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All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Mathematics, Computation and Statistics (JMATHCOS)
Zakiyah Mar'ah
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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Journal : VARIANSI: Journal of Statistics and Its Application on Teaching and Research

 1 
PEMODELAN FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP ANGKA BUTA HURUF DI PROVINSI SULAWESI SELATAN DENGAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) Beddu Solo, Nurul Era Natasyah; Muhammad Nusrang; Zakiyah Mar'ah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm141

Abstract

Geographically Weighted Logistic Regression (GWLR) is the development of a logistic regression model applied to spatial data from non-stationary processes with categorical response variables. The high rate of illiteracy is one of the crucial problems in the field of education that has not been resolved to date. South Sulawesi is the 4th province with the highest percentage of illiteracy in Indonesia in 2022. This research aims to obtain the GWLR model and the factors that have a significant influence on the illiteracy rate in South Sulawesi in 2022. In this research, we compare three functions Kernel weightings are Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. Selection of the best model uses the smallest AIC value. The results of this research are that the GWLR model with the Adaptive Tricube Kernel weighting function is the best model in modeling cases of illiteracy in South Sulawesi in 2022 which is obtained based on the smallest AIC value and the factor that has a significant influence on the illiteracy rate is the Open Unemployment Rate (X1), percentage of poor population (X2), Elementary School Enrollment Rate (X3), and area with city status (X4).
PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINE (MARS) PADA INDEKS HARGA SAHAM GABUNGAN (IHSG) TAHUN 2018 – 2023 Zakiyah Mar'ah; Ruliana, Ruliana; Magfirah Septiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm144

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).
Co-Authors Beddu Solo, Nurul Era Natasyah H. S., Rahmat Magfirah Septiana Muhammad Nusrang Rahmat Hidayat Ruliana Ruliana, Ruliana