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This research aims to evaluate the effectiveness of the Missouri Mathematics Project (MMP) Learning Model in improving mathematics learning outcomes for junior high school students. This study employs a comparative approach, comparing mathematics learning outcomes between students taught using MMP and those taught using conventional teaching models. Data were collected through hypothesis testing of mathematics learning outcomes for both groups. The data analysis results indicate a significant difference in mathematics learning outcomes between the two groups, with a significance value (sig.) smaller than the commonly used significance level. These findings suggest that the implementation of MMP effectively improves mathematics learning outcomes for junior high school students. The practical implications of these findings are discussed in the context of developing more effective and inclusive mathematics teaching strategies.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

This study explores the integration of hybrid AI control models, combining reinforcement learning (RL) and robust adaptive control, to improve the adaptability, performance, and stability of autonomous manufacturing systems. Traditional control systems, while effective under stable conditions, often struggle to cope with disturbances and varying production demands. Hybrid AI models, which integrate classical control methods such as Proportional Integral Derivative (PID) with machine learning techniques like RL, deep Q-networks (DQN), and deep deterministic policy gradient (DDPG), enhance decision-making capabilities in dynamic production environments. The study develops a hybrid RL robust control framework and tests it in both simulation and real-world scenarios. Performance metrics, including production efficiency, system stability, and adaptability, are assessed under various disturbance conditions, such as machine failures and fluctuating demands. The hybrid model significantly outperforms traditional PID control in terms of efficiency and stability, demonstrating faster convergence and better adaptability in dynamic environments. Statistical analysis confirms the superiority of the hybrid system over standalone RL models and traditional PID control. This model’s scalability and adaptability make it a promising solution for Industry 4.0 applications, addressing key challenges in real-world manufacturing systems by ensuring computational efficiency and the ability to manage large-scale data. The findings contribute to the development of more robust and efficient control strategies for autonomous manufacturing systems in uncertain environments.

The change in the Merdeka Curriculum has made learning tools in various subjects including biology in class X inadequate. This study aims to determine the feasibility of Problem-Based Learning (PBL) based LKPD (student worksheet) accompanied by Science Process Skills (KPS) on the material of environmental changes in class X. The development model refers to the research and development (R&D) method with 5 stages. The stages are potential and problems, data collection, product design, design validation, and design revision. The research instruments used were instrument validation questionnaires and validation sheet questionnaires. The validated aspects are the format, content, language, and benefits/usability of LKPD. Validation was carried out by 5 validators. Analysis of validation results using face validity Aiken's V with criteria V = 0.87. The results showed that the LKPD met the valid criteria with a total average of 0.947. It can be concluded that problem-based learning-based LKPD accompanied by science process skills is declared valid and ready to proceed to the trial stage.

Accurate image segmentation is a pivotal process in medical imaging, essential for supporting diagnosis, treatment planning, and monitoring disease progression. This study evaluates the effectiveness of machine learning algorithms, including U-Net, Fully Convolutional Networks (FCNs), and Mask R-CNN, in achieving high-precision segmentation of medical images. Experimental results demonstrate that these models significantly enhance segmentation accuracy, enabling more precise diagnostic outcomes in clinical settings and advancing the development of automated medical imaging technologies.

Background: Public opinion analysis has become increasingly important in the digital era, where social media platforms generate large-scale textual data reflecting public perceptions toward environmental policies. Advances in Natural language processing (NLP) and machine learning enable systematic sentiment classification to support data-driven decision-making. Objective: This study aims to evaluate the effectiveness of several sentiment classification models in analyzing Indonesian-language social media data related to environmental policies. Method: The research employed a text mining pipeline including data crawling, preprocessing (case folding, tokenization, stopword removal, and stemming), and vectorization using TF-IDF. Three classification models Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) were trained and evaluated using accuracy and F1-score metrics. Results: Experimental findings indicate that LSTM achieved the highest performance with 91.7% accuracy and 91.2% F1-score, outperforming SVM (88.5%) and Logistic Regression (84.2%). Sentiment distribution analysis shows that public opinion is dominated by positive sentiment (47.5%), followed by neutral (32.0%) and negative (20.5%). Overall: The results demonstrate that deep learning-based models provide more robust contextual understanding and more reliable sentiment mapping for environmental policy analysis.

Power systems are critical infrastructure that face significant challenges due to increasing demand and inherent complexity. Predicting failures in power systems is crucial for enhancing grid reliability, minimizing downtime, and optimizing maintenance processes. This study evaluates various deep learning models, specifically convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models, for predicting power system failures. By analyzing these models’ performance metrics on historical power grid data, the study provides insights into the strengths and weaknesses of each approach. The findings contribute to the development of more robust predictive models for power system reliability.

This research was motivated by the large number of students who scored below the KKM in the History of Islamic Culture subject at MTsS Thawalib Gunung Padang Panjang class VIII. To help solve this problem, researchers are interested in trying out a Reciprocal Learning learning model to improve student learning outcomes. This learning model will enable students to help students so that they are able to jointly use rational thinking skills and emotional awareness, to understand feelings, values, attitudes and behavior patterns. This research was conducted from November 8, 2023 to December 18, 2023. This research was an experimental study with a Randomized Control Group Posttest Only Design. The population in this study was class VIII MTsS Thawalib Gunung Padang Panjang. The sample was selected purposively from a certain population. Researchers chose class VIII C as the experimental class, and class VIII A as the control class. The instrument used is a test. To test the research hypothesis, the researcher used the t-test and processed the data using SPSS. Based on data analysis, the tcount results are greater than ttable. tcount is 3.180 and ttable is 1.679 with the average value of the experimental class being 84.38 and the control class being 72.57. So it can be concluded that the Reciprocal Learning learning model is effective on student learning outcomes in the Islamic Cultural History subject.    

This research is motivated by low learning outcomes and teachers have not used appropriate models in the learning process. The aim of this research is to find out the influence of the Storytelling learning model on the ability to listen to illustrated stories in Class V at MIS Insan Ikhlas Islamic School. The population in this study was 65 students. With a sample of class V A as the experimental class totaling 34 students and class V B as the control class totaling 31 students. Sampling used total sampling. To analyze the data, the Independent sample t-test was used using SPSS version 20. The results obtained after using the Storytelling learning model, namely the experimental class, obtained an average score of 84.4. Students' Listening Ability is assessed in the very Good category. There is an influence of the Storytelling learning model on the ability to listen to picture stories in Class V with the results of the calculated t test showing that at a significant level of 5%, the calculated value shows a tcount value of 4,646. Meanwhile for ttable at N = 65, namely 1.668. So tcount 4,646 > ttable 1,668.

This research aims to explain a review of the relationship between Self-efficacy and the ability to understand mathematical concepts, and how to develop and measure student Self-efficacy. In this research, the Systematic Literature research method was used which aims to obtain data from relevant research. With research sources coming from Google Scholar, Researchgate, Scopus, Syntax, and DOAJ. In this research, 17 reference sources were collected to obtain data and information. Information collected regarding differences in mathematical concept abilities in students' Self-Efficacy criteria, Development of Self-Efficacy in students in mathematics learning, Impact of students' Self-Efficacy on their ability to understand mathematical concepts, Measurement techniques (Indicators) of Self-Efficacy Efficacy of students' understanding of mathematical concepts. From the data collected, it was found that the technique that is often used is the Bandura technique. To develop self-efficacy in mathematics learning, you can focus on learning objectives, adding learning models, as well as 4 triggers, including: Mastery of experience, represented experience, social persuasion, and physical and affective conditions. In activating or applying these abilities, self-efficacy can have a positive impact on learning for students through external and internal learning activities of individual students. And self-efficacy is also useful in maintaining students' resilience in answering difficult questions, as well as understanding the concepts more deeply.

The main problem in this research is whether the use of the Giving Question and Getting Answer Learning Model can improve student learning outcomes in Class V Social Sciences learning at SDN No. 14 Cikowang Presidential Instruction. This research aims to improve student learning outcomes by using the Giving Question and Getting Answer Learning Model in Class V Social Sciences learning at SDN No. 14 Cikowang Presidential Instruction. This type of research is Classroom Action Research (PTK) which consists of two cycles. This research procedure includes planning, implementing actions, observing and reflecting. The subjects in this research were fifth grade students at SDN No. 14 Inpres Cikowang as many as 15 people. The results of the research showed that in the first cycle only 6 out of 15 students or 40% completed the KKM and obtained an average score of 55.53. Meanwhile, in cycle II, out of 15 students, 12 students or 80% had fulfilled the KKM, namely the average score obtained was 85.00%. Based on the results of the research above, it can be concluded that student learning outcomes in social studies subjects for class V SDN No. . 14 of Presidential Instruction Cikowang through the application of the Giving Question and Getting Answer learning model has increased.

This research aims to determine whether there is an influence of the think pair share cooperative learning model on students' mathematical communication skills. This research was conducted at SMA Markus Medan. The research method used is a quasi experimental method. The sample in this study consisted of two classes, namely class XI IPA-1 as the experimental class and class XI IPA-2 as the control class. The experimental class uses the think pair share learning model, and the control class uses conventional learning. The average result of the final test score (post-test) for experimental class students was 84.33, while in the control class the average final test score for students was 70.14. In the hypothesis test, the results of the mean difference test (t-test) were obtained with the statistical value  >  (8,342 > 1,671), which means that there is an influence from the use of the think pair share learning model applied in the experimental class. This indicates that the mathematical communication skills of experimental class students are better than those in the control class.    

The application of the STAD type cooperative learning method (Student Teams-Achievement Divisions) is a relevant strategy in efforts to improve students' mathematical abilities. In this research, we examine the effectiveness of using the STAD type cooperative learning style in improving students' mathematical abilities. This approach involves dividing students into small teams to encourage collaboration, joint problem solving, and knowledge exchange. We evaluated the impact of implementing this learning model on students' mathematical abilities and gained insight into effective learning strategies in the context of improving mathematics achievement.

The development of technology has affected the world of education in the content of the material and the learning process. At SMP Negeri 1 Besuki, it is known that technology-based learning media has never been developed during learning. The learning media that runs at SMP Negeri 1 Besuki is considered boring for students. This learning media is expected to help students to understand learning independently and improve the ability to understand informatics subjects.This research uses the ADDIE development model which consists of five stages, namely: analysis, design, development, implementation and evaluation. The ADDIE model provides an opportunity to evaluate development activities at each stage, this has a positive impact on the quality of development products. Based on the results of the feasibility test from the media expert of 83.63%, the results of the feasibility test on the material test amounted to 84.44% and student respondents amounted to 82%. If calculated the average is 83, 35%. Based on the criteria guidelines above this product is declared "Very Feasible". Mobile-based learning media using Construtc 3 in informatics subjects at SMP Negeri 1 Besuki can be utilized properly, introducing this learning media to teachers and other students. To Mr. / Mrs. Teacher in order to use and utilize mobile-based learning media using Construtc 3.

Integrating deep learning methodologies is pivotal in shaping the continuous evolution of computer-aided design (CAD) and computer-aided engineering (CAE) systems. This review explores the integration of deep learning in CAD and CAE, particularly focusing on generative models for simulating 3D vehicle wheels. It highlights the challenges of traditional CAD/CAE, such as manual design and simulation limitations, and proposes deep learning, especially generative models, as a solution. The study aims to automate and enhance 3D vehicle wheel design, improve CAE simulations, predict mechanical characteristics, and optimize performance metrics. It employs deep learning architectures like variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs) to learn from diverse 3D wheel designs and generate optimized solutions. The anticipated outcomes include more efficient design processes, improved simulation accuracy, and adaptable design solutions, facilitating the integration of deep learning models into existing CAD/CAE systems. This integration is expected to transform design and engineering practices by offering insights into the potential of these technologies.

21st-century learning always goes hand in hand with technology. Technology will always be involved in learning, and teachers are required to look for alternatives if learning experiences obstacles. The Covid-19 pandemic has hampered face-to-face learning. Therefore, teachers apply the blended learning model as a solution to combine online and offline learning. This research aims to find out how blended learning is implemented in elementary school to high school education. This research uses a literature study method by searching for various related reference materials. The results of the research show that the application of the blended learning model in education produces significant and positive results in student development.

English as an international language is an important language to be proficient in since it is used all over the globe. English is not an easy language to learn. Understanding phrases or expressions is very important when speaking English. The foundation for making sentences in English is to first understand words that are often used in everyday life (everyday vocabulary) and being able to effectively construct sentences that can be understood by others is a prerequisite for being able to compose sentences in English. The method used in the implementation of community service at SMPI Darul Muminin Bekasi school is the visual method and English card. The method of forming sentences using English cards emphasizes the ability of students to understand sentences using learnt vocabulary that is used to be assembled in a plot of a story. The application of the game technique method of composing sentences using English cards is considered very effective and fun, it is hoped that one day this method can be used as a model to develop learning activities in the classroom.

One of the aims of this research is to identify students' writing skills taught by the Somatic, Auditory, Visual and Intellectual (SAVI) learning model in expository texts written by students in class X SMA N 14 Medan. Another aim is to find out how the problem-based learning model (PBL) and the SAVI (Somatic, Auditory, Visual and Intellectual) learning model have an impact on the exposition text. This research involved all students in class X SMA N 14 Medan, with a simple random sample, consisting of 36 students in class X-1 and 36 students in class X-2. A two-group post-test experimental method was used and the research instrument was an expository text writing test. The "t" test data is used to test the hypothesis. The results show that students' ability to write expository texts using the PBL learning model is in the fair category with an average score of 70.36, while their ability with the SAVI learning model is in the very good category with an average score of 89.36. Furthermore, the results of the hypothesis test show that the null hypothesis (H0) is rejected, and the alternative hypothesis (Ha) is accepted, because tcount is greater than ttable, namely 9.82 is greater than 1.934. Thus, it can be concluded that the expository text writing skills of class X SMA N 14 Medan students are greatly influenced by the use of the SAVI learning model.

The role of teachers in Christian-based character education is crucial in shaping individuals who are not only intellectually smart but also rich in values and ethics, especially amidst the challenges of globalization and rapid technological advancements. This research aims to develop teaching methodologies that can support Christian religious education teachers in their duties, specifically in designing and implementing an effective curriculum to help students understand and apply Christian values in daily life. The research method to be used is literature research, involving the collection of data related to the discussed theme from primary sources such as books, scientific journals, and others. The research findings indicate that Christian Religious Education teachers play a vital role in forming students' character to reflect the teachings of Lord Jesus Christ through the teaching of Christian values and by being models in behavior, attitude, and ethics. Effective character education requires support from a conducive learning environment, including the commitment of the school community, a curriculum that is integrated with values and ethics, visionary leadership from the school principal, and solid teamwork, despite challenges such as limited resources, lack of parental support, and apathetic attitudes from some parties.

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.