Publication Search

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

This study examines the transformation of business education curricula in response to the rapid expansion of the digital economy. The increasing integration of artificial intelligence, big data analytics, cloud computing, and digital platforms has significantly reshaped industrial demands and professional competencies. Using a qualitative research approach and content analysis, this study explores the challenges and strategic opportunities associated with curriculum transformation in higher education institutions. The findings reveal that universities face critical challenges related to technological infrastructure, faculty readiness, curriculum relevance, and industry alignment. However, digital transformation also provides opportunities for interdisciplinary learning, experiential education, global collaboration, and technology-driven innovation. The study emphasizes the importance of adaptive leadership, faculty development, industry partnerships, and student-centered pedagogies in supporting sustainable curriculum reform. Ultimately, transforming business education curricula is essential for preparing graduates with digital competencies, critical thinking abilities, and innovative skills necessary to compete effectively in the global digital economy.

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

Transformasi digital di sektor kesehatan, khususnya melalui penerapan Rekam Medis Elektronik (RME) dan telemedicine, telah membawa perubahan signifikan dalam efisiensi dan kualitas pelayanan kesehatan di Indonesia. Namun, di balik berbagai manfaat tersebut, muncul persoalan serius terkait perlindungan privasi dan keamanan data pasien, interoperabilitas sistem, serta kepastian hukum yang masih belum memadai. Penelitian ini bertujuan untuk mengkaji penerapan RME dalam layanan kesehatan, mengidentifikasi permasalahan keamanan dan perlindungan data pasien, serta menganalisis kepastian hukum dalam layanan kesehatan berbasis teknologi di Indonesia. Metode yang digunakan adalah studi literatur sistematis dengan pencarian artikel melalui database Google Scholar, dibatasi pada publikasi tahun 2022–2026. Dari 8.780 artikel yang ditemukan, proses seleksi bertahap menghasilkan 3 artikel final yang memenuhi kriteria inklusi dan dianalisis secara deskriptif kualitatif. Hasil penelitian menunjukkan bahwa interoperabilitas RME di Indonesia masih terhambat oleh ketidaksesuaian standar data, keterbatasan infrastruktur, dan regulasi yang belum memadai. Penerapan teknologi mutakhir seperti blockchain, cloud computing, dan kecerdasan buatan terbukti mampu meningkatkan keamanan dan efisiensi pengelolaan data pasien. Sementara itu, regulasi telemedicine yang ada belum terintegrasi secara komprehensif dan belum mampu memberikan kepastian hukum yang memadai bagi tenaga kesehatan maupun pasien. Kesimpulannya, diperlukan harmonisasi regulasi, standardisasi data nasional, peningkatan infrastruktur, serta penguatan kapasitas sumber daya manusia agar ekosistem kesehatan digital Indonesia dapat berkembang secara aman, terintegrasi, dan berkeadilan bagi seluruh pihak yang terlibat.

.This study aims to analyse the role of Financial Information Systems (FIS) in supporting risk management, decision-making, and organisational performance in the digital transformation era. This study employs the Systematic Literature Review (SLR) method to examine articles indexed in Scopus from 2016 to 2026. The PRISMA framework is used to ensure a systematic, transparent article selection process, resulting in the selection of 37 relevant articles for further analysis. The results of the study show that Financial Information Systems make a major contribution to improving financial transparency, operational efficiency, the quality of strategic decision-making, and organisational risk mitigation. In addition, the integration of emerging technologies such as Artificial Intelligence (AI), FinTech, big data analytics, and cloud computing further strengthens the effectiveness of financial information systems in modern organisations. This study contributes theoretically by mapping research trends and identifying research gaps, while providing practical benefits for organisations seeking to increase competitiveness through digital financial systems. For future research, it is recommended to develop a more predictive and intelligent Financial Information Systems model to address future business dynamics.

Cloud job-length prediction remains challenging when the target distribution is highly skewed and contains rare extreme values. This study proposes a log-transformed, regime-based machine learning framework for robust prediction of cloud job length, represented in million instructions (MI). The approach integrates sequential feature engineering, logarithmic target transformation, weighted learning, and regime-aware modeling to distinguish between normal and extreme job-length behavior. Using an ordered GoCJ-derived cloud job-length sequence of 1000 jobs, the dataset exhibits a heavy-tailed distribution, with a mean of 129,662 MI, a median of 93,000 MI, a 95th percentile of 525,000 MI, a 99th percentile of 900,000 MI, and a skewness of 3.695. The proposed model is evaluated against sequential baselines and stronger machine learning baselines, including Naive_Last, RollingMean_5, Global_Log_ExtraTrees, RandomForest, GradientBoosting, and MLP_Log. On the main test split, the proposed Regime_Log_ExtraTrees achieved the best RMSE of 206,255.66 and the least negative R² of −0.01062, while Global_Log_ExtraTrees remained competitive in terms of MAE, MedAE, and RMSLE. Additional walk-forward validation confirms that the regime-aware model consistently achieves the best mean RMSE and mean R² across temporal folds. Ablation results further show that regime-aware learning is the primary contributor to robustness, although accurate prediction of extreme jobs remains challenging. These findings indicate that log-transformed, regime-based learning provides a practical and more robust strategy for cloud job-length prediction under heavy-tailed workload conditions.

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.

Penelitian ini bertujuan untuk mengidentifikasi manfaat utama, tantangan, dan peluang dalam mengadopsi cloud computing dalam lingkungan akuntansi syariah serta merumuskan kerangka konseptual untuk mengintegrasikan cloud accounting dengan nilai dan prinsip Islam. Penelitian ini menggunakan tinjauan literatur sistematis (SLR) sesuai dengan pedoman PRISMA. Sebanyak 12 artikel relevan yang diterbitkan antara tahun 2020-2025 dianalisis melalui tahapan seleksi berdasarkan kriteria inklusi-eksklusi, penyaringan basis data, dan sintesis deskriptif. Hasil kajian menunjukkan bahwa cloud accounting berkontribusi terhadap peningkatan efisiensi operasional, akurasi, dan ketepatan waktu pelaporan keuangan, dan mendorong pergeseran peran akuntan ke arah fungsi analitis dan pendukung pengambilan keputusan. Namun, tantangan signifikan tetap ada termasuk masalah keamanan dan kerahasiaan data, literasi digital yang terbatas, dan ketidakhadiran kerangka regulasi eksplisit untuk memastikan kepatuhan Syariah. Penelitian ini menegaskan perlunya pengembangan kerangka konseptual cloud accounting yang selaras dengan nilai dan prinsip syariah guna menjembatani kesenjangan etika, operasional, dan regulasi dalam praktik akuntansi digital.

This study investigates the adoption of adaptive DevOps practices in embedded systems used in safety-critical industrial applications. Traditional DevOps models, which are primarily designed for cloud-based systems, face significant challenges when applied to embedded platforms due to hardware constraints, real-time performance requirements, and stringent safety standards. The research focuses on developing a tailored DevOps framework that integrates continuous integration/continuous delivery (CI or CD) pipelines, automation, real-time monitoring, and safety assurance processes to enhance system reliability, performance, and compliance with regulatory standards. The study uses a case study methodology, involving embedded system teams across multiple industrial sectors, to assess the impact of these adapted DevOps practices on system stability and operational efficiency. Key findings show that the adoption of adaptive DevOps practices led to significant improvements in system reliability, performance, and deployment stability. Continuous feedback mechanisms allowed for early issue detection and faster resolution, leading to enhanced system uptime and responsiveness. Additionally, the integration of safety assurance into the DevOps pipeline ensured that safety-critical systems complied with required safety integrity levels and certification standards. The study further explores the integration of DevOps with embedded safety-critical systems, highlighting the benefits of cross-domain collaboration, enhanced communication, and the ability to address the unique challenges of these platforms. The research also underscores the limitations of conventional DevOps models in embedded systems and presents practical implications for the wider adoption of DevOps in safety-critical industrial applications. Future research is recommended to refine DevOps frameworks for embedded systems, integrating emerging technologies like the Industrial Internet of Things (IIoT) and Digital Twins to further optimize performance, security, and predictive maintenance.

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.

The increasing demand for real time parallel processing in cloud computing environments necessitates the development of more efficient and fault-tolerant scheduling algorithms. Traditional scheduling methods, such as static algorithms, often fall short when handling dynamic workloads and system failures, leading to increased task latency and reduced system performance. In contrast, adaptive scheduling algorithms dynamically adjust to changes in system conditions and workloads, ensuring timely task completion and optimized resource utilization. This study evaluates the performance of adaptive scheduling algorithms in real time cloud environments, focusing on key factors such as task latency, system resilience, and fault tolerance. Simulation experiments were conducted using cloud computing models that incorporate fault injection scenarios, including network failures and virtual machine crashes. The results show that adaptive algorithms significantly outperform traditional static schedulers in terms of task latency reduction and improved system resilience. These algorithms demonstrated better fault recovery times and ensured consistent real time performance, even under failure conditions. The findings highlight the advantages of adaptive scheduling in cloud environments, particularly for applications requiring rapid data processing and high system reliability. Despite the promising results, challenges remain regarding the scalability and complexity of these algorithms in large-scale cloud systems. Further research is needed to optimize adaptive scheduling algorithms for efficiency, scalability, and comprehensive performance evaluation, taking into account factors such as energy consumption, cost, and reliability. This research contributes to advancing cloud computing infrastructures that can dynamically handle real time tasks and maintain high performance under varying workloads and failures.

Real time decision making applications, such as those used in autonomous vehicles, smart cities, and industrial IoT, require fast, scalable, and accurate analytics to ensure timely responses and optimized operations. Traditional cloud-based systems face significant challenges in meeting these requirements due to high latency, limited scalability, and bottlenecks in data processing. This study explores the use of a hybrid Edge Cloud architecture to optimize End to end machine learning (ML) pipelines for real time applications. The proposed system offloads time-sensitive tasks to edge devices, while computationally intensive processes are handled by the cloud, ensuring efficient use of resources and reduced latency. Experimental results demonstrate that the hybrid model reduces inference latency by up to 70% compared to cloud-only systems, while maintaining model accuracy and increasing throughput. Additionally, the scalability of the hybrid architecture is highlighted, as it can handle large-scale data streams and adapt to varying workloads. The findings show that hybrid Edge Cloud architectures are well-suited for applications where fast decision making is critical, such as autonomous systems and real time analytics in smart cities. However, challenges remain in managing resources across edge and cloud systems, particularly in balancing computational loads and ensuring system reliability. Future research should focus on optimizing task partitioning, integrating advanced edge AI models, and exploring the use of 5G networks to enhance performance further. Overall, the study demonstrates the potential of hybrid Edge Cloud systems in overcoming the limitations of traditional cloud-based ML pipelines and provides insights into the future of real time data processing.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

This study focuses on analyzing the application of cloud computing as a supporting infrastructure for digital transformation in the implementation of Smart City at the Communication and Information Agency (Diskominfo) of Muara Enim Regency. In the era of digital transformation and accelerated urbanization, the need for smart city management based on information technology has become increasingly urgent. Cloud computing plays a strategic role in providing integrated, scalable, and efficient data services to support the effectiveness of public services and data-driven decision-making. This study aims to analyze the extent to which cloud computing has been implemented in the Muara Enim Diskominfo environment, identify the supporting and inhibiting factors of its implementation, and evaluate its contribution to the achievement of Smart City objectives. This study uses a comparative approach with data collection techniques through interviews, observation, and documentation studies. The results of the study show that the implementation of cloud computing at the Muara Enim Communication and Information Agency is still in the development stage, with positive achievements in data management efficiency and inter-unit collaboration, but facing obstacles in terms of system integration and human resources. This research contributes to strengthening academic understanding of cloud computing implementation strategies in the context of local government, as well as providing practical recommendations for policy makers to improve digital infrastructure readiness towards a sustainable Smart City.

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.

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

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.