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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.

Modernization of education in Islamic boarding schools is an important need to maintain the relevance of learning in the digital era. Salafiyah Islamic boarding schools, which are synonymous with the traditional approach through the study of the Yellow Book, face the challenge of adapting technological developments without losing their scientific character. This research aims to develop a model of curriculum modernization through the integration of deep learning-based digital technology  to improve the effectiveness of learning and the quality of academic evaluation. The methods used include literature studies, observation of the learning process, and trial implementation of digital learning tools equipped with deep learning algorithms  for student ability analysis. The results of the study show that the application of deep learning technology  is able to speed up the competency mapping process, provide automatic feedback on reading and comprehension of classical texts, and assist teachers in monitoring the development of students more accurately. This integration also increases interest in learning and strengthens the mula curriculum system without shifting the traditional values of the Islamic boarding school. Thus, technology-based modernization can be a strategic solution to bridge the classical approach and the needs of 21st century learning in Salafi Islamic boarding schools.

The development of artificial intelligence (AI) in learning media presents challenges related to the uneven readiness of human resources and infrastructure, thus affecting the effectiveness of its implementation. This study aims to examine the effect of AI implementation in learning media development on learning outcomes of students of Faculty of Economics, University of Balikpapan. The method used is quantitative with survey design and Structural Equation Modeling (SEM) analysis using AMOS. The sample consists of 113 students selected by simple random sampling from the population of 376 active students. The results of the analysis showed that the readiness of lecturers and the quality of AI-based learning media had a significant effect on improving student learning outcomes. However, the success of AI implementation is also strongly influenced by infrastructure support and educator training. The findings provide important implications for learning media developers and policy makers to strengthen lecturers' capacity and improve technology infrastructure to support inclusive and sustainable digital transformation of education. In addition, ethical aspects and data privacy should be the main concerns in the development of AI-based learning media.

This study aims to examine the impact of the Jigsaw cooperative learning strategy on student learning outcomes at the secondary school level. Using a literature review method, the research synthesizes theoretical perspectives on cooperative learning, detailed applications of the Jigsaw model, and empirical findings from previous studies that focus on its effects across cognitive, affective, and psychomotor domains. The findings indicate that the Jigsaw strategy significantly improves students’ cognitive achievements and fosters affective development such as motivation, empathy, and positive attitudes toward learning. In addition, this strategy enhances students’ social interaction skills and promotes collaborative behavior, contributing to a more inclusive and engaging classroom environment. The collaborative nature of the Jigsaw model allows students to take responsibility for their own learning while also supporting their peers, which contributes to deeper understanding and retention of the material. These outcomes suggest that the Jigsaw method is an effective instructional approach for enhancing student engagement and academic performance. The implications of this study emphasize the importance of providing professional development opportunities for teachers to effectively implement the Jigsaw strategy. Furthermore, schools are encouraged to foster a classroom climate that supports teamwork and active student participation. Overall, this review highlights the Jigsaw model as a promising pedagogical tool for improving student outcomes and fostering 21st-century skills in secondary education settings.

The purpose of this research is to examine how the CTL Learning Model affects third graders' conceptual understanding at MI Al-Munawwarah in Jambi City.  The research goals informed the selection of class 3.2, which yielded a research sample of 30 kids, for this third grade study that employed a sampling approach known as purposive sampling.  A one-group pretest and posttest design was used in this investigation.  In this work, we employed a test-and-observation design to gather data; we administered the test before (pretest) and after (posttest) learning using the CTL model.  To determine the impact, a t-test is used in a hypothesis test.  The significance value of 0.000 <0.05, obtained from the SPSS 26 statistical calculation using the paired sample t-test, indicates that the calculated statistics (output t number) <table statistics (t table) concluded that H0 was rejected and ha was accepted. Consequently, the CTL learning model influenced the comprehension of the concept of learning outcomes among class III students at MI Al-Munawwarah, Jambi City.  

This research is based on the background of the problems found in grade IX of SMP N 1 Gunuang Omeh District that the low motivation of students in learning mathematics, this is characterized by students who look lazy in learning and only a few students pay attention when the teacher explains the material. Then, from the results of the students' daily test scores, it was also found that the low ability to understand mathematical concepts of grade IX students at SMP N 1, Gunuang Omeh District. The formulation of the problem in this study is how is the motivation of students to learn in mathematics subjects using the Student Teams Achievement Division (STAD) type cooperative learning model for grade IX at SMP N 1 Gunuang Omeh District for the 2023/2024 Academic Year and whether there is a significant positive influence using the Student Teams Achievement Division (STAD) type cooperative learning model on the ability to understand mathematics concepts of grade IX students at SMP N 1 Kecamatan Gunuang Omeh for the 2023/2024 Academic Year. This type of research is a quasi-experimental experiment with a Randomized Control Group Only Design research design. The population in this study is all grade IX students at SMP N 1, Gunuang Omeh District. Sampling was determined at random first by a normality test, a homogeneity test, and an average similarity test on population data. The sample in this study is class IX-2 as the experimental class and class IX-3 as the control class. The results of the analysis of the student mathematics learning motivation questionnaire data were obtained with a percentage of 72.28% with high motivation criteria and the test of students' ability to understand mathematical concepts was calculated using the t-test obtained tcount = 3.180 and and ttable = 1.672 because tcount > ttable means subtracting H0 and accepting H1 at the real level α=0.05, and by using SPSS26 Sig = 0.003 was obtained which means that sig ≤α with α=0.05.

The teaching of Professional Ethics in the Culinary Arts Study Program is often delivered in a theoretical manner, make lack of contextual and reflective learning experiences for students. This study aims to develop and evaluate an interactive pocket book as a learning medium to support students' understanding of ethical values in real culinary work settings. The research employed the 4-D development model (Define, Design, Develop, Disseminate), with data collected through observation, interviews, expert validation, practicality questionnaires, and pretest–posttest design. The study was conducted over three months at Universitas Negeri Medan. Validation results indicated that the media was highly feasible in terms of content, layout, and language. Practicality testing showed that students found the media very practical, while the effectiveness test revealed an improvement in learning outcomes with a gain score of 0.60 (moderate to high category). These findings demonstrate that the interactive pocket book is effective in promoting ethical learning that is contextual, reflective, and applicable. The media is recommended for integration into learning strategies focused on values and professional attitudes and is suggested for further development into digital formats to enhance flexibility and wider accessibility.

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

The purpose of this study is to determine the effect of self-confidence on student learning motivation at Junior High School 1 Suwawa Tengah. This study uses a quantitative approach, with a Survey research method. The data used are primary data obtained from distributing questionnaires to students at Junior High School 1 Suwawa Tengah. The number of samples drawn in this study was 51 respondents. The data analysis technique used simple linear regression with the help of the SPSS program. The results of the analysis, the study showed that there was a positive and significant influence between self-confidence and student learning motivation at Junior High School 1 Suwawa Tengah. The comparison of the t-count values obtained is still greater than the t-table value so that Ho is rejected. Thus, at a confidence level of 95%, it can be concluded that there is a positive and significant influence of self-confidence on learning motivation. The coefficient of determination value of the previously obtained regression model is 0.170, this value means that 17% of the variation in learning motivation is explained by the self-confidence of a student.

This article examines the implementation of digital literacy within the Islamic Religious Education (PAI) curriculum as a response to educational transformation in the digital era. The study aims to analyze strategies for integrating digital literacy into the PAI curriculum, identify the challenges of its implementation, and formulate a contextual model for developing PAI learning based on digital literacy. Using a qualitative research method with a literature review approach and analysis of current curriculum documents, the study found that the implementation of digital literacy in the PAI curriculum remains partial and unsystematic. The findings indicate the need to reformulate the PAI curriculum by comprehensively integrating digital literacy competencies through digital project-based learning, online collaborative learning, and the use of social media for reflective learning. The implications of these findings highlight the urgency of transforming the PAI curriculum to prepare students to become critical, creative, and ethical digital Muslims in the era of technological disruption.

The interaction between genetic and environmental factors plays a crucial role in determining phenotypic traits in organisms. This study aims to analyze these interactions using computational approaches, including statistical models and machine learning algorithms. The data used include genetic factors (genotypes) and simulated environmental factors. Results indicate that machine learning models such as Random Forest can detect interaction patterns with high accuracy, as demonstrated by significant R² values. Additionally, heatmap visualizations provide deeper insights into the non-linear effects of genetic-environment interactions. This study highlights the potential of computational methods in exploring complex interactions, with broad applications in health, agriculture, and biotechnology.

Environmental education plays a crucial role in modern curricula, emphasizing sustainability and ecological responsibility to address global challenges. This study compares faith-based and secular environmental education strategies, focusing on how theological reflection and ecological consciousness are integrated into teaching frameworks. Faith-based schools, particularly those rooted in Christianity and Islam, incorporate ecological ethics within religious teachings, framing environmental stewardship as a moral and spiritual duty. In contrast, secular models prioritize scientific literacy and environmental problem-solving. The study reveals that faith-based models promote both scientific understanding and moral engagement with environmental issues through religious values like Imago Dei (in Christian schools) and khalifah (in Islamic schools). These values instill a sense of moral accountability, motivating students to act sustainably. The research also highlights how curriculum design, teacher involvement, and experiential learning contribute to the effectiveness of both models in fostering ecological responsibility. By comparing these approaches, the study proposes an integrative eco-theological pedagogical framework that combines the strengths of both methods to promote long-term commitment to sustainability. The findings have implications for educational policy, curriculum development, and teacher training, especially in pluralistic school settings where diverse perspectives must be considered. Integrating ecological ethics into education can cultivate a generation committed to sustainability and ethical responsibility.

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.

This study investigates the dual impact of teacher competence on student academic performance and the development of leadership qualities in Senior High Schools and Vocational High Schools in Kabupaten Serang, Banten Province. Using a qualitative research approach, data were collected through interviews, focus groups, and classroom observations involving teachers and students. The findings reveal that teacher competence encompasses not only mastery of subject matter and pedagogical skills but also emotional intelligence and leadership modeling. Competent teachers create supportive and participatory learning environments that enhance both academic achievement and leadership development among students. The research highlights that effective teachers are those who not only deliver content but also engage students in meaningful ways, fostering a sense of belonging and motivation. This engagement is crucial, as it encourages students to take ownership of their learning and develop critical thinking skills. Furthermore, the study identifies that teachers who model leadership behaviors—such as ethical decision-making, effective communication, and collaboration—significantly influence students' perceptions of leadership and their willingness to adopt similar traits. However, challenges such as limited access to professional development and resource constraints affect the full realization of teacher competence. Many teachers express a desire for ongoing training and support to enhance their skills, yet systemic barriers often hinder their professional growth. The study underscores the need for comprehensive teacher development programs and policy support to improve educational outcomes holistically. These insights are valuable for educators, policymakers, and school administrators aiming to foster academic excellence and leadership skills in students. By prioritizing teacher competence and providing the necessary resources, schools can create an environment that not only promotes academic success but also prepares students to become effective leaders in their communities. 

This study employed a descriptive qualitative approach. Data collection methods included structured interviews, non-participant observation, and documentation. The informants were the principal, the Islamic aqidah and akhlak (belief and moral) teacher, and students at MA Minhajut Thullab. Data analysis techniques included data collection, data reduction, data presentation, and conclusion drawing. Data validity was verified using data source triangulation. This research discusses how Akidah Akhlak learning is implemented to prevent juvenile delinquency at MA Minhajut Thullab Sumberberas in the 2024/2025 academic year. The study was conducted through observation, interviews, and documentation. The results show that Akidah Akhlak learning is designed with thorough planning, interactive implementation, and comprehensive evaluation focusing not only on students’ knowledge but also on their attitudes and behavior. Teachers apply approaches that relate to students’ daily lives, along with motivation and role-modeling. Supporting factors include the religious school environment and competent teachers. Meanwhile, obstacles include limited facilities and the diverse backgrounds of the students. Overall, Akidah Akhlak learning has proven effective in helping students develop discipline, respect, and avoid deviant behavior.

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.

This study explores the integration of Artificial Intelligence (AI) with thermal optimization in Waste-to-Energy (WtE) systems to enhance both energy recovery and emission control. Introduction: The growing need for sustainable urban waste management has highlighted the importance of optimizing WtE systems. AI technologies, including machine learning and deep learning, have shown potential in improving the efficiency of WtE processes, especially in reducing emissions and enhancing energy recovery. Literature Review: Previous research indicates that AI has been successfully applied to various WtE technologies such as pyrolysis, gasification, and incineration, yet the integration of AI specifically for thermal optimization remains underexplored. Most studies focus on predictive models for emission reduction rather than real time thermal optimization. Materials and Method: The study proposes the development of an AI-driven framework that integrates real time data collection from IoT sensors, predictive modeling, and real time control algorithms. The system optimizes key parameters such as combustion temperature and fuel flow to enhance energy recovery and minimize emissions. The method includes data collection from operational WtE plants, followed by model development using machine learning algorithms. Results and Discussion: Initial simulations and pilot testing showed significant improvements in energy efficiency and emission reduction. AI-driven systems outperformed conventional WtE systems by optimizing operational parameters in real time. The study identifies gaps in AI integration for thermal optimization and suggests future research directions, including the integration of AI with smart grids and carbon credit systems for more sustainable WtE operations.

This study aims to analyze the level of understanding of PSPK 23 B students at Medan State University on atomic theory questions. With a quantitative descriptive approach, data were collected through multiple-choice tests filled out by 30 students. The results showed an average understanding of 75.33%, which is quite good. However, there was significant variation where some students mastered basic concepts (eg Dalton and Thomson's atomic models), while others had difficulty with abstract topics (eg Bohr's model and quantum mechanics). This finding is in line with previous studies on misconceptions in atomic structure. This study emphasizes the importance of innovative learning strategies, such as visual media and conceptual approaches, to improve student understanding. Periodic evaluation is needed to detect learning gaps and adjust teaching methods. The results of this study can be a basis for developing more effective chemistry education practices.

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.

This community service program aims to empower student entrepreneurs through digitalization and strengthening sustainable business capacity. The activities are comprehensively designed with a participatory and experiential learning approach, so that participants not only understand the concepts theoretically but also are able to apply them practically. The program consists of various activities, such as interactive workshops on digital marketing and sustainable business models, business clinics to analyze current business problems, digitalization simulations using online platforms, individual mentoring sessions by business practitioners, and presentations of business plans developed by each participant. A total of 30 students from various study programs were actively involved and demonstrated high enthusiasm throughout the process. A total of 30 students from various study programs were actively involved and demonstrated high enthusiasm throughout the process. Program evaluation was conducted through observation, questionnaires, and assessments of the developed digital action plans. The evaluation results showed significant improvements in cognitive aspects (understanding of digitalization concepts, sustainable business models, and digital marketing strategies), affective (interest and commitment to business development), and application (the ability to develop and present innovative business plans relevant to digital challenges). Ninety percent of participants were able to develop digital action plans that encompassed the use of social media, e-commerce, digital payment systems, and technology integration in business operations. Furthermore, the program encouraged collaboration among students in building business networks and young entrepreneur communities. Positive participant feedback demonstrated that the participatory approach and hands-on practice were effective in increasing student motivation and readiness to face business challenges in the digital era. The program has proven effective in enhancing students' entrepreneurial competencies and fostering an adaptive business mindset.