Mayori Permitha, . (2025) PENENTUAN ALGORITMA OPTIMASI 4 PARAMETER ANTENA MIKROSTRIP FRAKTAL SIERPINSKI GASKET BERBASIS MACHINE LEARNING UNTUK WI-FI 7 FREKUENSI 2,4 GHZ. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
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Abstract
This research aims to determine the most suitable machine learning-based optimization algorithms for four parameters of Sierpinski Gasket fractal microstrip antennas at 2.4 GHz for Wi-Fi 7 applications: return loss, gain, VSWR, and bandwidth. The four machine learning algorithms evaluated were K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Regression (SVR). A comprehensive dataset was built from 424 variations of antenna designs simulated using CST Studio Suite software with two types of substrates, FR-4 and RT/duroid 5880. The performance analysis showed that different algorithms excelled for each parameter: KNN for return loss (R2=0.9837), SVR for VSWR (R2=0.9986), and Random Forest for both bandwidth (R2=0.7042) and gain (R2=0.8701). These best-performing algorithms were then integrated with the Random Search method, equipped with a weighted scoring system, to find the optimal antenna design. The optimization recommended the first antenna model design with an RT/duroid 5880 substrate. Verification through simulation confirmed the superior performance of the optimized antenna with a return loss of -41.65 dB, VSWR of 1.01, bandwidth of 166.4 MHz, and gain of 4.56 dBi at a frequency of 2.4086 GHz. This study successfully demonstrated that the integration of machine learning can effectively transform initially non-functional antenna designs into highly efficient ones that fully meet Wi-Fi 7 specifications.
Item Type: | Thesis (Skripsi) |
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Additional Information: | [No.Panggil: 2110314060] [Pembimbing 1: Silvia Anggraeni] [Pembimbing 2: Yosy Rahmawati] [Penguji 1: Didit Widiyanto] [Penguji 2: Muhammad Alif Razi] |
Uncontrolled Keywords: | Microstrip Antenna, Sierpinski Gasket Fractal, Machine Learning, Optimization, Wi-Fi 7 |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Fakultas Teknik > Program Studi Teknik Elektro (S1) |
Depositing User: | MAYORI PERMITHA |
Date Deposited: | 08 Aug 2025 02:53 |
Last Modified: | 08 Aug 2025 02:53 |
URI: | http://repository.upnvj.ac.id/id/eprint/39471 |
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