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All Journal Journal of Digitalization in Physics Education (JDPE) Journal of Current Studies in SDGs Journal of Current Studies in SDGs Journal of Law and Bibliometrics Studies Open Access DRIVERset
Noer Risky Ramadhani
Universität für Weiterbildung Krems
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Physics Social Sciences Other

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Design Optimization of Rectangular Microstrip Antenna Using Deep Neural Network for 3 GHz Applications in Support of SDG 9 Riski Ramadani; Afiyah Nikmah; Arum Vonie Rachmawati; Rohim Aminullah Firdaus Firdaus; Noer Risky Ramadhani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.130

Abstract

Objective: This study aims to investigate the effectiveness of Deep Neural Networks (DNN) for optimizing the design of a rectangular microstrip antenna operating at a target frequency of 3 GHz. The research focuses on improving antenna design efficiency by predicting antenna performance parameters based on geometric characteristics. Method: The study employed a computational simulation approach combined with machine learning techniques. A synthetic dataset consisting of 6000 antenna configurations was generated using analytical microstrip antenna equations. The dataset included geometric parameters such as dielectric constant, substrate thickness, patch width, patch length, and inset feed position. A Deep Neural Network model was trained to predict resonant frequency, return loss, and input impedance. The trained model was then used as a surrogate model to evaluate 30,000 candidate antenna designs and identify the optimal configuration. Result: The proposed model achieved high predictive accuracy with values of 0.9987 for resonant frequency prediction and 0.9988 for input impedance prediction. The optimized antenna design produced a resonant frequency of 2.996 GHz, return loss of −18.70 dB, and input impedance of 53.95 Ω, which closely match the target specifications for S-band wireless applications. Novelty: The study demonstrates that Deep Neural Networks can significantly accelerate antenna design optimization by replacing repetitive electromagnetic simulations with data-driven prediction models.
Design Optimization of Rectangular Microstrip Antenna Using Deep Neural Network for 3 GHz Applications in Support of SDG 9 Riski Ramadani; Afiyah Nikmah; Arum Vonie Rachmawati; Rohim Aminullah Firdaus Firdaus; Noer Risky Ramadhani
Journal of Current Studies in SDGs Vol. 2 No. 1 (2026): March
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.1.130

Abstract

Objective: This study aims to investigate the effectiveness of Deep Neural Networks (DNN) for optimizing the design of a rectangular microstrip antenna operating at a target frequency of 3 GHz. The research focuses on improving antenna design efficiency by predicting antenna performance parameters based on geometric characteristics. Method: The study employed a computational simulation approach combined with machine learning techniques. A synthetic dataset consisting of 6000 antenna configurations was generated using analytical microstrip antenna equations. The dataset included geometric parameters such as dielectric constant, substrate thickness, patch width, patch length, and inset feed position. A Deep Neural Network model was trained to predict resonant frequency, return loss, and input impedance. The trained model was then used as a surrogate model to evaluate 30,000 candidate antenna designs and identify the optimal configuration. Result: The proposed model achieved high predictive accuracy with values of 0.9987 for resonant frequency prediction and 0.9988 for input impedance prediction. The optimized antenna design produced a resonant frequency of 2.996 GHz, return loss of −18.70 dB, and input impedance of 53.95 Ω, which closely match the target specifications for S-band wireless applications. Novelty: The study demonstrates that Deep Neural Networks can significantly accelerate antenna design optimization by replacing repetitive electromagnetic simulations with data-driven prediction models.
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.
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.
Effectiveness of Learning With PBL Model Based on E-Modules to Improve Critical Thinking Skills Aulia Betha Novianti; Budi Jatmiko; Binar Kurnia Prahani; Noer Risky Ramadhani
Journal of Digitalization in Physics Education Vol. 1 No. 3 (2025): December
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jdpe.v1i3.42451

Abstract

Objective: This study aims to examine the effectiveness of physics e-modules based on the Problem-Based Learning (PBL) model in improving students’ critical thinking skills on static fluid concepts. The focus is to evaluate the implementation of learning, measure the improvement of students’ critical thinking ability, and analyze student responses after applying the PBL-based e-modules. Method: The research employed a proper experimental design with a pre-test and post-test control group, conducted at MA Ma’arif Bangil, Pasuruan, Indonesia. A total sampling technique was used, with class XA as the experimental group and class XB as the control group. Data were collected using observation sheets, pre-test and post-test instruments, and student response questionnaires. Data analysis included N-Gain scores, normality and homogeneity tests, t-tests, and descriptive analysis for student responses. Results: The findings showed that the implementation of PBL-based e-modules reached an average of 91.8% categorized as very good. Students in the experimental class demonstrated a significant improvement in critical thinking skills, with an average N-Gain of 0.512 (moderate), compared to 0.212 (low) in the control group. Furthermore, student responses were predominantly in the “good” and “very good” categories, indicating positive perceptions of the learning approach. Novelty: This study highlights the integration of PBL with interactive e-modules as an innovative learning strategy that not only enhances students’ critical thinking skills but also addresses the limitations of conventional print modules. It provides empirical evidence that digital-based problem-oriented instruction is highly relevant to fostering 21st-century competencies in science learning.
Co-Authors Afiyah Nikmah Afiyah Nikmah Arum Vonie Rachmawati Aulia Betha Novianti Binar Kurnia Prahani Budi Jatmiko Nisaul Fadhilah Riski Ramadani Riski Ramadani ROHIM AMINULLAH FIRDAUS Rohim Aminullah Firdaus Firdaus