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Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia

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@article{IJASEIT9400,
   author = {Ifran Lindu Mahargya and Guruh Fajar Shidik},
   title = {Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {6},
   year = {2020},
   pages = {2261--2269},
   keywords = {farmer term of trade; FTT; support vector machine; neural network; multi linear regression; genetic algorithm.},
   abstract = {The welfare of farmers is a strategic problem in Indonesia. The Farmer term of the trade (FTT) is one indicator to measure the welfare of farmers. FTT is a measurement of the comparison of the price index received by farmers (It) with the price index paid by farmers (Ib). Some models for FTT prediction on previous research are using ANN, SVM, MLR, Markov Chain - Predictive Probabilistic Architecture Modeling Framework (P2AMF), Singular Spectrum Analysis (SSA) - ARIMA and ANN-PSO. Previous FFT research in 2018 used three prediction methods, namely using the ANN, SVM and MLR algorithms with the best RMSE being 0.00098. Then in 2019, FTT research was followed by optimization of the ANN parameters using PSO for weighting (ANN-PSO) and obtaining the best RMSE was 0.00062. This study evaluates the robustness of the prediction models of FTTs in the Central Java region using SVM. Then proceed with increasing SVM prediction accuracy using GA (SVM-GA). SVM-GA has resulted in an increase in FTT prediction accuracy. This study has found that the SVM method has better robustness than the ANN method. The development of research related to the accuracy for the FTT prediction model in the Central Java Province has increased, starting from 2018 with RMSE 0.00098; in 2019 with RMSE 0.00062 and the results of this study resulted in the best RMSE of 0.00037.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9400},
   doi = {10.18517/ijaseit.10.6.9400}
}

EndNote

%A Mahargya, Ifran Lindu
%A Shidik, Guruh Fajar
%D 2020
%T Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia
%B 2020
%9 farmer term of trade; FTT; support vector machine; neural network; multi linear regression; genetic algorithm.
%! Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia
%K farmer term of trade; FTT; support vector machine; neural network; multi linear regression; genetic algorithm.
%X The welfare of farmers is a strategic problem in Indonesia. The Farmer term of the trade (FTT) is one indicator to measure the welfare of farmers. FTT is a measurement of the comparison of the price index received by farmers (It) with the price index paid by farmers (Ib). Some models for FTT prediction on previous research are using ANN, SVM, MLR, Markov Chain - Predictive Probabilistic Architecture Modeling Framework (P2AMF), Singular Spectrum Analysis (SSA) - ARIMA and ANN-PSO. Previous FFT research in 2018 used three prediction methods, namely using the ANN, SVM and MLR algorithms with the best RMSE being 0.00098. Then in 2019, FTT research was followed by optimization of the ANN parameters using PSO for weighting (ANN-PSO) and obtaining the best RMSE was 0.00062. This study evaluates the robustness of the prediction models of FTTs in the Central Java region using SVM. Then proceed with increasing SVM prediction accuracy using GA (SVM-GA). SVM-GA has resulted in an increase in FTT prediction accuracy. This study has found that the SVM method has better robustness than the ANN method. The development of research related to the accuracy for the FTT prediction model in the Central Java Province has increased, starting from 2018 with RMSE 0.00098; in 2019 with RMSE 0.00062 and the results of this study resulted in the best RMSE of 0.00037.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9400
%R doi:10.18517/ijaseit.10.6.9400
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 6
%@ 2088-5334

IEEE

Ifran Lindu Mahargya and Guruh Fajar Shidik,"Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 6, pp. 2261-2269, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.6.9400.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mahargya, Ifran Lindu
AU  - Shidik, Guruh Fajar
PY  - 2020
TI  - Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 6
Y2  - 2020
SP  - 2261
EP  - 2269
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - farmer term of trade; FTT; support vector machine; neural network; multi linear regression; genetic algorithm.
N2  - The welfare of farmers is a strategic problem in Indonesia. The Farmer term of the trade (FTT) is one indicator to measure the welfare of farmers. FTT is a measurement of the comparison of the price index received by farmers (It) with the price index paid by farmers (Ib). Some models for FTT prediction on previous research are using ANN, SVM, MLR, Markov Chain - Predictive Probabilistic Architecture Modeling Framework (P2AMF), Singular Spectrum Analysis (SSA) - ARIMA and ANN-PSO. Previous FFT research in 2018 used three prediction methods, namely using the ANN, SVM and MLR algorithms with the best RMSE being 0.00098. Then in 2019, FTT research was followed by optimization of the ANN parameters using PSO for weighting (ANN-PSO) and obtaining the best RMSE was 0.00062. This study evaluates the robustness of the prediction models of FTTs in the Central Java region using SVM. Then proceed with increasing SVM prediction accuracy using GA (SVM-GA). SVM-GA has resulted in an increase in FTT prediction accuracy. This study has found that the SVM method has better robustness than the ANN method. The development of research related to the accuracy for the FTT prediction model in the Central Java Province has increased, starting from 2018 with RMSE 0.00098; in 2019 with RMSE 0.00062 and the results of this study resulted in the best RMSE of 0.00037.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9400
DO  - 10.18517/ijaseit.10.6.9400

RefWorks

RT Journal Article
ID 9400
A1 Mahargya, Ifran Lindu
A1 Shidik, Guruh Fajar
T1 Improvement Support Vector Machine Using Genetic Algorithm in Farmers Term of Trade Prediction at Central Java Indonesia
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 6
YR 2020
SP 2261
OP 2269
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 farmer term of trade; FTT; support vector machine; neural network; multi linear regression; genetic algorithm.
AB The welfare of farmers is a strategic problem in Indonesia. The Farmer term of the trade (FTT) is one indicator to measure the welfare of farmers. FTT is a measurement of the comparison of the price index received by farmers (It) with the price index paid by farmers (Ib). Some models for FTT prediction on previous research are using ANN, SVM, MLR, Markov Chain - Predictive Probabilistic Architecture Modeling Framework (P2AMF), Singular Spectrum Analysis (SSA) - ARIMA and ANN-PSO. Previous FFT research in 2018 used three prediction methods, namely using the ANN, SVM and MLR algorithms with the best RMSE being 0.00098. Then in 2019, FTT research was followed by optimization of the ANN parameters using PSO for weighting (ANN-PSO) and obtaining the best RMSE was 0.00062. This study evaluates the robustness of the prediction models of FTTs in the Central Java region using SVM. Then proceed with increasing SVM prediction accuracy using GA (SVM-GA). SVM-GA has resulted in an increase in FTT prediction accuracy. This study has found that the SVM method has better robustness than the ANN method. The development of research related to the accuracy for the FTT prediction model in the Central Java Province has increased, starting from 2018 with RMSE 0.00098; in 2019 with RMSE 0.00062 and the results of this study resulted in the best RMSE of 0.00037.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=9400
DO  - 10.18517/ijaseit.10.6.9400