PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA INDEKS HARGA SAHAM GABUNGAN (IHSG) TAHUN 2018 – 2023

Zakiyah Mar'ah; Ruliana Ruliana; Magfirah Septiana

doi:10.35580/variansiunm144

Zakiyah Mar'ah Universitas Negeri Makassar
Ruliana Ruliana Department of Statistics, Universitas Negeri Makassar
Magfirah Septiana Universitas Negeri Makassar

DOI: https://doi.org/10.35580/variansiunm144

Keywords: Regresi Nonparametrik, MARS, IHSG, GCV

Abstract

Nonparametric regression is one of the methods used to estimate the pattern of the relationship between response variables and predictor variables where the shape of the regression curve is unknown and is generally assumed to be contained in an infinite dimensional function space and is a smooth function (Eubank, 1999). The MARS method is one method that uses a nonparametric regression approach and high-dimensional data. These namely data has a number of predictor variables of 3 ≤ k ≤ 20 and data samples of size 50 ≤ n ≤ 1000. This research discusses Multivariate Adaptive Regression Spline (MARS) Modeling on the Composite Stock Price Index (JCI) 2018 - 2023. MARS modeling is obtained from a combination of basis function (BF), maximum interaction (MI), and minimum observation (MO) based on the minimum Generalized Cross Validation (GCV) value. The results of this study were obtained from the combination value of BF = 16, MI = 1, and MO = 2 with GCV = 60710.98. The factors that affect the Jakarta Composite Index (JCI) are Inflation (X1), Rupiah to USD Exchange Rate (X3), and Money Supply (X4).