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In Indonesia, maritime safety remains a problem. Ship captains and ship owners often disregard maritime safety standards, particularly for fishing vessels. Provisions for criminal liability for domestic fishing vessels that fail to meet maritime safety standards are an important legal instrument for regulating and enforcing maritime safety regulations in the fisheries sector. Failure of vessels to comply with safety requirements, such as the completeness of safety equipment, engine condition, and vessel structure, can potentially lead to accidents that endanger the lives of crew members and negatively impact the marine environment. This study uses a normative research method with a legislative approach. This article focuses on examining aspects of criminal liability, including the subjects, objects, and legal sanctions imposed for violations of maritime safety standards. The results indicate that perpetrators of violations can be subject to criminal sanctions in the form of fines, detention, and even revocation of vessel operating permits, taking into account human rights and environmental protection. Effective and synergistic law enforcement between relevant authorities is essential to provide a deterrent effect, encourage compliance with safety standards, and improve the system for the sustainability of marine resources.

Highways play a strategic role in supporting economic development, so the quality of pavement materials (flexible pavement) must meet previously established quality standards. The Asphalt Concrete Wearing Course (AC-WC) layer, as the topmost layer of flexible pavement, is susceptible to damage caused by aging triggered by environmental conditions, particularly ambient temperature. Temperatures above 30°C are known to accelerate plastic deformation (rutting) and reduce pavement bearing capacity. The Meteorology, Climatology, and Geophysics Agency indicates that temperatures in Lhokseumawe City, North Aceh, range from 33.7–35.3°C with day-night fluctuations that could potentially accelerate the degradation of mechanical properties. This study aims to analyze the Marshall characteristics of AC-WC asphalt mixtures under ambient temperature conditions. The testing was conducted with varying exposure times of 0, 15, 30, 45, 60, 75, and 90 days at a minimum temperature of 23.90°C and a maximum temperature of 31.80°C, with an average humidity of 87.93%. The test results showed that the AC-WC asphalt mixture experienced a decrease in stability value of 21.37% and an increase in flow value of 36.36%, resulting in a decrease in the Marshall Quotient (MQ) value of 42.52%.

The use of Information Systems and Information Technology (IS/IT) is a crucial factor in improving the efficiency and accuracy of retail sector operations. PT XYZ, a retail-level staple goods provider, has implemented IS/IT to support sales recording, inventory management, security, and operational reporting. This study aims to assess the effectiveness, efficiency, security, and compliance of the information system used. The audit was conducted through observation, interviews, document review, and system testing, referring to the COBIT framework and the ISO/IEC 27001 standard. The audit results indicated that the system supported core operational functions, but weaknesses remained, including insufficient system documentation, limited access controls, and suboptimal data security and backup mechanisms. Furthermore, reliance on manual procedures has the potential to lead to errors and slow down service delivery. Therefore, system improvements, strengthening security controls, developing standard operating procedures, and enhancing the competency of human resources and IT infrastructure are needed. This audit is expected to support improved service quality and secure, effective, and sustainable IT governance at PT XYZ.

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

Composite materials have the potential to be used as an alternative material for e-scooter frames due to their low weight and good mechanical strength. The mechanical properties of composite materials are strongly influenced by their constituent materials, particularly the fiber fraction. This study aims to analyze the effect of fiberglass composition variations on the tensile strength of composite materials as an alternative for e-scooter frames. The results show that increasing the fiberglass content leads to an improvement in the tensile strength of the composite material. The tensile strength increased by 51.52% when the fiberglass content was increased from 40% to 50%, while an increase of 19.5% was observed from 50% to 60%, indicating a decreasing rate of improvement. At higher fiberglass compositions, voids were observed due to imperfections in the manufacturing process, which affected the bonding between the fiber and the resin. In addition, increasing the fiberglass content resulted in a reduction in tensile strain from 1.1% at 40% fiberglass to 0.55% at 60% fiberglass, indicating that the material became stiffer and more brittle. These results indicate a trade-off between increased tensile strength and reduced ductility of the composite material.

The advancement of automotive technology has accelerated the adoption of renewable‑energy‑based electric vehicles, including the integration of solar panels on electric scooters. Indonesia’s tropical climate provides abundant solar energy potential; however, the limited surface area of scooters often restricts panel placement to the footrest section. This study aims to evaluate the impact of using a 10 mm clear acrylic cover on the performance of a 50 Wp monocrystalline solar panel in an electric scooter battery‑charging system. An experimental method was employed by comparing the panel’s performance under two conditions: without a cover and with the acrylic cover installed. Key parameters observed included voltage, current, and charging power, recorded using a data logger. Tests were conducted for 30 minutes under varying solar radiation intensities. The results indicate that the acrylic cover reduces the panel’s output power, from 55 W to 45 W at a solar radiation intensity of approximately 1100 W/m². These findings suggest that the use of an acrylic cover must be carefully considered to maintain optimal charging system performance.

Leaf springs serve as vehicle weight supports and vibration dampers from uneven roads. Reducing vehicle weight can support fuel consumption reduction. The use of composite materials allows for a reduction in leaf spring weight without reducing load capacity and stiffness. The purpose of this study was to find the composition of composite leaf springs with a polyurethane matrix that were resistant to tensile and flexural tests using e-glass, epoxy, and polyurethane materials. This study used an experimental method, in which specimens were tested using a tensile and flexural testing machine. The variations included polyurethane matrices of 10%, 20%, and 30%. The data was statistically analyzed using Excel to determine the significant effect of the variables. The results showed the effect of polyurethane variation on the composite. The tensile test showed that the greatest tensile stress was on the 30% polyurethane specimen at 1.574 N/mm² and the smallest was on the 10% specimen at 7.007 N/mm². In the flexural test, the greatest effect on flexural strength was observed in the 30% specimen at 14.36 MPa and the smallest in the 10% specimen at 25.82 MPa. Without the addition of polyurethane, the tensile stress was 39.678 N/mm² and the flexural strength was 157.09 MPa. Conclusion: The addition of polyurethane reduces the mechanical strength of composite leaf spring material without polyurethane addition.

Mr. Ujang Cracker Factory, located in Pakiskembar Village, Pakis District, Malang Regency, is one of the UMKM that produces crackers. Most of the machines used in cracker processing at Mr. Ujang Cracker Factory still use a simple mechanical system. One of these machines is a cracker molding machine, where the speed is still adjusted manually by replacing the pulley. This study aims to recondition the cracker molding machine by adding a control panel in the form of a VSD inverter with the aim of facilitating the speed control process in operating the cracker molding machine. This study was conducted by analyzing the consistency of the influence of frequency and pulley diameter variations on the speed of the electric motor and mechanical movements in the cracker molding machine. The results of the study show that the frequency of the VSD inverter and the pulley diameter have a significant effect on the entire mechanism of the cracker printing machine. Based on the standard deviation value, the consistency of the pressing movement speed was obtained at a frequency of 40 Hz with a pulley diameter of 2 inches. From this study, it can be concluded that the overall frequency of the VSD inverter is the main parameter in determining the speed and stability of the cracker printing machine. Meanwhile, the variation in pulley diameter only serves as an additional mechanical adjustment to increase or decrease the output speed from the electric motor to the mechanical movement of the cracker printing machine.

Equitable vaccine distribution is a global issue that has received increasing attention, especially since the increasing need for vaccines in the face of modern pandemics. This study aims to analyze the inequality in vaccine distribution and the factors influencing vaccine hesitancy through a descriptive qualitative approach, utilizing literature studies and supporting data in the form of graphs. The analysis results show that high-income countries have significantly greater access to vaccines than middle- and low-income countries, creating inequalities that impact public health protection. Furthermore, levels of vaccine hesitancy were found to vary across social groups, with adolescents being the group with the highest rate of rejection due to the influence of misinformation and low trust in health institutions. These findings confirm that the success of a vaccination program is determined not only by the availability of equitable distribution but also by public acceptance, which is influenced by social, psychological, and ethical factors. Overall, this study emphasizes the importance of applying bioethical principles such as justice, beneficence, and autonomy in formulating effective and inclusive vaccination policies.

This study identified the use of abbreviations in Medical Treatment Consent Forms (SPTK) at X Hospital Indonesia. A quantitative cross-sectional descriptive approach was applied to 76 SPTKs in September 2024, and questionnaires were administered to 30 patient-responsible physicians (DPJP). The results showed that 75% of SPTKs contained abbreviations, even though 97% of respondents understood the risk of miscommunication to patient safety. The state of the art includes accreditation standards that prohibit the use of abbreviations in informed consent, with global orthopedic studies reporting a decrease from 54% to 22% after educational interventions, as well as Indonesian regulations, namely Peraturan Mentri Kesehatan (Permenkes) Republik Indonesia No. 24/2022, which emphasizes that medical records must be complete. The novelty lies in the first empirical analysis in Indonesian hospitals to reveal the disparity between high physician knowledge and low documentation compliance, contributing to the development of evidence-based monitoring for patient safety. These findings support recommendations for daily review of SPTK, ongoing socialization, and integration of digital checklists to reduce medical errors.

Distributed software systems face significant challenges related to data quality due to their complex, decentralized architecture. These systems often involve multiple nodes responsible for processing and storing data, making it difficult to maintain consistency and ensure accurate data across the entire network. In particular, issues like data inconsistency, latency, and data fragmentation are prevalent in distributed environments. To address these challenges, this study proposes an integrated data quality governance strategy that combines real time monitoring and automated anomaly detection using machine learning models. The proposed strategy aims to improve data consistency, enhance anomaly detection capabilities, and reduce the need for manual intervention, ultimately improving overall data governance in distributed systems. Real time monitoring ensures immediate identification of data issues as they occur, while machine learning models, such as autoencoders and Isolation Forests, automate the detection of anomalies based on high reconstruction errors and data isolation techniques. The study evaluates the proposed strategy through real-world distributed system scenarios, comparing its effectiveness to traditional approaches like periodic audits and manual validation. Results demonstrate that the integrated approach leads to faster anomaly detection, reduced data inconsistencies, and improved overall system performance. The use of advanced machine learning techniques and real time analytics significantly enhances the system's ability to maintain high data quality standards across multiple distributed nodes. This strategy has wide-ranging implications for industries that rely on distributed systems, such as finance, healthcare, and IoT, where data integrity is essential for operational success. Future research can focus on integrating more advanced machine learning techniques and optimizing the real time monitoring framework to handle larger and more complex systems.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

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 complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

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.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

This study investigates the integration of computational thinking (CT) principles with adaptive curricula to enhance problem-solving skills in undergraduate programming education. Traditional programming curricula often emphasize syntax and basic concepts, neglecting critical problem-solving strategies. The adaptive curriculum framework used in this study combines CT skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking with personalized learning experiences. A mixed-method approach, combining qualitative and quantitative research, was employed to assess the effectiveness of this integrated approach. The results show significant improvements in students' problem-solving abilities, conceptual understanding, and engagement compared to a control group following a traditional curriculum. Students in the experimental group, which received the adaptive curriculum, demonstrated better performance in applying algorithms and debugging code. Additionally, students expressed higher levels of engagement and motivation, suggesting that the personalized learning environment fostered greater academic involvement. The study highlights the importance of integrating CT principles with adaptive learning frameworks to create a more inclusive and effective learning environment that accommodates diverse learning needs. The findings suggest that adaptive curricula can bridge gaps in traditional education by providing personalized support and ensuring that students progress at their own pace. This approach is especially beneficial for programming education, where both conceptual understanding and practical problem-solving skills are critical for success. Future research should explore the long-term impact of adaptive learning frameworks and investigate how these technologies can be integrated with traditional teaching methods to maximize their effectiveness.