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This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.

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.

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.

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 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.

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.

This community service activity aims to enhance students’ literacy through the application of ecoprint as an environmentally based creative learning medium at MAT Daarut Tahfidz Al-Ikhlas Banda Aceh. This program was motivated by the limited utilization of practice-based art activities as a means of strengthening students’ literacy. The implementation method employed a participatory-educative approach consisting of material socialization, ecoprint technique demonstrations, group-based hands-on practice, reflective discussions, and activity evaluation. The results indicate that students were able to understand the concepts and stages of ecoprint, produce works with various natural motifs, and demonstrate improvements in visual literacy and creativity. In addition, the activity increased students’ awareness of the use of natural materials and the importance of environmental friendly practices. Group-based activities also encouraged the development of social literacy through collaboration and communication among participants. Therefore, ecoprint can be considered an effective and contextual alternative learning medium to support the enhancement of students’ literacy in the school environment and has the potential to be sustainably developed.

The rapid development of digital technology has brought significant changes to the business landscape, transforming how products are marketed, services are delivered, and business relationships are built. In this context, students, as future members of the workforce and potential entrepreneurs, are required to possess strong digital literacy skills in order to effectively face challenges and seize emerging business opportunities. This study aims to analyze the importance of digital literacy in supporting students’ readiness to respond to future business trends. The research employed a descriptive approach using a literature review and observations of the entrepreneurship learning process in vocational schools. The findings indicate that digital literacy plays a crucial role in enhancing students’ creativity, adaptability, and ability to utilize digital platforms for online marketing, branding, and business communication. Furthermore, digital literacy helps students understand market dynamics, analyze consumer behavior, and adopt innovative business models that align with technological developments. Students with adequate digital literacy are better prepared to face rapid changes in the business environment and demonstrate higher confidence in applying technology to entrepreneurial activities. In conclusion, the integration of digital literacy into entrepreneurship education is essential to produce competitive, innovative, and adaptable graduates who are capable of thriving in the digital era and contributing to sustainable economic development.

Benign Paroxysmal Positional Vertigo (BPPV) is the most common cause of peripheral vertigo, characterized by brief episodes of vertigo due to otoconia displacement. Although most previous studies have focused on intrinsic factors such as age, gender, osteoporosis, and metabolic disorders, evidence regarding the role of environmental factors, particularly occupational noise exposure, is limited. Chronic noise has the potential to affect vestibular function through both sensory and vascular mechanisms. This study aims to narratively review the effect of occupational noise exposure on the risk of BPPV by integrating clinical, epidemiological, and experimental findings. The method used is a literature-based narrative review of the PubMed, Scopus, Web of Science, and Google Scholar databases without year restrictions, using the keywords "BPPV", "occupational noise exposure", "vestibular dysfunction", "VEMP", and "otoconia displacement". The search results obtained 25 relevant articles linking BPPV to otolith, hormonal, vascular, lifestyle factors, and occupational noise exposure. The results indicate that chronic noise can cause sensory damage (otoconia and vestibular hair cells), vascular disorders (hypertension, cardiovascular disorders, and inner ear microvascular circulation disorders), and exacerbate lifestyle comorbidities (sedentary lifestyle, osteoporosis, hypertension, diabetes). The discussion confirms that these multifactorial mechanisms explain the susceptibility of industrial workers to BPPV despite normal hearing function. The conclusion of this study is that workplace noise exposure has been shown to play a significant role as a risk factor for BPPV, therefore, prevention strategies, vestibular health monitoring, and healthy lifestyle interventions need to be optimized in occupational health programs.

Low back pain (LBP) is one of the most common musculoskeletal complaints globally and is the main cause of decreased work productivity. One of the risk factors that has been widely studied in the literature is prolonged sitting duration. Prolonged sitting activities cause increased static pressure on the lumbar spine, fatigue of the supporting muscles, as well as a decrease in local blood flow that can trigger chronic pain. This study is a literature review with a meta-analytical approach that aims to analyze the relationship between long-term sitting and the incidence of LBP. The review process was carried out following PRISMA guidelines through searches on PubMed, Scopus, ScienceDirect, and Google Scholar databases for publication in 2020–2025. Of the 20 previous studies analyzed, most showed a positive association between sitting time and an increased risk of LBP, both in adults, adolescents, and children. Sitting for more than 6 hours per day increases the risk of LBP by 33%, especially in individuals with low physical activity. In addition, longer sitting durations with poor posture and minimal active rest were found in the group of office workers and professional drivers who had a high prevalence of LBP. Physical activity has been shown to play a protective role against the risk of LBP, while ergonomic interventions such as lumbar support can reduce pain complaints. Although most studies were observational, the consistency of results supports the importance of prevention through reduced sitting time, increased physical activity, and ergonomic work environment settings. These findings provide a scientific basis for multidimensional interventions to effectively reduce the incidence of LBP.  

Academic stress has become a significant psychological issue among university students, particularly during periods of high academic pressure such as examinations, intensive coursework, and academic performance demands. Excessive academic stress may negatively affect students’ psychological well-being, learning motivation, and academic performance. This study aims to explore in depth the experiences of academic stress and psychological responses among students during periods of high academic pressure. A qualitative descriptive approach with a phenomenological perspective was employed to capture students’ lived experiences. Data were collected through in-depth semi-structured interviews with six student participants selected using purposive sampling. The data were analyzed using thematic analysis, involving open, axial, and selective coding to identify emerging themes. The findings reveal that the main sources of academic stress include excessive academic workload, time management difficulties, high self-expectations, and external academic pressures. Students experienced varying levels of stress ranging from mild to severe, manifested through psychological responses such as anxiety, emotional instability, mental fatigue, decreased concentration, and reduced motivation. The study also found that students applied diverse coping strategies, including adaptive strategies such as time management, seeking social support, and self-reflection, as well as maladaptive strategies such as procrastination and emotional withdrawal. These findings highlight that academic stress is a complex and subjective experience influenced by individual perceptions and coping capacities. The study implies the importance of supportive academic environments and accessible mental health services in higher education institutions to promote students’ psychological well-being and resilience. The results may serve as a reference for developing preventive and promotive mental health programs for students facing academic stress..    

Formal organizational structures are crucial for ensuring the effectiveness, efficiency, and accountability of work processes within an institution. However, many organizations still face challenges such as overlapping tasks, ineffective communication, and inadequate control mechanisms. This study aims to analyze formal organizational structure design strategies through systematic steps, implementation mechanisms, and ongoing evaluation. The research method used is a literature review, based on a review of the latest scientific sources (books, journals, and research reports) related to organizational theory and structural design. The results indicate that organizational structure design should begin with defining objectives, analyzing activities, departmentalizing, establishing hierarchical relationships, coordinating mechanisms, and evaluating. Implementation mechanisms include socialization, human resource capacity development, process integration with technology, monitoring, and change management. The literature review also confirms that alignment between strategy, structure, and business processes is a key factor in improving organizational performance. Therefore, organizational structures must be adaptive, responsive, and data-driven to face the challenges of an ever-evolving strategic environment.

This study aims to analyze the effectiveness of waste management in preventing fires at the Alak Landfill (TPA) in Kupang City using a qualitative case study approach. Data was collected through interviews, observations, and documentation involving officials from the Environmental and Sanitation Office, landfill workers, waste pickers, and surrounding communities. The findings indicate that the effectiveness of waste management remains low. In terms of input, financial resources, infrastructure, and human resources are insufficient to support optimal operations. During the process stage, waste collection and transportation are hindered by low public compliance, limited vehicles, and minimal waste segregation. Final waste processing still relies on the open dumping method, which increases waste accumulation, methane generation, and fire risks. In terms of output, various mitigation efforts have not successfully reduced the recurring fire incidents due to limited facilities, inadequate compaction, and the absence of gas monitoring systems. The results highlight the need for increased budget allocation, improved facilities, enhanced human resource capacity, and the implementation of safer and more sustainable waste management methods to reduce fire risks at the Alak Landfill.

Drug abuse and risky social behavior remain significant social issues among adolescents, particularly in rural areas experiencing rapid information exposure and lifestyle changes. This community service program aimed to enhance adolescents’ awareness and resilience toward the dangers of drug abuse (NAPZA) while promoting a healthy lifestyle through the cultivation of family medicinal plants (TOGA) in Karangmalang Village, Ngawi Regency. The program employed a participatory descriptive approach, actively involving adolescents as the primary beneficiaries. The implementation consisted of two main stages: interactive socialization on drug abuse and risky behavior, and a workshop on cultivating medicinal plants as a form of community-based healthy living practice. The results indicated that the socialization activities improved adolescents’ understanding and awareness of drug abuse risks, as reflected in their active participation during discussions. Furthermore, the TOGA cultivation workshop provided practical benefits by introducing simple herbal remedies and offering positive, sustainable activities for the community. Overall, the integration of educational and practical approaches proved effective as a preventive and empowerment strategy to strengthen adolescent resilience and foster a healthier and more productive village environment.

This research aims to analyze the influence of job placement, fringe benefits, and work facilities on the performance of employees at Jawa Pos Kediri, both partially and simultaneously. The method used is quantitative with a descriptive and associative approach. Data were collected through the distribution of questionnaires to 50 respondents who are employees of Jawa Pos Kediri. The data analysis techniques used include multiple linear regression, t-test (partial), F-test (simultaneous), and the coefficient of determination (R²). The results indicate that both partially and simultaneously, job placement, fringe benefits, and work facilities have a positive and significant impact on employee performance. The R² value of 0.944 indicates that 94.4% of the variation in employee performance can be explained by these three variables, while the remaining 5.6% is influenced by other factors. Therefore, this study emphasizes that appropriate job placement, adequate benefits, and sufficient work facilities are important factors in improving employee performance at Jawa Pos Kediri. Thus, the company management needs to pay more attention to these three aspects to create a productive and sustainable work environment.

Collaborative governance is a decision-making approach that brings together multiple stakeholders to work toward a shared objective. This study aims to describe the collaborative governance process in poverty alleviation through the Tasik Bageur Program in Tasikmalaya City. Poverty in the city is a multidimensional issue that affects social, economic, and environmental aspects of community life. In response, the Tasikmalaya City Government initiated this program as a collaborative effort involving government institutions, private sector actors, and academic organizations. The program is implemented every Wednesday and is designed to foster cooperation and mutual support in addressing poverty-related needs.Although the program has succeeded in attracting participation from several private and academic actors, this study identifies a major challenge: the number of poor residents remains significantly higher than the number of collaborating partners. This imbalance limits the amount and diversity of assistance provided and reduces the program’s overall effectiveness.This research employs a qualitative descriptive approach, with data collected through observations, interviews, and document analysis. The findings indicate that the collaborative process within the Tasik Bageur Program has not yet reached optimal implementation. Limited understanding and low awareness among private-sector actors hinder broader participation. As a result, the gap between community needs and the available collaborative resources remains substantial.

Health workers contribute up to 80% to the success of health development. For this reason, effective employee performance management is needed according to service performance for communicable and non-communicable diseases. The aim of this research is to analyze the factors that influence the performance of nurses in health services for non-communicable diseases and infectious diseases at the UPT Puskesmas Kuala, Kuala District, Langkat Regency.The research design used was an analytical survey with a cross sectional approach. The population of this study were all 31 nurses with a sample using a total sampling technique of 31 people. The data analysis used is the binary logistic regression test.The research results show that ability has a sig-p value of 0.025, attitude sig-p 0.014, learning sig-p 0.036, motivation sig-p 0.022, leadership sig-p 0.031 and work environment sig-p 0.048, which means it has an influence on nurse performance , while age sig-p 1.000 and perception sig-p 0.496 have no influence on nurse performance. Attitude is the dominant factor with an OR value of 28.446. In conclusion, ability, attitude, learning, motivation, leadership, and work environment influence nurse performance, while age and perception have no effect. Attitude is the most dominant factor influencing nurse performance.

This study aims to describe the condition of clean water access and its impact on public health in Swadaya Village. The research employed a qualitative approach through in-depth interviews with household heads, community leaders, health workers, and local water managers. The findings reveal that access to clean water in Swadaya Village remains very limited. Most residents rely on dug wells and river water for daily needs, while only a small portion use water from the local water utility (PDAM) due to high installation costs and limited pipeline coverage. The water quality often decreases during the dry season, becoming cloudy, odorous, and causing skin irritation. These conditions contribute to the increasing incidence of waterborne diseases such as skin infections, diarrhea, and typhoid fever. Besides infrastructure limitations, the lack of community awareness regarding hygiene and sanitation practices worsens the situation. Although the village government has proposed clean water development programs, their implementation has been hampered by budget constraints. The study concludes that clean water problems in Swadaya Village are not only related to resource availability but also to environmental management and community behavior. Therefore, improving water infrastructure must be accompanied by community education to promote a healthy, safe, and sustainable living environment.

The phenomenon of burnout has evolved into a significant occupational health problem with a prevalence reaching 82% across various global work sectors, especially post-COVID-19 pandemic. This study aims to identify and analyze the causal factors of burnout through a systematic literature review approach using PRISMA 2020 guidelines. Literature search was conducted on four main electronic databases yielding 309 articles which were then selected down to 10 high-quality articles meeting inclusion criteria for the 2021-2025 publication period. Synthesis results indicate that burnout is a multidimensional phenomenon influenced by complex interactions between organizational and individual factors. Dominant organizational factors include work overload, lack of managerial support, unsafe working conditions, and resource inadequacy with burnout prevalence ranging from 17-94%. Individual factors encompass demographic characteristics, coping strategies, resilience, and internal psychological conflicts that serve as moderator variables. Job satisfaction and work meaningfulness function as mediator variables connecting organizational conditions with burnout outcomes. These findings provide theoretical contributions in understanding burnout etiology and can practically serve as a foundation for developing evidence-based preventive interventions to reduce burnout prevalence in work environments.

This study examines community capacity building for flood mitigation in flood-prone regions of Central Kalimantan through disaster education, technical training, and the construction of an amphibious house prototype. Using a community-based disaster risk reduction (CBDRR) framework, the program integrates participatory training, field surveys, and adaptive structural innovation. Findings indicate a 40% increase in community knowledge based on  Community Empowerment Level Analysis results, active engagement of 35 participants in disaster education, and significant improvement in technical skills among 22 trainees involved in amphibious foundation construction. The prototype achieved 100% completion within four effective working days, demonstrating the feasibility of amphibious technology using locally available materials. Strengthening youth organization structures further enhances community readiness and institutional resilience. Overall, the integration of participatory learning and adaptive technology effectively builds community self-efficacy and disaster preparedness in flood-prone environments.