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This study aims to determine the effect and impact of the overall research on the Problem-based learning model in science learning. This type of research is a meta-analysis. The data sources in this study are 17 national and international journals published from 2017-2022. The process of searching for data sources through Google Scholar, ScienceDirect, Wiley, ProQuest, and Eric Journal. Inclusion criteria are research on problem-based learning models with experimental or quasi-experimental methods and measurement of learning outcomes to evaluate Problem-Based Learning learning models. The results showed that the average effect size value of all studies (ES = 1.40) was very high. This finding explains that the Problem-Based Learning model provides a very high positive impact on science learning. In addition, the Problem-Based Learning learning model is effective to be applied to students' science learning at school. Effect measurement in this study is influenced by the level of education, year of publication, learning outcomes, and sample size. Overall, the Problem-Based Learning model is very useful in increasing students' potential in facing the 21st century.

In the modern era of education, the focus of learning has shifted from teacher orientation to student orientation. As educators, it is important to design learning methods that are in accordance with technological developments. Today, learning that is too teacher-centered and theoretical tends to reduce student interest. Therefore, the use of audio-visual media such as learning videos can increase student engagement, provide deep meaning, and arouse high enthusiasm for learning. This study aims to 1) explain the level of student enthusiasm when using audio-visual learning media, and 2) evaluate the impact and interest of students on audio-visual based learning media in the learning process. This research was conducted at Madrasah Ibtidaiyah Ibn Aqil Pekanbaru. This research is a descriptive qualitative research that uses data collection methods in the form of direct observation, interviews, and literature studies. Research findings show that lecture-dominated learning tends to make learners lose interest and focus on themselves. The results of the study showed that 1) when students were introduced to the use of audio-visual media such as learning videos, most of them felt very enthusiastic and excited in following the learning process, while only one student showed less interest. 2) Thus, it can be concluded that the use of audio-visual learning media can increase student enthusiasm at the elementary school level.

Indonesia requires Islamic Religious Education (PAI) as an important part in making students competent in cognitive, affective and psychomotor aspects (IQ and EQ). PAI functions to shape students' personalities so that they become virtuous and noble human beings (SQ). To increase competence in Islamic religious education, it is necessary to develop varied learning strategies. Varied learning strategies function to design learning methods and models, so that they are able to design teaching and learning environment systems and implement effectively and efficiently what has been planned in the learning objectives. Learning strategies are classified into 5 types: (1) direct learning strategies, (2) indirect learning strategies, (3) interactive learning strategies, (4) empirical learning strategies, (5) independent learning strategies.

PJOK plays an important role in movement activities for students at school. In the learning process, there are learning methods that can be used, one of which is the peer teaching method. PJOK material in junior high school has big ball games, one of which is basketball. This study aims to find out whether the application of peer teaching learning methods can improve basketball shooting free throw learning outcomes. This study used an experimental method with a randomized control group pre-test post-test design. The instruments used in this study are knowledge and skill tests. The study sample used cluster random sampling with class VIII A as the experimental class and VIII E as the control class with a total of 64 students. The results showed that the use ofpeer teachinglearning methods can improve the learning outcomes of shooting free throw basketball grade VIII at SMP.The value of increasinglearning outcomes in the medium category, namely knowledge increased by54.51% and skills by 42.40%.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.

A method is a set of methods or methods that teachers use in learning so that students can achieve certain learning goals and skills. Many verses in the Koran explain the use of methods in teaching. Some of them we find in Surah Ali Imran verse 159 and Al-Maidah verse 67. The aim of this research is to find out the method of learning or teaching the Al-Qur'an. This research method uses descriptive qualitative methods with a literature review. The main data sources come from books, magazines and the latest articles related to this research theme. The results of this research indicate that the methods of teaching the Al-Quran are (1) Hiwar, namely alternating conversations between two or more parties through questions and answers regarding a topic leading to a goal, and (2) Tabligh, namely a systematic, orderly and measurable method used by preachers in presenting tabligh material to their audiences.    

This study investigates the efficacy of dynamic assignment strategies in enhancing English language proficiency, engagement, and motivation among learners compared to traditional assignment methods. Through a mixed-methods approach, involving quantitative analyses of language proficiency scores and engagement levels, as well as qualitative insights from student perspectives and teacher observations, the research highlights the significant advantages of integrating dynamic assignments in English language learning. Results indicate that students participating in the dynamic assignment group showed statistically significant improvements in overall language proficiency, particularly in speaking and writing skills (p < 0.05). Additionally, these students reported higher levels of cognitive, emotional, and behavioral engagement (p < 0.05), which were positively correlated with their language proficiency improvements (r = 0.62, p < 0.01). This suggests that the more engaged students are in their learning process, the better their language learning outcomes. From the qualitative data, students expressed a greater motivation and interest in learning English, attributing this to the real-world relevance and applicability of the dynamic assignments. Teachers corroborated these findings, noting enhanced active participation, collaboration, and improvements in students’ communication skills and confidence. However, both students and teachers identified challenges, including increased preparation time and the need for clearer guidelines to maximize the benefits of dynamic assignments. The study concludes that dynamic assignment strategies significantly contribute to improving language proficiency and engagement among English language learners. It suggests that addressing the identified challenges could further optimize the effectiveness of these strategies. This research advocates for a shift towards more interactive, relevant, and engaging teaching methodologies in language education to better prepare learners for real-world communication.

This study investigates the impact of gamified learning platforms on the development of critical thinking and problem-solving skills among middle school students. With the increasing reliance on digital tools, fostering these essential skills has become a critical educational goal. Traditional teaching methods often fail to engage students effectively, which has led educators to explore innovative approaches such as gamified learning. The experimental design of this study involved two groups: an experimental group using gamified learning platforms and a control group following conventional teaching methods. Data were collected through pre-tests and post-tests measuring students' critical thinking and problem-solving abilities. Results indicated that the experimental group showed a significant improvement in both skills, with a 20-point increase in their scores, while the control group exhibited minimal progress. The findings highlight that gamified learning, which integrates game mechanics such as points, rewards, and leaderboards, enhances engagement and motivation, leading to improved cognitive skills. This study emphasizes the potential of gamification to revolutionize educational practices, suggesting that its integration can be a powerful tool to equip students with the necessary skills for the future.

This study explores the potential of mobile educational applications to enhance student-centered inquiry-based learning (IBL) in secondary school classrooms. As traditional, teacher-centered pedagogies fail to adequately engage students in critical thinking and problem-solving, IBL offers a promising alternative that encourages active participation and deeper learning. The research investigates how mobile applications can support IBL by facilitating the inquiry process, such as data collection, hypothesis formulation, and collaboration. Through a quasi-experimental design involving secondary school students, the study compares the effectiveness of traditional teaching methods and mobile-assisted IBL. Results indicate that students using mobile applications showed significant improvements in critical thinking, engagement, and academic performance compared to those taught through traditional methods. Teachers and students both reported high satisfaction with mobile apps, particularly in terms of ease of use and educational value. The findings suggest that integrating mobile technologies into the classroom can create more interactive, accessible, and personalized learning experiences, fostering critical thinking and enhancing student outcomes. However, challenges related to infrastructure, teacher training, and digital literacy must be addressed to fully harness the potential of mobile-assisted inquiry-based learning.

The rapid growth of data-intensive applications has posed significant challenges for classical machine learning (ML) algorithms, particularly in terms of computational efficiency and scalability. This study explores the role of quantum computing in optimizing machine learning performance through the implementation of Quantum Machine Learning (QML), specifically using the Quantum Support Vector Machine (QSVM) model. The research adopts a Design Science Research approach, involving problem identification, model development, system implementation, and performance evaluation. Both classical Support Vector Machine (SVM) and QSVM models are developed and tested using benchmark classification datasets. The results indicate that QSVM outperforms the classical SVM model across multiple evaluation metrics, including accuracy, precision, recall, and F1-score. Additionally, QSVM demonstrates improved computational efficiency by reducing training time, particularly when handling high-dimensional data. These improvements are attributed to the ability of quantum computing to utilize quantum kernel methods and map data into higher-dimensional feature spaces, enabling better pattern recognition and classification performance.  Despite these promising outcomes, the study also identifies several limitations related to current quantum hardware, such as noise, decoherence, and limited qubit availability, which may affect scalability and practical implementation. Therefore, further research is required to enhance quantum hardware reliability and develop hybrid quantum-classical models. In conclusion, quantum machine learning offers a promising solution to overcome the limitations of classical approaches, providing enhanced performance and efficiency for complex data processing tasks in future intelligent systems.

This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates advanced security solutions that can detect and respond to evolving cyberattacks. Literature Review: Traditional Intrusion Detection Systems (IDS), including signature based and anomaly based methods, are not equipped to handle the dynamic nature of 5G networks, leading to high false positives and low detection accuracy. In contrast, RL offers significant improvements in adaptability, detection accuracy, and response time. Materials and Method: The study simulates 5G network traffic and develops an RL-based IDS using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) techniques. The performance of the RL-based system is compared to traditional IDS systems, focusing on detection accuracy, false positive rates, and response times. Results and Discussion: The RL-driven IDS demonstrated superior performance, achieving higher detection accuracy (95%) and faster response times (30 milliseconds) compared to traditional methods. However, challenges such as computational cost and model interpretability were identified. The study emphasizes the importance of adaptive learning mechanisms and the integration of RL into Zero Trust Architecture (ZTA) to enhance the security of 5G networks.

Canva app is a graphic design platform that has become a favorite among users who don't have a graphic design background. With a variety of easy-to-use features and templates, Canva is perfect for teachers who want to create engaging and interactive learning materials. The purpose of building a Canva application is to make users easy to use and free of charge with hundreds of attractive design templates for the community. Canva is also a multifunctional application that can be used for the development of learning media. One of the main benefits of Canva app training is to increase teacher creativity in creating engaging learning. The methods used in this community service activity are discussion, training and practice. The results of this canva application training activity are (1) the canva application can improve the quality of learning materials delivered by teachers, (2) the canva application increases the attractiveness of learning, (3) the canva application can encourage teacher creativity better, (4) increase the use of technology for teachers in learning materials.

The rapid growth of social media platforms has generated massive volumes of unstructured textual data containing valuable information about public opinions and sentiments. Extracting meaningful insights from this data has become increasingly important for decision-making in various domains, including business, politics, and social analysis. This study aims to evaluate the effectiveness of deep learning techniques for sentiment analysis of social media data, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM model. A quantitative experimental approach is employed, where datasets are preprocessed through text cleaning, tokenization, and feature representation using word embeddings. The models are trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that all models perform effectively in sentiment classification tasks, with the hybrid CNN-LSTM model achieving the highest performance due to its ability to capture both local textual features and long-term contextual dependencies. This demonstrates that combining CNN and LSTM architectures enhances classification accuracy compared to individual models. Furthermore, the findings confirm that deep learning approaches are more robust in handling the complexity and noisiness of social media data compared to traditional methods. This study contributes to the development of more adaptive and accurate sentiment analysis models and highlights the potential of hybrid deep learning architectures for real-world applications.

This paper presents a comparative analysis of various battery management systems (BMS) in electric vehicles, with a focus on incorporating machine learning techniques to improve battery safety and extend battery life. The study evaluates conventional BMS against machine learning-enhanced models in predicting thermal runaway, state of charge (SOC), and state of health (SOH) under diverse operating conditions. Results indicate that machine learning algorithms outperform conventional methods, providing more accurate SOC and SOH estimations, thus enhancing vehicle safety and longevity.

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

Islam is basically a religion that requires continuous efforts to acquire knowledge. Muslims are obliged to gain knowledge from birth to death. In educating students, educators have an obligation to provide the best possible learning. In this case, educators can imitate the lessons taught by the Prophet. This article aims to describe the obligation to study according to an Islamic perspective in terms of focusing on the hadith of the Prophet regarding the obligation to study. This can be seen in the Al Quran and Hadith, the type of research used in this research is library research. The results of this research are that the teaching and learning process should be related to the characteristics of Prophetic teaching. Be kind to your students, protect them, motivate them, relate events in the learning process, familiarize yourself with dialogue and mental strategies, and maintain anonymity when criticizing or praising students. So that education remains embedded in the minds of students, we need to apply methods that provide good Uswatun Hasana to students    

Autonomous vehicles (AVs) are revolutionizing transportation by relying on advanced AI techniques like deep learning and reinforcement learning for decision-making and navigation. However, concerns about the opacity of traditional AI models in safety-critical applications such as autonomous driving raise issues related to safety, accountability, and trust. This study explores the integration of Explainable AI (XAI) techniques in AV systems to enhance transparency and interpretability while maintaining high prediction accuracy. XAI methods, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations), provide understandable justifications for AI-driven decisions, addressing biases, fairness, and accountability. These techniques also support regulatory compliance and foster public trust in AVs. A mixed-methods approach, combining experimental simulations and user surveys, was employed to integrate XAI into AV systems and test its performance in urban traffic and highway driving scenarios. Feedback from users, collected through questionnaires and in-depth interviews, revealed that XAI-enhanced systems significantly improved the interpretability of AV decisions, leading to higher user trust and satisfaction. The study highlights the importance of balancing model complexity with interpretability, demonstrating that XAI techniques are crucial for building trust and ensuring accountability in autonomous driving systems.

This research aims to determine the influence of the Genially Platform on PAI learning outcomes at SMK Negeri 2 Semarang. The type of research used is field research with a quantitative approach. Data collection methods use observation, tests and documentation. This research design uses an experimental posttest only control design, with different treatments in two classes, namely the experimental class using the Genial platform and the control class using conventional learning. After being given different treatments, analysis prerequisite tests were carried out, namely data normality tests, data homogeneity tests, and hypothesis tests. In testing the hypothesis, researchers used the independent sample t-test. 

Pronunciation skills are fundamental in English language learning, especially in the correct articulation of regular past tense verbs. However, students often experience difficulties in accurately pronouncing such verbs due to the differences between English spelling and pronunciation. To address this issue, this study investigated the effect of the English Language Speech Assistant (ELSA) application on the pronunciation ability of tenth-grade students at SMA Negeri 2 Pematang Siantar. This research utilized a quantitative approach with a quasi-experimental design, involving 64 students divided equally into an experimental group (X-3) and a control group (X-9) selected through purposive sampling. The experimental group received instruction using the ELSA Speak application, while the control group was taught using conventional teaching methods. Data were collected through pre-tests and post-tests, then analysed using an independent sample t-test. The mean pre-test scores for the experimental and control groups were 50.34 and 50.21, respectively, while the mean post-test scores were 82.56 and 67.37. The t-test analysis revealed a significance value (2-tailed) of 0.000, which was lower than the significance level of 0.05 (0.000 < 0.05). Additionally, the computed t-value exceeded the t-table value (t-observed > t-table at df=62 and α=0.05), leading to the acceptance of the alternative hypothesis (Ha) and the rejection of the null hypothesis (H₀). This indicates that there is a significant difference between students taught using the ELSA Speak application and those taught using conventional methods. Based on the research findings, it can be concluded that the ELSA Speak application significantly improves students’ pronunciation abilities in pronouncing English regular past tense verbs.

The aim of this research is to find out vocal phrasing techniques for good and correct sentence fragments in singing the song Arise And Shine Forth. This research uses qualitative research methods using observation, interviews and documentation. This research was carried out by collecting information through field research to obtain results which were processed into accurate data. The research process carried out as data collection is: videos and interviews. By singing the song Arise And Shine Forth, the author uses accompaniment via digital media, namely karaoke. The research results show that the process of implementing the vocal phrasing technique training in singing the song Arise And Shine Forth, namely practicing body posture, breathing, articulation and learning the phrasing of the song Arise And Shine Forth. In the score for the song Arise And Shine Forth, it is known that the chord is 4/4, 42 bars and tempo 68 (Adagio), the tempo tends to be slow but tends to be relaxed. This song is performed in a duet consisting of a man and a woman using different voice melodies. In bars 6 to 10 it is sung by women. Bars 12 to 14 are sung by men. Bars 18 to bar 28, the lyrics are sung together using different melodies. From all the observations and methods that the author has used while conducting research, the vocal phrasing technique in singing the song Arise And Shine Forth is very important. To obtain good and correct phrasing techniques for phrasing sentences, you need to practice and master good vocal techniques to be able to sing the song. There are several ways that must be considered to master good phrasing techniques, namely: practicing breathing, understanding the song being sung, understanding the purpose or message of a song and looking at the sentences in the song where they are divided.