BibTex Citation Data :
@article{J.Gauss36643, author = {Aanisah Ishlah and Sudarno Sudarno and Puspita Kartikasari}, title = {IMPLEMENTASI GRIDSEARCHCV PADA SUPPORT VECTOR REGRESSION (SVR) UNTUK PERAMALAN HARGA SAHAM}, journal = {Jurnal Gaussian}, volume = {12}, number = {2}, year = {2023}, keywords = {Stock; Support Vector Regression; GridSearchCV; Kernel; Prediction}, abstract = { Stock is a sign of the capital participation of a person or authority in a company (PT). PT Anabatic Technologies Tbk (ATIC) is one of the service providers and IT consultants that is included in the technology sector, which is a new sector in the IDX-IC classification. ATIC stock trading was temporarily suspended due to a significant increase in cumulative prices. This indicates that stock prices tend to be volatile and non-linear. The Support Vector Regression (SVR) method can be used to predict stock prices. SVR is able to solve non-linear data problems by using kernel functions so it can overcome overfitting problems and will give good performance. The SVR problem is difficult to determine the optimal hyperparameters, so this research uses grid search cross validation (GridSearchCV). In this research, ATIC’s daily closing price data was used with 1007 training data and 252 testing data. The results show that the best model is SVR with a linear kernel and the hyperparameters used are Cost and epsilon . The linear kernel SVR model produces a MSE of 0,001237173; SMAPE of 0,1167301; and = 0,9206643 }, issn = {2339-2541}, pages = {276--286} doi = {10.14710/j.gauss.12.2.276-286}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/36643} }
Refworks Citation Data :
Stock is a sign of the capital participation of a person or authority in a company (PT). PT Anabatic Technologies Tbk (ATIC) is one of the service providers and IT consultants that is included in the technology sector, which is a new sector in the IDX-IC classification. ATIC stock trading was temporarily suspended due to a significant increase in cumulative prices. This indicates that stock prices tend to be volatile and non-linear. The Support Vector Regression (SVR) method can be used to predict stock prices. SVR is able to solve non-linear data problems by using kernel functions so it can overcome overfitting problems and will give good performance. The SVR problem is difficult to determine the optimal hyperparameters, so this research uses grid search cross validation (GridSearchCV). In this research, ATIC’s daily closing price data was used with 1007 training data and 252 testing data. The results show that the best model is SVR with a linear kernel and the hyperparameters used are Cost and epsilon . The linear kernel SVR model produces a MSE of 0,001237173; SMAPE of 0,1167301; and = 0,9206643
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