DOI : https://doi.org/10.18517/ijaseit.10.6.9400

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

Ifran Lindu Mahargya (1), Guruh Fajar Shidik (2)
(1) Faculty of Computer Science, Dian Nuswantoro University, Semarang City, Indonesia
(2) Faculty of Computer Science, Dian Nuswantoro University, Semarang City, Indonesia
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How to cite (IJASEIT) :
Mahargya, Ifran Lindu, 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, Dec. 2020, pp. 2261-9, doi:10.18517/ijaseit.10.6.9400.
Citation Format :
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.

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