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This research aims to develop an efficient and reliable information system for the financial report sorting process at the Office of the Provincial Financial and Asset Management Agency of North Sumatra, using a comprehensive project management approach. The research methodology involves user needs analysis, designing a responsive web-based system, development using the latest technology, and thorough testing to ensure system security and performance. The expected outcome is improved efficiency and accuracy in financial report sorting, with the potential for synergistic integration with existing systems for better public financial management in the region.

This research investigates the implementation of Customer Relationship Management (CRM) in the Micro, Small and Medium Enterprises (MSME) and startup sectors. Through a qualitative approach, data was collected from a number of MSMEs and startups to analyze the factors that influence the adoption and success of CRM implementation. The findings show that limited resources, lack of knowledge about the benefits of CRM, and dependence on technological infrastructure are the main challenges in adopting CRM systems. However, CRM has also been found to have great potential in increasing customer retention, developing more suitable products, and expanding into new markets. Practical implications of these findings include recommendations for appropriate resource allocation, employee training, and more effective digital marketing strategies. The contribution of this research lies in a deeper understanding of CRM implementation in the context of MSMEs and startups as well as practical guidance for improving business performance through effective CRM strategies.

Indonesian history records the important role of the Vereenigde Oost-Indische Compagnie (VOC) in shaping culture and government systems. The VOC, a Dutch trading company, was dominant in the colonial era, controlling territory that is now part of Indonesia, Malaysia and Singapore. In its influence, the VOC not only brought Western culture but also expanded corrupt practices in Indonesia. This study uses qualitative research methods with a focus on literature study. Data was obtained through analysis of primary and secondary sources, as well as interviews with historians. This approach allows for an in-depth understanding of VOC culture and the impact of the corruption it initiated. Corruption started by the VOC is still a continuing problem in Indonesia because it has affected culture, social structure and government. Efforts to overcome this problem require hard and consistent work from the government and society. The VOC's influence not only introduced corruption during the colonial period, but also left a lasting legacy that influences Indonesia to this day. This corruption has become integrated into the culture and system of government, requiring continued efforts to overcome its impacts.

Blockchain, originally developed as the technology behind cryptocurrencies such as Bitcoin, has gained attention for its potential to revolutionize cybersecurity. It offers a promising solution to the vulnerabilities of centralized systems by decentralizing data storage and implementing advanced encryption techniques. Blockchain technology and cryptocurrencies have presented significant challenges in the realm of cyber law to regulate them in the future, varying blockchain and crypto regulatory approaches in various countries, including regulations on crypto exchanges, ICOs, and compliance with anti- money laundering ( AML) regulations. and getting to know customers (KYC). explores the issue of data protection in blockchain transactions and i ts implications for cyber law, as well as the legality and enforcement of smart contracts. the impact of cryptocurrency use on cyber law, including challenges in tax compliance, law enforcement, and consumer protection. In the midst of various complexities regarding the direction of regulation of blockchain technology and cryptocurrencies in cyber law. with blockchain technology and cryptocurrencies and prepare views to face future challenges.

The purpose of writing this article is to determine the intersection of transformational leadership with visionary and situational leadership. This research uses a type of library research. Library research is research carried out by reviewing various journals and related reference books. The results of his research show that the transformative leadership model is a leadership model that can be an alternative to be applied in the current organizational system with various organizational dynamics and changes in line with the increasingly high demands of society. This is in line with the need for leaders who are able to bring about change. Transformative leadership is a leadership model that is different from traditional leadership models which view the relationship between leaders and employees (leadership and followership) as based solely on reward and punishment. A visionary leader is also understood as a leader who has a clear vision and mission in the organization. He has the ability to predict future events and express them in a clear and measurable vision and mission. Visionary leaders are able to raise the enthusiasm of their members with their motivation and imagination, to make the organization more alive and growing. Another situational leadership model is known as the Situational Contingency leadership model which revises the behavioral approach which is considered unable to explain ideal leadership. This approach illustrates that the style developed depends on the leader himself, the support of his followers, and a conducive situation. By analyzing the basic motivations of their subordinates, leaders can place them in appropriate situations. Transformative, visionary and situational leadership models have several elements in common, both in terms of systems, processes and objectives of Islamic Education Management. From the system side, there are elements of education unit leaders, educators, education staff, and various other elements; It is hoped that they can synergize in a process that is conducive to achieving goals.

This study aims to analyze the influence of human resource (HR) quality on the implementation of internal control systems at PT. Avia Avian. The background of this study is based on the importance of the role of quality HR in supporting the effectiveness of the implementation of internal control systems in companies. An effective internal control system is essential to maintain integrity, efficiency, and compliance with applicable regulations. The purpose of this study is to identify and analyze how HR quality can affect the implementation of internal control systems in companies. The method used in this study is a quantitative approach using a survey of employees in various departments of PT. Avia Avian. The data obtained were analyzed using regression techniques to examine the relationship between HR quality variables and internal control systems. The findings of the study indicate that there is a significant influence between HR quality and the implementation of internal control systems, where the better the quality of HR, the more effective the implementation of internal control systems. The implication of this study is that companies need to pay attention to improving HR quality as a strategic step to strengthen the existing internal control system, which in turn can improve the company's performance and compliance with established standards.Additionally, this research shows that continuous training and employee competency development can strengthen HR quality, which in turn contributes to the effectiveness of the internal control system. Companies that invest in improving HR quality will not only benefit in terms of compliance with regulations but also in enhancing operational efficiency and risk management. Therefore, it is crucial for the management of PT. Avia Avian to continue focusing on HR development to ensure the smooth implementation of internal control systems in accordance with applicable standards. This research contributes to the literature on risk management and internal control by highlighting the crucial role of HR quality in the success of internal control systems.

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).