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All Journal Inovasi Fisika Indonesia (IFI) Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Firdaus, Rohim Aminullah Firdaus
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Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Physics Social Sciences Other

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DESAIN ANTENA MIKROSTRIP FOLDED DIPOLE PADA JARINGAN SELULAR 5G arnando, steven; Firdaus, Rohim Aminullah Firdaus; Rahmawati, Endah; Khoiro, Muhimmatul; Winarno, Nanang
Inovasi Fisika Indonesia Vol. 14 No. 3 (2025): Vol 14 No 3
Publisher : Prodi Fisika FMIPA Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/ifi.v14n3.p429-435

Abstract

Abstrak Artikel penelitian ini membahas tentang antena patch mikrostrip yang dibuat untuk komunikasi nirkabel. Bahan substrat yang digunakan yaitu FR-4 (lossy) dengan permitivitas dielektrik 4,3. Antena ini dirancang dengan menggunakan software CST Studio Suite. Desain antena patch mikrostrip yang ukurannya kecil, mudah difabrikasi, dan biaya yang murah telah dianalisis dalam artikel ini. Dari antena yang diusulkan memiliki return loss -34 dB dengan bandwidth sebesar 268 MHz pada return loss dibawah -10 dB. VSWR terendah pada frekuensi 3,5 GHz yaitu sebesar 1,01868. Antena ini dapat diaplikasikan pada ponsel, dan aplikasi LAN nirkabel.   Abstract This research article discusses microstrip patch antennas designed for wireless communication. The substrate material used is FR-4 (lossy) with a dielectric permittivity of 4.3. This antenna is designed using CST Studio Suite software. The design of the small-sized microstrip patch antenna, which is easy to fabricate and low-cost, has been analyzed in this article. The proposed antenna has a return loss of -34 dB with a bandwidth of 268 MHz at a return loss below -10 dB. The lowest VSWR at a frequency of 3.5 GHz is 1.01868. This antenna can be applied to radar systems, mobile phones, and wireless LAN applications.
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.
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 arnando, steven Endah Rahmawati Khoiro, Muhimmatul Nanang Winarno Nikmah, Afiyah Rachmawati, Arum Vonie Ramadani, Riski Ramadhani, Noer Risky