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All Journal POSITRON Jurnal Kolaboratif Sains Journal of Current Studies in SDGs Journal of Current Studies in SDGs Journal of Law and Bibliometrics Studies Open Access DRIVERset
Nikmah, Afiyah
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Analisis Kinerja Sistem LiDAR (Light Detection and Ranging) dalam Pengukuran Jarak dengan Pendekatan Simulasi: Evaluasi Ketepatan dan Keandalan Pengukuran: Analysis of LiDAR (Light Detection and Ranging) System Performance in Distance Measurement using Simulation Approach: Evaluation of Measurement Accuracy and Reliability Nikmah, Afiyah; Riski Ramadani; Dzulkiflih; Muhimmatul Khoiro; Firdaus, Rohim Aminullah
Jurnal Kolaboratif Sains Vol. 7 No. 5: MEI 2024
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v7i5.5250

Abstract

Studi ini menyajikan analisis kinerja sistem LiDAR dalam pengukuran jarak dengan memanfaatkan pendekatan berbasis simulasi. Sistem LiDAR banyak digunakan dalam berbagai bidang karena kemampuannya untuk mengukur jarak secara akurat. Namun, menilai akurasi dan keandalan pengukuran LiDAR dalam berbagai kondisi sangat penting untuk memastikan efektivitasnya dalam aplikasi praktis. Dalam penelitian ini, sebuah kerangka kerja simulasi dikembangkan untuk meniru operasi sistem LiDAR dan menghasilkan pengukuran jarak dengan mempertimbangkan faktor lingkungan dan noise pengukuran. Pengukuran yang disimulasikan kemudian dianalisis untuk mengevaluasi akurasi dan keandalan sistem LiDAR dalam pengukuran jarak. Melalui analisis perbandingan dan evaluasi statistik, wawasan tentang kinerja sistem LiDAR diperoleh, yang menyoroti kelebihan dan keterbatasannya. Temuan dari penelitian ini memberikan kontribusi pada pemahaman yang lebih baik tentang teknologi LiDAR dan memberikan panduan untuk mengoptimalkan kinerjanya dalam aplikasi berbagai bidang. Penelitian ini menegaskan pentingnya penilaian berbasis simulasi dalam menilai kemampuan sistem LiDAR dan memberikan informasi bagi proses pengambilan keputusan dalam berbagai bidang yang bergantung pada pengukuran jarak yang akurat.
Three-Dimensional Soliton Propagation in Ruby for High-Stability Optical Communication Yantidewi, Meta; Firdaus, Rohim Aminullah; Khoiro, Muhimmatul; Winarno, Nanang; Ramadani, Riski; Nikmah, Afiyah; Alhusni, Hanan Zaki
POSITRON Vol 16, No 1 (2026): Vol. 16 No. 1 Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v16i1.93213

Abstract

Optical communication in digitalisation is a significant catalyst in supporting high-speed and large capacity data transmission. However, optical communication applications face substantial challenges, such as energy loss, dispersion effects, and signal stability over long transmission distances. One material that has the potential to overcome these problems is ruby due to its nonlinear characteristics. This research work numerically investigates soliton propagation in ruby material analysis as a Nonlinear Schrödinger Equation numerical modelling and three-dimensional visualisation with Cubic B-Spline, modified Korteweg-de Vries-Zakharov-Kuznetsov, and Boussinesq methods to understand ruby material characteristics more deeply. The simulations show that the soliton waveform remains stable during propagation, with its amplitude confined within approximately -1.05 to 0.35, indicating robust shape preservation under anisotropy and temperature variations. like most previous studies that are limited to one or two dimensional modelling or to dispersive fibre media, this study implement a full 3D NLSE framework tailored to the physical properties of ruby. The results show that both numerical modelling and three-dimensional visualisation indicate that ruby material can maintain the soliton waveform during propagation. This finding suggests that ruby has the potential to be applied in optical communication systems with high stability to support long-distance data transmission.
Trends and Mapping of Research on Artificial Intelligence-Based Antenna Optimisation: A Bibliometric Analysis Ramadani, Riski; Nikmah, Afiyah; Fadhilah, Nisaul; Firdaus , Rohim Aminullah; Ramadhani, Noer Risky
Journal of Law and Bibliometrics Studies Vol. 1 No. 2 (2025): July
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 Ramadani, Riski; Nikmah, Afiyah; Fadhilah, Nisaul; Firdaus , Rohim Aminullah; Ramadhani, Noer Risky
Journal of Law and Bibliometrics Studies Vol. 1 No. 2 (2025): July
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.
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 Alhusni, Hanan Zaki DZULKIFLIH Fadhilah, Nisaul Firdaus , Rohim Aminullah Firdaus, Rohim Aminullah Firdaus, Rohim Aminullah Firdaus Khoiro, Muhimmatul Muhimmatul Khoiro Nanang Winarno Rachmawati, Arum Vonie Ramadani, Riski Ramadhani, Noer Risky Riski Ramadani Yantidewi, Meta