scholarly journals Pemetaan Promosi dalam Penjaringan Calon Mahasiswa Menggunakan Algoritma Backpropagation

2020 ◽  
Vol 2 (1) ◽  
pp. 21-26
Author(s):  
Mhd Hary Kurniawan ◽  
Sarjon Defit ◽  
Yuhandri Yunus
Keyword(s):  
Neural Network ◽  
Learning Rate ◽  
Data Accuracy ◽  
Student Data ◽  
Input Layer ◽  
Excellent Method ◽  
Complex Patterns ◽  
Hidden Layer

Promotion requires a large fee if it is not targeted when doing it. Backpropagation is an excellent method of dealing with the problem of recognizing complex patterns. Backprogation neural network each unit in the input layer is connected to each unit in the hidden layer. Student data from 2014 to 2018 is a comparison point. The results of testing of this method are calculations using a sample value of 5 years before using a comparative value of 2014 to 2018 totaling 602 data. This research uses 5-5-1 architecture, epoch 2000 and learning rate so that the data accuracy reaches 71% with an error value of 0.0099. The results of this study are 16 districts that become promotion recommendations. Ordering of forecasting the highest number of students to the smallest number of students, so it can be concluded that this method is very useful in mapping promotions.

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

2017 ◽  
Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Centrifugal Pumps ◽  
Output Layer ◽  
Sample Data ◽  
Input Layer ◽  
Flow Induced Noise ◽  
Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

scholarly journals Analisis Pengenalan Bendera Negara Rumpun Melayu Menggunakan Metode Feed Forward Neural Network

2020 ◽  
Vol 10 (2) ◽  
pp. 144-152
Author(s):  
H Santoso ◽  
D Murdianto
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Learning Rate ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Brunei Darussalam ◽  
Hidden Layer

Telah dilakukan analisis pada sistem pengenalan gambar empat buah bendera negara rumpun melayu secara digital. Negara tersebut adalah Indonesia, Malaysia, Singapura, dan Brunei Darussalam. Tujuan dari penelitian ini adalah sebagai bentuk langkah awal dalam melatih sistem Artificial Intelligence (Kecerdasan Buatan) dalam membedakan empat buah negara rumpun melayu berdasarkan warna dan motif bendera pada sebuah peta digital. Proses analisis dan pelatihan pengenalan bendera tersebut menggunakan metode Feed Forward Neural Network (FFNN). Hasilnya menunjukkan bahwa penggunaan 4 buah Hidden Layer, serta penggunaan Learning Rate 0,5 memberikan kemampuan pengenalan citra bendera secara tepat dengan persentase akurasi rata-rata mencapai 74,15%.

scholarly journals Identifikasi Penyakit Diabetes Millitus Menggunakan Jaringan Syaraf Tiruan Dengan Metode Perambatan-Balik (Backpropagation)

2018 ◽  
Author(s):  
Sutedi Sutedi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Accuracy Rate ◽  
Layer 2 ◽  
Input Layer ◽  
Hidden Layer ◽  
Diabetes Melitus

Diabetes Melitus (DM) is dangerous disease that affect many of the variouslayer of work society. This disease is not easy to accurately recognized by thegeneral society. So we need to develop a system that can identify accurately. Systemis built using neural networks with backpropagation methods and the functionactivation sigmoid. Neural network architecture using 8 input layer, 2 output layerand 5 hidden layer. The results show that this methods succesfully clasifies datadiabetics and non diabetics with near 100% accuracy rate.

scholarly journals Artificial Neural Network dalam Menentukan Grading Histopatologi Kanker Payudara

2017 ◽  
Vol 19 (2) ◽  
pp. 176
Author(s):  
Agoes Santika Hyperastuty
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Rate ◽  
Gray Level ◽  
Artificial Neural ◽  
Hidden Layer

Abstrak Kanker payudara adalah jenis tumor ganas utama yang diamati pada wanita dan pengobatan yang efektif tergantung pada diagnosis awalnya. Standar emas pemeriksaan kanker payudara adalah pemeriksaan histopatologis sel kanker. Penentuan kadar pada kanker payudara ditentukan oleh tiga faktor: pleomorfik, pembentukan tubular dan mitosis sel. Dalam tulisan ini mengacu pada formasi pleumorfic dan tubular oleh gambar histopatologi sel payudara. Sistem yang diusulkan terdiri dari empat langkah utama: preprocessing, segmentation, ekstrasi fitur dan identifikasi. Pada proses segmentasi  menggunakan metode K-Means Clustering yaitu mengelompokkan data menurut kesamaan warna dan bentuk. Hasil dari K-Means tersebut berupa matrik.  Ekstraksi fitur menggunakan Gray level Cooccurence Matrix (GLCM) yaitu  tingkat keabuan masing-masing citra yang dilihat dari  4 fiturnya adalah kontras, energi, entropi dan homogenitas. Langkah terakhir adalah identifikasi menggunakan Backpropagation. Beberapa parameter penting akan divariasikan dalam proses ini seperti learning rate dan jumlah node pada hidden layer. Hasil penelitian menunjukkan bahwa fitur ekstraksi dalam 4 fitur adalah akurasi terbaik berdasarkan kelas 81,1% dan khususnya ketepatannya adalah 80%.Kata kunci—Histopatologic breast cancer, kmeans, GLCM, Backpropagation

scholarly journals Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network

2021 ◽  
Vol 7 (2) ◽  
pp. 108-118
Author(s):  
Erwin Yudi Hidayat ◽  
Raindy Wicaksana Hardiansyah ◽  
Affandy Affandy
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Application Programming Interface ◽  
Learning Rate ◽  
Application Programming ◽  
Hidden Layer ◽  
Programming Interface

Dalam menaikkan kinerja serta mengevaluasi kualitas, perusahaan publik membutuhkan feedback dari masyarakat / konsumen yang bisa didapat melalui media sosial. Sebagai pengguna media sosial Twitter terbesar ketiga di dunia, tweet yang beredar di Indonesia memiliki potensi meningkatkan reputasi dan citra perusahaan. Dengan memanfaatkan algoritma Deep Neural Network (DNN), neural network yang tersusun dari layer yang jumlahnya lebih dari satu, didapati hasil analisa sentimen pada Twitter berbahasa Indonesia menjadi lebih baik dibanding dengan metode lainnya. Penelitian ini menganalisa sentimen melalui tweet dari masyarakat Indonesia terhadap sejumlah perusahaan publik dengan menggunakan DNN. Data Tweet sebanyak 5504 record didapat dengan melakukan crawling melalui Application Programming Interface (API) Twitter yang selanjutnya dilakukan preprocessing (cleansing, case folding, formalisasi, stemming, dan tokenisasi). Proses labeling dilakukan untuk 3902 record dengan memanfaatkan aplikasi Sentiment Strength Detection. Tahap pelatihan model dilakukan menggunakan algoritma DNN dengan variasi jumlah hidden layer, susunan node, dan nilai learning rate. Eksperimen dengan proporsi data training dan testing sebesar 90:10 memberikan hasil performa terbaik. Model tersusun dengan 3 hidden layer dengan susunan node tiap layer pada model tersebut yaitu 128, 256, 128 node dan menggunakan learning rate sebesar 0.005, model mampu menghasilkan nilai akurasi mencapai 88.72%. 

scholarly journals Kp forecasting with a recurrent neural network

2019 ◽  
Vol 9 ◽  
pp. A19 ◽  
Author(s):  
Ernest Scott Sexton ◽  
Katariina Nykyri ◽  
Xuanye Ma
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Recurrent Neural Network ◽  
Historical Data ◽  
Time Step ◽  
Prediction Horizon ◽  
Sigma B ◽  
Input Layer ◽  
Solar Wind Parameters ◽  
Hidden Layer

In an effort to forecast the planetary Kp-index beyond the current 1-hour and 4-hour predictions, a recurrent neural network is trained on three decades of historical data from NASA’s Omni virtual observatory and forecasts Kp with a prediction horizon of up to 24 h. Using Matlab’s neural network toolbox, the multilayer perceptron model is trained on inputs comprised of Kp for a given time step as well as from different sets of the following six solar wind parameters, Bz, n, V, |B|, σB and $ {\sigma }_{{B}_z}$. The purpose of this study was to test which combination of the solar wind and Interplanetary Magnetic Field (IMF) parameters used for training gives the best performance as defined by correlation coefficient, C, between the predicted and actually measured Kp values and Root Mean Square Error (RMSE). The model consists of an input layer, a single nonlinear hidden layer with 28 neurons, and a linear output layer that predicts Kp up to 24 h in advance. For 24 h prediction, the network trained on Bz, n, V, |B|, σB performs the best giving C in the range from 0.8189 (for 31 predictions) to 0.8211 (for 9 months of predictions), with the smallest RMSE.

Deep Learning in the Healthcare Industry

2020 ◽  
pp. 220-245 ◽  
Author(s):  
Zahra A. Shirazi ◽  
Camila P. E. de Souza ◽  
Rasha Kashef ◽  
Felipe F. Rodrigues
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Input Data ◽  
Time Series Data ◽  
Short Term Memory ◽  
Healthcare Industry ◽  
Neural Network Models ◽  
Input Layer ◽  
Hidden Layer

Artificial Neural networks (ANN) are composed of nodes that are joint to each other through weighted connections. Deep learning, as an extension of ANN, is a neural network model, but composed of different categories of layers: input layer, hidden layers, and output layers. Input data is fed into the first (input) layer. But the main process of the neural network models is done within the hidden layers, ranging from a single hidden layer to multiple ones. Depending on the type of model, the structure of the hidden layers is different. Depending on the type of input data, different models are applied. For example, for image data, convolutional neural networks are the most appropriate. On the other hand, for text or sequential and time series data, recurrent neural networks or long short-term memory models are the better choices. This chapter summarizes the state-of-the-art deep learning methods applied to the healthcare industry.

Selecting the architecture of a class of back-propagation neural networks used as approximators

1997 ◽  
Vol 11 (1) ◽  
pp. 33-44 ◽  
Author(s):  
William C. Carpenter ◽  
Margery E. Hoffman
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Back Propagation ◽  
Region Of Interest ◽  
Input Output ◽  
Advantages And Disadvantages ◽  
Back Propagation Neural Networks ◽  
Input Layer ◽  
Hidden Layer

AbstractThis paper examines the architecture of back-propagation neural networks used as approximators by addressing the interrelationship between the number of training pairs and the number of input, output, and hidden layer nodes required for a good approximation. It concentrates on nets with an input layer, one hidden layer, and one output layer. It shows that many of the currently proposed schemes for selecting network architecture for such nets are deficient. It demonstrates in numerous examples that overdetermined neural networks tend to give good approximations over a region of interest, while underdetermined networks give approximations which can satisfy the training pairs but may give poor approximations over that region of interest. A scheme is presented that adjusts the number of hidden layer nodes in a neural network so as to give an overdetermined approximation. The advantages and disadvantages of using multiple output nodes are discussed. Guidelines for selecting the number of output nodes are presented.

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