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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.

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.

Energy efficiency has become a cornerstone in industrial optimization, reducing operational costs and contributing to sustainability. This paper reviews key innovative approaches in mechanical systems used to enhance energy efficiency within industrial applications. It covers advances in system design, smart technologies, automation, and predictive maintenance. By understanding these techniques, industries can make strides toward greener production processes, lower energy costs, and reduced environmental impact.

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.

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.

Applying ergonomic principles in work tool design for manufacturing industries is a crucial factor in improving productivity while maintaining worker health. This research aims to analyze the effectiveness of adaptive work tool design models based on cognitive and physiological ergonomic principles, identify interaction patterns between workstation design and operational performance, and develop a conceptual framework for integrating ergonomic principles into production cycles. The research method adopts a cognitive-physiological approach with qualitative analysis of human-machine interactions, biomechanical simulations using digital human modeling, and muscle load measurements through electromyography. Implementation was conducted using a participatory ergonomics approach and IMU sensor-based real-time monitoring systems. Results show that using materials with controlled deformation capabilities (15-20%) in work tools reduces muscle work by up to 27%, while adaptive automation system integration improves assembly accuracy by 18%. Workstations with ergonomic adjustments increase assembly speed by an average of 12%, and low-cost ergonomic interventions effectively improve productivity by 11-15% in resource-limited environments. Longitudinal analysis reveals that evidence-based ergonomic investments yield a 230% ROI through increased productivity, reduced injury compensation costs, and decreased employee turnover. IMU-based posture monitoring systems integrated with adaptive feedback loops reduced musculoskeletal disorder incidents by up to 41%. In conclusion, ergonomic optimization based on cognitive-physiological principles creates synergy between production efficiency and worker well-being, making it an essential component in achieving sustainable productivity.

This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.

This study examines the complex interplay between formal legal structures, customary practices, and rapid urban development in shaping land ownership dynamics in Batam's old villages (kampung tua). Through a qualitative case study approach, incorporating document analysis, semi-structured interviews, and field observations, we investigate the challenges in reconciling traditional land rights with modern property laws in the context of Batam's development as a free trade zone. Our findings reveal that approximately 70% of land parcels in the studied villages lack formal titles, highlighting the prevalence of informal ownership systems. The research identifies significant legal pluralism, where national land laws, local regulations, and customary (adat) practices coexist and often conflict. Notaries emerge as key actors navigating this complex landscape, often expanding beyond their formal mandate to mediate between different systems of authority and ownership. The study underscores the limitations of current legal frameworks in addressing the unique challenges of land administration in rapidly urbanizing areas with strong customary traditions. We propose the need for more adaptive land governance approaches that can accommodate both formal and informal ownership structures, including specialized legal frameworks for recognizing customary land rights in urban contexts. This research contributes to the broader understanding of land rights issues in the face of rapid urban development and offers insights for policymakers, legal practitioners, and urban planners grappling with similar challenges in other developing regions.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.

This study analyzes and optimizes production systems in the Industry 4.0 context, examining the fundamental shift from centralized, push-based production models to decentralized, adaptive, pull-based approaches. The research employs a mixed-method approach combining comprehensive literature review and multiple case studies across manufacturing sectors. Findings reveal that integration of Internet of Things (IoT), cyber-physical systems, artificial intelligence, and big data analytics enables real-time communication between production components, product personalization, and faster decision-making. Despite significant benefits in efficiency, flexibility, and competitiveness, implementation challenges persist, including high initial investment, employee resistance, technical expertise limitations, and integration complexity. Optimization approaches such as mixed-integer linear programming, digitally-integrated Lean Six Sigma, and digital twin simulations effectively enhance performance indicators including flexibility, reliability, and energy efficiency. The study concludes that successful production system transformation requires an integrated strategy encompassing process engineering, digital competency development, change management, and continuous evaluation to ensure sustainable optimization in the digital era

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.

This article explores the reframing of holistic education by integrating spirituality and ethics as core components to achieve transformative impact. The study highlights the limitations of conventional education systems that prioritize cognitive development while neglecting moral and spiritual dimensions. The objective is to propose a more comprehensive educational paradigm that nurtures learners as whole individuals—intellectually, emotionally, morally, and spiritually. Using qualitative literature analysis, this study identifies the role of spiritual and ethical integration in shaping character, enhancing empathy, and fostering social responsibility. The findings suggest that embedding these values within curriculum design and instructional practices contributes to meaningful personal development and community transformation. By addressing the growing need for a more inclusive approach to education, the article emphasizes how integrating spirituality and ethics can bridge gaps in traditional educational models. The research underscores the importance of fostering emotional and moral intelligence alongside intellectual growth, particularly in a world where global challenges require not only intellectual problem-solving but also ethical decision-making and empathy. The implications emphasize the need for inclusive and humanistic approaches in education, encouraging educators to act as facilitators of life values and agents of change. Through the integration of spirituality and ethics, education can help shape a new generation of compassionate leaders and engaged citizens. By reimagining holistic education through this lens, the article offers a framework that aligns academic achievement with ethical consciousness and spiritual growth, ultimately fostering a more just and compassionate society. This transformative approach offers a comprehensive model for educators and policymakers aiming to prepare students not only for professional success but also for meaningful lives rooted in integrity and responsibility.

Advances in digital technology in the current Industry 4.0 era have changed and influenced several aspects of human life. Suppose the Indonesian people do not want the identity and character of their community and their cultural riches to be lost at the hands of globalization. In that case, the community must have a robust defence mechanism. One way to build a defense system is through education. Technology and education are two things that cannot be separated. Pesantren is the oldest Islamic education and broadcasting institution in Indonesia. This research aims to examine and formulate the implications of the shift in orientation and value systems of Islamic boarding schools at the modern Islamic boarding school Nurussalam Sidogede (South Sumatra), the modern Islamic boarding school Darul A'mal (Metro Lampung), the Al-Hikmah Islamic boarding school (Bandar Lampung) and the modern Islamic boarding school Al -Muhajirin (Cikarang Bekasi) with an AGIL approach. This research uses a descriptive qualitative approach. The primary data used in this research is sourced from the author's interviews with the Islamic boarding school. The results show that there are several similarity and differences between the transformative Islamic boarding school and non-transformative Islamic boarding school groups in terms of variables or indicators, namely Adaptation (A), Integration (I), Goal (G), and Latent Patten (L).

National education faces challenges related to quality, efficiency and management. Several important problems in the education system include: (1) student moral behavior, (2) distribution of learning, (3) less efficient internal systems, (4) poor organization, (5) education management that is not in line with national development, and (6) lack of professionalism in resources. One effort to overcome this problem is to improve learning strategies, which are very important for the success of the learning process. One of the aspects studied in this journal is the teaching of the Islamic religion from Abuddin Nata's perspective. The results of the literature search show that an Islamic-based learning approach can shape student behavior. One effective strategy is to use an approach that is appropriate to the learning objectives and emphasizes skills and learning processes such as the Islamic education model which integrates skills, problem solving and memory formation.

The rapid growth of big data has significantly increased the demand for efficient and scalable data processing methods, particularly within cloud computing environments. This study aims to evaluate the effectiveness of distributed computing frameworks, specifically Apache Hadoop and Apache Spark, in optimizing big data processing. A qualitative approach using a Systematic Literature Review (SLR) method is employed to analyze existing studies related to distributed systems, cloud computing architectures, and performance optimization techniques. The analysis focuses on key performance indicators, including processing speed, resource utilization, and scalability, as well as the suitability of each framework for different data processing scenarios. The findings indicate that Apache Hadoop is highly effective for batch processing and storage-intensive tasks due to its disk-based architecture, while Apache Spark demonstrates superior performance in real-time and iterative processing through its in-memory computing capabilities. Additionally, system configuration factors such as cluster size, memory allocation, and network bandwidth are identified as critical elements influencing overall performance. The study also highlights emerging trends, including the adoption of hybrid cloud environments, the integration of artificial intelligence and machine learning, and the utilization of edge computing to enhance real-time data processing. In conclusion, distributed computing frameworks play a vital role in improving the efficiency and scalability of big data processing in cloud environments. The selection of an appropriate framework, combined with optimized system configuration, can significantly enhance operational performance and support data-driven decision-making.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

This article explores the role of religious education in shaping students' moral development through a comparative analysis of different educational systems. By examining the curricula and pedagogical approaches in various cultural and religious contexts, the study identifies commonalities and differences in how moral values are taught. The findings highlight the importance of integrating universal ethical principles alongside religious teachings to nurture well-rounded individuals. 

Negative ambition, thirst for power, which gives birth to fraud in constitutional politics, greed will tend to give birth to authoritarian government, which also facilitates uncontrolled corrupt behavior in all levels of the government system, the result is that corruption becomes a system that is difficult to judge effectively. right in front of the judiciary. The state's ideals which are the legal basis in the Proclamation are only historical, the State's goals as stated in Paragraph IV appear to be subject to multiple interpretations with negative ambitions and a monopoly in the control of natural resources by certain groups. The Constitutional Court has become a bone of contention. because it is considered the last bull of the constitution. The various systems and regulations as the basic foundation in the 1945 Constitution are the reason that it is no longer appropriate to the situation and must be repeatedly amended. The recruitment of State Rulers through the ELECTION system once every five years continues to be changed in a direction that is increasingly widening and even eliminating the true meaning of the goals of a State which is based on the Blessing of God. Almighty. Our constitutional history, which is quite long since the birth of the 1945 Constitution, will continue to be corrected for reasons of adjusting interests. In fact, the state is a tool to achieve the goals of the nation that agreed to form the state with the aim of protecting and ensuring the welfare of the people of that nation. The principles and foundations of the State have been established as the foundation in a state order, but our constitutional problems can only be resolved by changing the law and very minimally changing the concept of human thinking in that Pancasila has become the philosophy and goal of national life. The next question will be whether the Amendment to the 1945 Constitution will continue. carried out to adjust the circumstances of the desires or interests of the State authorities. Conflicts of interest in our state structure must return to the order of religious values ​​in religion which in Islamic psychology is known as Maqashidus syari'ah jurisprudence.

The growing environmental crisis underscores the need for education systems to foster ecological responsibility among students. This study explores the potential for multifaith schools to cultivate environmental moral education through an interreligious pedagogical model. By integrating diverse religious teachings on ecology, the proposed model aims to promote shared moral values for environmental protection and sustainability. The research addresses the gap in existing environmental education, which often lacks an integrated approach that incorporates various religious perspectives. Through a qualitative research design, the study analyzes curricula, observes classroom practices, conducts interviews with educators, and evaluates existing environmental education frameworks in multifaith schools. The study identifies key strategies, including the incorporation of eco-ethics from different religious traditions, project-based learning, and interfaith dialogues, as effective means of fostering ecological responsibility. However, challenges such as balancing doctrinal differences, overcoming biases, and developing inclusive pedagogy remain. The study emphasizes the importance of designing educational content that respects all faiths and promotes intercultural dialogue, thereby encouraging a collective commitment to sustainability. The findings suggest that multifaith schools can serve as powerful platforms for environmental moral education, highlighting the value of integrating religious perspectives into sustainability education. The study concludes with recommendations for incorporating interreligious eco-ethics into curricula and teacher training programs and suggests future research on the long-term impact of interreligious environmental education and its applicability in diverse cultural contexts.