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This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

The rapid advancement of Internet of Things (IoT) technologies has transformed conventional residential environments into intelligent smart housing systems capable of enhancing energy efficiency, security, comfort, healthcare services, and environmental sustainability. However, existing studies remain fragmented, focusing on specific technologies or applications rather than providing a comprehensive understanding of the smart housing ecosystem. Therefore, this study aims to systematically review the major technologies employed in IoT-based smart housing systems, examine their contributions to residential benefits, and identify the challenges and future opportunities associated with their implementation. This research adopted a Systematic Literature Review (SLR) approach using the Scopus database as the primary source of literature. The review process followed a structured screening and selection procedure, resulting in the inclusion of 26 studies that met the predefined eligibility criteria. The findings indicate that Artificial Intelligence, Blockchain, Edge Computing, Fog Computing, Cloud Computing, Digital Twin, Smart Grid technologies, renewable energy systems, and advanced sensor infrastructures constitute the primary technological foundations of modern smart housing systems. The reviewed studies indicate that these technologies contribute to energy optimization, cybersecurity enhancement, intelligent automation, healthcare monitoring, safety improvement, and sustainable resource management. However, challenges related to security, privacy, interoperability, scalability, computational complexity, and implementation costs remain important barriers to widespread adoption. In conclusion, IoT-based smart housing systems have evolved into integrated and intelligent residential ecosystems, and future development is expected to be driven by the convergence of Artificial Intelligence, distributed computing, Digital Twin technologies, and sustainable smart infrastructure solutions.  

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

This study investigates the relationship between design patterns, modular architecture, and the maintainability of distributed real time systems developed using agile practices. Distributed real time systems are critical in various sectors, including telecommunications, healthcare, and automotive, where strict timing constraints and reliability are essential. Agile methodologies, known for their flexibility and iterative development, have been widely applied to software engineering, but their impact on long-term system maintainability, especially in complex real time environments, has been insufficiently explored. This research employs an empirical analysis, combining both quantitative and qualitative data from multiple real time system projects using agile methods. The analysis focuses on the application of design patterns, such as Singleton, Observer, and Factory, and evaluates the effectiveness of modular architectures in enhancing system scalability, flexibility, and long-term sustainability. The study also explores how agile practices contribute to system performance and maintainability, despite challenges related to frequent updates and coordination among distributed teams. Key findings show a positive correlation between the consistent use of design patterns and modularity, which significantly improves the maintainability and adaptability of distributed real time systems. This research also highlights the challenges faced by agile methods in maintaining architectural consistency and managing non-functional requirements, particularly in distributed environments. The results contribute valuable insights into adapting agile practices to meet the specific demands of distributed real time systems, offering recommendations for developers and project managers to incorporate modular architecture and design patterns to enhance long-term system sustainability. Further research is suggested to explore new design patterns and investigate the broader impact of agile methodologies on system quality beyond maintainability.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

The development of music streaming services such as Spotify has increased the need to understand the factors that influence user loyalty, particularly among university students who are highly engaged with digital platforms. Despite the rapid growth in music streaming adoption, research specifically examining the relationship between user satisfaction, measured using the End-User Computing Satisfaction (EUCS) model, and user loyalty among Surabaya university students remains limited. This study aims to examine the influence of the five EUCS dimensions, namely content, accuracy, format, ease of use, and timeliness, on the loyalty of Spotify users. A quantitative research approach was employed, with data collected through a Likert-scale questionnaire distributed to 77 students who have used Spotify for at least three months. The collected data were analyzed using multiple linear regression through SPSS to determine both partial and simultaneous effects of each dimension. The findings reveal that only the ease-of-use dimension has a positive and significant impact on user loyalty, while content, accuracy, format, and timeliness show no significant effects. These results highlight that ease of use plays a crucial role in shaping and maintaining user loyalty among Spotify users in Surabaya. Furthermore, the study provides valuable insights for developers to prioritize usability improvements while also contributing to the enrichment of EUCS-based satisfaction studies in the digital music service domain.

WPS Office stands for Writer, Presentation, and Spreadsheets—a software suite offering diverse office functions, including document processing, spreadsheet creation, and presentation tools. This study analyzes user satisfaction levels and the influence of the variables Content, Accuracy, Format, Ease of Use, and Timeliness on WPS Office application users in Jambi City, using the End User Computing Satisfaction method. Data were gathered through an online questionnaire distributed to students in Jambi City who had used the application; created via Google Forms, it garnered 385 responses. Post-collection, analysis was conducted using Structural Equation Modeling in SmartPLS software version 4. Of the five hypotheses tested, four were accepted. The results reveal that accuracy, format, ease of use, and timeliness positively and significantly influence user satisfaction, while content shows no significant effect.

The modernization of smart grids through edge computing introduces significant cybersecurity challenges, most stemming from adversarial machine learning attacks that compromise distributed intelligence. Although Federated Learning is an appealing decentralized model training paradigm for edge smart grids, its resilience against coordinated injection and evasion attacks has not yet been thoroughly explored. To address this critical gap, we develop and evaluate a resilient Federated Learning model for edge-based innovative grid applications. Under a rigorous simulation-based experimental design, we created a controlled environment based on synthetic energy-demand data and implemented adversarial attack scenarios to ensure model robustness. We propose a resilience enhancement layer in our framework during the federated aggregation process to curtail malicious model updates and adversarial inferences. The results show significant improvement in the stability of the proposed model under attack, maintaining a robustness index above 0.62, whereas baseline approaches exhibit complete degradation. This corresponds to a reduction of approximately 34% in the attack impact rate across different-intensity attack scenarios, while maintaining high stability in aggregation. In addition to the adversarial testing framework in the domain of Federated Learning, this work provides a validated resilience model that secures analytics of smart grids without requiring access to raw data. Our methodology presents a resource-efficient alternative to physical testing and enables safe yet comprehensive security evaluation in critical infrastructure applications.

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 advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.

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.

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 .

Electronic Medical Record (EMR) systems have become a fundamental component of hospital information systems, aiming to improve the quality, efficiency, and continuity of patient care. However, the success of EMR implementation depends largely on user satisfaction. This study investigates user satisfaction with the EMR system implemented at Dr. Soetarto (DKT) Hospital, a mid-tier hospital in Yogyakarta, Indonesia. The research addresses issues related to usability, data accuracy, and system responsiveness that potentially hinder effective EMR usage. The objective is to evaluate user satisfaction using the End User Computing Satisfaction (EUCS) framework, which consists of five dimensions: content, accuracy, format, ease of use, and timeliness. A quantitative approach was applied through a structured questionnaire distributed to 40 healthcare workers, including medical records staff, nurses, pharmacists, and administrative personnel. Data analysis using Spearman correlation revealed significant positive relationships among all EUCS dimensions, with the strongest association found between format and ease of use. The findings indicate that while users are generally satisfied with the system’s performance, improvements are still needed in timely data retrieval and user interface consistency. This study highlights the critical need for continuous system evaluation and end-user involvement to optimize EMR functionality and healthcare service delivery.

The Agricultural Office of Central Lombok Regency is a government institution that utilizes a website to provide information regarding various agricultural services to the public. This website is used by both the staff of the Agricultural Office and the public. To assess the level of user satisfaction with the website of the Agricultural Office of Central Lombok Regency, this study employs the End User Computing Satisfaction (EUCS) method, which measures satisfaction through five dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. The purpose of this study is to evaluate the extent of user satisfaction with the website using a Likert scale as the measurement tool. Data were collected through a questionnaire distributed to 55 respondents, consisting of both the staff of the Agricultural Office and the public who use the website. The results show that the level of user satisfaction with the website of the Agricultural Office of Central Lombok Regency falls into the "agree" category for all dimensions examined. The average satisfaction percentages for each dimension are as follows: Content (78.18%), Accuracy (75.2%), Format (76%), Ease of Use (74%), and Timeliness (75%). The Content dimension showed the highest percentage, indicating that the completeness and relevance of the information on the website significantly influence user satisfaction. Based on these findings, it is recommended to improve the website’s interface and update the information regularly to enhance the user experience. This study provides a clear picture of user satisfaction with the website of the Agricultural Office of Central Lombok Regency and can serve as a reference for future website development.

Pengembangan sistem operasi pada platform terdistribusi, terutama dalam konteks Cloud Computing, memainkan peran penting dalam mengelola sumber daya komputasi secara efisien. Manajemen terdistribusi mengacu pada penggunaan terintegrasi dari berbagai komponen di berbagai lokasi, menghadirkan tantangan yang signifikan dalam mengelola dan memantau sumber daya yang tersebar. Komputasi awan menawarkan solusi infrastruktur fleksibel yang memungkinkan pengguna mengakses sumber daya komputasi tanpa memerlukan manajemen infrastruktur fisik. Penelitian ini bertujuan untuk mengoptimalkan manajemen terdistribusi dalam sistem operasi komputasi awan dengan memanfaatkan algoritma pembelajaran mesin untuk meningkatkan efisiensi alokasi sumber daya dan meningkatkan keamanan data. Pengumpulan data dilakukan melalui metode observasi sistematis dan tinjauan pustaka. Temuan menunjukkan bahwa penerapan algoritme prediktif di lingkungan cloud secara signifikan mengurangi waktu respons dibandingkan dengan metode manual dan memperkuat perlindungan data melalui deteksi intrusi yang ditingkatkan. Selain itu, sistem berbasis pembelajaran mesin memungkinkan skalabilitas yang lebih baik dan adaptasi yang lebih cepat terhadap permintaan sumber daya dinamis tanpa waktu henti sistem. Makalah ini menyumbangkan wawasan berharga dalam mengoptimalkan kinerja komputasi awan melalui manajemen sumber daya terdistribusi yang ditingkatkan.

This study aims to evaluate the level of user satisfaction with the website of SMAN 1 Wangon by applying the End User Computing Satisfaction (EUCS) method. This method assesses user satisfaction based on five main dimensions: system quality, information quality, service quality, end user, and individual benefits. Data collection was conducted through questionnaires distributed to students, teachers, and administrative staff. The results of the analysis show that overall user satisfaction is in a good category, although there are some aspects that need to be improved, especially in terms of information and service quality. This research is expected to provide suggestions for further development of the school website, in order to improve user experience and support the learning process.

Advances in information technology have penetrated several aspects of human activities, including educational activities. Muhammadiyah University of Ponorogo, a campus that has implemented technology, has developed a website to make lecture activities easier, especially for taking attendance, checking schedules, checking financial billing data, and viewing grades. A good website must make it easy for users to use it. Ease of use of the website will also give a good impression from users. In this research, the author conducted research to conduct an analysis of student satisfaction at Muhammadiyah University of Ponorogo towards the SIMTIK website.  The method used in this research is the End User Computing Satisfaction (EUCS) method. The population in this study were active students at Muhammadiyah University of Ponorogo, totaling 31 students. Data was obtained from a questionnaire that the author distributed via Google Form. The data that has been obtained is processed using SPSS software and statistically processed using a Likert scale. To measure the level of satisfaction, we also use a Likert scale satisfaction table. The results of each dimension are, the Content dimension has an average value of 3.87 (satisfied), the Format dimension has an average of 3.17 (quite satisfied), the Timeliness dimension 3.53 (satisfied), the Ease of Use 2.6 (quite satisfied), and Accuracy (quite satisfied). From these five dimensions, an average of 3.25 was obtained and was included in the quite satisfied category.

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.