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Semantic interoperability remains a major challenge in large scale distributed information systems due to heterogeneous data schemas, diverse contextual interpretations, and the dynamic nature of distributed environments. Traditional metadata-based interoperability approaches are often insufficient to address these challenges, as they lack semantic expressiveness and adaptability. This study proposes a context aware knowledge graph framework to enhance semantic interoperability across heterogeneous distributed systems. The research adopts a design-oriented methodology involving requirement analysis, knowledge graph construction, ontology modeling and alignment, context aware semantic representation, and semantic reasoning. A prototype implementation is developed to evaluate the effectiveness of the proposed framework through interoperability scenarios and cross-system semantic queries. The results demonstrate that the proposed approach significantly improves semantic alignment accuracy, query precision, and recall compared to conventional metadata-based solutions. The explicit integration of contextual information and ontology-based reasoning enables adaptive semantic interpretation and reduces ambiguity across systems. Overall, the findings confirm that combining knowledge graphs with ontology modeling and context aware mechanisms provides a robust and scalable solution for improving semantic interoperability in complex distributed information systems.

Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using incremental learning techniques and statistical drift detection mechanisms. The model integrates an Adaptive Drift Detector (ADD) and Incremental Learning System, enabling real-time adjustments to data distribution changes. The model is evaluated across synthetic and real-world datasets, demonstrating its superior ability to detect abrupt, gradual, and recurring drifts compared to traditional models. Experimental results indicate that the adaptive model maintains high prediction accuracy, minimizes false positive rates, and reduces detection delays. Furthermore, the model performs well in resource-constrained environments, making it suitable for real-time applications such as healthcare prediction, fault detection, and IoT systems. Despite its promising performance, the study identifies challenges related to computational complexity and the model’s performance with imbalanced datasets and noisy data. Future research should focus on optimizing the model’s scalability, computational efficiency, and adaptability to more complex data types to ensure broader applicability in dynamic environments. This work contributes to advancing the detection and adaptation of concept drift, offering a robust solution for dynamic and evolving data streams.

Cloud native architectures are essential for modern software systems due to their ability to handle dynamic environments, scalability, and high availability. However, ensuring resilience in these systems remains a significant challenge, particularly under varying operational conditions such as high-load periods and failure scenarios. This study aims to assess the resilience of cloud native architectures using quantitative metrics that objectively evaluate key attributes such as availability, fault tolerance, recovery time, and scalability. Through the application of these metrics, the study identifies the strengths and weaknesses of the architecture, providing insights into how the system performs under stress and recovers from failures. The results show that while the architecture demonstrates strong availability and scalability under typical conditions, recovery time and scalability under extreme load conditions reveal areas for improvement. Specifically, issues with resource allocation and self-healing capabilities were identified as key weaknesses affecting the overall resilience of the system. These findings highlight the importance of using data-driven metrics to gain detailed insights into system resilience and to guide architectural improvements. The study also emphasizes the need for continuous monitoring and adaptation of the architecture to optimize fault tolerance and recovery processes. The implications of this research extend to cloud application developers and architects, offering actionable recommendations for improving system resilience. Future research could focus on integrating real-time monitoring systems, developing more advanced resilience metrics, and incorporating AI-driven scaling techniques to further enhance the adaptability and robustness of cloud native systems. By addressing these challenges, cloud native architectures can be better equipped to maintain high performance and reliability in dynamic, real-world environments.

This study investigates the integration of high performance communication protocols with adaptive signal processing engines in multi-core systems, aiming to enhance scalability, throughput, and inter-core communication efficiency. The challenges inherent in traditional multi core architectures, such as communication overhead, latency, and scalability limitations, are addressed through the incorporation of Network-on-Chip (NoC) architectures and adaptive signal processing techniques. By using a multi-core digital signal processing (DSP) platform, the study evaluates the performance improvements achieved by this integration under varying workloads and core configurations. The experimental results show a 35% improvement in throughput and a 25% reduction in communication latency, highlighting the effectiveness of adaptive communication protocols in managing data traffic between cores and reducing bottlenecks. The integration of NoC architecture facilitates parallel data transfers, while adaptive signal processing engines ensure that data flows more efficiently across the cores, enhancing system responsiveness, especially under high data rate conditions. Furthermore, the study explores the scalability of the proposed system, demonstrating its ability to maintain high performance as core counts increase. The findings emphasize the potential of combining advanced communication protocols with adaptive signal processing for optimizing multi-core system performance. Practical implications of this research include the design of scalable, flexible, and efficient multi core architectures suitable for complex, data-intensive applications. Future research should focus on further refining communication protocols and exploring additional integration strategies to enhance the adaptability and scalability of multi-core systems in next-generation computing environments.

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.

The rapid development of globalization and the acceleration of digital transformation have encouraged organizations to adopt more adaptive and collaborative work practices. In this context, collaborative culture has become a strategic element that plays a crucial role in enhancing organizational effectiveness and competitiveness. This study aims to comprehensively examine the concept, characteristics, forming factors, and theoretical foundations of collaborative culture in modern organizations. The research employs a qualitative approach through a literature review, analyzing reputable national and international journal articles, textbooks, and relevant institutional reports. Data analysis is conducted using a descriptive-analytical technique by synthesizing findings from previous studies. The results indicate that collaborative culture significantly contributes to improved communication quality, work coordination, adaptability, and both individual and organizational performance. Collaborative culture is shaped through the integration of shared vision, open communication, trust, willingness to share resources, collaborative leadership, flexible organizational structures, and the support of collaborative technologies. This study also highlights that the success of digital transformation largely depends on the strength of an effectively internalized collaborative culture. The findings are expected to provide a theoretical reference for organizations and researchers in developing sustainable collaborative culture strategies.

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 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 presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

Digital transformation initiatives have become essential for organizations seeking to remain competitive in today’s rapidly evolving technological landscape. However, many organizations face challenges due to ineffective Information Systems (IS) governance, which hampers strategic decision-making and the successful execution of these initiatives. This study aims to develop an IS governance framework that enhances decision-making quality by aligning IT decisions with organizational goals during digital transformation efforts. The proposed framework addresses existing gaps in current IS governance models, offering a solution to common challenges such as inadequate governance structures, resource constraints, and misalignment between IT and business strategies. The framework was developed through a mixed-method approach, including conceptual framework development, expert consensus via the Delphi method, and organizational validation studies. Key findings reveal that the framework improves transparency in decision-making, enhances accountability for IT decisions, and ensures better alignment between IT strategies and organizational objectives. By embedding agile leadership and data-driven decision-making principles, the framework enables organizations to respond effectively to the fast-changing dynamics of digital transformation. This study also compares the proposed framework to existing models such as COBIT and ITIL, highlighting its unique features, including its adaptability to the fluid nature of digital transformation. The framework's strengths include its comprehensiveness and flexibility, though its application may face challenges in organizations with limited digital maturity or rigid governance structures. Future research directions include exploring the integration of emerging technologies into the framework and its applicability across different organizational contexts.

Traffic accidents not only cause physical injuries but also significantly impact the psychological state of victims, such as trauma, anxiety, excessive fear, and other emotional disturbances. Post-accident psychological trauma can hinder daily activities and reduce the victim's quality of life if not properly treated. This study aims to examine the role of individual counseling in assisting the recovery process of psychological trauma in traffic accident victims. The study used a qualitative approach with a phenomenological design with three informants who were traffic accident victims and experienced psychological trauma. Data collection was conducted through in-depth interviews to explore the victims' subjective experiences during the recovery process. The data obtained were analyzed using thematic analysis techniques. The results of the study indicate that individual counseling plays a significant role in helping victims recognize and manage negative emotions, reduce anxiety and fear levels, and rebuild a sense of security, self-confidence, and adaptability. Thus, individual counseling is an effective intervention in supporting the ongoing psychological recovery of traffic accident victims.

Communication is a fundamental element in the development of entrepreneurship, particularly within edupreneurship, which integrates educational innovation with entrepreneurial values to address contemporary challenges in education. In an increasingly competitive and digitally driven educational landscape, edupreneurs are required not only to possess pedagogical competence but also strong communication skills to sustain and scale their initiatives. This study aims to analyze the role of effective communication in strengthening edupreneurship competencies. The research employs a literature review method by synthesizing and analyzing relevant theories and previous studies on communication, entrepreneurship, and edupreneurship. The findings indicate that effective communication plays a significant role in enhancing key edupreneurship competencies, including negotiation skills, leadership capacity, personal branding, and adaptability to digital transformation. Communication also functions as a strategic tool for building trust, expanding professional networks, fostering collaboration, and creating supportive learning ecosystems within educational enterprises. Moreover, effective communication enables edupreneurs to articulate value propositions clearly, manage stakeholders efficiently, and respond flexibly to dynamic changes in educational markets. The implications of this study emphasize that communication competence is not a complementary skill but a core requirement for sustainable edupreneurship development. Therefore, systematic communication training and the integration of communication-based learning strategies are strongly recommended within entrepreneurship education programs, particularly for prospective edupreneurs, to support long-term innovation, competitiveness, and sustainability in the education sector.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

This research extends the previous work of Tsaqila, Winiarti, and Widaningrum (2024), who applied the Complex Proportional Assessment (COPRAS) method within a decision support system for supermarket branch location selection. Unlike the prior study, which focused on Ponorogo through a web-based framework, this study expands the implementation of COPRAS to the Medan Area, Medan Kota, Medan Polonia, dan Medan Maimun districts, adapting it to local urban, social, and economic characteristics. The main objective is to identify the most strategic site for a new supermarket by analyzing multiple criteria, including land cost, population density, accessibility, safety, cleanliness, and disaster risk. Data were collected from both field surveys and official government publications. The findings reveal that the COPRAS method provides reliable and objective assessments among the evaluated alternatives, with Medan Area emerging as the most suitable location for supermarket development. Overall, this study broadens the practical scope of the COPRAS method in a different regional context and reinforces its reliability and adaptability as a multi-criteria decision-making tool in the modern retail industry.

This study developed the Weton-Based Leadership Model as a leadership framework that integrates Javanese cultural values from the weton system with modern leadership theories, such as transformational, servant, charismatic, and situational leadership. Using a postmodern paradigm with an exploratory qualitative approach, this study utilizes pattern matching and explanation building methods through in-depth interviews with cultural experts and human resource management practitioners, as well as analysis of Javanese cultural documents. The results of the study identify five key components in the model, namely self-awareness, value alignment, situational adaptability, team harmony, and risk governance. These five components interact with each other to form contextual leadership that is in harmony with personal identity, organizational culture, and environmental demands. The practical implications of this study include the use of weton as a reflective instrument in recruitment, personalized leadership development, and the strengthening of an inclusive organizational culture. Further research is recommended to test this model in various industrial contexts through quantitative methods and longitudinal approaches.

The rapid advancement of information technology has encouraged business actors to adopt digital transformation; this situation is also experienced by Pabrik Tahu Macanan, a small scale tofu factory in Magelang that still relies on manual systems in operation. This  study aims to analyze the implementation of management information systems in supporting digital transformation and risk management at Pabrik Tahu Macanan; a descriptive qualitative approach was applied, using interviews, observations, and documentation as date collection methods. The findings reveal that digital information systems have the potential to improve efficiency, recording accuracy, and internal control; however, their implementation remains limited due to human resource constraints and low adaptability to new technologies. The research also found that simple risk management practices such as regular machine maintenance and manual bookkeeping remain effective in maintaining business stability. The implication of this study indicates that a gradual implementation of digital based information systems, supported by training and supervision, can serve as a strategic step to enhance competitiveness, operational efficiency, and sustainability for traditional SMEs like Pabrik Tahu Macanan.

The aim of this research is to develop a new strategic model for Indonesian border defense by recalibrating the Lykke Framework. It assesses the relevance of the traditional ends–ways–means framework in addressing modern border threats and proposes adding a risk pillar to improve adaptability and multi-domain integration. Using a Systematic Literature Review (SLR), the study examines policy developments, defense doctrines, surveillance technologies, and geopolitical dynamics influencing Indonesian border security. Literature was sourced from international and national databases (ScienceDirect, SpringerLink, SINTA, BRIN, etc.) for publications between 2018–2025. The findings reveal that Indonesia's border regions face complex threats such as sovereignty violations, transnational crime, cyberattacks, and ideological penetration. These challenges highlight the inadequacy of the traditional ends–ways–means framework without incorporating a fourth risk pillar. The study introduces the New Lykke Model, which enhances the strategic framework for integrated military management, considering geopolitical, operational, socio-cultural, and environmental risks. This model offers practical guidance to stakeholders like the Indonesian National Armed Forces (TNI), Bakamla, and the National Police (Polri), aiming to improve border security operations and policy planning. The study is among the first to adapt the Lykke Model to Indonesian border defense, incorporating an integrated risk pillar for a more comprehensive security strategy.

This study aims to examine the influence of product innovation, digital marketing, and business networking on the competitiveness of small and medium-sized enterprises (SMEs), with customer engagement positioned as a mediating variable. Employing a Systematic Literature Review (SLR) approach, thirty Scopus-indexed articles published between 2020 and 2025 were analysed to synthesise theoretical and empirical insights related to SME competitiveness in contemporary digital and urban business environments. The findings indicate that product innovation, digital marketing, and business networking each play a significant role in strengthening SME competitiveness, particularly within markets characterised by rapid technological change. Customer engagement emerges as a critical mediating mechanism that connects these strategic variables to sustainable competitive advantage. It enhances the impact of innovative and digital strategies by fostering stronger emotional, behavioural, and participative interactions between SMEs and their customers. The review also highlights that SMEs adopting integrated digital management practices, such as the utilisation of human-resource information systems (HRIS) and data-driven decision-making tend to demonstrate greater adaptability, market responsiveness, and long-term performance. The study contributes theoretically by integrating resource-based and dynamic capability perspectives, offering a holistic understanding of how digital and relational capabilities interact to elevate competitiveness. Practically, the findings provide strategic guidance for policymakers, SME managers, and practitioners in designing innovation-oriented and digitally enabled initiatives that support sustainable SME growth in the digital era.