scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

scholarly journals New Method for Locker Door with Face Security System Using Backpropagation Algorithm

2018 ◽  
Vol 7 (2.13) ◽  
pp. 402
Author(s):  
Y Yusmartato ◽  
Zulkarnain Lubis ◽  
Solly Arza ◽  
Zulfadli Pelawi ◽  
A Armansah ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Back Propagation ◽  
The Face ◽  
Artificial Neural ◽  
Hidden Layer

Lockers are one of the facilities that people use to store stuff. Artificial neural networks are computational systems where architecture and operations are inspired by the knowledge of biological neurons in the brain, which is one of the artificial representations of the human brain that always tries to stimulate the learning process of the human brain. One of the utilization of artificial neural network is for pattern recognition. The face of a person must be different but sometimes has a shape similar to the face of others, because the facial pattern is a good pattern to try to be recognized by using artificial neural networks. Pattern recognition on artificial neural network can be done by back propagation method. Back propagation method consists of input layer, hidden layer and output layer.  

Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density

2017 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Anjar Wanto ◽  
Agus Perdana Windarto ◽  
Dedy Hartama ◽  
Iin Parlina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Population Density ◽  
Network Architecture ◽  
Activation Function ◽  
Sigmoid Function ◽  
Backpropagation Algorithm ◽  
Architecture Model ◽  
Artificial Neural

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

scholarly journals Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance

2019 ◽  
Vol 14 (1) ◽  
pp. 58-79 ◽  
Author(s):  
Gaetano Bosurgi ◽  
Orazio Pellegrino ◽  
Giuseppe Sollazzo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Performance Evaluation ◽  
Network Architecture ◽  
Training Data ◽  
Structural Performance ◽  
Reliable Technique ◽  
Artificial Neural

Artificial Neural Networks represent useful tools for several engineering issues. Although they were adopted in several pavement-engineering problems for performance evaluation, their application on pavement structural performance evaluation appears to be remarkable. It is conceivable that defining a proper Artificial Neural Network for estimating structural performance in asphalt pavements from measurements performed through quick and economic surveys produces significant savings for road agencies and improves maintenance planning. However, the architecture of such an Artificial Neural Network must be optimised, to improve the final accuracy and provide a reliable technique for enriching decision-making tools. In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. In particular, input factor quality (structural, traffic, climatic), “homogeneity” of training data records and the actual net topology have been investigated. Finally, these results further prove the approach efficiency, for improving Pavement Management Systems and reducing deflection survey frequency, with remarkable savings for road agencies.

Multilayer Backpropagation Neural Networks for Implementation of Logic Gates

2021 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Santosh Giri ◽  
Basanta Joshi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Specific Problem ◽  
Logic Gates ◽  
Backpropagation Algorithm ◽  
Processing Elements ◽  
Artificial Neural ◽  
Hidden Neurons

ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained by using a backpropagation algorithm until the model satisfies the predefined error criteria (e) which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively.

scholarly journals Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

2017 ◽  
Vol 1 (1) ◽  
pp. 34 ◽  
Author(s):  
Sandy Putra Siregar ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Backpropagation Algorithm ◽  
Neural Structure ◽  
Data Prediction ◽  
Architectural Models ◽  
Artificial Neural ◽  
North Sumatra

Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.

scholarly journals IDENTIFIKASI POLA KARAKTER ANAK DENGAN ALGORITMA PERCEPTRON

2019 ◽  
Vol 3 (1) ◽  
pp. 86
Author(s):  
Sri Rahmadhany
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Computational Method ◽  
Input Output ◽  
Perceptron Algorithm ◽  
Behavioral Variables ◽  
Artificial Neural

Abstract - Artificial Neural Network is a computational method that works like a human brain. The Perceptron algorithm is one method that exists in Artificial Neural Networks. The research carried out was the identification of children's character patterns using the Perceptron algorithm. The Perceptron algorithm is very reliable in recognizing patterns, one of which is the child's character pattern as was done in this study. The Perceptron algorithm identifies the character patterns of children through three inputs and two outputs. The three outputs are taken from nature variables, attitude variables and behavioral variables. The output is four human temperaments according to Hipocrates, namely sanguin, melancholy, choleric and plegamatic. All inputs and outputs will be converted into binary numbers to be trained with Matlab software.Keywords - Artificial Neural Networks, Perceptron Algorithms, child character patterns, input, output, binary numbers. Abstrak - Jaringan Syaraf Tiruan merupakan salah satu metode komputasi yang dapat bekerja seperti layaknya otak manusia. Algortima Perceptron merupakan salah satu metode yang ada pada Jaringan Syaraf Tiruan. Penelitian yang dilakukan adalah identifikasi pola karakter anak dengan menggunakan algoritma Perceptron. Algoritma Perceptron sangat handal dalam mengenali pola salah satunya yaitu pola karakter anak seperti yang dilakukan dalam penelitian ini. Algoritma Perceptron mengidentifikasi pola karakter anak melalui tiga input dan dua output. Tiga output tersebut diambil dari variabel sifat, variabel sikap dan variabel tingkah laku. Adapun output merupakan empat temperamen manusia menurut Hipocrates yaitu sanguin, melankolis, koleris dan plegamatis. Seluruh input dan output akan diubah menjadi bilangan biner untuk dilatih dengan software Matlab.Kata Kunci - Jaringan Syaraf Tiruan, Algoritma Perceptron, pola karakter anak, input, output, bilangan biner.

scholarly journals Jaringan Syaraf Tiruan untuk Memprediksi Penjualan Kelapa Sawit Menggunakan Algoritma Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 418
Author(s):  
Delima Sinaga ◽  
Solikhun Solikhun ◽  
Iin Parlina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Palm Oil ◽  
Learning Process ◽  
Grouped Data ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Matlab Software ◽  
Artificial Neural

This study discusses the prediction of palm oil sales using artificial neural networks, which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses a backpropagation algorithm where the data entered is the number of sold. Then artificial neural networks are formed by determining the number of units per layer. After the networks is formed, training is carried out from the grouped data. Experiments are carried out with an architecture consisting of input units, hidden units, output units and architecture. Testing is done with matlab software. For now the competition for palm oil sales is getting tougher. Predictions with the best accuracy use the 12-2-1 architecture with an accuracy rate of 92% and the lowest level of accuracy using 12-6-1 architecture with an accuracy rate of 58%

scholarly journals ANALISIS ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI TINGKAT KEBERHASILAN PELATIH SEPAKBOLA SEBAGAI SALAH SATU KEMAJUAN OLAHRAGA DI INDONESIA

2019 ◽  
Vol 6 (3) ◽  
pp. 264
Author(s):  
Muhammad Ridwan Lubis
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Technology ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Backpropagation Algorithm ◽  
Network Methods ◽  
Effective Decision ◽  
Artificial Neural

<p><em>The development of sports is an important role of a trainer and the management role that is in it. Determining the success of the trainer by using the criteria of experience, strategy and understanding of the trainer on the mental and spiritual conditions of each player is the first step in achieving success. Research using computational-based information technology is very much developed mainly by using neural network methods. Research using Artificial Neural Networks has been widely used, especially in the field of sports, especially football, including prediction results of soccer matches. In this study, the study of determining the success rate of soccer coaches as one of the advances in Indonesian football sports using the backpropagation algorithm was the goal of researchers to produce an effective decision in determining the success of football sports in Indonesia</em><em>.</em></p><p><em><strong>Keywords</strong></em><em>: Coach, Football, Artificial Neural Network, Backpropagation Indonesian</em> </p><p><em>Perkembangan olahraga merupakan peran penting dari seorang pelatih dan peran manajemen yang ada didalamnya. Menentukan tingkat keberhasilan pelatih dengan menggunakan kriteria pengalaman, strategi dan pemahaman pelatih terhadap kondisi mental dan spiritual setiap pemain merupakan langkah awal dalam mencapai keberhasilan. Penelitian dengan menggunakan teknologi informasi berbasis komputasi sangat banyak dikembangkan terutama dengan menggunakan metode neural network. Penelitian dengan menggunakan Jaringan Saraf Tiruan sudah banyak digunakan terutama  dalam bidang olahraga terutama sepakbola, diantaranya adalah Prediksi hasil pertandingan sepak bola. Pada penelitian ini, penelitian tetang menentukan tingkat keberhasilan pelatih sepakbola sebagai salah satu kemajuan olahraga sepakbola diindonesia menggunakan algoritma backpropagation menjadi tujuan peneliti  untuk menghasilkan sebuah keputusan yang efektif dalam menentukan keberhasilan olahraga sepakbola di indonesia.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>Pelatih, Sepakbola, Jaringan Saraf Tiruan, Backpropagation, Indonesia</em></p>

scholarly journals Jaringan Syaraf Tiruan Backpropogation dalam Memprediksi Jumlah Tilang di Kejaksaan Negeri Simalungun

2019 ◽  
Vol 1 ◽  
pp. 697
Author(s):  
Sony Irwanda ◽  
Jaya Tata Hardinata ◽  
Irfan Sudahri Damanik
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Matlab Software ◽  
Artificial Neural

This study predicts the number of ticketing by applying Artificial Neural Networks. The application uses the Backpropogation algorithm where the data entered is the number of tickets. Then an Artificial Neural Network is formed by determining the number of units per layer. After the network is formed training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, output units and network architecture. Testing is done with Matlab software.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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