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All Journal Ahlussunnah: Journal of Islamic Education INOVTEK Polbeng - Seri Informatika
Ramadhani, Maya
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Religion Computer Science & IT Education Environmental Science Other

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Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Zamsuri, Ahmad; Nasution, Nurliana; Ramadhani, Maya
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
Enhancing Islamic Religious and Character Education through a Deep Learning Approach Nilpendra, Nestri Natasya; Faslah, Roni; Triana, Neni; Nursuadila , Nursuadila; Ramadhani, Maya
Ahlussunnah: Journal of Islamic Education Vol. 5 No. 1 (2026): April
Publisher : STIT Ahlussunnah Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58485/jie.v5i1.542

Abstract

Islamic Religious Education and Character learning in the era of the Industrial Revolution 4.0 and Society 5.0 faces complex challenges in balancing the transmission of authentic Islamic teachings with the need for creativity, collaboration, critical thinking, and digital literacy. Conventional approaches that emphasize memorization are often insufficient in fostering strong spiritual character. This study aims to explore the implementation of the Deep Learning approach in Islamic Religious Education and Character learning, focusing on how its principles mindful, meaningful, and joyful learning enhance students’ spiritual understanding and character development. This study employs a qualitative approach using a case study design. Data were collected through in-depth interviews, classroom observations, and documentation to obtain comprehensive insights into the application of Deep Learning principles. The data were analyzed through thematic interpretation to identify patterns of implementation and their impact on learning processes. The findings indicate that constructivist-based lesson planning encourages students to develop a deeper and more contextual understanding of Islamic teachings. The implementation of Deep Learning principles through reflective discussions and experiential practices enhances students’ spiritual awareness, value internalization, and real-life application. However, challenges such as limited instructional time and the need for pedagogical adaptation were also identified. The study implies that the Deep Learning approach has significant potential to transform Islamic Religious Education into a more meaningful, student-centered learning process. It contributes to strengthening character education and supports the development of students who are both religiously grounded and adaptable to contemporary challenges.
Co-Authors Ahmad Zamsuri, Ahmad Neni Triana, Neni Nilpendra, Nestri Natasya Nurliana Nasution, Nurliana Nursuadila , Nursuadila Roni Faslah Susandri, Susandri