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Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

This study aims to examine the application of situational leadership theory by the principal of SMP Negeri 2 Sampanahan, Kotabaru Regency, and its impact on organizational behavior within the school environment. A qualitative approach with a case study method was employed, involving in-depth interviews with the principal, teachers, and students. The main focus of the research is to understand how the principal adapts leadership styles according to the needs and conditions of the individuals being led. The findings reveal that the principal successfully applied various leadership styles—ranging from directive, supportive, participative, to delegative—flexibly based on the specific situations encountered. This adaptability positively influenced the organizational climate, fostered harmonious working relationships, and enhanced the motivation and active participation of both teachers and students. The school environment became more conducive and collaborative, ultimately leading to overall performance improvement. These findings highlight the importance of adaptive leadership in education to support the achievement of organizational goals and the development of human resources within schools. This study contributes to the development of leadership practices in educational settings, particularly in the context of educational management that is responsive to both internal and external institutional dynamics.

Social competence is the ability of teachers to communicate and interact with students during the learning process. This competence is very important so that teachers can be good examples for students, help students develop individuals who are full of empathy, attention, and concern for others. This study aims to assess in detail the influence of social competence possessed by classroom teachers. This study uses a qualitative approach. The main subjects of the study, namely teachers and supporting objects, namely students. Data collection techniques through observation, interviews, and documentation. Data were analyzed using Miles and Huberman's theory, including data reduction, data presentation, and drawing conclusions. The results of the study showed that teacher social competence, including inclusive attitudes, polite communication, adaptability, and communication with the professional community, had a positive effect on the learning process. Teachers who are effective in social competence create a pleasant learning atmosphere, increase comfort, motivation, and student learning achievement. This study concludes the importance of strengthening social competence in improving the quality of the learning process in the classroom.

This qualitative study aims to gain insights into how English lecturers perceive and integrate generative Artificial Intelligence (AI) in their teaching, an increasingly relevant topic in education. Guided by the Technology Acceptance Model (TAM), the research involved two lecturers from a higher education context. Findings reveal a shift in perception: although both participants were initially reluctant due to unfamiliarity and concerns about learning quality, they gradually embraced AI after firsthand experience. They now recognize its potential to support teaching while maintaining careful oversight to ensure it does not compromise the learning process. Their evolving views highlight the importance of adaptability and critical awareness in incorporating AI into English language education.

Final year students often experience difficulties in adapting to career and life demands, as well as maintaining their well-being. This study aims to examine the influence of social support and self-efficacy on life satisfaction and career adaptability among final-year students. Quantitative data were collected from 155 students in their final year of study and analyzed using Structural Equation Modelling (SEM). The results showed that social support did not affect the career adaptability of final-year students. Conversely, self-efficacy had a positive effect on career adaptability. Social support was found to have a positive impact on students' life satisfaction. Students who received social support from family, friends, and educational institutions tended to have higher levels of life satisfaction. Self-efficacy also positively affected life satisfaction, indicating that high self-confidence enhances individual well-being. However, life satisfaction did not significantly influence career adaptability, suggesting that other factors such as work experience and environmental support play a more significant role. This study has several limitations, including a limited sample size and potential respondent bias. Future research should involve larger and more diverse samples and employ a variety of data collection methods. Educational institutions are encouraged to develop programs that enhance self-efficacy and social support to help students reach their full potential in their careers and personal lives.

This literature review discusses the application of Agile methodology in modern software development. Agile offers a flexible, iterative approach that contrasts with traditional waterfall models. The objective of this study is to provide insights into how Agile frameworks such as Scrum, Kanban, and Extreme Programming have influenced development speed, software quality, and team collaboration. Methodologically, the study synthesizes findings from recent academic and industrial literature from the last five years. The results show that Agile enhances adaptability and customer satisfaction, although challenges remain in large-scale implementations.  

Cybersecurity is continuously challenged by increasingly sophisticated and dynamic cyber-attacks, necessitating advanced adaptive defense mechanisms. Deep Reinforcement Learning (DRL) has emerged as a promising approach, offering significant advantages over traditional intrusion detection methods through real-time adaptability and self-learning capabilities. This paper presents an advanced adaptive cybersecurity framework utilizing five prominent DRL algorithms: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Twin Delayed DDPG (TD3), Soft Actor-Critic (SAC), and Asynchronous Advantage Actor-Critic (A3C). The effectiveness of these algorithms is evaluated within complex, realistic simulation environments using live-streaming data, emphasizing key metrics such as accuracy (AUC-ROC), response latency, and network throughput. Experimental results demonstrate that the SAC algorithm consistently achieves superior detection accuracy (95% AUC-ROC) and minimal disruption to network performance compared to other approaches. Additionally, A3C provides the fastest response times suitable for real-time defense scenarios. This comprehensive comparative analysis addresses critical research gaps by integrating both traditional and novel DRL techniques and validates their potential to substantially improve cybersecurity defense strategies in realistic operational settings.

Digital bureaucracy is a new bureaucratic system that arises because of globalization, as well as a substitute for the classic bureaucratic concept, namely paper-based bureaucracy. Digital bureaucracy has a role in improving the good governance system, by increasing the four indicators of good governance principles, namely transparency, accountability, efficiency and effectiveness. Indonesia can take advantage of the existing demographic bonus. Human resource management as the foundation for running the digital governance wheel is an important key. Adoption of technology in various public service sectors and supported by human resources with high digital adaptability is expected to increase the competitiveness of our governance. The state has a goal in realizing the welfare of its people. So that the state needs to do various things in meeting the needs of the community through the implementation of public services. In order to meet the needs of the community to run well, the urgency related to the quality of public services is very large. As for what affects the quality of public services, one of them is internal factors, namely discretionary authority,actions determined, carried out by government officials to overcome concrete problems faced in the administration of government in terms of laws and regulations that provide choices, do not regulate, are incomplete or unclear,there is government stagnation. Discretion is used by the government for and on behalf of positions, carried out within the formal environment of the authority of government officials, its implementation is directed at effectiveness and efficiency, and must be in accordance with AUPB, and based on the principle of rationality. Any use of government officials' discretion is aimed at facilitating government administration, filling legal voids.

The irfani approach in the study of Islamic law is a method that integrates spiritual experience, intuition, and moral values in understanding and applying legal principles. This research aims to explore the relevance, implications, and challenges of the irfani approach within the context of Islamic law in Indonesia. By employing normative juridical analysis and literature study, this study finds that the irfani approach enriches the understanding of Islamic law by linking it to contemporary issues, such as social justice, environmental protection, and human rights. This perspective encourages a more holistic and ethical interpretation of legal principles, moving beyond rigid textual analysis. Furthermore, this research highlights the potential harmonization between Islamic law and customary law (adat law) in Indonesia. This integration can create a more inclusive, culturally adaptive, and community-responsive legal system, which aligns with Indonesia's pluralistic legal landscape. However, challenges remain in the widespread acceptance and implementation of the irfani approach. Resistance often arises from scholars and institutions that prefer conventional, textual, and rationalistic approaches to Islamic law, perceiving the irfani method as subjective or lacking legal rigor.Despite these challenges, strengthening education, scholarly dialogue, and interdisciplinary engagement between Islamic law scholars, legal practitioners, and policymakers can foster a greater appreciation for the ethical and spiritual dimensions of Islamic law. By incorporating mystical insights and moral considerations, the irfani approach has significant potential to contribute to the evolution of Islamic law in Indonesia, ensuring its relevance, inclusivity, and adaptability in addressing the complex challenges of modern society.

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the system progressively shifts to a collaborative filtering mechanism, leveraging cosine similarity to identify similar users within comparable contexts. The recommender system focuses on three categories of POIs: restaurants, hotels, and landmarks. These locations are retrieved through the Google Maps API, and only mapped locations are considered. The system was implemented on Android devices and evaluated through a user study involving 25 participants from diverse backgrounds, including software developers, IT students, and general users. Evaluation metrics such as normalized Discounted Cumulative Gain (nDCG) and classification accuracy were used to assess recommendation quality. Results demonstrate that the system performs better than traditional methods, with nDCG improvements reaching up to 83 percent. Users reported high satisfaction regarding the recommendations' accuracy, ease of use, and contextual relevance. While the system offers significant improvements, it also has certain limitations. Its dependency on Google Maps data may restrict its scope, and using only four contextual factors limits the system’s adaptability to more complex user preferences. Future enhancements could include additional dynamic contexts such as weather, POI popularity, and time-related trends, as well as integrating more advanced models to increase personalization and flexibility in real-world applications.

This article discusses the social guidance program carried out by adolescent victims of domestic violence with the aim of increasing their self-confidence at Putra Utama Children's Home 3. In empowering these adolescents, PSAA Putra Utama creates a social guidance program, in which there are various approaches, including daily assistance, positive reinforcement, individual counseling, and skills training. The type of approach in this research uses descriptive qualitative methods, with data collection through in-depth interviews, observation and documentation studies. The results of the study revealed that the social guidance program carried out by PSAA Putra Utama 3 can improve. increase adolescents' self-confidence, which is reflected in progress in social interaction, personal motivation, and life goal setting. The adolescents showed significant improvements in adaptability and living a better social life. However, another thing that must be improved is regarding the indicators of the success of empowerment, PSAA Putra Utama 3 has not fulfilled having specific indicators to systematically evaluate the program. This research provides an overall picture of the success of the program and shows the need for the development of evaluation indicators to ensure the empowerment program can be further optimized.  

Managing human resources and entrepreneurship are two crucial components of any successful organization. Human resources management focuses on recruiting, training, and retaining employees to ensure they can perform their job duties effectively. On the other hand, entrepreneurship involves the creation and development of new business ventures, products, or services. Both areas play a significant role in the overall success and growth of a company. In this paper, we will explore the intersection of human resources management and entrepreneurship, examining how these two disciplines can work together to drive innovation and organizational success. By leveraging the expertise and creativity of both HR professionals and entrepreneurs, companies can cultivate a culture of innovation and adaptability. This collaboration can lead to the development of cutting-edge products and services, as well as the implementation of forward-thinking HR practices that attract and retain top talent. However, integrating HR and entrepreneurship may also present challenges, such as balancing risk-taking with employee well-being and ensuring that organizational goals align with individual aspirations. Despite these obstacles, the potential benefits of combining these disciplines are vast, offering companies a strategic advantage in today's competitive business landscape.

This study aims to examine the role of differentiated instruction strategies in improving the quality of Islamic education. Using a qualitative descriptive approach through literature review, this research analyzes the concept, implementation, and impact of differentiation strategies on the effectiveness of learning in Islamic education. Differentiated instruction allows for the adaptation of teaching methods based on students' needs, learning styles, and individual potential, creating a more inclusive and adaptive learning environment. The findings indicate that implementing differentiation strategies whether in content, process, or product significantly enhances students' understanding of Islamic teachings. Additionally, these strategies increase students' learning motivation, active engagement, and help them develop character aligned with Islamic values. Teachers who apply differentiation strategies can be more flexible in delivering material and more responsive to individual student differences, making the learning process more effective and meaningful. The implications of this research emphasize that differentiation strategies play a crucial role in creating a higher-quality Islamic education system that remains relevant in an evolving era. Therefore, continuous efforts from educators and educational institutions are needed to adopt these strategies in order to produce a generation of Muslims who not only have a strong understanding of Islamic teachings but also possess the adaptability and moral character to face modern life challenges.

Self-esteem exerts a significant influence on an individual's confidence and adaptability, indirectly influencing students' academic success. This research seeks to examine the association between self-esteem levels and the Cumulative Grade Point Average (GPA) of students at the Faculty of Economic,  Sultan Agung Islamic University, Semarang. This study employs a quantitative approach with a cross-sectional design. The research sample consists of 185 respondents selected randomly using the simple random sampling method. Data were collected through the Rosenberg Self-Esteem Scale (RSES) questionnaire to measure self-esteem levels and students' latest GPA records. Data analysis was conducted using the Spearman correlation test. The findings of this investigation reveal a robust positive correlation between self-esteem levels and students' GPA (p = 0.000; r = 0.774). This suggests that elevated self-esteem is intrinsically linked to improved scholastic performance. This study is anticipated to serve as a foundational reference for support programs aimed at enhancing students' self-confidence. In conclusion, a profound and statistically significant association is evident between self-esteem and students' academic performance.  

Human Resource Management (HRM) is a critical determinant of organizational productivity and competitiveness in the era of globalization and digital transformation. This study employs a Systematic Literature Review (SLR) approach to synthesize empirical findings on HRM strategies published between 2019 and 2024. The review identifies six key domains of effective HRM: competency-based recruitment, continuous training and upskilling, transparent performance evaluation, reward and incentive systems, workforce diversification and inclusion, and technology-enabled HRM. Evidence demonstrates that competency-driven recruitment reduces turnover and enhances job fit, while continuous training fosters innovation and adaptability. Transparent evaluation systems increase motivation and fairness, and balanced reward packages combining financial and non-financial recognition improve retention and engagement. Workforce diversification enhances problem-solving and innovation when paired with inclusive leadership practices. Technology integration, particularly HRIS and people analytics, streamlines processes and strengthens decision-making. Importantly, bundled strategies yield stronger productivity outcomes than isolated interventions, with improvements occurring through capability, motivation, and coordination pathways. The findings underscore the need for organizations to align HRM practices with strategic goals and global trends. This study contributes to HRM literature by providing evidence-based insights and offers practical guidance for managers seeking to enhance productivity in competitive environments.

The maritime industry is undergoing rapid technological transformation, yet the role of scientific paradigms in shaping technological decision-making remains underexplored. This study examines how epistemological reasoning influences leadership approaches to technological innovation in maritime business management. The research is motivated by the need to bridge the gap between theory and practice, ensuring that technological advancements are implemented with structured decision-making rather than reactive adaptation. This study provides an original contribution by evaluating the intersection of leadership, epistemology, and technological adoption—a perspective largely overlooked in previous maritime innovation research. While digital transformation is widely discussed, few studies address how structured epistemological reasoning affects decision-making in technology adoption. The research seeks to answer: To what extent do scientific paradigms influence technological decision-making in maritime leadership, and what challenges hinder epistemological integration? Using a qualitative methodology, the study engages industry experts, lecturers, and postgraduate students, applying thematic and comparative analysis to explore epistemological competency development and sustainable innovation. Results indicate that leadership adaptability and epistemological literacy significantly enhance digital transformation efforts, yet barriers such as regulatory constraints and resistance to structured reasoning persist. The study concludes that embedding epistemology into maritime education and leadership training is essential for ensuring sustainable, data-driven technological decision-making in the industry.

Optimizing human resources (HR) in digital marketing through the synergy between creativity and technology is an important strategy to create effective and innovative marketing campaigns in the digital era. Creativity plays a crucial role in producing engaging and relevant content, which is able to build an emotional connection with consumers as well as strengthen the brand identity. Meanwhile, technology allows for personalization, data-driven analysis, and efficiency in the implementation of marketing strategies. This study examines how the combination of human resource creativity and the use of digital technology can create a competitive advantage for companies. The results of the study show that companies that successfully integrate these two elements can increase consumer engagement, customer loyalty, as well as the effectiveness of marketing campaigns. From a conceptual perspective, this approach is changing the way companies view innovation and cross-functional collaboration within organizations, which in turn improves responsiveness and adaptability to market changes. The practical implications of this research are the importance of investing in the development of creative and skilled human resources in technology, to create a sustainable digital marketing strategy and be able to compete in an increasingly competitive global market.

As 6G networks promise unprecedented speeds and ultra-low latency, AI-based resource allocation plays a crucial role in optimizing network performance. This study explores AI-driven techniques for spectrum management, energy efficiency, and real-time data processing. By leveraging machine learning and deep learning models, AI enhances network adaptability, reduces congestion, and improves overall efficiency. The proposed approaches enable intelligent decision-making, dynamic resource allocation, and predictive analytics to meet the growing demands of future wireless communication. The findings highlight the potential of AI in revolutionizing 6G networks, ensuring seamless connectivity, and maximizing network capacity while minimizing power consumption. These advancements contribute to the development of more sustainable and intelligent telecommunication infrastructures.  

This qualitative literature review examines the key predictors of SME recovery from severe financial distress, focusing on financial blockages, employee dynamics, and turnaround success. The study synthesizes findings from various sources to highlight how financial challenges such as liquidity shortages and limited access to credit hinder recovery, while government support and alternative financing offer potential solutions. Additionally, employee engagement, organizational culture, and strategic leadership are identified as critical factors in successful turnaround efforts. SMEs that foster innovation, adaptability, and retain key employees are more likely to overcome financial crises. The review emphasizes the importance of a holistic approach, combining financial relief, strategic leadership, and organizational resilience, for long-term SME recovery. The findings suggest avenues for future research on the interaction of these factors and their context-specific impacts.