scholarly journals Jaringan Syaraf Tiruan dalam Memprediksi Penjualan Air Minum dalam Kemasan dengan Metode Backpropogation

2019 ◽  
Vol 1 ◽  
pp. 512
Author(s):  
Hafiz Pratama ◽  
Poningsih Poningsih ◽  
Jalaluddin Jalaluddin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Bottled Water ◽  
Accuracy Rate ◽  
Matlab Software ◽  
Artificial Neural

This study predicts the sale of bottled water by applying Artificial Neural Networks. The application uses the Backpropogation Algorithm where the data entered is the number of sales, then Artificial Neural Networks are formed by determining the number of each 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. Predictions with the best accuracy use 3-10-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using architecture 3-40-1 with an accuracy rate of 33%.

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 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 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 Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

scholarly journals Structural Features for Furan-Derived Fruity and Meaty Aroma Impressions

2016 ◽  
Vol 11 (10) ◽  
pp. 1934578X1601101
Author(s):  
Bettina Wailzer ◽  
Johanna Klocker ◽  
Peter Wolschann ◽  
Gerhard Buchbauer
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Structural Features ◽  
Classification Rule ◽  
Classification Rate ◽  
Furan Derivatives ◽  
Information Index ◽  
The Neural Network ◽  
Artificial Neural

Furan derivatives are part of nearly all food aromas. They are mainly formed by thermal degradation of carbohydrates and ascorbic acid and from sugar-amino acid interactions during food processing. Caramel-like, sweet, fruity, nutty, meaty, and burnt odor impressions are associated with this class of compounds. In the presented work, structure-activity relationship (SAR) investigations are performed on a series of furan derivatives in order to find structural subunits, which are responsible for the particular characteristic flavors. Therefore, artificial neural networks are applied on a set of 35 furans with the aroma categories “meaty” or “fruity” to calculate a classification rule and class boundaries for these two aroma impressions. By training a multilayer perceptron network architecture with a backpropagation algorithm, a correct classification rate of 100% is obtained. The neural network is able to distinguish between the two studied groups by using the following significant descriptors as inputs: number of sulfur atoms, Looping Centric Information Index, Folding Degree Index and Petitjean Shape Indices. Finally, the results clearly demonstrate that artificial neural networks are successful tools to investigate non-linear qualitative structure-odor relationships of aroma compounds.

FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

2004 ◽  
Vol 03 (01) ◽  
pp. 145-165 ◽  
Author(s):  
WEI HUANG ◽  
K. K. LAI ◽  
Y. NAKAMORI ◽  
SHOUYANG WANG
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Network Architecture ◽  
Future Research ◽  
Promising Alternative ◽  
Practical Applications ◽  
Future Research Directions ◽  
Artificial Neural ◽  
Input Variables

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

scholarly journals EARTHQUAKE FORECASTING USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol XLII-5 ◽  
pp. 823-827 ◽  
Author(s):  
A. Bhatia ◽  
S. Pasari ◽  
A. Mehta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Short Term Memory ◽  
Economic Loss ◽  
Earthquake Forecasting ◽  
Accuracy Evaluation ◽  
Neural Network Architecture ◽  
Forecasting System ◽  
Artificial Neural

<p><strong>Abstract.</strong> Earthquake is one of the most devastating natural calamities that takes thousands of lives and leaves millions more homeless and deprives them of the basic necessities. Earthquake forecasting can minimize the death count and economic loss encountered by the affected region to a great extent. This study presents an earthquake forecasting system by using Artificial Neural Networks (ANN). Two different techniques are used with the first focusing on the accuracy evaluation of multilayer perceptron using different inputs and different set of hyper-parameters. The limitation of earthquake data in the first experiment led us to explore another technique, known as nowcasting of earthquakes. The nowcasting technique determines the current progression of earthquake cycle of higher magnitude earthquakes by taking into account the number of smaller earthquake events in the same region. To implement the nowcasting method, a Long Short Term Memory (LSTM) neural network architecture is considered because such networks are one of the most recent and promising developments in the time-series analysis. Results of different experiments are discussed along with their consequences.</p>

ANALYSIS OF DOCUMENTS RANKING ALGORITHMS BASED ON THE ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Author(s):  
Andrew Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Search Algorithm ◽  
Network Algorithms ◽  
Text Document ◽  
Ranking Algorithms ◽  
Selection For ◽  
Artificial Neural

The object of this study is to analyse the effectiveness of document ran­ king algorithms in search engines that use artificial neural networks to match the texts. The purpose of the study was to inspect a neural network model of text document ran­ king that uses clustering, factor analysis, and multi-layered network architecture. The work of neural network algorithms was compared with the standard statistical search algorithm OkapiBM25. The result of the study is to evaluate the effectiveness of the use of particular models and to recommend model selection for specific datasets.

A Review: Artificial Neural Network

2018 ◽  
Vol 8 (04) ◽  
Author(s):  
Jaspreet Kaur ◽  
Prabhpreet Kaur
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
High Speed ◽  
Parallel Architecture ◽  
Research Area ◽  
Neural Nets ◽  
Optimization Function ◽  
Artificial Neural

Neural networks are those information processing systems, which are built and performed to design the human brain. The main objective of the neural network research is to evolve a computational device for representing the brain to perform various evaluating tasks at a faster rate than the traditional systems. Neural networks are latest method of programming computers. Several programs that utilize neural nets are also proficient Neural networks have appeared in the past few years as an area of different opportunity for research area, development and application to a variety of real world problems because of their rapid feedback and parallel architecture. Artificial neural networks perform various tasks such as pattern-matching and classification, optimization function and data clustering. These tasks are very difficult for traditional for implementation of artificial neural networks, high-speed digital computers are used, which makes the simulation if neural processes feasible. This paper provides a broad overview of the wide array of artificial neural networks, some of the most commonly network architecture and various learning processes currently in use in research. Also concisely describes several applications of it.

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