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Optimization forecasting using back-propagation algorithm

Authors :
Putu Doddy Heka Ardana
Rima Herlina S Siburian
Budi Raharjo
Nurul Farida
Robbi Rahim
Purwo Subekti
Source :
Journal of Applied Engineering Science. 19:1083-1089
Publication Year :
2021
Publisher :
Centre for Evaluation in Education and Science (CEON/CEES), 2021.

Abstract

The purpose of this study was to evaluate the back-propagation model by optimizing the parameters for the prediction of broiler chicken populations by provinces in Indonesia. Parameter optimization is changing the learning rate (lr) of the backpropagation prediction model. Data sourced from the Directorate General of Animal Husbandry and Animal Health processed by the Central Statistics Agency (BPS). Data is the population of Broiler Chickens from 2017 to 2019 (34 records). The analysis process uses the help of RapidMiner software. Data is divided into 2 parts, namely training data (2017-2018) and testing data (2018-2019). The backpropagation model used is 1-2-1; 1-25-1 and 1-45-1 with a learning rate (0.1; 0.01; 0.001; 0.2; 0.02; 0.002; 0.3; 0.03; 0.003). From the three models tested, the 1-45-1 model (lr = 0.3) is the best model with Root Mean Squared Error = 0.028 in the training data. With this model, the prediction results obtained with an accuracy value of 91% and Root Mean Squared Error = 0.00555 in the testing data.

Details

ISSN :
18213197 and 14514117
Volume :
19
Database :
OpenAIRE
Journal :
Journal of Applied Engineering Science
Accession number :
edsair.doi...........897c276a986569f7fc7af97a4d644483
Full Text :
https://doi.org/10.5937/jaes0-30175