Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Variance : Journal of Statistics and Its Applications
Salsabilla, Arla
Unknown Affiliation
Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
IMPLEMENTATION GRID SEARCH OF RBF AND POLYNOMIAL ON SUPPORT VECTOR REGRESSON FOR CLOSING STOCK PRICES PREDICTION ON PT INDOFARMA (INAF) Salsabilla, Arla; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page133-142

Abstract

Stocks represent evidence of ownership of an asset. The highly volatile nature of stock prices makes it difficult for investors to predict stock prices, necessitating the analysis of stock investments. This research aims to forecast for the next 30 days the closing price of PT Indofarma (INAF) stocks using the best model, and the accuracy level of the employed model was analyzed based on the data from the last seven years. The research used the Support Vector Regression (SVR) method, which is known for its capability to handle nonlinear data through kernel functions. The Radial Basis Function (RBF) and polynomial kernels are used in this case. The challenge with SVR lies in determining the optimal hyperparameter, which can be addressed through hyperparameter tuning using grid search. The research results show that the best model is the SVR kernel RBF model with optimal hyperparameter C=1,γ=0.01, and ε=0.01. Based on the performance evaluation results of the best model, the MAPE, MSE, and MAE values are equal to 1.537%,1483.936, and 23.409.
Co-Authors Andani, Wirda Neva Satyahadewi