📅 30 July 2025
DOI: 10.51903/elkom.v18i1.2915
Predictive Modeling of Microstrip Antenna Slot Dimensions Using Random Forest Regression
Jurnal Elektronika dan Komputer
Universitas Sains dan Teknologi Komputer
📄 Abstract
This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.
🔖 Keywords
#Machine Learning; Microstrip Antenna; Prediction; Random Forest; Regression
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 July 2025
Volume / Nomor / Tahun
Volume 18, Nomor 1, Tahun 2025
📝 HOW TO CITE
Yusuf, Aisya Nur Aulia; Nurdiniyah, Elsa Sari Hayunah; Amalia, Norma, "Predictive Modeling of Microstrip Antenna Slot Dimensions Using Random Forest Regression," Jurnal Elektronika dan Komputer, vol. 18, no. 1, Jul. 2025.
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