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Journal : open access driverset

Trends and Mapping of Research on Artificial Intelligence-Based Antenna Optimisation: A Bibliometric Analysis Riski Ramadani; Afiyah Nikmah; Nisaul Fadhilah; Rohim Aminullah Firdaus; Noer Risky Ramadhani
Journal of Law and Bibliometrics Studies Vol. 1 No. 2 (2025): August
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jolabis.1.2.85

Abstract

Objective: This study aims to map the global research landscape on artificial intelligence (AI)-based antenna optimisation using a bibliometric approach. The objective is to identify publication trends, key contributors, collaborative networks, and emerging themes that define the development of this research domain. Method: The analysis was based on 4,814 documents retrieved from the Scopus database for the period 2010–2025. Data preprocessing included deduplication and keyword harmonisation. Bibliometric analysis was conducted using performance metrics (publication trends, influential authors, journals, countries) and science mapping (co-authorship, co-occurrence, co-citation) with VOSviewer and Bibliometrix. Results: Findings reveal three distinct publication phases: initial stagnation (2010–2016), growth (2017–2019), and exponential expansion (2020–2024), with a peak in 2023. China dominates global research output, followed by the United States and India. IEEE journals, particularly IEEE Access and IEEE Transactions on Antennas and Propagation, serve as the primary publication platforms. Co-authorship analysis indicates a highly centralised collaboration network with hubs like Zhang and Wang. At the same time, thematic mapping shows a strong focus on machine learning, deep learning, 5G or 6G technologies, and adaptive antenna design. Novelty: This paper provides a systematic, data-driven overview of the intellectual structure and thematic evolution of AI-based antenna optimisation research. It identifies gaps such as limited experimental validation, standardisation issues, and the need for AI-driven inverse design methods for next-generation communication systems.
Artificial Intelligence in Physics Learning for Education for Sustainable Development: A Bibliometric Analysis Riski Ramadani; Hanan Zaki Alhusni; Titin Sunarti; Madlazim Madlazim
Journal of Law and Bibliometrics Studies Vol. 1 No. 3 (2025): December
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jolabis.1.3.95

Abstract

Objective: This study aims to map the global research landscape on Artificial Intelligence (AI) in physics education within the framework of Education for Sustainable Development (ESD) using a bibliometric approach. The objective is to identify publication trends, key contributors, collaborative networks, and emerging themes that define the development of this research domain. Method: The analysis was based on 4,814 documents retrieved from the Scopus database for the period 2015–2025. Data preprocessing included deduplication and keyword harmonization. Bibliometric analysis was conducted using performance indicators (publication output, influential authors, journals, countries, institutions) and science mapping (co-authorship, co-occurrence, co-citation) with VOSviewer and Bibliometrix. Results: Findings reveal three phases of publication dynamics: initial emergence (2015–2018), growth (2019–2021), and accelerated expansion (2022–2024), with a peak in 2024. The United States dominates global output, followed by China and Indonesia. Physics-focused journals such as Physical Review Physics Education Research and Journal of Physics: Conference Series serve as major outlets. Co-authorship networks show a core cluster in Europe and North America, while Asian and Global South researchers are increasingly active. Thematic mapping highlights clusters on AI-enabled assessment, machine learning, Large Language Models (LLMs), and sustainability-oriented physics education. Novelty: This paper provides a systematic overview of the intellectual structure and thematic evolution of AI-based physics education for ESD. It identifies gaps, including limited cross-country collaboration, low experimental validation, and uneven global participation, while highlighting opportunities for ethical, inclusive, and sustainability-aligned AI integration in future physics learning.
Mapping the Global Research Landscape of Problem-Based Learning in Digital Learning Environments: A Bibliometric Analysis Toward Achieving SDG 4 Hanan Zaki Alhusni; Binar Kurnia Prahani; Budi Jatmiko; Riski Ramadani; Noer Risky Ramadhani; Lindsay Natalia Bergsma; Abd. Hadi Bunyamin
Journal of Law and Bibliometrics Studies Vol. 2 No. 2 (2026): August
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jolabis.2.2.137

Abstract

Objective: The study aims to map the global research landscape of Problem-Based Learning (PBL) in Digital Learning Environments (DLE) using a bibliometric approach. The objective is to identify publication trends, major contributors, collaboration patterns, and emerging research themes supporting the development of sustainable digital education aligned with Sustainable Development Goal 4 (SDG 4). Method: The analysis was conducted on 2,134 documents retrieved from the Scopus database covering publications up to 2025. Data preprocessing involved duplicate removal, document filtering, and keyword harmonization. Bibliometric analysis employed performance indicators (annual publications, productive authors, sources, affiliations, and countries) and science mapping techniques (co-authorship, keyword co-occurrence, and citation analysis) using Bibliometrix and VOSviewer to visualize the intellectual structure of the field. Results: The findings reveal a substantial growth of research in this field, particularly following the global acceleration of digital education. China and the United States dominate research productivity, followed by emerging contributions from countries such as Indonesia. Publication sources are largely dominated by conference proceedings and journals in educational technology and computer science, highlighting the field's interdisciplinary nature.  Novelty: This study provides a comprehensive mapping of the intellectual structure and thematic evolution of PBL research in Digital Learning Environments. It identifies research gaps, including limited international collaboration and the need for greater integration of emerging technologies such as artificial intelligence. The findings offer directions for future research to strengthen sustainable digital learning innovation and support the achievement of SDG 4.