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@article{J.Gauss39315, author = {Risma Ashali Fauziah and Suparti Suparti and Arief Rachman Hakim}, title = {PENERAPAN REGRESI SPLINE TRUNCATED DATA LONGITUDINAL DUA VARIABEL PREDIKTOR UNTUK PEMODELAN HARGA SAHAM PERBANKAN}, journal = {Jurnal Gaussian}, volume = {13}, number = {2}, year = {2024}, keywords = {Stock; Inflation; Exchange Rate; Spline Truncated; Longitudinal Data; MSE, GUI}, abstract = {Stock prices movement are influenced by several factors, including inflation and the exchange rate. The bank is one of the institutions that needs to pay attention to those factors. In this study, analysis of factors that affect stock price can be carried out with the spline truncated approach. The advantage of spline is being able to estimate the data pattern and adjusts to its movement. Stock prices tend to increase and decrease at certain sub-intervals, thus these can be applied in spline truncated. Spline truncated best model for longitudinal data with two predictor variables is determined by choosing the order and optimal knots using the smallest MSE (Mean Square Error). This study used monthly data from January 2019 to December 2022 with a comparison of in sample data and out sample data, which is 90%:10%. The results of the analysis showed the best model obtained on the 2nd orde with 3 knot points. The value is 97.49%, meaning that the model is strong and the MAPE value is 12.71%, which is means that the model has good forecasting ability because it is in the category of 10% ≤ MAPE <20%.}, issn = {2339-2541}, pages = {383--393} doi = {10.14710/j.gauss.13.2.383-393}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/39315} }
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