ESTIMASI PARAMETER ANTENA MIKROSTRIP MENGGUNAKAN JARINGAN SYARAF TIRUAN

The parameters of a rectangular microstrip antenna are intensely determined by the permittivity of the substrate, the thickness of the substrate, and the resonant frequency. Generally, to get the antenna parameters, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine antenna’s parameters more easily. In this study, an artificial neural network method with backpropagation algorithm is used to overcome the problem. The network is trained using the Levenberg–Marquardt algorithm. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method was successfully utilized to calculate the patch length, the patch width, and the feed point of a rectangular microstrip antenna, where the precision of the resonant frequency obtained of 93.33% at an error of ≤ 0.5%, and 100% at an error of ≤ 1%. However, the artificial neural network method with backpropagation algorithm is quite accurate for determining the parameters of rectangular microstrip antennas.Keywords: Artificial neural network, Backpropagation, Microstrip antenna, Resonant frequency

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Analisa JST Untuk Memprediksi Pembuatan SIM Menggunakan Metode Algoritma Backpropagation

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v4i1.1742 ◽

2020 ◽

Vol 4 (1) ◽

pp. 124

Author(s):

Mirza Rahul ◽

Indra Gunawan ◽

Fitri Anggraini ◽

Sumarno Sumarno ◽

Ika Okta Kirana

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Customer Satisfaction ◽

Backpropagation Algorithm ◽

Neural Network Method ◽

Service Company ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural

In a service company there are customers who become consumers of the company. Customer satisfaction is formed from the level of performance and company loyalty. If the company does not know about the level of customer satisfaction, the company also cannot develop their services. So from that Pematangsiantar Police Satpas need to know the level of customer satisfaction in order to improve their performance and loyalty. So research is needed to determine the level of customer satisfaction through the Artificial Neural Network method with the backpropagation algorithm which is then predicted and the best results will be searched to be used to give the best results and will display the results of the problems encountered.

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Penerapan Jaringan Saraf Tiruan dalam Menentukan Kelayakan Calon Penerima Bantuan Renovasi Rumah pada Kantor Pangulu Nagori Tangga Batu Dengan Metode Backpropogation

Prosiding Seminar Nasional Riset Information Science (SENARIS) ◽

10.30645/senaris.v1i0.46 ◽

2019 ◽

Vol 1 ◽

pp. 405

Author(s):

Chyntia Irwana ◽

M. Safii ◽

Iin Parlina

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Poverty Alleviation ◽

Basic Needs ◽

Backpropagation Algorithm ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

The Government

Home is one of the basic needs for humans, where the house serves as a place to shelter and shelter. Apart from having a function as a place to live, the house also functions as a place for fostering and chatting with a family. Poverty is a condition where a person is unable to fulfill his basic needs. In Nagori Tangga Batu there are still many people who have homes that are not habitable. Based on these problems, the government organized a poverty alleviation program through a home renovation route for residents of Nagori Tangga Batu village. In determining whether or not a house is suitable for renovation, it is necessary to use an Artificial Neural Network using the Backpropogation algorithm to determine whether or not the house of Nagori Tangga Batu residents is eligible for home renovation assistance. The best research with Artificial Neural Network method in determining the feasibility of recipients of home renovation assistance using the backpropagation algorithm is the model 6-3-1 with the repetition process (epoch) during training with epoch value = 1673 and MSE achievement during testing with MSE = 0.00797068 . This research is expected to be a reference for further researchers relating to the user algorithm used.

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