scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI FREKUENSI RESONANSI ANTENA MIKROSTRIP

2019 ◽  
Vol 12 (1) ◽  
pp. 33-40
Author(s):  
Khairi Budayawan ◽  
Yuhandri Yuhandri ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Neural Network Learning ◽  
Artificial Neural Network Method ◽  
Dimensional Parameters ◽  
Artificial Neural

The resonant frequency of an antenna is determined by the dimensional parameters and permittivity of the antenna substrate. Generally, to get the resonant frequency, a complex mathematical formula is needed to solve. For this reason, an intelligent method is offered to determine the resonant frequency more easily. In this study, an artificial neural network method with Backpropagation algorithm is used to overcome the problem. The data used were consisting of 80 training data and 15 testing data. The results have shown that the artificial neural network learning method with the backpropagation algorithm was successfully utilized to calculate the resonant frequency of microstrip antennas, where the precision of the resonant frequency obtained of 93.33% at an error of = 1%, and 100% at an error of = 2%.

scholarly journals ESTIMASI PARAMETER ANTENA MIKROSTRIP MENGGUNAKAN JARINGAN SYARAF TIRUAN

2019 ◽  
Vol 7 (2) ◽  
pp. 182
Author(s):  
Khairi Budayawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antenna ◽  
Backpropagation Algorithm ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Rectangular Microstrip Antenna

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

scholarly journals Identification Of Number Using Artificial Neural Network Backpropagation

2018 ◽  
Vol 215 ◽  
pp. 01011
Author(s):  
Sitti Amalia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Training Data ◽  
Simulation Test ◽  
Backpropagation Algorithm ◽  
Network Parameter ◽  
Artificial Intelligent ◽  
Design And Implementation ◽  
Network Training ◽  
Artificial Neural

This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

scholarly journals An Application of Artificial Neural Network to Compute the Resonant Frequency of E–Shaped Compact Microstrip Antennas

2013 ◽  
Vol 64 (5) ◽  
pp. 317-322 ◽  
Author(s):  
Ali Akdagli ◽  
Abdurrahim Toktas ◽  
Ahmet Kayabasi ◽  
Ibrahim Develi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resonant Frequency ◽  
Microstrip Antennas ◽  
Multilayer Perceptrons ◽  
Ann Model ◽  
Resonant Frequencies ◽  
Average Percentage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract An application of artificial neural network (ANN) based on multilayer perceptrons (MLP) to compute the resonant frequency of E-shaped compact microstrip antennas (ECMAs) is presented in this paper. The resonant frequencies of 144 ECMAs with different dimensions and electrical parameters were firstly determined by using IE3D(tm) software based on the method of moments (MoM), then the ANN model for computing the resonant frequency was built by considering the simulation data. The parameters and respective resonant frequency values of 130 simulated ECMAs were employed for training and the remaining 14 ECMAs were used for testing the model. The computed resonant frequencies for training and testing by ANN were obtained with the average percentage errors (APE) of 0.257% and 0.523%, respectively. The validity and accuracy of the present approach was verified on the measurement results of an ECMA fabricated in this study. Furthermore, the effects of the slots loading method over the resonant frequency were investigated to explain the relationship between the slots and resonant frequency.

scholarly journals Implementation of the Perceptron Method for Recognizing the Patterns of Types of Intestinal Nematode Worms

2020 ◽  
Vol 4 (1) ◽  
pp. 180-186
Author(s):  
Erni Rouza ◽  
Jufri ◽  
Luth Fimawahib
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Training Data ◽  
Perceptron Algorithm ◽  
Intestinal Nematode ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
The Right ◽  
Nematode Worm

The purpose of pattern recognition is do the process of classifying an object into one particular class based on the pattern it has, so it can be used to recognize patterns of intestinal nematode worm types. One of the methods used in pattern recognition is by utilizing the artificial neural network method, the artificial neural network is able to represent a complex Input-Output relationship. For that the algorithm used is the perceptron algorithm. Perceptron is one method of Artificial Neural Networks. In the introduction of types of intestinal nematode worms, a computer must be trained in advance using training data and test data, this study discusses how a computer can recognize a pattern of types of intestinal nematode worms using the perceptron method. Based on the results of testing trials with input in the form of worm image scan results, based on the results of the perceptron method testing is able to recognize the pattern recognition of the types of intestinal nematode worms and be able to analyze with the right results of 100% for pinworms patterns, hookworm patterns, and 40- 50% for roundworms, by comparing the output value and the target value entered first.

scholarly journals Automation of (Macro)molecular Properties Using a Bootstrapping Swarm Artificial Neural Network Method: Databases for Machine Learning

2019 ◽  
Author(s):  
Blerta Rahmani ◽  
Hiqmet Kamberaj
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Quantum Mechanics ◽  
Molecular Properties ◽  
Training Data ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

AbstractIn this study, we employed a novel method for prediction of (macro)molecular properties using a swarm artificial neural network method as a machine learning approach. In this method, a (macro)molecular structure is represented by a so-called description vector, which then is the input in a so-called bootstrapping swarm artificial neural network (BSANN) for training the neural network. In this study, we aim to develop an efficient approach for performing the training of an artificial neural network using either experimental or quantum mechanics data. In particular, we aim to create different user-friendly online accessible databases of well-selected experimental (or quantum mechanics) results that can be used as proof of the concepts. Furthermore, with the optimized artificial neural network using the training data served as input for BSANN, we can predict properties and their statistical errors of new molecules using the plugins provided from that web-service. There are four databases accessible using the web-based service. That includes a database of 642 small organic molecules with known experimental hydration free energies, the database of 1475 experimental pKa values of ionizable groups in 192 proteins, the database of 2693 mutants in 14 proteins with given values of experimental values of changes in the Gibbs free energy, and a database of 7101 quantum mechanics heat of formation calculations.All the data are prepared and optimized in advance using the AMBER force field in CHARMM macromolecular computer simulation program. The BSANN is code for performing the optimization and prediction written in Python computer programming language. The descriptor vectors of the small molecules are based on the Coulomb matrix and sum over bonds properties, and for the macromolecular systems, they take into account the chemical-physical fingerprints of the region in the vicinity of each amino acid.Graphical TOC Entry

scholarly journals Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

2020 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Chrisani Waas ◽  
D. L. Rahakbauw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Processing System ◽  
Disease Diagnosis ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Neural Network Learning ◽  
Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

scholarly journals Analisa JST Untuk Memprediksi Pembuatan SIM Menggunakan Metode Algoritma Backpropagation

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.

scholarly journals Prediksi Volume Penggunaan Air PDAM Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation

2018 ◽  
Vol 2 (3) ◽  
Author(s):  
Budy Satria
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Consumption ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Training Data ◽  
Clean Water ◽  
Customer Data ◽  
Artificial Neural Network Method ◽  
Artificial Neural

As the population growth rate in Duri increases, the need for clean water also increases as needed. In Indonesia, PDAM is an institution that regulates and manages the provision of clean water for the community. So the amount of water produced and distributed should be adjusted to the demand for water. However, the problem arises in the form of waste of water at PT. PDAM Duri. Purpose of this study is to predict the amount of water consumption at PT. PDAM Duri by implementing Backpropagation  Artificial Neural Network method. Variables of data taken from customer data were social, general social, household 1, household 2, household 3, commerce 1, commerce 2 and commerce 3. Data used in the prediction process was training data in 2016 and data testing in 2017. Actual amount of data at PT. PDAM Duri City 2016 until 2017 was 2.840.165 when the prediction result using artificial neural network back propagation method was 2.843.388. The number of training epochs was 4595 and the achievement of MSE (Mean Squared Error) on the test was 0,001 and the result of accuracy was 99,99900000%. Final result of this research was artificial neural network using back propagation method could predict the using of water consumption at PT. PDAM  Duri for next year.  

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

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.

scholarly journals IMPLEMENTASI ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI KELULUSAN MAHASISWA

2018 ◽  
Vol 5 (2) ◽  
pp. 169
Author(s):  
Muhammad Dedek Yalidhan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Prediction Accuracy ◽  
Confusion Matrix ◽  
The Other ◽  
Training Data ◽  
Backpropagation Algorithm ◽  
Testing Data ◽  
Artificial Neural

<p><em>Student’s graduation is one kind of the college accreditation elements by BAN-PT. Because of that. Information System is one of the department in STMIK Banjarbaru, there is no application has been implemented to predict imprecisely of student’s graduation time so far, which causes on time graduation percentage tend low every year. Therefore the accurate student’s graduation prediction can help the committe to choose the correct decisions in order to prevent the imprecisely of student’s graduation time. In this research, the backpropagation algorithm of artificial neural network will be implemented into the application with the output result as delayed and on time graduation. This reseach is using 318 data samples which the 70 % of it will be used as the training data and the other 30 % will be used as testing data. From the calculation of confusion matrix table’s the percentage of the prediction accuracy is 98.97 %.</em></p><p><em></em><em><strong>Keywords</strong>: student’s graduation, artificial neural network, backpropagation, confusion matrix</em></p><p><em></em><em>Kelulusan mahasiswa merupakan salah satu elemen dalam standar akreditasi perguruan tinggi oleh BAN-PT. Sistem Informasi adalah salah satu program studi yang ada di STMIK Banjarbaru, selama ini belum ada aplikasi yang diimplementasikan untuk memprediksi ketidaktepatan waktu kelulusan mahasiswanya yang menyebabkan angka kelulusan tepat waktu cenderung rendah setiap tahunnya. Oleh sebab itu, prediksi kelulusan mahasiswa yang akurat dapat membantu pihak Program Studi dalam mengambil keputusan-keputusan yang tepat untuk mencegah ketidaktepatan waktu kelulusan mahasiswanya. Pada penelitian ini, artificial neural network algoritma backpropagation diimplementasikan pada aplikasi yang dibuat dengan output lulus terlambat dan lulus tepat waktu. Penelitian ini menggunakan sebanyak 318 sampel data yang mana 70 % data digunakan sebagai data training dan 30 % data digunakan sebagai data testing. Dari hasil perhitungan tabel confusion matrix diperoleh persentase akurasi prediksi sebesar 98.97 %.</em></p><p><em></em><em><strong>Kata kunci</strong>: kelulusan mahasiswa, artificial neural network, backpropagation, confusion matrix</em></p>

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