platforms. public, has trigger improvement frequency and complexity Distributed Denial of Service (DDoS) attacks. Intelligent DDoS attacks AI based capable copy pattern Then cross user valid, so that difficult detected and mitigated. The majority approach mitigation moment This nature reactive, no scalable, and tends to sacrifice availability service for authorized users. Research​ This aiming develop architecture proactive and adaptive defense​ For ensure continuity service during attack ongoing. Security model proposed hybrid​ integrating Zero Trust Architecture (ZTA), adaptive bandwidth control, and isolation service container -based. Architecture consists of from three layer Main: (1) ZTA Policy Engine which performs verification identity and assessment behavior through tokens and policies intelligent; (2) Adaptive Bandwidth Load Balancer which automatically dynamic separate and arrange Then cross based on reputation and level trust ; and (3) Containerized Service Cluster which groups request to in different containers For user trusted and not known . Components addition such as blockchain -based smart contracts are used For recording request and verification access , as well as lightweight AI module used for profiling then cross in real-time. Simulation results show that this model succeed increase availability service for user trusted during attack , press false positive rate , as well as optimize allocation source power. Integration of zero trust policies with intelligence Then cross and segmentation service in real-time forming framework effective and scalable defense​ to modern DDoS threats . In conclusion , the study This contributes a robust , adaptive , and modular architectural model for maintain continuity cloud services in condition network at risk .

The digitalization of Micro, Small, and Medium Enterprises (MSMEs) has become a crucial strategy for enhancing competitiveness in the digital economy era. This study examines the utilization of digital technology by MSMEs in Bandung City, as well as the challenges and opportunities faced during the digital transformation process. Employing a literature review approach and the Systematic Literature Review (SLR) method, this research identifies the impact of the digital economy on MSMEs, including the use of cloud computing for data storage and analytics to understand consumer behavior. Despite challenges such as digital literacy gaps and budget constraints, support from the government through initiatives like the UMKM Go Digital Movement and Bandung Smart City has accelerated digitalization efforts. These programs provide significant training and assistance, with over 5,000 MSMEs receiving digital training and reporting an average revenue increase of 30%. The study concludes that collaboration among government, academia, and the private sector is essential to ensure the sustainability of MSME digital transformation. Policy recommendations are also suggested to support MSMEs in adopting digital technology more effectively

The advancement of digital technology has revolutionized the way companies manage their business operations. Operational management is no longer limited to process efficiency, but has evolved into an adaptive, data-driven system supported by advanced technologies such as artificial intelligence, the Internet of Things (IoT), big data analytics, and cloud computing. This article aims to explore the evolution of operational management in the digital era by identifying major challenges such as the digital skill gap, technological infrastructure limitations, and organizational resistance to change, while also examining strategic opportunities such as automation, enhanced efficiency, and data-based decision making. Using a qualitative approach through literature review, this study provides both theoretical and empirical perspectives on the importance of operational innovation in building competitive advantage amid ongoing digital disruption.