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All Journal FAJAR Jurnal Pendidikan Islam International Journal of Review in Mathematics Education
Saputra, Muhammad Akhyar Aji
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Religion Education

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TEACHER PROFESSIONALISM AND COMPETENCE IN THE PERSPECTIVE OF CONTEMPORARY ISLAMIC EDUCATION Anisaturrizqi, Rita; Saputra, Muhammad Akhyar Aji; Hanifiyah, Fitriyatul
FAJAR Jurnal Pendidikan Islam Vol. 5 No. 1 (2025): FAJAR Jurnal Pendidikan Islam (Maret)
Publisher : Program Studi Pendidikan Agama Islam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56013/fj.v5i1.3988

Abstract

This study aims to examine the focus of scientific studies related to teacher competence and professionalism in the perspective of education, especially in the context of Islamic education. The results of the study show that the themes of teacher professionalism and teacher competence are the center of attention in the literature, which is closely linked to important aspects such as pedagogic competence, educator professional ethics, quality of education, and the social image of teachers. In addition, the analysis of temporal developments shows a shift in focus from fundamental issues to more contextual and applicative topics, such as strengthening writing competence and improving the quality of public perception of educational institutions. These findings show that the development of teacher professionalism cannot be separated from the integration of Islamic values, as well as the importance of building the character of educators who excel spiritually, morally, and intellectually. This study is expected to be a strategic reference for curriculum development, improving the quality of Islamic education, and forming ideal teachers in the perspective of Islamic values.
Evolution of Deep Learning and Its Reflection on Statistical Mathematics Learning Saputra, Muhammad Akhyar Aji; Crismono, Prima; Hudi, Saman
International Journal of Review in Mathematics Education Volume 1 No. 1: March 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijrime.14812

Abstract

This study aims to evaluate the development, topic interconnections, and global research directions in the field of Deep Learning during the period 2019–2024, while also examining its implications for teaching statistical mathematics in the digital era. A bibliometric approach was used to analyze publication trends, citation patterns, and keyword relationships with the assistance of VOSviewer software. Data were obtained from the Scopus database using the main keywords “Deep Learning,” “Neural Networks,” and “Artificial Intelligence.” The results indicate that peak research activity occurred in 2022 with a significant surge in citations, followed by a decline in 2023–2024, marking a phase of research stabilization. Network analysis revealed that topics such as computer vision, medical imaging, and unsupervised learning dominate, while emerging trends like federated learning and edge computing are beginning to develop toward privacy and computational efficiency. Geographically, the United States and China are the main contributors to scientific publications, followed by Germany, the United Kingdom, and Australia. These findings highlight that the core success of Deep Learning is fundamentally grounded in statistical mathematics, particularly in optimization and probabilistic modeling. Accordingly, the implications for teaching statistical mathematics involve reorienting curricula toward applied, data-driven contexts emphasizing probabilistic thinking, algorithmic reasoning, and the integration of computational tools. Such an approach encourages students to bridge theoretical understanding with real-world problem solving in artificial intelligence and data science.
Co-Authors Anisaturrizqi, Rita Crismono, Prima Cristi Hanifiyah, Fitriyatul Hudi, Saman