Publication Search

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.

Sharia-compliant investment in Indonesia has experienced rapid growth, in line with increasing public interest in instruments compliant with Islamic principles. However, market fluctuations remain a major challenge in maintaining the performance of sharia investments, particularly sharia mutual funds. This article analyzes the dynamics of sharia investment in Indonesia in the face of market volatility, focusing on the performance of sharia mutual funds. The research method used is a quantitative approach, with secondary data analysis from various scientific studies and recent statistical data. The results indicate that macroeconomic fluctuations and market conditions significantly influence the performance of sharia mutual funds. Nevertheless, sharia mutual funds continue to demonstrate resilience and certain advantages compared to conventional mutual funds, particularly in the face of market uncertainty. These findings have important implications for sharia investors, investment managers, and policymakers in designing more optimal investment strategies and strengthening the position of sharia mutual funds in an increasingly dynamic market.

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.

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.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

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.

This study aims to analyze the effect of audit tenure on audit quality at PT Wijaya Karya (Persero) Tbk, which is listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period. This research employs a quantitative approach with a causal associative research method. The data used are secondary data obtained from the company’s annual reports and financial statements. Data collection was conducted through documentation, while sample selection used purposive sampling. The data analysis method applied was simple linear regression to examine the effect of audit tenure on audit quality. The results indicate that audit tenure has a significant effect on audit quality, suggesting that the length of the relationship between the auditor and the client has implications for the quality of audit outcomes. These findings are expected to contribute to the development of accounting literature and serve as a consideration for regulators and companies in determining auditor engagement policies to maintain audit quality.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

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 increasing integration of Cyber physical Systems (CPS) into industrial environments has highlighted the need for secure, scalable, and efficient cryptographic key management systems. Traditional centralized key management protocols are often limited by vulnerabilities such as single points of failure, scalability issues, and significant overhead. Blockchain technology presents a promising solution to these challenges by leveraging decentralization, immutability, and transparency to enhance security and efficiency in CPS. This study investigates the use of blockchain based cryptographic key management systems, focusing on smart contracts for automated key distribution and rotation. Experimental results demonstrate that blockchain based systems significantly improve system integrity, auditability, and resilience, offering enhanced protection against cyber-attacks and reducing the risks associated with centralized systems. Blockchain’s decentralized architecture eliminates the need for a central authority, making it more resistant to tampering and operational failures. Additionally, smart contracts automate the key management process, improving efficiency while maintaining a high level of security. The study also evaluates the impact of blockchain on communication performance, finding that it reduces latency and overhead by automating processes and eliminating the need for centralized control. Despite these advantages, challenges such as scalability, latency, and integration with legacy systems remain. The study concludes by suggesting future research directions, including the development of lightweight blockchain protocols tailored for industrial applications and the integration of blockchain with emerging technologies like Artificial Intelligence (AI) to further enhance key management in CPS. Blockchain based solutions have the potential to transform the security landscape of industrial environments, offering greater robustness, reliability, and trust.

The increasing sophistication of cyber threats has rendered traditional cybersecurity models insufficient in safeguarding enterprise networks. This study introduces a risk aware cybersecurity governance model that integrates real time threat intelligence with predictive anomaly detection to proactively mitigate potential threats. By leveraging advanced machine learning and AI techniques, the model enhances the ability to identify and address cyber threats before they can escalate into significant incidents. The model’s ability to predict anomalies, analyze real time threat intelligence feeds, and provide early warnings allows for faster response times and reduced risk exposure compared to traditional reactive models. Through simulations and real-world use cases, the proposed model demonstrated a 30% reduction in response time and a 25% decrease in overall risk exposure, showing its potential to improve security decision-making and resilience in dynamic threat environments. Unlike traditional models that rely on static rules and periodic policies, the proposed model uses predictive analytics to stay ahead of evolving threats, ensuring continuous monitoring and rapid adaptation. This proactive approach enhances organizational resilience, particularly in handling sophisticated cyber threats such as ransomware, malware, and phishing attacks. Despite its effectiveness, challenges such as data overload, scalability, and the need for interpretability in AI models remain. Future research will focus on refining predictive models, improving scalability for larger networks, and enhancing the explainability of machine learning models to foster greater trust in automated cybersecurity systems. This study contributes to the ongoing evolution of cybersecurity governance by demonstrating the value of integrating predictive and real time monitoring technologies for enhanced threat detection and mitigation.

This study investigates the role of Information Technology (IT) governance in enhancing risk management performance and ensuring regulatory compliance within multinational digital enterprises. As digital transformation continues to reshape the global business landscape, organizations face increasing challenges in managing technological risks and complying with complex regulatory requirements across various jurisdictions. The study adopts a quantitative approach, using a survey methodology to collect data from senior IT and compliance managers in multinational digital enterprises. The survey focuses on how IT governance frameworks, such as COBIT 2019 and ISO 27000, are utilized to align IT strategies with business objectives, mitigate risks, and maintain regulatory compliance. The findings indicate that organizations with well-established IT governance structures are better positioned to proactively identify and mitigate risks, ensuring greater consistency in meeting regulatory requirements. These organizations demonstrate improved risk management effectiveness, especially concerning cybersecurity, data privacy, and compliance with global regulations like GDPR. In contrast, organizations with ad hoc or decentralized governance structures struggle with fragmented risk management and compliance efforts. The study further highlights the importance of integrating IT governance frameworks with internal audit functions, specifically the Chief Audit Executive (CAE), to enhance cybersecurity resilience and ensure compliance with global standards. This research contributes to the literature by providing empirical evidence on the integration of IT governance, risk management, and regulatory compliance in multinational enterprises. It also highlights the need for a structured and systematic approach to IT governance to improve organizational performance in managing risks and ensuring consistent regulatory adherence. The study offers practical insights for organizations looking to optimize their IT governance structures in the face of rapid digital transformation.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

This research examines criminal law policy in handling special narcotics crimes and their social impacts on Indonesian society. Narcotics crime constitutes an extraordinary crime that threatens public health, security, and social resilience. Therefore, the state implements criminal law policies through penal and non-penal approaches regulated in statutory provisions, particularly the Narcotics Law. This research aims to analyze the effectiveness of criminal law policies in combating narcotics crimes and to examine the social impacts arising from the implementation of such policies. The research method used is normative legal research employing statutory and conceptual approaches. The findings indicate that criminal law policies in narcotics control still face various challenges, including ineffective law enforcement, prison overcapacity, and social stigma against narcotics users. The social impacts are not only experienced by offenders but also affect families and the wider community. Consequently, criminal law policies that prioritize restorative justice, rehabilitation, and preventive measures are urgently needed to reduce negative social impacts and to ensure sustainable protection for society in Indonesia.

This community service activity aimed to enhance the security and efficiency of halal certification mentoring services at the Aurum First Sunrise community in Surakarta. The main challenge faced by the partner was the risk of sensitive SME data leakage such as ID cards, recipes, and supply chain information, due to the lack of an adequate document security mechanism. The core solution implemented was Technology Implementation in the form of a Cryptographically-based Document Management Information sistem (utilizing the Light Weight PDAC algorithm) integrated with digital access rights management and user Training. Evaluation demonstrated successful implementation, evidenced by an increase in the average satisfaction of SMEs regarding data security to 97.8%, confirming enhanced trust. Furthermore, digitalization successfully improved the efficiency of the mentoring team, reflected by a satisfaction score of 85.0%. In conclusion, this service successfully transformed the partner into a secure, efficient, and credible mentoring institution, significantly supporting SMEs in accessing halal certification.

The culture of patronage is a key characteristic of Indonesian political dynamics, having developed from the pre-colonial era to the contemporary democratic era. Despite decentralization and political reforms in Indonesia, patronage practices persist through relationships between political elites, bureaucracy, business actors, and communities, particularly at the local level. This study analyzes patronage using Pierre Bourdieu's Social Practice Theory framework, which emphasizes the interaction between habitus, capital, and the arena as factors shaping social practices. The method used is descriptive qualitative research with data collection techniques through desk studies, which allows researchers to examine various academic literature to build a comprehensive conceptual analysis. The research findings indicate that internalized political habitus, the accumulation of economic, social, cultural, and symbolic capital, and a competitive local political field are key elements in perpetuating patronage. Patronage is not merely a transactional political practice, but a social structure that is continuously reproduced and impacts the politicization of bureaucracy, the strengthening of oligarchy, power inequality, and the vulnerability of the poor to political manipulation. This research confirms that efforts to strengthen democracy in Indonesia require transformation of the political habitus of society, bureaucratic reform, and restrictions on the dominance of economic actors in the political arena to break the entrenched patron-client chain.

The academic crisis among Physics students often arises due to the pressure of the credit semester sistem (SKS), grade demands, and heavy study loads. This condition drives students to focus solely on achieving academic results, while the essence of learning as worship is often overlooked. This study aims to explore how the values of Islamic education, particularly the orientation towards rewards, can help students cope with academic pressure. The methods used include literature review and a quantitative survey with an online questionnaire completed by 56 Physics and Physics Education students at Universitas Pendidikan Indonesia. The analysis results show a significant correlation between the SKS pressure and changes in learning behavior (r = 0.807), while the orientation towards rewards acts as a spiritual balance. The findings suggest that Islamic education plays a crucial role in building academic resilience through values of sincerity (ikhlas), patience (sabar), trust in God (tawakal), and balance (tawazun). Thus, academic achievements can become a means of worship while contributing positively to society. This study highlights the importance of spiritual values in achieving balanced academic success.

A high-risk pregnancy is a pregnancy that can affect both the pregnancy, delivery, newborn, and postpartum period. There are several high-risk pregnancies, including pregnant women who are <145cm tall, have low birth weight, are too young, and give birth too closely spaced, or what is known as the 4T. Prevention is carried out by conducting comprehensive supervision, this is a way to reduce MMR and IMR. The Central Java government has developed the One Student One Client (OSOC) program, it is hoped that the implementation of this program can reduce MMR in Central Java. MMR according to WHO in 2023 reached 189/100,000 live births, MMR based on ASEAN reached 235/100,000 live births, MMR according to the Indonesian Ministry of Health reached 205/100,000 live births, MMR in Central Java in 2023 has reached 485/100,000 live births, MMR in Brebes in 2023 54/100,000 live births and at the Bantarkawung Community Health Center MMR in 2023 amounted to 2 cases of maternal death. In this study, researchers used Varney and SOAP to document midwifery care comprehensively, with direct qualitative descriptive approach methods such as observation, interviews, and documentation.

The flood disaster that hit Medan City caused a multidimensional impact on the community, especially the psychological impact experienced by the victims. In addition to material losses, flood victims often face emotional distress in the form of anxiety, stress, trauma, and a sense of loss. This study aims to examine the role and effectiveness of individual counseling in dealing with the psychological problems of post-flood victims. The research method uses a qualitative approach with case studies through in-depth interviews, observations, and documentation of victims who receive individual counseling services. The results show that individual counseling plays an important role in helping victims understand their emotional state, manage stress, and rebuild hope and resilience after disasters. Individualized counseling also provides a safe space for victims to express their feelings, gain support, and develop adaptive strategies in dealing with trauma. These findings confirm that psychological interventions based on individual counseling can be one of the effective approaches in disaster victim recovery, especially floods, so that it can strengthen psychosocial aspects and improve the quality of life after disasters. Thus, this research contributes to the development of more responsive and sustainable counseling services in the context of disaster management in Indonesia.

Having a child with an intellectual disability requires parents to face greater challenges in parenting, encompassing not only physical needs but also emotional and social aspects. This study aims to explore the resilience of parents of children with intellectual disabilities and the factors that influence it. Parental resilience encompasses the ability to survive, adapt, and thrive despite facing high levels of psychological stress. This study used a narrative literature review method, reviewing 11 international and national journal articles published between 2015 and 2025. The results indicate that psychological stress, such as anxiety, stress, and frustration, is often experienced by parents, but high resilience can mitigate the negative impact of this stress. Factors influencing resilience include the individual characteristics of parents, the characteristics of children with intellectual disabilities, and social and environmental support. Good resilience not only supports child development but also the well-being of parents. Based on these findings, this study suggests the importance of social support and the formation of a community of parents to share experiences and provide emotional support.