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User experience (UX) evaluation plays a crucial role in understanding how users interact with digital platforms and in improving product design. Traditional UX evaluation methods, such as surveys and interaction logs, often rely on a single data source, which limits the depth of analysis. This study explores the integration of multimodal data processing techniques in UX research, aiming to enhance the accuracy and comprehensiveness of UX evaluations. By combining interaction logs, visual attention data, and physiological measurements, this approach provides a more holistic understanding of user behavior, emotional responses, and satisfaction. Interaction logs offer objective data on user actions, while eye-tracking and physiological data capture users' emotional states, providing richer insights into usability and user experience. This study highlights the effectiveness of multimodal integration in identifying patterns that traditional methods overlook, such as emotional responses to interface elements and real-time feedback from users. The findings reveal that multimodal data processing improves the precision of UX assessment by combining objective behaviors with subjective emotional responses, offering a more complete view of user interactions. The study also discusses the challenges of data synchronization and the potential ethical concerns related to the use of physiological data. The integration of these data sources shows great potential for enhancing the design process, allowing designers to make informed decisions based on comprehensive insights. Finally, this research underscores the future potential of multimodal analytics in UX research, suggesting further exploration of additional data modalities and real-time applications in various digital environments.

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.

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.

Socio-economic challenges faced by tuberculosis (TB) survivors in Indonesia remain substantial, particularly stigma that restricts employment opportunities and hinders social reintegration. This community service program aimed to strengthen the economic capacity of TB survivors through a community-based empowerment approach and health information dissemination via social media. The program involved a TB survivor community in the partner area of the Lampung Sehat Initiative. A participatory method was applied through entrepreneurship training, digital content production assistance, and the distribution of health education materials using Instagram, TikTok, and YouTube. The results indicate that social media was effective not only as a platform for health education but also as a promotional tool for participants’ micro-business products. In addition to improving public literacy regarding TB, social media contributed to stigma reduction by delivering positive narratives and reinforcing the collective identity of the survivor community. The program also encouraged behavioral change, supported the emergence of local leadership, and expanded participants’ social and economic networks. These findings suggest that integrating social media into community-based empowerment initiatives can serve as a social transformation strategy that enhances the sustainability of economic development and social support for TB survivors.

Introduction: Clean and Healthy Living Behavior (PHBS) is an important indicator in improving health status, particularly among university students as a productive age group. Health knowledge plays a role as a predisposing factor in the formation of healthy behavior. Individuals with a good level of health knowledge tend to have higher awareness and motivation to implement PHBS. Conversely, a lack of knowledge may lead to behaviors that pose health risks. Objective: This study aimed to determine the relationship between the level of health knowledge and clean and healthy living behavior (PHBS) among students at Universitas Muhammadiyah Semarang. Methods: This study employed a quantitative design with a cross-sectional approach. The sample consisted of 55 students, including students from health-related and non-health-related faculties. Samples were selected using an accidental sampling technique, namely respondents who were encountered and willing to complete the questionnaire at the time of data collection. The research instruments were a health knowledge questionnaire and a PHBS questionnaire. Data analysis was conducted using univariate and bivariate analysis with the Spearman test. Results: The results showed that the majority of respondents had good health knowledge (89.1%) and good PHBS (90.9%). The Spearman test results showed a p-value of 0.001 (p < 0.05), indicating a significant relationship between the level of health knowledge and PHBS. Conclusion: It can be concluded that the better the level of students’ health knowledge, the better the implementation of clean and healthy living behavior.

The increasing demand for rapid software delivery has led to the widespread adoption of Continuous Integration (CI) and Continuous Deployment (CD) pipelines. These pipelines automate the processes of code integration, testing, and deployment, significantly improving the speed and reliability of software development. However, traditional CI or CD pipelines often overlook security testing, leading to vulnerabilities in the deployed software. To address this gap, this study proposes an integrated framework that embeds automated security testing within the CI or CD process. The framework incorporates security testing tools such as Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Vulnerability Assessment and Penetration Testing (VAPT) to ensure continuous security checks throughout the development lifecycle. The experimental results show that the proposed framework enhances early vulnerability detection, with detection rates increasing from 30% to 70%. Additionally, the framework reduces deployment failures from 50% to 20%, demonstrating its effectiveness in improving software dependability. While the integration of automated security testing adds a slight 5% increase in pipeline execution time, this minimal impact does not significantly affect the overall speed of the pipeline. The proposed approach successfully balances security and efficiency, ensuring that software is both secure and delivered at high speed. This research highlights the importance of integrating security into CI or CD pipelines and demonstrates that it is possible to achieve high security without sacrificing the speed of software development. The study also discusses the practical implications for software development teams and suggests areas for future research, including the integration of advanced AI-driven security testing tools and the expansion of the framework's applicability across different software projects.

Women micro, small, and medium enterprises (MSMEs) play a strategic role in strengthening both family and community economies; however, they experience to obtain fundamental skill in financial management. One of the main problems is the mixing of personal and business finances, which turn into challenges for entrepreneurs to monitor business performance and to address access financing. This community service activity aims to improve insight of women entrepreneurs in Medan regarding the importance of separating personal and business finances through simple, practice-based training. The activity was conducted in 7 November 2025 in Medan and was attended by 150 women MSME participants from a local entrepreneurship community. The training was held for one day using interactive lectures, group discussions, and hands-on cash recording exercises. The results showed a significant improvement in participants’ understanding of financial separation concepts and their ability to record business transactions. A total of 85% of participants were able to create a simple cash record format and expressed a commitment to applying separate financial practices. This activity contributed to improving the financial literacy of women micro-entrepreneurs and strengthening their managerial capacity toward sustainable business growth.

Model driven Development (MDD) has emerged as an efficient software engineering methodology that focuses on using high-level models as primary artifacts throughout the software development process. The methodology involves transforming abstract models into detailed designs, and eventually into executable code, with the assistance of automated tools. This study evaluates the impact of MDD on the Verification and Validation (V&V) processes within secure enterprise software systems. By comparing MDD-based projects with traditional code-centric development approaches, the study highlights the advantages of MDD in reducing verification time, minimizing defect leakage, and improving the traceability of security requirements. MDD significantly enhances V&V efficiency by automating key processes, which allows for earlier error detection and better resource utilization. Additionally, MDD strengthens security compliance by integrating security requirements early in the development lifecycle, ensuring better alignment between system requirements and their implementation. Despite the clear benefits, challenges such as the lack of standardized tools and the need for specialized expertise in model development were also encountered during the study. The findings of this research offer important insights for enterprise software development teams looking to adopt MDD for more efficient and secure V&V processes. Future research should focus on the long-term impact of MDD on security compliance, as well as its adoption across different industries, to fully understand the practical benefits and challenges of implementing MDD in diverse real-world environments.

Corporate financial performance is an important factor in maintaining business sustainability amid increasingly intense competition. One of the commonly used indicators of financial performance is Return on Assets (ROA), which reflects a company’s ability to generate profits through the efficient use of its assets. Corporate profitability is influenced by various internal factors, including capital structure and liquidity. This study aims to analyze the effect of Debt to Equity Ratio (DER) and Current Ratio (CR) on Return on Assets (ROA). This research employs a quantitative approach using secondary data obtained from corporate financial statements. The research sample consists of 36 observations selected through purposive sampling. Data analysis techniques include descriptive statistical analysis and multiple linear regression analysis using SPSS software. The results show that, partially, the Debt to Equity Ratio does not have a significant effect on Return on Assets, while the Current Ratio has a positive and significant effect on Return on Assets. Simultaneously, Debt to Equity Ratio and Current Ratio have a significant effect on Return on Assets, with Current Ratio being the most dominant variable. The findings indicate that effective liquidity management plays a crucial role in improving corporate profitability. The implications of this study are expected to provide useful insights for corporate management in making financial decisions, particularly related to liquidity management and capital structure.

The development of information technology encourages organizations to adopt a more efficient, flexible, and secure data management system, especially in the field of financial management that requires high accuracy and reliability. One of the technologies that is widely used is cloud computing, which offers easy access to data and an integrated security system. This article aims to analyze the utilization of cloud technology in improving the security and accessibility of financial management data. The method used in this study is a literature study by examining various scientific sources, books, and online news relevant to the topic of cloud computing and financial data management. The results of the study show that cloud technology is able to improve data security through the implementation of encryption, multi-layered access control, user authentication, and a reliable data backup system. In addition, cloud technology also improves the accessibility of financial data because it allows users to access information in real-time, flexibly, and without location or device restrictions. Thus, the application of cloud technology can be a strategic solution for organizations in improving operational efficiency, data security, and the quality of decision-making in financial management.

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.

Reading ability is a basic skill that plays a crucial role in the success of students' learning process. However, various studies indicate that the reading ability of junior high school students remains low. This study aims to analyze the reading ability of students at the UPT SMP Negeri 2 Mappakasunggu using Alfred Schutz's social phenomenology perspective. The research approach used was qualitative with descriptive methods. Data collection techniques included in-depth interviews, observation, and documentation of students and teachers. The results indicate that students' low reading ability is not solely caused by cognitive factors but is also influenced by subjective meanings formed through students' social experiences. The lack of a literacy culture in the family and school environment results in reading not being perceived as an important or enjoyable activity. Furthermore, the dominant use of gadgets for entertainment creates habits that reduce students' interest and concentration in reading texts. From Alfred Schutz's social phenomenology perspective, these conditions are related to students' lifeworlds and stock of knowledge, which shape their perspectives and actions toward reading. This study concludes that improving students' reading ability requires a comprehensive approach, taking into account experiences, social interactions, and the formation of meaning in reading in students' daily lives.

This study aims to analyze the implementation of Good Corporate Governance (GCG) principles at PT Bank Mandiri (Persero) Tbk as one of the state-owned banks that plays a strategic role in the Indonesian banking system. The implementation of GCG is crucial in maintaining public trust, improving performance, and ensuring business sustainability in the banking sector. This research employs a qualitative method with a descriptive approach, focusing on secondary data analysis obtained from annual reports, corporate governance reports, sustainability reports, and official information published on the website of PT Bank Mandiri (Persero) Tbk. The results indicate that Bank Mandiri has consistently implemented the principles of transparency, accountability, responsibility, independency, and fairness in its corporate governance system. These principles are reflected in information disclosure practices, clear organizational structures, regulatory compliance, independent decision-making processes, and fair treatment of all stakeholders. Overall, the implementation of GCG at PT Bank Mandiri (Persero) Tbk contributes positively to strengthening internal control systems, enhancing public trust, and supporting the stability and sustainability of banking operations.

The integration of Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems has emerged as a critical strategy for modern digital enterprises aiming to enhance customer experience and operational efficiency. This study examines the impact of CRM-ERP integration on customer satisfaction, personalized service, and organizational responsiveness. By adopting a mixed-methods approach, this research combines quantitative customer data analysis and qualitative managerial interviews to assess the benefits and challenges of CRM-ERP integration. Key findings highlight significant improvements in customer experience, with increased satisfaction and personalized interactions facilitated by a unified view of customer data. Operational efficiencies were also realized through streamlined processes, better alignment of departments, and enhanced decision-making based on real-time, data-driven insights. Despite these positive outcomes, challenges such as system integration complexities, data fragmentation, and resistance to change were identified, which hindered the speed of integration and full utilization of the systems. This study demonstrates that CRM-ERP integration provides a competitive advantage by improving both customer service and business agility, particularly in industries undergoing digital transformation. For digital enterprises, integrating these systems is crucial for maintaining a seamless customer experience across various touchpoints and achieving greater operational effectiveness. The paper concludes by suggesting future research on the long-term impact of CRM-ERP integration on customer loyalty, business growth, and the potential role of emerging technologies like AI and blockchain in further enhancing these systems.

This study explores the transformative role of big data-driven Decision Support Systems (DSS) in global digital enterprises, particularly focusing on their impact on operational efficiency and corporate governance. By leveraging big data analytics, DSS offer organizations the tools to process vast amounts of real-time data, enabling executives to make more informed decisions that optimize resources, improve productivity, and reduce operational costs. The research highlights the integration of predictive analytics, machine learning, and real-time data processing within DSS, which allows businesses to gain strategic insights and anticipate market trends. Furthermore, the study emphasizes the significant role of DSS in enhancing corporate governance, improving transparency, accountability, and compliance with regulations. These systems foster better decision-making processes, which contribute to building trust among stakeholders and ensuring long-term organizational success. However, the study also identifies several challenges in implementing big data-driven DSS, including data management complexities, technological integration difficulties, and the need for skilled personnel. Despite these challenges, the findings demonstrate that big data-driven DSS are pivotal in driving competitive advantage, operational optimization, and governance improvements. The research concludes with actionable recommendations for executives to adopt and implement big data-driven DSS, emphasizing the importance of continuous support, training, and system integration. The study also suggests future research directions, including exploring the integration of emerging technologies like AI and IoT into DSS and assessing their long-term impact on sustainability and corporate governance.

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.

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.

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.

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.

Adolescents are an age group that is vulnerable to reproductive health problems, including the risk of Sexually Transmitted Infections (STIs) and risky sexual behaviors, due to limited knowledge and psychosocial maturity. Schools have a strategic role in providing reproductive health education as a promotive and preventive effort. This counseling activity aimed to determine adolescents’ knowledge regarding the prevention of Sexually Transmitted Infections (STIs) among students at Parulian 3 School, Medan.This study employed a one-group pretest–posttest approach. The participants were all seventh-grade students who met the inclusion criteria. The intervention consisted of reproductive health education delivered through lectures, discussions, and visual media. Data were collected using questionnaires measuring knowledge and attitudes before and after the counseling session, and then analyzed descriptively by comparing pretest and posttest results. The results showed an increase in students’ knowledge and attitudes after receiving reproductive health education. The differences in scores before and after the intervention indicate that the counseling provided was effective in improving students’ understanding and fostering positive attitudes toward the prevention of STIs and risky sexual behaviors. Reproductive health education in the school setting is an effective effort to enhance adolescents’ knowledge and attitudes regarding reproductive health. Therefore, reproductive health education activities should be implemented continuously and integrated into school programs.