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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.

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.

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.

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.

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

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.

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

This community service programme was implemented in the coastal community of Padelegan Village, Pademawu Subdistrict, Pamekasan Regency, with a focus on strengthening health literacy and behaviour-based disease prevention at the household level. The background of the activity is based on the vulnerability of coastal communities to health problems related to hygiene, household drinking water management, and environmental cleanliness, as well as the need for an approach that not only increases knowledge but also encourages consistent preventive practices. The programme aims to improve residents' ability to understand and use health information in an applicable manner, while strengthening key disease prevention behaviours through education and mentoring. The activity will be carried out from February to April 2025 with a behaviour-based education design combined with practice demonstrations and community mentoring, involving 48 participants (40 residents/households and 8 posyandu/PKK cadres). The stages included initial assessment, development of KIE media (brief modules and leaflets/posters), two education-demonstration sessions, two home mentoring sessions, and final evaluation. The evaluation was conducted using pre-post knowledge, attitude, and practice (KAP) assessments and practice observation through a checklist. The results showed an increase in health literacy and improved consistency in preventive behaviour; knowledge increased from 5.4 to 7.8, and compliance with hand washing at critical times increased from 41.7% to 72.9%. Programme outputs included an information, education and communication (IEC) package, a household monitoring checklist, and capacity building for cadres as local facilitators. It was concluded that the integration of participatory education, practical demonstrations and cadre mentoring has the potential to be effective in encouraging preventive behavioural change in coastal communities, with recommendations for integrating monitoring into the routine agenda of integrated health service posts (posyandu) and community empowerment groups (PKK) and for follow-up monitoring for 3–6 months to maintain the sustainability of practices.

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.

This study examines the impact of Dutch Military Aggression I (1947) and Dutch Military Aggression II (1948–1949) on the political development of Indonesia during the early years of independence. Employing the historical method (heuristics, source criticism, interpretation, and historiography) and a qualitative diachronic-synchronic approach, the research analyses primary and secondary sources concerning internal political dynamics and the Republic of Indonesia’s international relations in the period 1947–1949. The findings reveal that both Dutch military aggressions paradoxically served as crucial catalysts for the acceleration of national political consolidation and maturation in Indonesia. The First Aggression triggered international condemnation, transformed the Republic’s status from “rebels” to a sovereign state defending its independence, strengthened national solidarity, and gave birth to the integration of armed struggle and diplomacy—the precursor to the Total People’s Defense doctrine. Meanwhile, the Second Aggression, marked by the occupation of Yogyakarta and the arrest of republican leaders, led to the establishment of the Emergency Government of the Republic of Indonesia (PDRI), closer civil-military coordination, unification of political parties and societal organizations, and heightened global pressure that ultimately resulted in the recognition of sovereignty in 1949. Overall, Dutch military pressure expedited the transformation of political structures, bolstered the legitimacy of the Republican government, and secured both de facto and de jure recognition of Indonesian independence.

Production planning and inventory control are critical aspects of operations management, as they directly influence cost efficiency, resource utilization, and the continuity of the production process. Ineffective planning and inventory decisions may lead to excessive costs, production delays, or imbalances between supply and demand. The complexity of these problems, which often involve multi-period horizons and multi-stage decision-making processes, has encouraged the application of quantitative optimization methods, one of which is dynamic programming. This study aims to analyze and synthesize the application of dynamic programming in production planning and inventory control through a Systematic Literature Review (SLR) approach. The SLR process was conducted by systematically identifying, selecting, and analyzing 15 relevant national journal articles published between 2015 and 2024 and obtained from various recognized scientific databases. The reviewed literature indicates that dynamic programming is effective in supporting optimal decision-making by determining appropriate production quantities and inventory levels, minimizing total production and holding costs, and managing fluctuating demand conditions. In addition, this method helps reduce the risks associated with overstock and stockouts by considering sequential decision structures. However, the findings also reveal several limitations of dynamic programming, including high computational complexity, strong dependence on deterministic data assumptions, and limited flexibility in handling high levels of uncertainty. These constraints suggest the need for further methodological development or integration with other approaches to enhance practical applicability.

The rapid growth of the creative economy, particularly in the culinary subsector, has prompted micro, small, and medium enterprises (MSMEs) to adopt digital marketing strategies as a means to enhance competitiveness and expand market reach. This study employs a systematic literature review (SLR) approach with content analysis to map key concepts, identify research gaps, and explore future research directions concerning the transformation of culinary MSMEs from local kitchens to digital markets. Findings reveal that organizational learning mediated by motivation, culture, leadership, and continuous digital marketing practices plays a pivotal role in this transformation. However, significant research gaps persist, including limited longitudinal studies, inadequate integration of organizational culture and leadership into digital marketing frameworks, and insufficient comparative analyses across diverse MSME profiles. This study contributes theoretically by synthesizing fragmented literature on culinary marketing in the digital creative economy and offers practical insights for MSME actors, mentors, and policymakers in designing adaptive and sustainable digital marketing strategies.

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

This study focuses on analyzing the application of cloud computing as a supporting infrastructure for digital transformation in the implementation of Smart City at the Communication and Information Agency (Diskominfo) of Muara Enim Regency. In the era of digital transformation and accelerated urbanization, the need for smart city management based on information technology has become increasingly urgent. Cloud computing plays a strategic role in providing integrated, scalable, and efficient data services to support the effectiveness of public services and data-driven decision-making. This study aims to analyze the extent to which cloud computing has been implemented in the Muara Enim Diskominfo environment, identify the supporting and inhibiting factors of its implementation, and evaluate its contribution to the achievement of Smart City objectives. This study uses a comparative approach with data collection techniques through interviews, observation, and documentation studies. The results of the study show that the implementation of cloud computing at the Muara Enim Communication and Information Agency is still in the development stage, with positive achievements in data management efficiency and inter-unit collaboration, but facing obstacles in terms of system integration and human resources. This research contributes to strengthening academic understanding of cloud computing implementation strategies in the context of local government, as well as providing practical recommendations for policy makers to improve digital infrastructure readiness towards a sustainable Smart City.

The advancement of automotive technology has accelerated the adoption of renewable‑energy‑based electric vehicles, including the integration of solar panels on electric scooters. Indonesia’s tropical climate provides abundant solar energy potential; however, the limited surface area of scooters often restricts panel placement to the footrest section. This study aims to evaluate the impact of using a 10 mm clear acrylic cover on the performance of a 50 Wp monocrystalline solar panel in an electric scooter battery‑charging system. An experimental method was employed by comparing the panel’s performance under two conditions: without a cover and with the acrylic cover installed. Key parameters observed included voltage, current, and charging power, recorded using a data logger. Tests were conducted for 30 minutes under varying solar radiation intensities. The results indicate that the acrylic cover reduces the panel’s output power, from 55 W to 45 W at a solar radiation intensity of approximately 1100 W/m². These findings suggest that the use of an acrylic cover must be carefully considered to maintain optimal charging system performance.

This study identified the use of abbreviations in Medical Treatment Consent Forms (SPTK) at X Hospital Indonesia. A quantitative cross-sectional descriptive approach was applied to 76 SPTKs in September 2024, and questionnaires were administered to 30 patient-responsible physicians (DPJP). The results showed that 75% of SPTKs contained abbreviations, even though 97% of respondents understood the risk of miscommunication to patient safety. The state of the art includes accreditation standards that prohibit the use of abbreviations in informed consent, with global orthopedic studies reporting a decrease from 54% to 22% after educational interventions, as well as Indonesian regulations, namely Peraturan Mentri Kesehatan (Permenkes) Republik Indonesia No. 24/2022, which emphasizes that medical records must be complete. The novelty lies in the first empirical analysis in Indonesian hospitals to reveal the disparity between high physician knowledge and low documentation compliance, contributing to the development of evidence-based monitoring for patient safety. These findings support recommendations for daily review of SPTK, ongoing socialization, and integration of digital checklists to reduce medical errors.

Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.