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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.

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 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 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.

An investigation was conducted at A State Senior High School in Kupang City, NTT.to assess how Project-Based Learning (PjBL) and Discovery Learning (DL) models differentially impact student learning activities and outcomes when teaching atomic structure. Adopting a quasi-experimental design with a nonequivalent control group, the study collected data through comprehensive activity questionnaires, observation sheets, and pretest/posttest instruments. The findings clearly showed that PjBL significantly boosted student learning activities, with an average score of 81.19, considerably higher than the 66.15 observed in the DL group. This significant difference was statistically confirmed by an independent samples t-test. Regarding learning outcomes, the PjBL group's average (70.97) was marginally better than the DL group's (68.89), but this difference did not reach statistical significance. It is suggested that limitations in learning time may have played a role in the learning outcomes. This study concludes that PjBL is superior for enhancing student engagement and activity, while both PjBL and DL are equally effective in improving student learning outcomes. Ultimately, the choice of a learning model should be strategically aligned with educational goals, subject matter, and the unique characteristics of the classroom to maximize student achievement.

Character development is an important part of the learning process in AUD. The study was conducted using qualitative descriptive techniques with sources from interview results, documentation, and observation . The purpose of the study focused on the development of children's character through classroom management strategies located in Pertiwi DWP Kindergarten, Wonosobo Regency. The learning process is not limited to the level of knowledge, but is also optimal in conveying character values in AUD. The good or bad character-based classroom management has an impact on the effectiveness of character development carried out by teachers. The results of the study showed that teachers carried out classroom management strategies by setting class rules , building positive relationships , modeling good behavior, giving appreciation to children , carrying out reflection activities. Based on the results of the implementation of classroom management, teachers are able to develop positive characters, namely discipline, responsibility, empathy, honesty and self-confidence.

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.

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 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.

This study aims to describe the development of a deep learning-based learning model to improve the speaking skills of students at SMP Muhammadiyah Purworejo. This study uses research and development with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The research location was at SMP Muhammadiyah Purworejo in the 2024/2025 Academic Year for class VIII students with a population selection of class VIII students. Meanwhile, the research sample with a purposive sampling technique was 30 students representing diverse speaking abilities. Data were collected through tests, questionnaires, and observations. The test measures speaking skills, the questionnaire collects student responses, and the observation sheet observes the activities of teachers and students during the learning process. Interactive methods were used to analyze the data. The results showed an increase of 11.3 points in pretest data of 28.9 and posttest of 40.2. In addition, the questionnaire and observation showed that both teachers and students showed positive responses to the application of the deep learning model. Therefore, it can be concluded that students of Muhammadiyah Purworejo Middle School can acquire better speaking skills with this model.

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 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.

Batik in Indonesia is growing very rapidly, almost every region or city in Indonesia has a variety of batik motifs. The batik motifs owned by each region are their own wealth and heritage in each region that must be preserved and maintained properly. So the Indonesian people need to collaborate with each other to place batik preservation as a top priority in the field of preserving the nation's culture. Because knowledge of batik motifs in Indonesia is important in order to maintain the culture of the Indonesian nation, including knowledge of batik motifs in each of their respective regions, so there is a need to make it easier for humans to recognize batik motifs in regions in Indonesia quickly and easily. This study aims to build a system model that can assist humans in recognizing batik motifs in Indonesia through this batik motif detection system. This research produces a model that can detect batik motifs from every area in Central Java. The conclusion of this study is a batik detection model that can help and introduce to the public about various batik motifs from each region.Batik in Indonesia is growing very rapidly, almost every region or city in Indonesia has a variety of batik motifs. The batik motifs owned by each region are their own wealth and heritage in each region that must be preserved and maintained properly. So the Indonesian people need to collaborate with each other to place batik preservation as a top priority in the field of preserving the nation's culture. Because knowledge of batik motifs in Indonesia is important in order to maintain the culture of the Indonesian nation, including knowledge of batik motifs in each of their respective regions, so there is a need to make it easier for humans to recognize batik motifs in regions in Indonesia quickly and easily. This study aims to build a system model that can assist humans in recognizing batik motifs in Indonesia through this batik motif detection system. This research produces a model that can detect batik motifs from every area in Central Java. The conclusion of this study is a batik detection model that can help and introduce to the public about various batik motifs from each region.

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

This study aims to identify and analyze the implementation strategies for discipline at SDIT Bunayya Lhokseumawe, a school committed to developing an intellectually intelligent and morally superior Islamic generation. In the context of modern education, educational institutions no longer merely serve as a place to transfer knowledge but also have a significant responsibility in instilling moral, ethical, and spiritual values. Implementation is a crucial aspect of discipline in achieving the goal of developing a holistic student character. However, the reality on the ground shows that challenges to implementing discipline are increasingly complex. The negative influence of social media, rapid technological developments, and changes in parenting patterns in families often influence student behavior. The strategic role of educational institutions is crucial, especially in building a system capable of consistently and sustainably instilling disciplinary values. This study used a qualitative approach involving the principal, teachers, and students as the main subjects. Data collection techniques were carried out through direct observation, in-depth interviews, and documentation of school activities. The results showed that the strategies implemented at SDIT Bunayya include a collaborative approach involving all elements of the school, including parents. Character training programs, regular training, clear enforcement of rules, and targeted extracurricular activities are integral parts of the school's discipline system. A disciplined and conducive learning environment has been proven to support both academic achievement and character development. Therefore, the strategies implemented at SDIT Bunayya can serve as a model for other schools, particularly integrated Islamic schools, which share a similar vision of developing a generation that excels not only academically but also possesses integrity and noble character.

This research is motivated by the problems in class VIII of Al-Irsyad TI Bulaan Kamba Islamic Boarding School that the learning process is still centered on the teacher, lack of student response to mathematics learning, lack of student courage to ask the teacher, students still think mathematics is difficult and scary, lack of interest in learning mathematics, and students are less enthusiastic when participating in mathematics learning. One alternative learning that can be done to overcome these problems is to apply the Problem Based Learning learning model. This study aims to determine the effect of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School. The hypothesis in this study is "there is a significant effect of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School". This type of research is a pre-experiment with a one group pretest-posttest research design, the population in this study were all students of class VIII of Al Irsyad TI Bulaan Kamba Islamic Boarding School, the sampling used was Random Sampling, first a normality test, homogeneity test, and average similarity test were carried out on the population data. The sample in this study was class VIII A as the sample class. The instrument in this study was a questionnaire on students' interest in learning mathematics. The data analysis technique used in this study was the paired sample t test. The results obtained from the paired sample t test were t count = 2.81 and t table = 2.05 and sig = 0.0045 at a real level of α = 0.05. From these results it can be concluded that H0 is rejected and H1 is accepted. So it can be concluded that there is a significant influence of the Problem Based Learning learning model on the interest in learning mathematics of class VIII students of Al Irsyad TI Bulaan Kamba Islamic Boarding School.    

Environmental awareness is an effort to preserve, prevent damage to, and restore natural conditions. This attitude can be instilled through the integration of environmental values in science learning. The Science-Technology-Society (STS) approach is one effective method that fosters students' thinking skills and scientific literacy in line with technological and scientific developments. This study aims to determine whether environmental awareness in science subjects can be improved through the STS approach at MI Salamah in Jambi City, and to describe the implementation of the STS approach in enhancing environmental awareness in science subjects at the school. This research is a classroom action research using the Kemmis and McTaggart model, which consists of two cycles. Each cycle includes two meetings with four stages: planning, implementation, observation, and reflection. The research results show an increase in students' environmental awareness attitudes in both Cycle I and Cycle II. In Cycle I, the average score for environmental awareness attitudes rose from 60.10% to 76.63%, while in Cycle II, it increased more significantly from 88.58% to 92.6%. In addition, test results also showed a significant improvement in students' understanding of the material. The implementation of the Science-Technology-Society (STS) approach in enhancing environmental awareness in science subjects at MI Salamah has been effective. It was applied through experiential learning, discussions, and creative activities that actively involved students in understanding and applying environmental concepts.

This research is motivated by the challenges faced by teachers in implementing effective and smooth learning processes, particularly in implementing the Problem Based Learning (PBL) model in Islamic Cultural History (SKI) learning at MAN 2 Bengkulu City. Teachers, as crucial components in the learning process, need to pay attention to students' needs and characteristics in preparing meaningful learning. The PBL model is one of the alternative learning approaches that uses problems as a starting point for students to develop critical thinking skills. This research employed a qualitative approach with research subjects comprising teachers and eleventh-grade students at MAN 2 Bengkulu City. Data collection was conducted through observation, in-depth interviews, and documentation. The research results identified several obstacles in PBL implementation, namely: (1) limited learning time; (2) minimal supporting facilities; (3) variations in student ability levels and characteristics; and (4) lack of interaction and communication between teachers and students. This research also produced strategies to overcome these obstacles, including thorough preparation, effective time management, and development of teachers' abilities to facilitate problem-based learning. These findings contribute to the development of more effective SKI learning practices with a PBL approach in madrasas.

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 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.