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Science lessons in primary school significantly contribute to the early development of logical, scientific and systematic reasoning skills.   However, learning science can be challenging, especially when it comes to understanding abstract concepts and cause-and-effect interactions.   This is due to the fact that, as per Piaget's theory, primary school students are still in the concrete operational stage and have limited cognitive growth. Data regarding abstraction ability and causal reasoning in the context of science education were collected from various scientific sources using the literature review research methodology.  According to the research findings, STEAM integration, project-based learning strategies such as Project Based Learning (PBL), and the use of real and visual aids can help lower learning barriers. Successful learning is also influenced by the teacher's role as facilitator, which involves presenting the material in a relevant and contextualized way and fostering a positive learning environment. Real-world examples and exceptional examples, such as the use of worksheets based on cause-and-effect relationships and basic experiments, demonstrate how useful these strategies are for improving abstract thinking and understanding of cause-and-effect relationships. Developing innovative teaching methods based on practical, exploratory and integrative experiments is essential for maximizing the scientific understanding of primary school students.

This study aims to examine and compare various findings from previous research on the effectiveness of online learning among university students in higher education settings in order to obtain a comprehensive general conclusion. The research method employed is a literature review using a content analysis approach of scholarly publications published between 2019 and 2025. The data sources include relevant national and international journal articles focusing on online learning in higher education. The results of the review indicate that the effectiveness of online learning is influenced by three interrelated main factors. Psychological factors include learning motivation, students’ perceptions of online learning, and the emotional conditions experienced during the learning process. Pedagogical factors encompass lecturers’ creativity in designing learning activities, the selection of appropriate teaching methods, and the use of interactive and varied learning media. Meanwhile, technical or environmental factors include the availability and ease of access to learning platforms, the quality of internet connectivity, and supportive learning environments. Online learning offers several positive impacts, such as flexibility in learning time, cost efficiency, and enhanced student experience in utilizing digital technology. However, it also has the potential to generate negative effects, including boredom, stress, and difficulties in understanding course material when technical barriers are not resolved and learning activities are monotonous. Therefore, optimal management of psychological, pedagogical, and technical factors is essential to improve the effectiveness of online learning for university students.

This study aims to analyze teachers' teaching strategies in improving the quality of science learning for upper grade students (IV, V, and VI) at the SD Inpres Perumnas II Makassar. The results show that teachers implement various strategies including selecting approaches that suit students' needs, using a variety of learning methods, actively involving students in learning activities, utilizing media that support conceptual understanding, and implementing continuous evaluation to assess learning success. All of these strategies complement each other and help create more engaging, understandable, and relevant science learning for students. Research findings also revealed that the choice of teaching strategy is influenced by several important factors: student characteristics, the characteristics of the science material being taught, the availability of learning media and facilities, the learning environment, and the desired learning objectives. These five factors serve as the basis for teachers to determine the most appropriate strategy, ensuring a more effective, focused learning process that enhances students' understanding of the material. Furthermore, this study identified several challenges teachers face in implementing science teaching strategies, such as diverse student abilities, time constraints, a lack of supporting media and facilities, sub-conducive classroom conditions, and uneven student motivation. These challenges require teachers to be creative and adapt strategies to ensure learning objectives are achieved and the quality of science instruction continues to improve.

This study explores the integration of deep learning based approaches in real time video content analysis for intelligent human computer interaction (HCI) in multimedia systems. Traditional video analysis techniques, such as rule-based methods and offline processing, struggle with real time performance and adaptability to complex video data. In contrast, the deep learning model used in this research, particularly Convolutional Neural Networks (CNNs), provides high accuracy in object detection, feature extraction, and real time processing. The integration of CNNs with interactive visualization modules enables dynamic adjustments to video content based on user interactions, ensuring a seamless and engaging user experience. The system was benchmarked in terms of its processing speed, accuracy, and responsiveness, showing significant improvements over traditional approaches in real time video analysis. Moreover, the study demonstrates that combining deep learning with real time visualization enhances the efficiency of interactive multimedia applications, making it suitable for dynamic environments such as surveillance, security monitoring, and interactive media. Despite the system's strong performance, challenges such as computational demands in high-resolution video processing were identified, highlighting the need for further optimization. Future work will focus on optimizing the system for different hardware platforms, incorporating multimodal inputs, and refining deep learning models to address computational bottlenecks. This research contributes to advancing HCI by providing insights into the integration of deep learning for real time video content analysis, which is pivotal for enhancing the interactivity and adaptability of intelligent multimedia systems.

This article aims to describe the teaching methods and practices of the Philosophy of Islamic Education course at STAI Darussalam. This research uses a qualitative descriptive approach with a field study method that focuses on the learning process, the competencies to be achieved, teaching methods, assignment formats, evaluation systems, and obstacles encountered during the course. Data were obtained through observation and information gathering related to the implementation of the Philosophy of Islamic Education course. The results show that the teaching of the Philosophy of Islamic Education at STAI Darussalam focuses on understanding the nature of Islamic education, developing students' critical thinking patterns, improving educational literacy, and linking philosophical studies to social and political realities. The lecture method remains the dominant method used by lecturers, with an emphasis on students' ability to define and explain basic philosophical concepts accurately as an indicator of understanding. Learning success is measured by changes in students' thinking, which becomes increasingly critical, reflective, and contextual in responding to educational issues and social justice issues.

 Autonomous robot navigation in dynamic and unstructured environments remains a critical challenge due to unpredictable obstacles, sensor uncertainty, and limited adaptability of traditional planning algorithms. Although conventional navigation methods such as graph-based, potential field–based, and sampling-based approaches have been widely adopted, their performance under real-time dynamic conditions is still constrained. This study aims to design and implement a comprehensive experimental framework to evaluate the effectiveness and limitations of conventional navigation algorithms for autonomous mobile robots operating in dynamic unstructured environments. The research adopts an experimental and comparative methodology by implementing A*, Dijkstra, Artificial Potential Field (APF), and Rapidly-Exploring Random Tree (RRT) algorithms in simulated static and dynamic scenarios. Performance is assessed using quantitative metrics including path length, computation time, success rate, collision rate, and path smoothness. The experimental results demonstrate that graph-based algorithms achieve high success rates and optimal path efficiency in static environments but exhibit limited adaptability to dynamic changes. APF offers fast computation but suffers from high collision rates due to local minima, while RRT shows better adaptability in dynamic environments at the cost of longer and less smooth paths. These findings confirm that conventional navigation methods are insufficient for robust autonomous navigation in highly dynamic and unstructured environments. The study highlights the necessity of adaptive and learning-based navigation frameworks, such as deep reinforcement learning, to enhance real-time decision-making, robustness, and autonomy in future robotic systems.

Physical motor development is an essential and easily observable indicator of children’s growth. Parental involvement plays a crucial role in optimizing this development, particularly during the child’s golden age. This study aims to analyze the importance of motor development and examine how parents support children’s learning through a developmental psychology perspective. Using a literature review method, the findings reveal that child development is shaped by genetic and environmental factors, while active support from parents and teachers has long-term effects on children’s physical, emotional, and social readiness. Understanding developmental psychology enables parents to provide appropriate stimulation that maximizes children’s potential. Using literature review methods, it was found that motor development is influenced by genetic and environmental factors, while positive interactions between children, parents, and educators contribute to long-term physical, social, and emotional development. A deep understanding of developmental psychology enables parents to design appropriate stimulation strategies to maximize their children's potential.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

This study investigates the integration of computational thinking (CT) principles with adaptive curricula to enhance problem-solving skills in undergraduate programming education. Traditional programming curricula often emphasize syntax and basic concepts, neglecting critical problem-solving strategies. The adaptive curriculum framework used in this study combines CT skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking with personalized learning experiences. A mixed-method approach, combining qualitative and quantitative research, was employed to assess the effectiveness of this integrated approach. The results show significant improvements in students' problem-solving abilities, conceptual understanding, and engagement compared to a control group following a traditional curriculum. Students in the experimental group, which received the adaptive curriculum, demonstrated better performance in applying algorithms and debugging code. Additionally, students expressed higher levels of engagement and motivation, suggesting that the personalized learning environment fostered greater academic involvement. The study highlights the importance of integrating CT principles with adaptive learning frameworks to create a more inclusive and effective learning environment that accommodates diverse learning needs. The findings suggest that adaptive curricula can bridge gaps in traditional education by providing personalized support and ensuring that students progress at their own pace. This approach is especially beneficial for programming education, where both conceptual understanding and practical problem-solving skills are critical for success. Future research should explore the long-term impact of adaptive learning frameworks and investigate how these technologies can be integrated with traditional teaching methods to maximize their effectiveness.

Explainable Artificial Intelligence (XAI) has become a critical area of research within artificial intelligence, focusing on improving the transparency and interpretability of machine learning (ML) models, often referred to as "black-box" models. The need for XAI techniques arises from the inherent complexity of ML models, which can make their decision-making processes difficult for users to understand. This study investigates various XAI techniques, including LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), to assess their impact on model interpretability without significantly compromising predictive performance. A comparative experimental design was used, applying these XAI methods to different ML models, including deep neural networks and ensemble methods, within large-scale enterprise data analytics systems. The results indicate that XAI methods significantly enhance model transparency and decision traceability, allowing users to understand the influence of individual features on predictions. While a slight reduction in predictive accuracy was observed, especially with simpler models, the trade-off between interpretability and performance was deemed acceptable, particularly in fields requiring transparency, such as healthcare, finance, and autonomous systems. The use of XAI in enterprise data systems has practical implications for fostering trust and enabling informed decision-making among stakeholders. Furthermore, the study discusses the challenges and limitations of applying XAI techniques, such as complexity, scalability, and model-specific limitations. Future research is suggested to focus on developing more scalable and efficient XAI methods, enhancing their applicability across various model types, and addressing the challenges of real-time applications. This will be crucial in ensuring the widespread adoption of XAI in critical domains, promoting the ethical use of AI while maintaining predictive accuracy.

The increasing complexity and scale of modern cybersecurity threats necessitate the development of advanced systems capable of efficiently detecting, analyzing, and mitigating incidents in real time. This paper proposes an automated framework for digital forensics and incident response that leverages big data analytics and real time network traffic profiling. The framework integrates cutting-edge technologies, including Apache Spark for real time data processing and Hadoop for scalable data storage, combined with machine learning models like LSTM and Autoencoders to detect anomalies and threats in network traffic. By automating the process of incident detection and response, this framework significantly reduces the time required to identify threats and improves the accuracy of forensic evidence correlation across heterogeneous network environments. The study highlights the advantages of using machine learning models and big data tools to address the limitations of traditional manual and semi-automated systems, which often struggle to keep pace with large-scale data generation. Testing results demonstrate that the proposed framework can handle large data volumes efficiently, providing real time, actionable insights with significantly reduced response times. Additionally, the framework improves forensic analysis by enabling the correlation of evidence from different devices and protocols, making it more effective than traditional methods in identifying the root cause of security incidents. However, challenges related to data heterogeneity, scalability, and system integration were encountered during testing. The proposed framework holds promise for significantly enhancing the efficiency and effectiveness of cybersecurity operations, with future work focusing on further integration of advanced AI techniques and machine learning models for dynamic and adaptive incident response.

User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

The increasing adoption of cloud native microservices has brought about significant improvements in scalability, flexibility, and resilience. However, these advancements also introduce substantial security challenges, particularly in distributed environments where traditional perimeter-based security models prove inadequate. This paper proposes a secure architecture for cloud native microservices that integrates Zero trust Network Access (ZTNA) and multi layered encryption techniques to address these security concerns. The architecture operates on the principle of "never trust, always verify," ensuring that access to resources is strictly controlled and continuously monitored. By incorporating multi layered encryption methods such as RSA and AES, the architecture ensures data protection both in transit and at rest, significantly reducing the risk of data breaches and unauthorized access. Through experimental evaluations, the proposed architecture demonstrated its effectiveness in preventing lateral movement, mitigating data leakage, and resisting common attack vectors such as man-in-the-middle (MITM) attacks and privilege escalation. Additionally, the performance of the system remained optimal, with minimal overhead despite the additional security layers. The architecture's scalability and robust security mechanisms make it a viable solution for real-world microservices environments, where both security and performance are crucial. This paper discusses the potential impact of this secure architecture on the broader field of distributed system security and offers recommendations for future work, including the integration of advanced machine learning techniques for real-time threat detection and automated responses, as well as the adaptation of the architecture for emerging technologies like edge computing and 6G networks.

Reading ability is a basic skill that plays a crucial role in the success of students' learning process. However, various studies indicate that the reading ability of junior high school students remains low. This study aims to analyze the reading ability of students at the UPT SMP Negeri 2 Mappakasunggu using Alfred Schutz's social phenomenology perspective. The research approach used was qualitative with descriptive methods. Data collection techniques included in-depth interviews, observation, and documentation of students and teachers. The results indicate that students' low reading ability is not solely caused by cognitive factors but is also influenced by subjective meanings formed through students' social experiences. The lack of a literacy culture in the family and school environment results in reading not being perceived as an important or enjoyable activity. Furthermore, the dominant use of gadgets for entertainment creates habits that reduce students' interest and concentration in reading texts. From Alfred Schutz's social phenomenology perspective, these conditions are related to students' lifeworlds and stock of knowledge, which shape their perspectives and actions toward reading. This study concludes that improving students' reading ability requires a comprehensive approach, taking into account experiences, social interactions, and the formation of meaning in reading in students' daily lives.

This study aims to examine the effect of contextual learning on students’ learning motivation in Indonesian language learning for second-grade students of Madrasah Ibtidaiyah (MI) Salafiyah 1 Kauman. Contextual learning is an instructional approach that connects learning materials with students’ real-life experiences, which is expected to increase their engagement and learning motivation. This study employed a quantitative approach with an experimental research design. The subjects of the study were second-grade MI students divided into an experimental class and a control class. Data were collected through learning motivation questionnaires and classroom observations during the learning process. The collected data were analyzed using statistical techniques to determine differences in learning motivation between the two groups. The results indicate that contextual learning has a positive and significant effect on students’ learning motivation in Indonesian language learning. Students who were taught using contextual learning showed higher learning motivation than those who were taught using conventional learning methods. Therefore, contextual learning can be considered an alternative instructional strategy to enhance students’ learning motivation at the elementary or Madrasah Ibtidaiyah level.

This study aims to conceptually examine the role of visual media in Arabic vocabulary learning and its relevance in the context of education in the digital era. Vocabulary mastery is a fundamental component in learning Arabic; however, in practice, vocabulary learning often encounters various challenges, such as the abstract nature of vocabulary, low student motivation, and the dominance of conventional teaching methods. This study employed a qualitative approach using library research. Data were obtained through a review of relevant scholarly sources, including books, journal articles, and previous research related to visual media and Arabic language learning. Data were collected through documentation techniques and analyzed using content analysis to examine the concepts, roles, and effectiveness of visual media in Arabic vocabulary instruction. The findings indicate that visual media, both conventional and digital, play a significant role in improving vocabulary comprehension, strengthening memory retention, and increasing students’ motivation and engagement in the learning process. Visual media also help transform abstract vocabulary into more concrete and contextual representations, making learning more meaningful and effective. Nevertheless, the implementation of visual media still faces several challenges, including limited facilities, teachers’ digital competence, and the suboptimal use of technology in learning activities. Therefore, it is necessary to develop innovative instructional strategies and enhance teachers’ competencies to ensure that visual media can be utilized effectively and sustainably in Arabic language learning.

The Muhammadiyah Orphanage in Pamekasan faces major challenges in developing life skills and digital education for its children due to limited facilities, teaching staff, and conventional learning methods. To address these issues, an edutainment-based approach and digital pedagogy intervention were implemented to enhance learning quality, motivation, and preparedness for future social and technological challenges. The activities included workshops and training on Digital Pedagogy and Edutainment learning materials, as well as simulations and role-plays using a Game-Based Learning approach, followed by evaluations and participant plan presentations. The program significantly improved the wards’ digital literacy, particularly in personal security (online safety), digital ethics (cyber ethics), gadget usage, and information management, with the average score rising from 2.84 to 4.10 on a 5-point scale, surpassing the target of 75% of participants in the “good” category. Beyond cognitive aspects, the program also boosted motivation, engagement, communication, problem-solving, and independence. Caregiver training was also provided to ensure program sustainability. It is recommended that the orphanage integrate the Game-Based Learning Digital Safety module into its non-formal curriculum, enhance caregiver capacity through advanced training, and improve IT infrastructure.

This study aims to determine the effectiveness of the use of the artificial intelligence-based application (AI-Based Application) Duolingo in improving vocabulary mastery of EFL (English as a Foreign Language) students in Indonesia. This study used a descriptive quantitative approach with a single-group pre-test and post-test design involving 20 students of class VII C of SMP Negeri 1 Mappakasunggu. Data were collected through vocabulary tests, questionnaires, and classroom observations. The results showed a significant increase in students' vocabulary mastery, marked by an increase in the average score from 61 (fair category) in the pre-test to 78 (good category) in the post-test. Most students gave a positive perception of the use of Duolingo, especially regarding the gamification features, instant feedback, and simple and attractive display, which can increase motivation and learning engagement. The results of the observation also showed that students were more active and enthusiastic in using this application compared to traditional learning methods. Thus, Duolingo can be said to be effective as an AI-based learning medium to improve vocabulary mastery of junior high school students in Indonesia.  

This study aims to describe the implementation of multisensory strategies in improving the early reading skills and comprehension of dyslexic children in grade 2 of Bugel 2 Public Elementary School. The subject of the study was a student with dyslexia characteristics who had shown difficulties in phonological aspects, letter recognition, reading syllables, and reading comprehension. The method used was direct observation using an early reading test instrument, a comprehension test, and a learning behavior observation sheet. The results showed that the implementation of multisensory strategies (Visual-Auditory-Kinesthetic-Tactile) through letter tracing activities, reading with the help of sounds, arranging letter cards, and reading together exercises was able to improve reading accuracy, strengthen letter-sound relationships, and foster students' self-confidence. Thus, multisensory strategies were proven effective in helping dyslexic children master early reading skills and comprehend simple texts. This study provides an important contribution to the development of more inclusive and effective teaching methods, especially for students with dyslexia, who require a more holistic and comprehensive approach to improve their literacy skills. As a suggestion, the implementation of this strategy can be expanded by involving more students and considering variations in the types of texts and teaching materials used.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.