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The global ecological crisis is not only a scientific issue but also reflects the decline of human moral and spiritual awareness toward nature. This study aims to examine the acculturation approach of Islam Nusantara in Islamic Religious Education (PAI) learning as an effort to foster students’ ecological awareness. The research employed a qualitative library research method using various relevant sources, including books, national and international journal articles, and educational policy documents. Data analysis was conducted through data reduction, thematic presentation, and interpretive-analytical conclusion drawing. The findings indicate that Islam Nusantara, as a product of the acculturation between universal Islamic values and local Nusantara wisdom, contains various ecological traditions such as sasi, subak, and hutan larangan (forbidden forests), which can be contextually integrated into PAI learning. The developed learning model includes the use of local wisdom as learning resources, participatory project-based learning methods, and thematic Qur’anic interpretation related to environmental issues. The ecotheological approach of Islam Nusantara is considered effective in connecting religious teachings with students’ ecological realities, thereby fostering a religious-ecological character that views environmental preservation as part of worship. Although challenges remain in theological, pedagogical, and institutional aspects, this approach contributes strategically to shaping environmentally responsible Muslim generations.

This study explores the integration of Creativity Education and Growth Mindset in Catholic Religious Education (CRE) at Senior High Schools and examines its alignment with the Sustainable Development Goal (SDG) 4, focusing on quality and inclusive education. Using a Systematic Literature Review based on the PRISMA 2020 protocol, the study analyzed 1,263 articles from scientific databases, narrowing down to 106 relevant studies. The findings highlight that Creativity Education fosters critical thinking, cognitive flexibility, problem-solving, and the integration of moral and spiritual values. Growth Mindset enhances students’ intrinsic motivation, perseverance, and resilience, creating an adaptive, innovative, and inclusive learning environment. The synthesis led to the development of an integrative learning model, which includes the Creative Reflective Learning Cycle, Faith-Based Project Learning, Creative Growth Dialogue, and Digital Creativity Integration. This model aims to holistically develop students’ cognitive, creative, moral, and spiritual capacities. The study demonstrates that the combination of Creativity Education and Growth Mindset enriches CRE pedagogical practices and supports the development of 21st-century skills, such as critical thinking, collaboration, digital literacy, and moral literacy. It concludes that project-based learning, digital technology integration, and curriculum adjustments are vital steps in improving learning quality and inclusiveness. The study recommends further empirical research to test the effectiveness of the proposed model.

Education plays an important role in shaping the quality of human resources, particularly in developing students' critical thinking skills. In the learning process at school, teachers not only act as providers of information but also as facilitators who are able to create active, creative, and participatory learning environments. However, in practice, the learning process is still often dominated by one-way lecture methods, which tend to make students passive and less involved in the learning process. This condition affects the development of students’ critical thinking skills. Therefore, learning strategies that actively involve students are needed, one of which is through the implementation of Problem Based Learning (PBL).This study aims to analyze the strategies used by Islamic Religious Education teachers in implementing Problem Based Learning and its impact on students’ critical thinking skills at SMA Pembangunan V YAPIS Waena. This research employed a qualitative approach with a descriptive research design. Data were collected through observation, interviews, and documentation, while data analysis used the Miles and Huberman model which includes data reduction, data display, and conclusion drawing.The results of the study indicate that the implementation of Problem Based Learning is carried out through several stages, including presenting problems related to daily life, group discussions, problem analysis, and the presentation of discussion results by students. The implementation of this learning model increases students’ activeness in the learning process and encourages the development of critical thinking skills, such as identifying problems, analyzing information, expressing opinions, and drawing logical conclusions. Therefore, Problem Based Learning can be considered an effective learning strategy for improving students’ critical thinking skills in Islamic Religious Education learning.

This study aims to analyze the implementation of Artificial Intelligence (AI)-based learning media and its implications for student learning motivation in Islamic Religious Education (PAI). This study uses a systematic literature review approach by examining various reputable scientific articles discussing the integration of AI in education and the dynamics of learning motivation in the context of PAI. The results of the study indicate that the use of AI through adaptive learning systems, educational chatbots, gamification, and learning analytics can increase the effectiveness, personalization, and interactivity of learning. This implementation has a positive impact on cognitive motivation through increased conceptual understanding, affective motivation through active participation and emotional engagement, and spiritual motivation through strengthening reflection and internalization of Islamic values. However, ethical challenges, the risk of depersonalization of the teacher's role, and inequality in digital access are crucial issues that require policy attention and human-centered pedagogical design. Theoretically, this study offers an integrative conceptual framework that combines technological innovation with Islamic educational epistemology. Practically, the results of this study provide recommendations for teachers, schools, and policymakers to develop AI-based PAI learning models that are adaptive, ethical, and oriented towards character building.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

This outreach program evaluates the efficacy of the Lingkari Aku cooperative learning model in enhancing conceptual mathematical grasp among elementary students. Carried out at SD Negeri 054945 Dogang during the 2023/2024 odd semester, this study employed a qualitative case study methodology. The target participants were students engaged in integer operation topics. Data acquisition involved systematic observation, assessment tests, and documentation. The findings demonstrate that the Lingkari Aku method markedly bolstered student engagement, stimulated proactive group dialogue, and refined their grasp of integer concepts. Beyond academic gains, the approach fostered essential social competencies, such as teamwork, interpersonal communication, and accountability. Although high student excitement necessitated rigorous classroom management, the method successfully established a dynamic and pleasurable mathematical learning atmosphere. Consequently, this model is endorsed as a viable pedagogical alternative for elementary mathematics, especially within the framework of educational community service initiatives.

Physics learning requires higher-order thinking skills, particularly critical thinking and conceptual understanding. However, students’ critical thinking skills and physics learning outcomes at the senior high school level are still relatively low due to teacher-centered instruction and the limited use of innovative learning models and media. This study aimed to examine the effect of the CORE (Connecting, Organizing, Reflecting, Extending) learning model assisted by Lumio by Smart media on students’ critical thinking skills and physics learning outcomes. This research employed a true experimental design with a post-test only control group design conducted at SMAN 4 Jember in the 2025/2026 academic year. The samples consisted of two classes, with class XI Umum 2 as the experimental class and class XI Teknik 3 as the control class. Data were collected through tests and analyzed using the Mann–Whitney U test. The results showed significance values of 0.027 ≤ 0,05 for critical thinking skills which means H0 is rejected and Ha is accepted and 0.020 ≤ 0,05  for physics learning outcomes which means H0 is rejected and Ha is accepted. Therefore, it can be concluded that the CORE learning model assisted by Lumio by Smart media has a significant effect on students’ critical thinking skills and physics learning outcomes.

Education plays a crucial and essential role in improving the quality of human resource globally, thus a research was conducted with the aim of producing a product, namely a learning module. This module is one of the learning resources that can be used by students independently. Therefore, the module was developed to function as a learniing guide for students. The lerning model used in this research is Creative Problem Solving. In this learning model, the teacher presents problems so that students can find answers innovatively and sharpen  their critical thinking skills. The suitability of this learning module will be evaluated based on three aspect, namely validity, practicality, and effectiveness. Based on the research sample data, this consists of students from class XI Electrical Power Installation Techniques (TITL) 1 at SMKN 1 Driyorejo. This method uses the Research & Development (R&D) approach. The analysis of differences in learning outcomes was carried out using the One Group Pretest-Posttest Method, the treatment in the form of a learning module based on the Creative Problem Solving model was given to student. The research result show that (1) the modules suitability is stated as very valid wth a score of 89,60, (2) aspect, it is stataed as very practical with a total average reaching 90,60, (3) effectiveness is proven from the improvement in learning outcomes in terms of knowledge and domains with an average pretest score of 51,3, while the average posttest score is 85 with a significance of 0.000.

This study aims to: (1) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension and self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan; (2) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the mathematical concept comprehension of 10th-grade students at SMA Negeri 3 Tarakan; (3) Determine the effect of the discovery learning model with the aptitude treatment interaction strategy on the self-efficacy of 10th-grade students at SMA Negeri 3 Tarakan. The research population included all tenth-grade students of SMA Negeri 3 Tarakan in the 2024/2025 academic year. The research sample consisted of two classes selected randomly: one experimental class receiving discovery learning with an aptitude treatment interaction strategy and one control class receiving conventional learning. The research instruments consisted of a test measuring mathematical concept understanding on trigonometry material and a self-efficacy questionnaire. The data obtained were tested for prerequisites through normality and homogeneity tests before being analyzed using inferential statistical tests in the form of an independent samples t-test with the assistance of SPSS software version 26.0. The research results show that the implementation of the discovery learning model with the aptitude-treatment interaction strategy has a significant impact on students' mathematical concept understanding and self-efficacy simultaneously, with a significance value of 0.006 < 0.05. Partially, this learning model has a significant effect on students' mathematical concept understanding, with a significance value of 0.018 < 0.05. However, the effect of the discovery learning model with the aptitude-treatment interaction strategy on students' self-efficacy is not statistically significant, as indicated by a significance value of 0.089 > 0.05, even though there is a tendency for increased self-efficacy among students participating in the experimental class learning. Nevertheless, the influence of the discovery learning model with the aptitude treatment interaction strategy on students' self-efficacy is not statistically significant in partial terms, although there is a tendency for an increase in self-efficacy among students participating in the experimental class. These findings suggest that the discovery learning model with the aptitude treatment interaction strategy is effective in improving students' understanding of mathematical concepts in trigonometry material and has the potential to support the development of self-efficacy in mathematics learning.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

This study aims to analyze the effect of implementing the Realistic Mathematics Education (RME) learning model on improving students' mathematical literacy, particularly in social arithmetic material at SMP Negeri 2 Wonorejo. The research used a quantitative method with a quasi-experimental design in the form of a non-equivalent control group design. The population consisted of seventh-grade students from three classes, samples selected through purposive sampling based on preliminary test results: class VII B as the control group and class VII C as the experimental group. Research instruments included a validated observation sheet on student activities and an essay-type mathematical literacy test. Data analysis was conducted using normality tests, homogeneity tests, independent t-tests, and effect size calculation. The results indicated that student activities in the RME learning model were categorized as excellent. Hypothesis testing with an independent t-test yielded tcalculated = 2.81 > ttable = 1.56. The average post-test score of the experimental group (73.00) was higher than that of the control group (50.41). The effect size calculation resulted in d = 1.75, which falls into the large effect category. Thus, it can be concluded that the RME learning model has a positive and significant influence on improving students' mathematical literacy.

Fine motor development is a fundamental prerequisite for academic readiness and independence in early childhood. Still, currently, challenges such as sedentary behaviour and screen exposure hinder the optimisation of these abilities. This community service program aims to implement a Play-Based Learning model of fine-motor stimulation to improve the physical and academic readiness of children at Tamalate State Kindergarten in Makassar City. The intervention program was conducted over two weeks through three main stimulation zones: Texture Zone (playdough and finger painting), Precision Zone (beading, origami, and cutting), and Manipulative Zone (busy board and puzzles). The target subjects were children in group B with fine-motor impairments, and the study evaluated 20 children using periodic observation checklists. The results showed a significant transformation, with 95% of children (19 out of 20 participants) experiencing an increase in achievement from the Starting to Develop (MB) category to Developing as Expected (BSH) and Developing Very Well (BSB). These findings confirm that a structured intervention through play zones is not only effective in strengthening intrinsic hand muscles and eye-hand coordination but also in building children's resilience and perseverance in learning. The program recommends integrating a micro-curriculum based on stimulation zones into daily learning plans and strengthening partnerships with parents to maintain consistency in children's development.

This study aims to determine how the implementation of the cooperative learning model can contribute to students' cognitive development in Islamic Religious Education (PAI) at SMA Negeri 1 Kasihan. The background of this study is based on the low level of active student involvement in the PAI learning process, which is still dominated by conventional lecture methods. The study used a qualitative approach with data collection techniques through observation, interviews, and documentation of PAI teachers and grade 11 students. The results showed that the implementation of cooperative learning encouraged students to be more active in discussions, think critically, and understand religious concepts in depth. This model also improved social skills, responsibility, and collaboration among students. Thus, cooperative learning not only strengthens the cognitive domain but also develops students' affective and psychomotor aspects in a balanced manner. These findings demonstrate that cooperative learning is effectively used in PAI learning as an effort to create an interactive, collaborative, and meaningful learning process.

This study aims to determine the effectiveness of using vlogs as a Project Based Learning (PBL) media in the Fantasy Makeup course to enhance students’ soft skills, particularly in communication, public speaking, creativity, and problem solving. This research employed a pre-experimental design with a one group pre-test and post-test design. The sample consisted of 25 students selected through a census sampling technique, in which the entire population was used as the research sample. Data were collected through observation and questionnaires administered in two stages: a pre-test before the implementation of the learning model and a post-test afterward. The data were analyzed using a Paired Sample T-Test to identify the mean differences in students’ soft skill scores before and after the learning treatment. The results showed a significant improvement in communication, public speaking, creativity, and problem-solving abilities after the implementation of the vlog-based PBL model. Therefore, the use of vlogs is proven to be an effective and innovative learning medium to strengthen students’ soft skills in the context of the Fantasy Makeup course.

Physics learning requires students’ multirepresentational ability to understand concepts through verbal, mathematical, pictorial, or graphical forms. However, instruction at SMAN 1 Natal is still dominated by conventional methods, resulting in less active student participation and low multirepresentational skills. This study aims to determine the improvement of students’ multirepresentational ability after the implementation of the Problem Posing learning model on the topic of sound waves. The research employed a quantitative approach with a quasi-experimental design. The sample consisted of class XI MIPA 1 as the control class and class XI MIPA 2 as the experimental class. The research instrument was a multirepresentation test administered through pre-test and post-test. Data were analyzed using the Shapiro–Wilk test, the Mann–Whitney test, and the N-Gain test. The results of the normality test indicated that the data were not normally distributed; therefore, hypothesis testing was continued using the Mann–Whitney test, which yielded a significance value of 0.00 < 0.05. This result indicates a difference in the improvement of multirepresentational ability between the experimental and control classes. The N-Gain result for the experimental class was 49.40%, categorized as moderate. Thus, the implementation of the Problem Posing learning model in the experimental class resulted in an improvement that was lower than that of the control class

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

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.