scholarly journals Analisis Metode Backpropagation Memprediksi Penerimaan Santri/Wati di Pondok Pesantren Modern Al-Kautsar

2021 ◽  
Vol 6 (1) ◽  
pp. 140
Author(s):  
Parta Wijaya ◽  
Rahmat Widiya Sembiring ◽  
Saifullah S
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Direct Observation ◽  
Boarding School ◽  
Observation Data ◽  
Process Data ◽  
Target Value ◽  
Backpropagation Method ◽  
Artificial Neural

The development of technology today with the existence of artificial intelligence, conducted research to prove the Backpropagation method can predict students / wati in the modern boarding school Al-Kautsar. Artificial neural network is a method that is able to perform mathematical processes in predicting santri / wati. Bacpropagation algorithm is used to process data that is implemented with Matlab. Where data is collected through direct observation. Data is grouped by majors. The results obtained from the Matlab test performace and epoch values of each architecture are not the same as the results of the tests are displayed in the form of a graph comparing the target value with the research and testing process. The results of this study provide information on the modern Al-Kautsar boarding school on the number of registrants in 2020

scholarly journals Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data

2021 ◽  
Vol 004 (02) ◽  
pp. 115-126
Author(s):  
Aprianto Nomleni ◽  
Ery Suhartanto ◽  
Donny Harisuseno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tropical Rainfall Measuring Mission ◽  
Polynomial Equation ◽  
Observation Data ◽  
Artificial Neural Network Analysis ◽  
Backpropagation Method ◽  
Discharge Model ◽  
Artificial Neural ◽  
The Relationship

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK IN PREDICTING BIRTH RATE IN BATAM CITY USING BACKPROPAGATION METHOD

JURTEKSI ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 85-94
Author(s):  
Muhammad Jufri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Family Planning ◽  
Population Growth ◽  
Birth Rate ◽  
The Past ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
The City

Abstract: The population growth in Indonesia is increasing rapidly every year, so to help the government control the population growth through family planning programs, especially in the city of Batam. This study explains and describes one of the Artificial Terms Network methods, namely Backpropagation, where this method can predict what will happen in the future using data and information in the past. This study aims to predict the birth rate in the city of Batam to help the government with the family planning program. The data used is the annual data on the number of births in the city of Batam in 2016-2020 at The Civil Registry Office. To facilitate the analysis of research data, the data were tested using Matlab R2015b. In this study, the training process was carried out using 3 network architectures, namely 4-10-1, 5-18-1, and 4-43-1. Of these 3 architectures, the best is the 4-43-1 architecture with an accuracy rate of 91% and an MSE value of 0.0012205. The Backpropagation method can predict the amount of population growth in the city of Batam based on existing data in the past.           Keywords: artificial neural network; backpropagation; prediction   Abstrak: Pertumbuhan jumlah penduduk diindonesia yang setiap tahun meningkat dengan pesat, maka untuk membantu pemerintah mengendalikan jumlah pertumbuhan penduduk melalui program keluarga berencana khususnya dikota Batam. Penelitian ini  menjelaskan dan memaparkan tentang salah satu metode Jaringan Syarat Tiruan yaitu Backpropagation, dimana metode ini dapat memprediksi apa yang akan terjadi masa yang akan datang dengan menggunakan data dan informasi dimasa lalu. Penelitian ini bertujuan untuk memprediksi tingkat kelahiran di kota Batam sehingga membatu pemerintah untuk perencanaan keluarga berencana. Data yang digunakan yaitu data tahunan jumlah kelahiran di kota Batam pada tahun 2016-2020 pada Dinas Kependudukan dan Catatan Sipil. Untuk mempermudah analisis data penelitian maka, data diuji menggunakan Matlab R2015b. Pada penelitian ini dilakukan proses pelatihan menggunakan  3 arsitektur jaringan yaitu 4-10-1, 5-18-1, dan 4-43-1. Dari ke-3 arsitektur ini yang terbaik adalah arsitektur 4-43-1 dengan tingkat akurasi sebesar 91% dan nilai MSE 0,0012205. Metode backpropagation mampu memprediksi jumlah pertumbuhan penduduk di kota Batam berdasarkan data yang ada dimasa lalu. Kata kunci: backpropagation; jaringan syaraf tiruan; prediksi 

scholarly journals PENDUGAAN FLUKS PANAS DAN EVAPOTRANSPIRASI DENGAN JARINGAN SYARAF TIRUANHEAT FLUX AND EVAPOTRANSPIRATION ESTIMATION USING ARTIFICIAL NEURAL NETWORK

Agromet ◽  
2011 ◽  
Vol 25 (1) ◽  
pp. 24
Author(s):  
Satyanto Krido Saptomo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Latent Heat ◽  
Measurement Data ◽  
Heat Fluxes ◽  
Similar Process ◽  
Observation Data ◽  
Data Set ◽  
Dissipation Process ◽  
Artificial Neural

<em>Artificial neural network (ANN) approach was used to model energy dissipation process into sensible heat and latent heat (evapotranspiration) fluxes. The ANN model has 5 inputs which are leaf temperature T<sub>l</sub>, air temperature T<sub>a</sub>, net radiation R<sub>n</sub>, wind speed u<sub>c</sub> and actual vapor pressure e<sub>a</sub>. Adjustment of ANN was conducted using back propagation technique, employing measurement data of input and output parameters of the ANN. The estimation results using the adjusted ANN shows its capability in resembling the heat dissipation process by giving outputs of sensible and latent heat fluxes closed to its respective measurement values as the measured input values are given.  The ANN structure presented in this paper suits for modeling similar process over vegetated surfaces, but the adjusted parameters are unique. Therefore observation data set for each different vegetation and adjustment of ANN are required.</em>

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

scholarly journals Application of Wavelet Neural Network in Building Settlement Prediction

2020 ◽  
Vol 198 ◽  
pp. 03014
Author(s):  
Ruijie Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Building Design ◽  
Wavelet Neural Network ◽  
Deformation Monitoring ◽  
Polynomial Method ◽  
Observation Data ◽  
Bp Artificial Neural Network ◽  
Artificial Neural

Deformation monitoring, as a key link of information construction, runs through the entire process of the building design period, construction period and operation period[1]. At present, more mature static prediction methods include hyperbolic method, power polynomial method and Asaoka method. But these methods have many problems and shortcomings. In this paper, based on the characteristics of building foundation settlement and the methods widely discussed in this field, a wavelet neural network model with self-learning, self-organization and good nonlinear approximation ability is applied to the prediction problem of building settlement[2]. Using comparative analysis and induction method. The 20-phase monitoring data representing the deformation monitoring points of different settlement states of the line tunnel, using the observation data sequence of the first 15 phases respectively to take the cumulative settlement and interval settlement as training samples, through the BP artificial neural network and the improved wavelet neural network, for the last five periods Predict the observed settlement.Through the comparison, it is found that whether the interval settlement or the cumulative settlement is used, the prediction results of the wavelet neural network are basically better than the prediction results of the BP artificial neural network, and the number of trainings is greatly reduced. The adaptive prediction of the wavelet neural network. The ability is particularly obvious, and the prediction accuracy is significantly improved. Therefore, it can be shown that the wavelet neural network is indeed used in the settlement monitoring and forecast of buildings, which can obtain higher prediction accuracy and better prediction effect, and is a prediction method with great development potential.

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Tamer Moussa ◽  
Salaheldin Elkatatny ◽  
Mohamed Mahmoud ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Well Log ◽  
Well Test ◽  
Log Data ◽  
Hydrocarbon Reservoir ◽  
Reservoir Permeability ◽  
Artificial Neural ◽  
Core Permeability

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

Business viability of potential software projects using artificial neural network

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
O. Okolo ◽  
B.Y Baha
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Intelligent Systems ◽  
Project Selection ◽  
Business Value ◽  
Synthetic Approach ◽  
Software Project ◽  
Unseen Data ◽  
Artificial Neural

Selection of a software project is a critical decision. This selection involves prediction to ascertain a project that provides the best business value to the organization. The process of selection is carefully undertaken to optimize scarce resources available, which makes it impossible to simultaneously invest in all business ideas and systems. The current traditional method of software selection does not consider risk factors among the many variables necessary to predict a project that could provide the best business value. More so, the current method such as an artificial intelligence approach, where project managers use more robust models to make predictions have not received the needed attention in developing models for software project selection. This research applied a branch of Artificial Intelligence called Artificial Neural Network to classify projects into three levels. The research designed an artificial neural network of four inputs, one hidden layer with twenty-seven (27) neurons, and three outputs. Keras, a python deep learning library that runs on a theano background was used to implement the model. This research used a secondary dataset, which was enhanced by the synthetic approach, to make the required data features needed in machine learning applications. Backpropagation Algorithm enabled the model to train and learn from the data, and K-fold cross-validation was used to measure the accuracy of the model on unseen data. The results of the simulation showed that the model performed up to 98.67% accuracy with a standard deviation of 2.6% performance on unseen data. The research concludes that using the artificial neural network for software project selection unleashes a new vista of opportunities in artificial i ntelligence where intelligent systems are developed based on robust models from data forproject selection.Keywords: Artificial Neural Network, Project selection, Machine LearningVol. 26, No. 1, June 2019

scholarly journals Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

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