Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Gaussian
Nurina Salma Alfiyyah
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Other

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

Found 1 Documents
Search

 1 
PEMODELAN HARGA SAHAM PERUSAHAAN PROPERTI DAN REAL ESTATE MENGGUNAKAN REGRESI LONGITUDINAL SPLINE TRUNCATED DILENGKAPI GUI R Nurina Salma Alfiyyah; Suparti Suparti; Sugito Sugito
Jurnal Gaussian Vol 12, No 1 (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.12.1.42-51

Abstract

Stocks are one of the most popular financial instruments traded in the capital market. One of stock prices fluctuate up and down due to the influence of several factors, one of which is inflation. Stocks in the property and real estate sectors are important indicators to determine the level of a country economy. Data on several stock prices is one case of longitudinal data in economic field. The data is divided into 2 parts, namely in sample data from January 2016 to October 2020 and out sample data from November 2020 to December 2021. In this study, longitudinal stock price data is modeling using nonparametric spline truncated. The best spline truncated model is determined by the order and the optimal number of knot points based on the minimum Generalized Cross Validation value. Spline truncated nonparametric regression modeling for longitudinal data in this study is equipped with Graphical User Interface (GUI) that can facilitate the data processing. The results of the analysis show that the best longitudinal spline truncated regression model obtained on 2nd order with 5 knot points. 95.04% value of  indicates the model is a strong model. In the evaluation of the best model, the MAPE data out sample value is 16.45%. It indicates the model has good forecasting ability.
Co-Authors Sugito Sugito Suparti Suparti