PREDIKSI PERTUMBUHAN PENDUDUK PADA PROVINSI SUMATERA BARAT DENGAN MENGGUNAKAN METODE BACKPROPAGATION

2020 ◽  
Vol 7 (4) ◽  
pp. 216
Author(s):  
Hijrah Yanti Sitanggang ◽  
Vera Irma Delianti
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Population Growth ◽  
Population Growth Rate ◽  
Backpropagation Neural Networks ◽  
West Sumatra ◽  
Artificial Neural ◽  
The City

The problem of population is one of the problems in the Province of West Sumatra, especially in the City of Padang, Kota Bukitinggi, and the City of Payakumbuh which has a very fast population growth rate, this occurs due to several factors such as births, deaths, residents who come, and residents who leave. The highest population growth occurred in Padang City in 2018 amounting to 939,112 residents and the smallest population growth occurred in the City of Bukitinggi in 2014 amounting to 120,491 residents. The purpose of this study is to predict population growth that will occur in 2019 in the cities of Padang, Bukittinggi and Payakumbuh. The method used in this research is descriptive correlational by applying backpropagation neural networks. The application used is Matlab. Based on the problems and methods obtained, the predicted results in 2019 in Padang City amounted to 124,7150, Bukittinggi numbered 126,8040 and Payakumbuh totaled 128.7830.  Keywords: Artificial Neural Networks, Backpropagation, Matlab.

scholarly journals A Simulation of the Broiler Growth Rate Using Artificial Neural Networks

2015 ◽  
Vol 104 (1) ◽  
pp. 81-90
Author(s):  
Raquel Salazar ◽  
Fernando Rojano ◽  
Abraham Rojano
Keyword(s):  
Neural Networks ◽  
Growth Rate ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Redes Neuronales Artificiales: factores que determinan la cosecha de caña en la industria azucarera.// Artificial Neural Networks: factors that determine the cane harvest in a sugar industry.

Ciencia Unemi ◽  
2019 ◽  
Vol 12 (29) ◽  
pp. 36-50
Author(s):  
Italo Mendoza-Haro ◽  
Hiram Marquetti-Nodarse
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sugar Industry ◽  
Expert Assessment ◽  
Mathematical Tool ◽  
Development Environment ◽  
Thermal Oscillation ◽  
Historical Database ◽  
Artificial Neural ◽  
The City

La investigación muestra lo importante de las redes neuronales artificiales dentro de la industria azucarera, como una herramienta útil para la predicción del cultivo de la caña de azúcar, tomando como entradas la información climatológica: temperaturas máximas y mínimas, oscilación térmica, precipitaciones, heliofanía, humedad relativa, evaporación y hectáreas de los cultivos sembrados, para obtener una salida: toneladas de caña. Se desarrolló una herramienta de trabajo predictiva con resultados confiables, comparados con métodos tradicionales utilizados, como los aforos de expertos para la cosecha de la caña de azúcar. Se analizó la base de datos histórica de la organización, mediante un software MATLAB, herramienta matemática, que ofrece un entorno de desarrollo integrado (IDE) con lenguaje M de programación propio. La investigación se desarrolló en Compañía Azucarera Valdez S.A. Ubicada en la Ciudad de Milagro-Provincia del Guayas-Ecuador.AbstractThe research shows the importance of artificial neural networks within the sugar industry, as a useful tool for the prediction of the cultivation of sugarcane, taking as input the climatological information: maximum and minimum temperatures, thermal oscillation, rainfall, heliophany, relative humidity, evaporation and hectares of crops planted, to obtain tons of cane as an output. A predictive work tool with reliable results was developed, compared with traditional methods used, such as expert assessment for sugarcane harvesting. The historical database of the organization was analyzed through MATLAB software, a mathematical tool which offers an integrated development environment (IDE) with its own M programming language. The research was developed at Compañía Azucarera Valdez S.A. located in the City of Milagro-Province of Guayas-Ecuador.

scholarly journals The Use of Fuzzy BackPropagation Neural Networks for the Early Diagnosis of Hypoxic Ischemic Encephalopathy in Newborns

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Liu Li ◽  
Huo Liqing ◽  
Lu Hongru ◽  
Zhang Feng ◽  
Zheng Chongxun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Early Diagnosis ◽  
Recognition Rate ◽  
Hypoxic Ischemic Encephalopathy ◽  
Correct Recognition ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
Correct Recognition Rate ◽  
Ischemic Encephalopathy

Objective. To establish an early diagnostic system for hypoxic ischemic encephalopathy (HIE) in newborns based on artificial neural networks and to determine its feasibility.Methods. Based on published research as well as preliminary studies in our laboratory, multiple noninvasive indicators with high sensitivity and specificity were selected for the early diagnosis of HIE and employed in the present study, which incorporates fuzzy logic with artificial neural networks.Results. The analysis of the diagnostic results from the fuzzy neural network experiments with 140 cases of HIE showed a correct recognition rate of 100% in all training samples and a correct recognition rate of 95% in all the test samples, indicating a misdiagnosis rate of 5%.Conclusion. A preliminary model using fuzzy backpropagation neural networks based on a composite index of clinical indicators was established and its accuracy for the early diagnosis of HIE was validated. Therefore, this method provides a convenient tool for the early clinical diagnosis of HIE.

scholarly journals Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)

2020 ◽  
Vol 11 (29) ◽  
pp. 114-128
Author(s):  
Ali Mahdavi ◽  
Mohsen Najarchi ◽  
Emadoddin Hazaveie ◽  
Seyed Mohammad Mirhosayni Hazave ◽  
Seyed Mohammad Mahdai Najafizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Correlation Coefficients ◽  
Mean Square ◽  
Artificial Neural ◽  
The City

Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respectively for the optimal neural network model. Genetic programming has been more accurate than artificial neural networks and is recommended as a good way to accurately predict.

scholarly journals Identifying Olive (Olea europaea) Cultivars Using Artificial Neural Networks

1999 ◽  
Vol 124 (5) ◽  
pp. 527-531 ◽  
Author(s):  
S. Mancuso ◽  
F.P. Nicese
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Olea Europaea ◽  
Mediterranean Basin ◽  
Training Phase ◽  
Backpropagation Neural Networks ◽  
Artificial Neural ◽  
The Mediterranean Basin ◽  
Olea Europaéa

Backpropagation neural networks (BPNNs) were used to distinguish among 10 olive (Olea europaea L.) cultivars, originating throughout the Mediterranean basin. Identification was performed on the basis of 17 phyllometric parameters resulting from image analysis. Different BPNN architectures were attempted and best performance was achieved using a 17 × 20 × 10 BPNN. Networks were tested with sets of phyllometric parameters not involved in the training phase. Results enabled identification with certainty all cultivars tested.

scholarly journals PROPAGASI BALIK MENENTUKAN PREDIKSI PRODUKSI USAHA SONGKET SILUNGKANG KOTA SAWAHLUNTO

2018 ◽  
Vol 6 (2) ◽  
pp. 69
Author(s):  
Rima Liana Gema ◽  
Devia Kartika
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Small And Medium Enterprises ◽  
Cell System ◽  
City Government ◽  
Export Market ◽  
West Sumatra ◽  
Artificial Neural ◽  
The Government ◽  
Medium Enterprises

Artificial Neural Networks is a computational paradigm in which the way it works mimics the biological nerve cell system based on the characteristics of the function of the human brain. One method used in Artificial Neural Networks is a backpropagation algorithm that is widely used, especially in dealing with the problem of identification, prediction, recognition of complex patterns because this method is able to predict what will happen in the future based on patterns that existed in the past. Songket is one of the works of skilled hands of the original Silungkang craftsmen, Sawahlunto City, West Sumatra who have varied and unique patterns and motifs. Sawahlunto City Government, West Sumatra prioritizes the development of Silungkang songket craft business, which is a regional specialty, to enter the export market. At the initial stage, the regional government's priority is to increase the production of crafters by facilitating the development of micro, small and medium enterprises (MSMEs), especially those engaged in songket, to continue to be developed by improving quality and creativity. The city of Sawahlunto can help several parties such as the government, micro, small and medium enterprises in making good handling and decision making efforts to increase the production of Songket Silungkang MSMEs in Sawahlunto City.  

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