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Insomnia is the inability or difficulty sleeping both in terms of quantity and quality. Complaints for insomnia sufferers are characterized by complaints of difficulty sleeping, feeling short of sleep, sleeping with frightening dreams, and complaints of disturbed health. The causes of insomnia come from internal factors (intrinsic), namely: anxiety, stress, motivation and age while external factors (extrinsic), namely: use of drugs, general medical disorders, environmental and lifestyle factors such as smoking, using gadgets without time, irregular physical activity, and consuming caffeinated drinks. This study aims to determine what factors are related to the occurrence of insomnia in students of the Faculty of Public Health, Halu Oleo University in 2025. The research method used in this study is an analytical research method with a cross-sectional approach. The population in this study was 766 students of the Faculty of Public Health, Halu Oleo University, class of 2021 and 2022. The number of samples in this study was 256 students. The sampling technique used in this study was probability sampling using stratified random sampling technique. The results of this study indicate that there is a relationship between the duration of gadget use (0.000), physical activity (0.005), stress level (0.000), and anxiety level (0.000) with the incidence of insomnia, and there is no relationship between family support (1.000) with the incidence of insomnia in students of the Faculty of Public Health, Halu Oleo University in 2025. It is hoped that students will pay more attention to their lifestyle because a healthy lifestyle is the key to achieving a prosperous and disease-free life.

The increasing demand for low latency and high-throughput multimedia applications has spurred significant advancements in hardware software co design. This study explores the integration of custom digital signal processing (DSP) hardware accelerators with optimized software frameworks to enhance deep learning accelerated DSP tasks. The proposed co design approach significantly reduces latency and improves throughput compared to traditional software-only DSP implementations. Through the development of custom hardware accelerators built with FPGA technology, the system achieves up to a 1.85x reduction in latency and a 1.5x improvement in throughput for real-time multimedia tasks such as image recognition, video decoding, and audio processing. The combination of hardware and software optimizations allows for better resource utilization, enabling the parallel processing of computationally intensive tasks while the software framework handles less demanding operations. Additionally, the co design system demonstrated improved energy efficiency, making it highly suitable for embedded systems. The results show that the hardware software co design approach offers substantial advantages in performance, latency reduction, and energy efficiency, positioning it as a viable solution for real-time multimedia applications. The findings have important implications for applications requiring fast data processing, such as autonomous driving, healthcare, and disaster management. Future research could explore alternative hardware accelerators, advanced software optimizations, and AI-based resource management to further improve the system’s efficiency and scalability for more complex multimedia tasks.

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.

The integration of Augmented Reality (AR) technology into higher education has emerged as a promising approach to enhance collaborative learning experiences. This study aims to design and evaluate an AR multimedia framework that facilitates real time interaction and spatial visualization, creating immersive and engaging learning environments for students. The AR framework was developed with a focus on improving student engagement, collaboration, and learning outcomes through interactive 3D models and real time feedback. By leveraging AR technology, the study sought to address common challenges in traditional learning environments, such as limited student interaction and engagement, and lack of real time feedback. The experimental evaluation involved two student groups: one using the AR-based system and the other using conventional multimedia tools. Findings revealed that students using the AR framework showed significant improvements in engagement, interaction frequency, and collaborative task performance. Additionally, the AR framework contributed to better learning outcomes, including enhanced comprehension, retention of complex concepts, and improved problem-solving skills. The study also highlighted the importance of incorporating a user-centered design approach in developing AR applications to ensure that the system meets the needs and preferences of learners. Qualitative feedback from students indicated that the AR system provided an enriched learning experience, although challenges such as interface navigation were noted. Overall, the study demonstrates the effectiveness of AR in fostering collaborative learning and offers practical insights for its integration into higher education curricula. Future research should explore the integration of AR with other immersive technologies to further enhance collaborative learning experiences.

Forestry crimes remain a serious and recurring problem that threatens forest sustainability in Lampung Province and causes long-term environmental damage. The use of repressive penal approaches alone has been considered ineffective, as such measures tend to focus on punishment after violations occur rather than preventing crimes in advance. Therefore, this study aims to analyze non-penal efforts in preventing forestry crimes and to identify factors that hinder their effective implementation. The research used a normative juridical approach supported by an empirical approach through literature studies and interviews. The results show that non-penal strategies, including guidance, counseling, community empowerment, and conservation partnership programs implemented by the Tahura Wan Abdul Rachman KPHK UPTD, have contributed to reducing forestry crimes. Nevertheless, several obstacles continue to limit their effectiveness, such as overlapping regulations, limited numbers of law enforcement officers and supporting infrastructure, challenging socio-economic conditions of communities living around forest areas, and low levels of legal awareness and legal culture among local populations.

This article examines the integration of constructivist theory in Christian Religious Education (PAK) by emphasizing the role of personal experience as the basis for faith formation. Through a reflective-theological qualitative approach, this research combines the constructivist thought of Piaget, Vygotsky, and Fosnot with a theological perspective on the incarnation of Christ and the work of the Holy Spirit. The analysis suggests that constructivism can be theologically reinterpreted into a new paradigm called the Theo-Constructive Epistemology of Incarnational Faith, in which faith is not simply taught cognitively, but is constructed through life experiences infused by the presence of God. This approach emphasizes that the learning of faith is relational, participatory, and contextual, in line with the nature of God who works in human history. The findings of this study offer a new direction for PAK praxis to be more relevant to digital contexts and cultural plurality, without losing its theocentric orientation. Thus, the integration of constructivism and incarnational theology opens up space for more dynamic, contextual, and transformative learning of faith, while strengthening the relevance of PAK in facing the challenges of modern times.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

This research explores the impact of Cache Aware optimizations on signal processing pipelines in High Throughput computing systems. The growing demand for efficient memory management in modern computing systems, especially for data-intensive applications such as artificial intelligence (AI) and multimedia processing, necessitates the development of optimized memory hierarchies. Traditional memory systems often suffer from memory bottlenecks, significantly reducing the performance of these systems. This study investigates how memory hierarchy optimizations, particularly cache line aware optimization, dependency-aware caching, and adaptive cache replacement algorithms, can mitigate these challenges and improve system performance. Through analytical modeling and experimental benchmarking, this work evaluates various memory hierarchy configurations, including processing-in-memory (PIM) and three-dimensional integrated circuits (3D ICs), comparing them to conventional systems. The results demonstrate that Cache Aware optimizations lead to a reduction in memory access latency by up to 30%, while throughput improved by up to 40%. Additionally, cache hit rates increased by 25%, and energy consumption was reduced by up to 20%, highlighting the effectiveness of optimized memory management. The research contributes to the field by providing valuable insights into the design and implementation of efficient signal processing pipelines. It also identifies key challenges, including the need for dynamic occupancy mechanisms and DAG-aware scheduling algorithms, and suggests potential areas for future research, such as the exploration of collaborative caching approaches and further optimization of cache-adaptive algorithms. This work lays the foundation for more efficient, high-performance computing systems that can handle large datasets and complex tasks in real-time applications.

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.

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.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.

The increasing adoption of collaborative robots in modern manufacturing environments requires reliable perception systems that can ensure both safety and operational efficiency during human–robot collaboration. This study proposes a CNN-based real-time computer vision system for object and human detection in shared robotic workspaces. The research focuses on developing and evaluating a single-stage deep learning detection model optimized for real-time performance while maintaining high detection accuracy. The proposed methodology includes dataset preparation, model training using transfer learning, real-time system implementation, and comprehensive performance evaluation. Experimental results demonstrate that the developed system achieves high detection accuracy, as reflected by strong precision, recall, and mean Average Precision (mAP) values, while maintaining low inference latency suitable for real-time operation. The system consistently operates above real-time frame-rate thresholds, ensuring timely perception updates required for safety-related decision-making in collaborative robotic environments. Graphical and quantitative analyses further confirm the stability of inference performance under dynamic interaction scenarios involving human movement and multiple objects. Compared with existing approaches, the proposed system provides a balanced trade-off between accuracy and computational efficiency, making it practical for deployment in safety-aware human–robot collaboration scenarios. Overall, the findings indicate that CNN-based real-time object detection systems can effectively support perception and situational awareness in collaborative robotics, contributing to safer and more efficient industrial automation.

Autonomous decision-making systems increasingly rely on complex artificial intelligence models to operate in dynamic and safety-critical environments. While these models provide strong predictive capabilities, their black-box nature limits transparency, trust, and accountability. This study proposes a structured research methodology for integrating Explainable Artificial Intelligence (XAI) into autonomous decision-making systems. The research adopts a conceptual–analytical approach to develop an explainability-oriented framework that embeds transparency across perception, decision-making, and action execution stages. The methodology includes literature-driven problem identification, conceptual framework construction, classification and mapping of XAI methods, and formulation of explainability evaluation criteria. The results demonstrate that effective explainability in autonomous systems requires a hybrid integration strategy, combining in-model transparency with post-hoc explanation mechanisms. A structured mapping of XAI techniques to autonomous system components and a conceptual decision-flow diagram are presented to illustrate explainability integration. The findings highlight that layered and context-aware explainability enhances system interpretability, supports human oversight, and improves safety relevance without compromising autonomous operation. This study contributes a reusable methodological foundation for the design and evaluation of explainable autonomous systems, offering practical guidance for future empirical validation and real-world deployment in safety-critical applications.

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.

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.

The increasing volume and complexity of data have made traditional 2D visualization methods insufficient for effectively exploring and understanding high-dimensional datasets. Immersive Virtual Reality (VR) presents a promising solution by providing an interactive 3D environment that enhances spatial understanding, task efficiency, and user satisfaction. This research aims to evaluate the user experience (UX) and interaction design quality of immersive VR interfaces for 3D data visualization in complex environments. The study employs a mixed-methods approach, combining usability testing, UX questionnaires, and task-based performance analysis. Participants interacted with VR prototypes designed to visualize complex data and were assessed on their ability to manipulate and explore the data efficiently. The findings show that immersive VR interfaces significantly improve spatial comprehension, reduce cognitive load, and increase task performance efficiency compared to traditional 2D systems. Additionally, user satisfaction was notably high, with participants appreciating the intuitive and engaging interaction methods. The study concludes that immersive VR can provide substantial benefits in real-world data visualization applications, particularly in domains requiring the exploration of complex and high-dimensional data. However, further research is needed to optimize VR interfaces and address challenges such as motion sickness and interaction complexity.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

The advancement of information technology has increased young children’s exposure to digital devices, which potentially affects language development. This study aims to analyze the relationship between daily screen time duration and language development of children aged 5–6 years in Desa Mendalo Indah, Kecamatan Jambi Luar Kota, Kabupaten Muaro Jambi. This research employed a descriptive qualitative approach with a case study design. The subjects were six children aged 5–6 years who had more than two hours of daily screen time. Data were collected through observations of children’s language abilities, interviews with parents, and documentation of daily activities, and analyzed using Miles and Huberman’s (2014) interactive model, including data reduction, data display, and conclusion drawing. The results showed variations in children’s language abilities despite having relatively similar screen time durations. Children who received active parental guidance and accessed educational content demonstrated better receptive and expressive language skills, including following sequential instructions, forming simple sentences, and expressing ideas verbally. Conversely, children who used screen time passively without supervision tended to have limitations in vocabulary acquisition, concentration, and verbal interaction. These findings indicate that screen time duration alone is not the sole determinant of children’s language development; it is also influenced by the quality of parental guidance, the type of content accessed, and social interaction experiences. This study is expected to provide guidance for parents, teachers, and early childhood education practitioners in managing digital media use wisely to optimally support children’s language development.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.