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Science lessons in primary school significantly contribute to the early development of logical, scientific and systematic reasoning skills.   However, learning science can be challenging, especially when it comes to understanding abstract concepts and cause-and-effect interactions.   This is due to the fact that, as per Piaget's theory, primary school students are still in the concrete operational stage and have limited cognitive growth. Data regarding abstraction ability and causal reasoning in the context of science education were collected from various scientific sources using the literature review research methodology.  According to the research findings, STEAM integration, project-based learning strategies such as Project Based Learning (PBL), and the use of real and visual aids can help lower learning barriers. Successful learning is also influenced by the teacher's role as facilitator, which involves presenting the material in a relevant and contextualized way and fostering a positive learning environment. Real-world examples and exceptional examples, such as the use of worksheets based on cause-and-effect relationships and basic experiments, demonstrate how useful these strategies are for improving abstract thinking and understanding of cause-and-effect relationships. Developing innovative teaching methods based on practical, exploratory and integrative experiments is essential for maximizing the scientific understanding of primary school students.

K-Pop has developed into a key instrument of South Korean cultural diplomacy with global reach, including in Indonesia. Behind its success as a soft power tool, K-Pop harbors complex dynamics of gender representation, particularly regarding the position and role of women. This study aims to analyze women's representation and agency in South Korean cultural diplomacy through K-Pop in Indonesia during the 2022–2023 period. This study uses a qualitative approach with a descriptive-interpretive design, combining a poststructuralist feminist perspective and cultural diplomacy theory. Data were collected through a documentary study of K-Pop cultural products and in-depth interviews with K-Pop fans in Indonesia. The results show that women play a central role in K-Pop's global success, both as idols and as active fans. However, women's representation in K-Pop is still dominated by standardized beauty standards and images of femininity. On the other hand, women's agency emerges through fandom practices that allow female fans to actively interpret, critique, and negotiate the meaning of gender representation. This study also found that gender issues have not been explicitly integrated into South Korean cultural diplomacy policies. These findings confirm that cultural diplomacy through K-Pop is dynamic and involves active interaction between the country, industry, and the recipient society.

Social media provides significant benefits in life, one of which is as a communication and promotional tool. Instagram, as the platform with the second most users in the world, is a strategic medium for promotion. This study analyzes RFAS Music's strategy in utilizing Instagram to promote the song "Berona" by Rizky Febian. This study uses a descriptive qualitative approach with structured interviews, observation, and documentation methods. The collected data are analyzed through the process of data reduction, data presentation, and drawing conclusions. The theories used are the Technology Acceptance Model (TAM) which emphasizes technology acceptance and Quesenberry's Theory which emphasizes marketing communications. The results of the study indicate that RFAS Music successfully implemented an Instagram social media marketing strategy to create interaction and attract the attention of the target audience. This strategy not only increases audience awareness of the new song but also strengthens RFAS Music's position in the music industry. Thus, this study can be concluded that RFAS Music's strategy is a social media promotion strategy that pays attention to the level of efficiency and the times. By implementing the strategy and linking it to marketing communications, RFAS Music successfully promoted the launch of Rizky Febian's song "Berona." The study suggests that it would be better if this promotional activity also used other platforms that are currently equivalent and as widely used as Instagram. and continue to improve interesting strategies.customers

Cloud-native systems are essential for modern software development, offering enhanced scalability, flexibility, and resilience through cloud computing environments. However, ensuring the reliability and performance of these systems presents a challenge due to their dynamic and distributed nature. Traditional testing methods, such as unit and integration testing, while valuable for detecting individual component defects and interactions, are insufficient for predicting failure rates in complex, cloud-native applications. This study explores the effectiveness of various testing techniques and quality metrics in predicting failure rates within scalable cloud-native systems. A comparative experimental study was conducted using three primary testing techniques: unit testing, integration testing, and chaos testing. The results indicate that chaos testing, when combined with advanced quality metrics such as migration rate and mismigration rate, significantly outperforms traditional methods in predicting failure rates and evaluating system resilience. These findings suggest that chaos testing offers a more comprehensive evaluation, simulating real-world disruptions to test system behavior under stress, which is essential for cloud-native environments where high availability and fault tolerance are critical. The study also highlights the importance of integrating predictive quality metrics, which improve the accuracy of failure predictions and enhance system reliability. The study concludes that for cloud-native systems, a combination of advanced testing techniques and predictive metrics is essential for ensuring high availability, scalability, and reliability in dynamic environments. Future research should focus on refining predictive testing approaches, developing standardized frameworks, and empirically validating new testing methods to address the growing complexity of cloud-native systems.

Physical motor development is an essential and easily observable indicator of children’s growth. Parental involvement plays a crucial role in optimizing this development, particularly during the child’s golden age. This study aims to analyze the importance of motor development and examine how parents support children’s learning through a developmental psychology perspective. Using a literature review method, the findings reveal that child development is shaped by genetic and environmental factors, while active support from parents and teachers has long-term effects on children’s physical, emotional, and social readiness. Understanding developmental psychology enables parents to provide appropriate stimulation that maximizes children’s potential. Using literature review methods, it was found that motor development is influenced by genetic and environmental factors, while positive interactions between children, parents, and educators contribute to long-term physical, social, and emotional development. A deep understanding of developmental psychology enables parents to design appropriate stimulation strategies to maximize their children's potential.

Teaching fiction in Indonesian language classrooms is often constrained by text-centered instruction and limited use of engaging digital media. In the era of Generation Z learners who are visually and digitally oriented, innovative learning materials are needed to enhance motivation, interpretation, and comprehension of literary texts. This study aims to develop and evaluate a Scribus-based digital magazine for fiction learning by integrating the ADDIE model within a qualitative descriptive framework. The research adopted a qualitative descriptive design comprising five stages of the ADDIE model: Analysis, Design, Development, Implementation, and Evaluation. The study was conducted at SMP Negeri 10 Semarang, involving one Indonesian language teacher and fifteen Grade VIII students, who were selected through purposive sampling. Data were collected through observation, interviews, and documentation, and analyzed using the Miles and Huberman model (data reduction, data display, conclusion drawing). Media and content experts conducted validation to assess the feasibility, usability, and pedagogical quality of the content. Findings revealed that the Scribus-based learning media effectively increased students’ engagement and comprehension in analyzing fictional elements such as plot, character, and setting. Expert validation results indicated a high feasibility level, with mean scores above 85% in design and content quality. Teachers reported improved classroom interaction and creativity, while students expressed enthusiasm for the visual and interactive format. The study concludes that open-source tools, such as Scribus, can provide cost-effective and pedagogically sound alternatives for developing literary learning media. The integration of ADDIE and multimodal design promotes contextual, engaging, and sustainable learning experiences. Future research should explore multimedia enhancements, such as audio storytelling and cross-curricular applications, to broaden the pedagogical impact.

Electronics learning in higher education continues to face various challenges, particularly in the provision of interactive learning media capable of concretely and engagingly visualizing the form, characteristics, and working principles of electronic components. The limitations of conventional learning media often result in abstract learning processes, which in turn lead to a low level of student understanding of basic electronics concepts. This study aims to develop the AMPERE as an innovative and technologically relevant interactive learning medium. The research employed R&D approach using the Borg & Gall model, which includes the stages of needs analysis, design, product development, validity testing, and limited implementation. The AMPERE application was developed using marker based AR technology, in which a smartphone camera detects markers to display and interact with 3D electronic component objects in real time. The results indicate that the AMPERE application achieved a high level of validity, with a score of 0.88 from subject-matter experts and 0.84 from media experts, and was therefore deemed suitable for use as a learning medium. The small-group trial results showed a practicality level of 82.07%, while the practicality test during the implementation stage reached 85.67%. These findings demonstrate that AMPERE is effective in enhancing learning interactivity and assisting students in understanding the form, function, and working principles of electronic components through smartphone-based digital visualization. Theoretically, these results are consistent with constructivist learning theory, which emphasizes active knowledge construction through direct experience and interaction with learning objects.

Sleep is a fundamental biological need for adolescents and plays a crucial role in supporting cognitive function, emotional regulation, and overall mental health. Despite its importance, sleep quality among adolescents has increasingly declined due to the interaction of individual characteristics, social environments, and daily sleep behaviors. This article aims to analyze the factors influencing adolescent sleep quality using the Social Cognitive Theory framework through a literature review of national and international journals published between 2021 and 2025. The findings show that adolescent sleep quality is shaped by personal factors, including attitudes toward sleep, self-efficacy, and psychological conditions. Environmental factors, such as parental support, peer influence, and academic demands, also significantly affect sleep patterns. In addition, behavioral factors, particularly sleep hygiene practices, play a key role in determining sleep quality. Consistently, unhealthy sleep behaviors are associated with poor sleep quality. Therefore, comprehensive interventions are needed, involving the improvement of sleep habits, strengthening adolescents’ self-regulation abilities, and enhancing family and school support to promote better sleep quality.

The development of digital marketing has become a strategic factor in the growth of creative culinary businesses, particularly for micro, small, and medium enterprises facing increasing competition and changes in consumer behavior in the digital era. The use of digital platforms, such as social media and online marketplaces, enables business owners to expand market reach, enhance customer interaction, and optimize promotional strategies more efficiently. Nevertheless, various studies indicate that the implementation of digital marketing has not always produced optimal outcomes due to limitations in digital literacy, human resource capacity, and organizational adaptability. This study aims to systematically examine the role of digital marketing in the development of creative culinary businesses through a Systematic Literature Review approach. The review maps key concepts, research trends, and empirical findings related to digital marketing and organizational learning within the context of creative culinary enterprises. In addition, this study identifies existing research gaps and formulates recommendations for future research issues. The findings are expected to provide theoretical contributions in the form of comprehensive conceptual mapping as well as practical contributions for business practitioners, MSME facilitators, and policymakers in designing adaptive and sustainable digital marketing strategies.

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.

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.

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 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 adaptive streaming models with edge computing to optimize multimedia delivery, particularly in real-time applications such as video conferencing, live streaming, and virtual reality. The proposed model leverages adaptive compression techniques, including scalable video coding (SVC) and hybrid adaptive compression (HAC), which adjust video quality based on real-time network conditions. The use of edge computing further enhances the model by processing and delivering content closer to the user, reducing latency and optimizing bandwidth usage. The research demonstrates that the edge computing-based adaptive streaming model significantly improves latency by up to 30%, reduces bandwidth consumption, and ensures higher visual quality during video playback, even under fluctuating network conditions. This model addresses key challenges in multimedia streaming, such as maintaining video quality in bandwidth-constrained environments and minimizing buffering times. Furthermore, it enhances the overall Quality of Experience (QoE) for users by providing smoother interactions and real-time responsiveness. The study highlights the potential impact of this model on various fields, including remote education, entertainment, and interactive applications, where low-latency content delivery and high-quality streaming are critical. The findings suggest that integrating AI algorithms for even more efficient compression and expanding edge computing infrastructures will further optimize multimedia streaming in the future, ensuring reliable and high-quality user experiences in increasingly connected environments.

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.

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.

This research explores the role of human algorithm interaction mechanisms in enhancing trust, reliability, and user confidence in Decision Support Systems (DSS). Traditional DSS models often focus solely on algorithmic accuracy and performance, neglecting crucial factors such as transparency and user engagement, which are essential for building trust. By incorporating explainable AI (XAI) techniques like SHAP and LIME, real-time feedback mechanisms, and user-friendly interfaces, the study develops structured interaction models that improve the interpretability of AI-driven decisions. The results show that transparent decision-making processes and interactive features significantly enhance user trust, making DSS more reliable and easier to adopt. Users interacting with systems that provide clear, understandable explanations of decisions, along with real-time updates on the system’s confidence, reported higher levels of decision-making confidence, especially in high-stakes scenarios. These improvements lead to greater user engagement and adoption of the system in various domains, including healthcare and finance. The study also highlights the importance of balancing interpretability with efficiency in user interface design to ensure both trust and usability. The findings contribute to the design of more user-centric DSS that prioritize trust, interpretability, and cognitive factors, providing a framework for the successful integration of intelligent decision support systems in complex decision-making environments. Future research should focus on refining interaction models and exploring the broader applicability of these systems in different sectors.

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.

Derivatives of substituted oxazol-5-one (1–3) and substituted oxazolo-isoxazole (4–6) were successfully synthesized through a multi-step reaction process. Initially, glycine was reacted with acetic anhydride to produce acetyl glycine as a key intermediate. This intermediate subsequently underwent a condensation reaction with various substituted benzaldehydes, leading to the formation of oxazol-5-one derivatives (1–3). In the final synthetic step, these oxazol-5-one derivatives were reacted with hydroxylamine hydrochloride to yield the corresponding oxazolo-isoxazole compounds (4–6). The chemical structures of all synthesized compounds were characterized and confirmed using Fourier Transform Infrared (FT-IR) spectroscopy and proton nuclear magnetic resonance (¹H NMR) spectroscopy. Furthermore, molecular docking studies were carried out to evaluate the potential biological interactions of the prepared compounds. Docking simulations were performed using PyRx software, while visualization and interaction analysis were conducted employing PyMOL and Discovery Studio. The combined experimental and computational approaches provide valuable insights into the structural properties and potential biological relevance of the synthesized compounds.

This study investigates how character education is embedded into the school culture of an Indonesian Islamic junior high school (madrasah), focusing on four core values: religiosity, honesty, tolerance, and discipline. Using a qualitative naturalistic approach, the research was conducted at MTsN 3 Halmahera Utara through in-depth interviews, participant observation, and document analysis involving school leaders, teachers, and students. The findings reveal that character education at the madrasah is not a standalone program but is institutionalized through consistent routines, religious practices, and role modeling. Religiosity is fostered through structured worship, integration of Islamic values into lessons, and spiritual habits. Honesty is promoted via trust-based learning environments, teacher exemplarity, and reinforcement of moral norms. Tolerance is cultivated through inclusive social interactions and respect for religious and cultural differences, while discipline is internalized through habitual rule-following, punctuality, and self-regulation. These findings align with existing theories and regional character education frameworks, confirming that character is best formed through embedded cultural processes and consistent adult exemplarity. The study contributes to the understanding of how Islamic schools can function as moral communities and highlights the importance of alignment between school culture, family support, and wider social contexts in achieving sustainable moral development.