TY - GEN
T1 - A Comparative Analysis of Machine Learning Algorithms for Predicting the Dimensions of Rectangular Microstrip Antennas
AU - Yusuf, Aisya Nur Aulia
AU - Purnamasari, Prima Dewi
AU - Zulkifli, Fitri Yuli
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Antenna design process is time-consuming often involves a trial-and-error approach, which is not efficient. Machine learning algorithms can save significant time and effort in the design process and result in better antenna performance. This paper provides an analysis of suitable machine learning algorithms for predicting microstrip antenna dimensions by comparing six machine learning models based on four commonly used metrics in multi-output regression problems. The result shows that Random Forest algorithm is the most suitable algorithm since it gives the highest R2 score, namely 0.757, and lowest error value among the other algorithms, i.e. 0.674 for MAE, 4.097 for MSE, and 2.024 for RMSE.
AB - Antenna design process is time-consuming often involves a trial-and-error approach, which is not efficient. Machine learning algorithms can save significant time and effort in the design process and result in better antenna performance. This paper provides an analysis of suitable machine learning algorithms for predicting microstrip antenna dimensions by comparing six machine learning models based on four commonly used metrics in multi-output regression problems. The result shows that Random Forest algorithm is the most suitable algorithm since it gives the highest R2 score, namely 0.757, and lowest error value among the other algorithms, i.e. 0.674 for MAE, 4.097 for MSE, and 2.024 for RMSE.
KW - antenna design
KW - machine learning
KW - microstrip antenna
KW - regression
UR - http://www.scopus.com/inward/record.url?scp=85184804949&partnerID=8YFLogxK
U2 - 10.1109/ISAP57493.2023.10388517
DO - 10.1109/ISAP57493.2023.10388517
M3 - Conference contribution
AN - SCOPUS:85184804949
T3 - 2023 IEEE International Symposium on Antennas and Propagation, ISAP 2023
BT - 2023 IEEE International Symposium on Antennas and Propagation, ISAP 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Symposium on Antennas and Propagation, ISAP 2023
Y2 - 30 October 2023 through 2 November 2023
ER -