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All Journal SCHOOL EDUCATION JOURNAL PGSD FIP UNIMED 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 Journal of Innovative Technology and Sustainability Education
Ramadhani, Noer Risky
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Journal : Journal of Current Studies in SDGs

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Design Optimization of Rectangular Microstrip Antenna Using Deep Neural Network for 3 GHz Applications in Support of SDG 9 Ramadani, Riski; Nikmah, Afiyah; Rachmawati, Arum Vonie; Firdaus, Rohim Aminullah Firdaus; Ramadhani, Noer Risky
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.
Co-Authors Binar Kurnia Prahani Budi Jatmiko Damayanti, Nina Afria Fadhilah, Nisaul Firdaus , Rohim Aminullah Firdaus, Rohim Aminullah Firdaus Hareni, Siska Agustin Sha Hidayatin, Nofri Irani, Dhea Wanda Lutfiani, Elvia Reza Melly Br Bangun Muawiyah, Nurul Natalia Silalahi, Natalia Nikmah, Afiyah Novianti, Aulia Betha Nurhudayah Manjani Rachmawati, Arum Vonie Ramadani, Riski Sipahutar, Rini Juliani SYAHRIAL SYAHRIAL Wasis Wasis Widi Astutik