Publication Search

In the era of the Industrial Revolution 4.0 and Society 5.0, digital technology is developing rapidly. This has prompted the world of education to make major changes in learning methods, including in Islamic Religious Education (PAI). Until now, PAI has used outdated methods. These methods are inadequate to meet the needs of children who are accustomed to technology. Therefore, there is a need for technology integration in the learning process. This study aims to determine how to use E-Learning, Learning Management Systems (LMS), and Artificial Intelligence (AI) in PAI learning. This study also aims to determine the benefits, challenges, and strategies for implementing these technologies. This study used a qualitative approach with a literature review type. Data were obtained from various sources, such as books, scientific articles, journals, and relevant documents. The results show that the use of E-Learning, LMS, and AI can improve the quality of PAI learning. E-Learning makes learning easier and more flexible. LMS helps manage learning more systematically. AI helps make learning more personalized and tailored to students' needs. However, the implementation of these technologies still faces several challenges, such as limited infrastructure and low digital skills of teachers and students. Therefore, a strategy is needed to address these challenges. This strategy includes enhancing digital capabilities, strengthening technological infrastructure, developing innovative learning media, and monitoring the use of technology based on Islamic values. This will ensure optimal integration of technology into Islamic Religious Education learning.

The development of information technology, particularly the use of smartphones, has changed human activity patterns and significantly impacted the education sector. The "one-stop-shop" concept reflects the use of mobile devices as centers for various activities, including learning. This situation drives the need for innovation in the development of adaptive and interactive learning media. One solution that can be developed is an Android-based mobile application using Flutter, which can increase the effectiveness and efficiency of the learning process. The Association of Multimedia and Information Technology Professionals (PPMultindo), an organization focused on technology, has developed an Android-based learning platform. This platform is designed to provide flexible access to materials, support interactivity, and facilitate collaboration between educators. Features such as learning analysis, material recommendations, and automatic evaluation are expected to improve the quality of the learning experience. However, the implementation of this technology still faces various challenges, including limited technological competence of educators, inadequate infrastructure, and adjustments to curriculum needs. Therefore, collaborative efforts are needed to optimize the use of mobile technology to support educational transformation in the digital era.

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 research presents the development of the Rembulan E Learning platform using Moodle as the primary Learning Management System for SMK Negeri 1 Pandeglang.The main objective is to provide a structured, accessible, and interactive digital learning environment that effectively supports teaching and learning activities, enhances student engagement, and facilitates teacher management of course materials. The study applies the Waterfall development model, which includes five stages requirement analysis, system design, implementation, testing, and deployment. Data collection methods involve observation of classroom practices, in depth interviews with teachers to identify pedagogical and technological needs, and comprehensive documentation review to ensure alignment with curriculum standards and user expectations. The resulting system integrates features such as digital classrooms, learning modules, assignments, discussion forums, quizzes, and student performance monitoring, offering a comprehensive digital learning experience. System testing was conducted using Black‑box Testing, complemented by limited user trials with teachers and students, which confirmed that the platform is functional, user friendly, and capable of supporting a variety of learning activities. This research contributes to the implementation of Moodle based LMS development in vocational schools, providing practical guidance for improving digital learning quality, promoting blended learning approaches, and facilitating sustainable adoption of educational technology in secondary education.