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Puspita Kartikasari
Departemen Statistika, Fakultas Sains dan Matematika, Undip
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IMPLEMENTASI GRIDSEARCHCV PADA SUPPORT VECTOR REGRESSION (SVR) UNTUK PERAMALAN HARGA SAHAM Aanisah Waliy Ishlah; Sudarno Sudarno; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.276-286

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
PEMODELAN DAN PREDIKSI HARGA SAHAM PERUSAHAAN FAST MOVING CUSTOMER GOODS MENGGUNAKAN VECTOR AUTOREGRESSIVE WITH EXOGENOUS VARIABLES (VARX) Marya Magdalena Simanjuntak; Tarno Tarno; Puspita Kartikasari
Jurnal Gaussian Vol 12, No 2 (2023): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.12.2.166-177

Abstract

The increase in the population of Indonesia causes consumption to increase. This has made the FMCG (Fast Moving Consumer Goods) industry in Indonesia grow rapidly and occupy the second largest proportion of market capitalization thereby attracting investors to invest. One way to choose the best stocks to invest is by modeling. Modeling is carried out on the share price of companies with large capitalization, namely Mayora Indah, Indofood CBP, and Siantar Top. One of the factors that influence a company's stock price is the stock price of a competitor, namely Unilever and Buyung Poetra. Therefore, to predict and determine the relationship between stocks, the VARX (Vector Autoregressive with Exogenous Variables) method is used. The data period in this study starts from January 4, 2021 to January 14, 2022 with the results of the analysis, namely VARX (1) is the model obtained for prediction. The errors from the model meet the white noise and multinormal assumptions. The SMAPE value of the Mayora, Indofood CBP, and Siantar Top variables is below 10% which means the model has very good predictive ability. In addition, the prediction results show that Indofood's share price is more stable than other stocks. 
Co-Authors Aanisah Waliy Ishlah Marya Magdalena Simanjuntak Sudarno Sudarno Tarno Tarno