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The increasing sedentary lifestyle in the digital era has the potential to cause various health problems due to lack of physical activity. One approach that can be taken to encourage physical activity is through the use of digital games with body movement-based control mechanisms. This study aims to develop a body gesture-based game character control system using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. CNN is used to extract spatial features from each video frame, while LSTM serves to model the temporal relationship between frames so that movement patterns can be recognized sequentially. The research method used refers to the Machine Learning Lifecycle stages, starting from data collection, preprocessing, model development, to implementation in the endless runner game genre. Testing results show that the CNN–LSTM model is capable of classifying body gestures and generating outputs that can be used as commands to control game characters. The implementation of this system enables more natural and interactive game interactions without conventional input devices, and has the potential to encourage players to lead a more active lifestyle.

This research compares development administration models between developed and developing countries. Using comparative analysis, this study evaluates policies and best practices from both contexts. Research findings reveal significant differences in development approaches and outcomes, as well as implications for policy development. The analysis demonstrates that developed countries predominantly employ post-bureaucratic, participatory, and innovation-driven models characterized by strong institutional capacity, high levels of digitalization, decentralized decision-making, and robust accountability mechanisms. In contrast, developing countries frequently rely on hybrid models combining traditional bureaucratic structures with nascent reforms, constrained by limited resources, capacity gaps, institutional weaknesses, and political economy challenges. Critical differentiators include governance quality, administrative capacity, technological infrastructure, resource availability, stakeholder participation levels, and policy implementation effectiveness. Despite contextual differences, successful development administration in both settings shares common elements, including political commitment, adaptive capacity, citizen engagement, evidence-based policymaking, and continuous learning mechanisms. The research identifies transferable lessons and contextual adaptation requirements for developing countries seeking to enhance their development administration systems.

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.

Arabic language instruction has traditionally centered on classical Islamic texts, emphasizing rote memorization, mastery of grammar (naḥw), and rhetorical eloquence (balāghah), which, while preserving linguistic authenticity, often fail to meet the evolving educational demands of the 21st century. In an age marked by rapid technological advancement and shifting pedagogical paradigms, there is a critical need to reform Arabic teaching by integrating traditional methods with digital tools and interdisciplinary approaches. This study adopts a qualitative, literature-based methodology to explore both the foundational principles of classical pedagogy and recent innovations, including learning management systems, gamification, and AI-enhanced learning technologies. Through comparative analysis, the research identifies key disjunctions and overlaps between heritage-based teaching and modern digital practices. From this synthesis, the study proposes a reconstructed pedagogical model built on four core elements: cognitive-linguistic scaffolding, digital integration, sociocultural contextualization, and multidisciplinary instruction. This integrative approach not only fosters greater student engagement but also promotes character development in line with the objectives of Islamic education (maqāṣid al-sharīʿah). Furthermore, it supports a more adaptive and culturally responsive learning environment, contributing to broader educational reform efforts aimed at holistic learning. The findings suggest that such a model is particularly well-suited to Islamic higher education contexts while also being adaptable to global academic systems. By bridging the gap between tradition and innovation, this framework offers both a theoretical and practical foundation for modernizing Arabic language pedagogy. It holds implications for curriculum development, instructional strategies, and educational policy, advocating for a future-oriented yet culturally rooted approach to language education that respects heritage while embracing necessary transformation.

The purpose of this study was to develop and produce hybrid learning in the subject of Christian Religious Education and Character Education of class VIII of Junior High Schools in Indonesia in the odd semester of the Independent Curriculum. This study uses a development method with the ADDIE approach model. The study was conducted at SMP Negeri 2 Pollung. Data collection used a validation questionnaire and a response questionnaire. The validation questionnaire was used for validator lecturers who are media experts, design experts, material experts, and language experts. The response questionnaire was used for PAK teachers of class VIII of SMP Negeri 2 Pollung and students of class VIII of SMP Negeri 2 Pollung totaling 24 people for small groups and 66 people for large groups. The data analysis techniques used in this study were qualitative descriptive analysis and quantitative descriptive analysis. The results of this study are in the form of hybrid learning media products using PowerPoint in the subject of Christian Religious Education and Character Education of class VIII of SMP in the Odd Semester of the Independent Curriculum. The results of the study indicate that the products made are valid and suitable to be used as learning media. The assessment is based on validation experts, teachers, and students, namely media experts obtained an assessment percentage of 100% (very valid), design experts 96% (very valid), material experts 100% (very valid), language experts 100% (very valid), PAK teachers 100% (very valid), and finally students in small and large groups obtained an average percentage of 100% (very good).