Geometry Prediction of the Antenna Design Using Machine Learning Method

Agusriandi; Eko Setijadi; Achmad Affandi; Mohammad Rifqi Firmansyah

doi:10.1109/ISRITI60336.2023.10467879

Geometry Prediction of the Antenna Design Using Machine Learning Method

Agusriandi
, Eko Setijadi
, Achmad Affandi
, Mohammad Rifqi Firmansyah

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

To design and optimise an antenna for a specific pur-pose, engineers combine engineering concepts, electromagnetic theory and practical factors that take much longer. However, in the midst of machine learning methods, this work can be done effectively and efficiently. Therefore, this paper is concerned with predicting the geometry of a microstrip patch antenna using machine learning. Parameter sweeps for the patch antenna geometry are proposed and obtained from a microwave simulator with varying width values to be used as a dataset. The data collected for the design was fed into a support vector machine algorithm to verify that the support vector regression modelling was correct. With an average prediction error of less than 0.0903 and a correlation coefficient of 0.98, the regression model predicted the results very accurately. The time taken by the machine learning method was shorter than that required by conventional methods.

Original language	English
Title of host publication	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	422-426
Number of pages	5
ISBN (Electronic)	9798350358346
DOIs	https://doi.org/10.1109/ISRITI60336.2023.10467879
Publication status	Published - 2023
Event	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Batam, Indonesia Duration: 11 Dec 2023 → …

Publication series

Name	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding

Conference

Conference	6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023
Country/Territory	Indonesia
City	Batam
Period	11/12/23 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

geometry prediction
machine learning method
microstrip patch antenna
support vector machine

Access to Document

10.1109/ISRITI60336.2023.10467879

Cite this

Agusriandi, Setijadi, E., Affandi, A., & Firmansyah, M. R. (2023). Geometry Prediction of the Antenna Design Using Machine Learning Method. In 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding (pp. 422-426). (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISRITI60336.2023.10467879

Agusriandi, ; Setijadi, Eko ; Affandi, Achmad et al. / Geometry Prediction of the Antenna Design Using Machine Learning Method. 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 422-426 (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding).

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title = "Geometry Prediction of the Antenna Design Using Machine Learning Method",

abstract = "To design and optimise an antenna for a specific pur-pose, engineers combine engineering concepts, electromagnetic theory and practical factors that take much longer. However, in the midst of machine learning methods, this work can be done effectively and efficiently. Therefore, this paper is concerned with predicting the geometry of a microstrip patch antenna using machine learning. Parameter sweeps for the patch antenna geometry are proposed and obtained from a microwave simulator with varying width values to be used as a dataset. The data collected for the design was fed into a support vector machine algorithm to verify that the support vector regression modelling was correct. With an average prediction error of less than 0.0903 and a correlation coefficient of 0.98, the regression model predicted the results very accurately. The time taken by the machine learning method was shorter than that required by conventional methods.",

keywords = "geometry prediction, machine learning method, microstrip patch antenna, support vector machine",

author = "Agusriandi and Eko Setijadi and Achmad Affandi and Firmansyah, \{Mohammad Rifqi\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 ; Conference date: 11-12-2023",

year = "2023",

doi = "10.1109/ISRITI60336.2023.10467879",

language = "English",

series = "6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Agusriandi, , Setijadi, E , Affandi, A & Firmansyah, MR 2023, Geometry Prediction of the Antenna Design Using Machine Learning Method. in 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding, Institute of Electrical and Electronics Engineers Inc., pp. 422-426, 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023, Batam, Indonesia, 11/12/23. https://doi.org/10.1109/ISRITI60336.2023.10467879

Geometry Prediction of the Antenna Design Using Machine Learning Method. / Agusriandi, ; Setijadi, Eko ; Affandi, Achmad et al.
6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc., 2023. p. 422-426 (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Geometry Prediction of the Antenna Design Using Machine Learning Method

AU - Agusriandi,

AU - Setijadi, Eko

AU - Affandi, Achmad

AU - Firmansyah, Mohammad Rifqi

PY - 2023

Y1 - 2023

N2 - To design and optimise an antenna for a specific pur-pose, engineers combine engineering concepts, electromagnetic theory and practical factors that take much longer. However, in the midst of machine learning methods, this work can be done effectively and efficiently. Therefore, this paper is concerned with predicting the geometry of a microstrip patch antenna using machine learning. Parameter sweeps for the patch antenna geometry are proposed and obtained from a microwave simulator with varying width values to be used as a dataset. The data collected for the design was fed into a support vector machine algorithm to verify that the support vector regression modelling was correct. With an average prediction error of less than 0.0903 and a correlation coefficient of 0.98, the regression model predicted the results very accurately. The time taken by the machine learning method was shorter than that required by conventional methods.

AB - To design and optimise an antenna for a specific pur-pose, engineers combine engineering concepts, electromagnetic theory and practical factors that take much longer. However, in the midst of machine learning methods, this work can be done effectively and efficiently. Therefore, this paper is concerned with predicting the geometry of a microstrip patch antenna using machine learning. Parameter sweeps for the patch antenna geometry are proposed and obtained from a microwave simulator with varying width values to be used as a dataset. The data collected for the design was fed into a support vector machine algorithm to verify that the support vector regression modelling was correct. With an average prediction error of less than 0.0903 and a correlation coefficient of 0.98, the regression model predicted the results very accurately. The time taken by the machine learning method was shorter than that required by conventional methods.

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KW - machine learning method

KW - microstrip patch antenna

KW - support vector machine

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DO - 10.1109/ISRITI60336.2023.10467879

M3 - Conference contribution

AN - SCOPUS:85190067807

T3 - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding

SP - 422

EP - 426

BT - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Agusriandi , Setijadi E , Affandi A, Firmansyah MR. Geometry Prediction of the Antenna Design Using Machine Learning Method. In 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding. Institute of Electrical and Electronics Engineers Inc. 2023. p. 422-426. (6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding). doi: 10.1109/ISRITI60336.2023.10467879

Geometry Prediction of the Antenna Design Using Machine Learning Method

Abstract

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UN SDGs

Keywords

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