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This study explores the development of Islamic economic thought during the Umayyad Dynasty (661-750 CE). Using descriptive and literature review methods, this research highlights the integration of Islamic principles into economic policies and practices during this era. Key findings indicate that the Umayyad rulers implemented zakat, kharaj, and jizyah systems to ensure wealth redistribution and welfare. Infrastructure development and monetization through dinar and dirham currencies also contributed to economic stability while adhering to Islamic ethical values. Despite challenges such as social inequality and administrative inefficiencies, the Umayyad Dynasty laid a foundation for future Islamic economic systems that prioritize justice and prosperity.

The rapid evolution of smart cities, driven by the integration of technologies such as the Internet of Things (IoT) and blockchain, has brought about significant advancements in urban infrastructure and services. However, these developments also introduce new cybersecurity challenges. Introduction: Smart cities are increasingly vulnerable to cyber threats due to the extensive use of interconnected devices and systems. A key security concern is the management of digital identities, which is essential for maintaining the integrity and reliability of city services. Literature Review: Traditional centralized identity management systems face significant security issues, including a single point of failure, data breaches, and limited user control over personal information. In contrast, decentralized solutions, particularly blockchain-based systems, offer enhanced security through their distributed nature, eliminating vulnerabilities associated with centralized models. Materials and Method: This research focuses on blockchain technology’s application in smart city identity management. A decentralized framework is proposed, leveraging cryptographic techniques, consensus mechanisms, and smart contracts to ensure data security, integrity, and privacy. Results and Discussion: The implementation of blockchain for identity management significantly improves attack tolerance, data integrity, and transparency. The decentralized approach mitigates the risks associated with central authorities, ensuring that user data remains secure and verifiable. However, scalability, interoperability, and regulatory compliance challenges remain. Blockchain solutions must be optimized for large-scale smart city applications and aligned with legal standards to achieve widespread adoption. Future research should focus on overcoming these challenges to create a more secure and resilient smart city infrastructure.

Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates advanced security solutions that can detect and respond to evolving cyberattacks. Literature Review: Traditional Intrusion Detection Systems (IDS), including signature based and anomaly based methods, are not equipped to handle the dynamic nature of 5G networks, leading to high false positives and low detection accuracy. In contrast, RL offers significant improvements in adaptability, detection accuracy, and response time. Materials and Method: The study simulates 5G network traffic and develops an RL-based IDS using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) techniques. The performance of the RL-based system is compared to traditional IDS systems, focusing on detection accuracy, false positive rates, and response times. Results and Discussion: The RL-driven IDS demonstrated superior performance, achieving higher detection accuracy (95%) and faster response times (30 milliseconds) compared to traditional methods. However, challenges such as computational cost and model interpretability were identified. The study emphasizes the importance of adaptive learning mechanisms and the integration of RL into Zero Trust Architecture (ZTA) to enhance the security of 5G networks.

Cloud-based resource allocation and VM/container orchestration play a crucial role in ensuring performance, scalability, and energy efficiency in modern distributed computing environments. This study investigates the effectiveness of centralized and decentralized scheduling models combined with heuristic and optimization-based allocation strategies in container-based cloud infrastructures. A quantitative experimental approach was employed to evaluate system performance under varying workload intensities. Key evaluation metrics included response time, throughput, resource utilization, SLA violation rate, and energy consumption. The experimental results indicate that centralized scheduling mechanisms experience scalability limitations and increased latency under high workload conditions. Although optimization-based allocation improves performance within centralized architectures, coordination bottlenecks remain significant. In contrast, decentralized scheduling models demonstrate superior adaptability, reduced response time, and improved throughput due to distributed decision-making and reduced control overhead. The integration of intelligent optimization techniques further enhances resource utilization and energy efficiency, achieving the lowest SLA violation rates and highest system stability. Overall, the findings confirm that combining decentralized scheduling with optimization-driven resource allocation provides a more scalable and sustainable orchestration strategy for modern cloud environments. This approach is particularly suitable for dynamic, large-scale, and latency-sensitive applications in container-based and edge-integrated cloud systems.

The integration of augmented reality (AR) technology in education is advancing, as seen in a project at Atisa Dipamkara School. This project developed a chemistry learning application using Unity, Android XR Plugin, and ARCore to address laboratory limitations. The app uses interactive 3D visualizations to help students grasp complex chemistry concepts. Blackbox testing showed that all main modules work well, except for a bug in the compound reset module. The app effectively improves students' understanding, interest, and motivation in chemistry, making abstract concepts more tangible. This application is both a learning aid and an educational innovation, promoting a more interactive and enjoyable learning experience.

The advent of 5G technology has marked a significant shift in the telecommunications industry, offering transformative improvements in service speed, latency, and network reliability. This study explores the impact of 5G on operational efficiency and service innovation in telecom companies. By examining the operational performance of three leading telecom companies that have implemented 5G networks, the research identifies key improvements in speed, cost reduction, and resource optimization. The findings highlight that 5G has enabled companies to achieve up to 100 times faster data transfer speeds compared to previous generations, drastically reducing latency and enhancing network reliability. These improvements contribute to increased customer satisfaction, faster response times, and reduced operational costs. Additionally, the integration of artificial intelligence (AI) for network management has optimized resource allocation and further enhanced the efficiency of telecom operations. The research also demonstrates how 5G has driven innovation in service offerings, such as enabling smart cities, IoT integrations, autonomous vehicles, and real-time patient monitoring in healthcare. While the deployment of 5G offers numerous benefits, the study acknowledges challenges such as high infrastructure costs, digital inequality, and regulatory hurdles. Telecom companies must invest significantly in infrastructure and navigate complex regulatory environments to fully realize the potential of 5G. The study concludes that 5G technology has the potential to reshape the telecom sector, fostering greater competitiveness, service quality, and innovation. Future research should focus on the long-term impact of 5G on customer loyalty, its expanded applications, and its role in advancing future technologies such as 6G.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

This study explores the integration of Islamic economic education with green economy principles, focusing on environmental stewardship and sustainability. Islamic education plays a pivotal role in fostering ecological awareness, integrating values such as khalifah (stewardship), maslahah (public welfare), and adl (justice), which align with green economic practices. The research investigates how these values are taught in Islamic educational settings, particularly in pesantren (Islamic boarding schools), and how they promote sustainable practices such as tree planting and livestock cultivation. The study highlights the role of Islamic financial mechanisms, such as zakat, waqf, and green financing, in supporting sustainable development and equitable resource distribution. It also examines how Islamic education instills eco-spiritual values and encourages sustainable behaviors among students. The findings suggest that Islamic universities emphasize ethical economic principles more than secular institutions, while secular institutions focus more on sustainability content. The study calls for a more integrated approach, where both ethical economic practices and sustainability principles are harmonized to equip students for leadership roles in a green economy. This integration is crucial for fostering responsible global citizens capable of addressing the intertwined challenges of economic growth and environmental preservation.

The integration of Sharia principles with Environmental, Social, and Governance (ESG) frameworks presents a unique opportunity to enhance ethical accountability and sustainability in Islamic financial institutions. This study employs an analytical-descriptive research design, utilizing secondary data from annual sustainability reports, Sharia compliance documentation, and regulatory publications, to examine the adoption of ESG principles in the Islamic finance sector. Findings indicate that Islamic banks have achieved high levels of governance (90%) and social (85%) implementation, while environmental initiatives lag (62%), reflecting the need for stronger alignment with the khalifah fil ardh (stewardship of the earth) principle. The research also demonstrates a positive correlation between ESG implementation and investor confidence, with institutions exceeding 80% ESG adoption achieving an Investor Confidence Index of 92 points compared to 65 points among lower-performing banks. Despite conceptual synergy between ESG and Sharia principles centered on justice (adl), social welfare (maslahah), and environmental stewardship (khalifah) practical integration faces challenges including limited green financing instruments, regulatory fragmentation, and insufficient standardized ESG reporting tailored to Islamic finance. To address these issues, the study proposes an integrative ESG Sharia model emphasizing ethical foundations as the core of sustainable practices. Recommendations include developing Maqasid al-Shariah–based ESG indicators, expanding engagement in green financing and renewable energy projects, and adopting digital sustainability reporting. This integrative approach supports both global sustainability goals and the ethical imperatives of Islamic finance, contributing to a value-based, socially responsible, and spiritually aware financial ecosystem.

This research investigates the integration of Sharia economic instruments into green economy policies in Indonesia, focusing on how these instruments can enhance sustainability efforts. Specifically, it evaluates the role of Green Sukuk, zakat, and waqf in supporting sustainable development projects. Sharia economic instruments are identified as essential tools for financing environmentally friendly projects while aligning with social and ethical values. The research explores how these instruments contribute to the green economy by expanding funding sources, increasing public participation, and promoting social welfare. The study finds that the hybrid approach of integrating Sharia instruments with green policies leads to more sustainable outcomes compared to secular-only frameworks. The integration fosters long-term stability, attracts ethical investors, and supports social inclusion, making green initiatives more resilient. This research highlights the potential of Sharia-compliant financing in advancing the Sustainable Development Goals (SDGs) and fostering a more inclusive and sustainable economic model. Recommendations are made for the Indonesian government to develop policies that incorporate Sharia instruments into the green economy framework to enhance financial support and community engagement.

This study investigates the impact of gamified learning platforms on the development of critical thinking and problem-solving skills among middle school students. With the increasing reliance on digital tools, fostering these essential skills has become a critical educational goal. Traditional teaching methods often fail to engage students effectively, which has led educators to explore innovative approaches such as gamified learning. The experimental design of this study involved two groups: an experimental group using gamified learning platforms and a control group following conventional teaching methods. Data were collected through pre-tests and post-tests measuring students' critical thinking and problem-solving abilities. Results indicated that the experimental group showed a significant improvement in both skills, with a 20-point increase in their scores, while the control group exhibited minimal progress. The findings highlight that gamified learning, which integrates game mechanics such as points, rewards, and leaderboards, enhances engagement and motivation, leading to improved cognitive skills. This study emphasizes the potential of gamification to revolutionize educational practices, suggesting that its integration can be a powerful tool to equip students with the necessary skills for the future.

This study explores the use of graphene-based nanofluids in enhancing the performance of solar-powered desalination systems. A laboratory-scale desalination system was developed to simulate the evaporation process, powered by solar energy, with the integration of graphene-based nanofluids to improve thermal efficiency. The experimental setup measured evaporation rates, energy consumption, and temperature profiles under varying solar radiation conditions (400–800 W/m²). Results revealed that the system with nanofluids demonstrated up to a 35% increase in evaporation rates compared to the baseline system without nanofluids, indicating enhanced heat transfer properties. Moreover, energy consumption was reduced by up to 20%, highlighting the improved energy efficiency of the system with nanofluids. The system with nanofluids exhibited higher temperatures in the evaporator, confirming more effective thermal utilization. Statistical analyses, including t-tests and regression analysis, confirmed the significant impact of nanofluids on both evaporation rates and energy consumption. This study demonstrates that graphene-based nanofluids offer a sustainable and energy-efficient solution for solar-powered desalination, particularly in areas with abundant solar radiation. The integration of nanofluids not only enhances the efficiency of the desalination process but also reduces operational costs, making it a promising alternative for addressing water scarcity in a sustainable manner. Further research is needed to optimize nanofluid formulations and assess their long-term feasibility for large-scale applications.

This systematic literature review explores the role of psychoneuroimmunology (PNI)-based mind-body interventions in improving wound healing within nursing practice. Wound healing is a complex, multifactorial process influenced not only by cellular and molecular factors but also by psychological and immune responses. Despite the growing body of evidence supporting the efficacy of mind-body practices such as guided imagery, hypnotherapy, and meditation in managing stress and modulating immune responses, there is limited integration of these interventions into clinical nursing practices. This review aims to bridge this gap by synthesizing studies published between 2020 and 2024 that examine the impact of these interventions on wound healing outcomes. The review follows the PRISMA protocol, analyzing data from 50 primary studies focusing on RCTs, systematic reviews, and quasi-experimental designs. The results show significant improvements in wound closure rates, pain reduction, and immune modulation (e.g., reduction in cortisol and pro-inflammatory cytokines) in patients who received mind-body interventions. The findings support the hypothesis that mind-body interventions, by addressing both psychological stress and immune function, enhance wound healing. The proposed framework for integrating PNI-based interventions into nursing practice could improve patient outcomes in chronic wound management. Future research should focus on long-term studies with larger sample sizes and standardized intervention protocols to further validate these findings.

Hospital Information Systems (HIS), or Sistem Informasi Rumah Sakit (SIRS), play a critical role in enhancing administrative efficiency, decision support, and healthcare service quality. However, their implementation and effectiveness vary significantly across healthcare settings, particularly in low- and middle-income countries (LMICs). This study aims to systematically evaluate the existing literature on HIS effectiveness, implementation barriers, and administrative impact. Using a PRISMA-based Systematic Literature Review (SLR) approach, we examined 14 high-quality studies from multiple scholarly databases including PubMed, Scopus, ScienceDirect, and Garuda. The review applied a hybrid thematic synthesis grounded in HOT-FIT and DeLone & McLean models, combined with a normalized quality scoring system. The findings reveal that HIS implementations positively influence administrative workflow, billing accuracy, and patient throughput, though outcomes are context-dependent. Key challenges include lack of interoperability, resistance to change, and insufficient training. Notably, regulatory mandates and national digital health policies were found to significantly enhance HIS adoption and sustainability. This review contributes a multidimensional synthesis of HIS performance, highlighting the importance of human, organizational, and policy alignment. It offers an evidence-backed framework for HIS evaluation that bridges theory and practice. We conclude that integrated, context-sensitive HIS models are essential for advancing hospital management and public health systems, and recommend further empirical studies on long-term impact and cross-sector integration.

With the rise of digital media, religious education faces new challenges and opportunities. This article investigates the integration of digital tools in teaching religious studies, focusing on their potential to make learning more engaging and accessible. The study analyzes both the benefits, such as interactivity and inclusivity, and the risks, including misinformation and digital distractions. Practical recommendations are provided for educators to effectively leverage digital platforms while maintaining the integrity of religious teachings.  

Industrial robotics has transformed the field of mechanical engineering, enhancing precision, productivity, and safety in various industrial applications. This article examines the key challenges and opportunities that industrial robotics presents within mechanical engineering, alongside an exploration of emerging technologies like AI-enhanced robotics, collaborative robots (cobots), and advanced sensor integration. By addressing the complex issues surrounding robotics and highlighting potential advancements, this paper provides insights into the future of robotics in industrial settings.

This article explores recent advancements in sustainable industrial innovation within the field of mechanical engineering. With the shift towards environmental consciousness, the integration of sustainable practices in manufacturing, energy use, and waste management has become crucial. This paper reviews current trends, emerging technologies, and projected future directions, providing insights into how mechanical engineering can play a pivotal role in fostering sustainable industrial practices.

This study presents the development of a smart grid system designed to efficiently integrate renewable energy sources into the existing electrical grid. The proposed system employs advanced communication technologies and real-time data analytics to optimize energy distribution and consumption. A simulation model was created to evaluate the system's performance under various scenarios, demonstrating significant improvements in energy efficiency and reliability. The findings indicate that the smart grid system can enhance the stability of the electrical network while promoting the use of sustainable energy sources.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.