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All Journal Jurnal Gaussian
Suparti Suparti
Departemen Statistika, Fakultas Sains dan Matematika, Undip
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PERBANDINGAN GULUD REGRESSION DAN PRINCIPAL COMPONENT REGRESSION (PCR) TERHADAP PEMODELAN INDEKS PEMBANGUNAN MANUSIA PROVINSI JAWA TIMUR Raihandika Ari Indhova; Suparti Suparti; Arief Rachman Hakim
Jurnal Gaussian Vol 12, No 2 (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.12.2.199-208

Abstract

Human resources is valuable asset in a country. Human Development Index (HDI) becomes important indicator of quality of human resources in an area. HDI value is affected by a variety of factors that are strongly related to each other so they cause multicollinearity. This observation aims to deal with multicollinearity optimally by comparing Gulud Regression to Component Regression in modeling factors that affect East Java HDI in 2020. Data that are used in this observation are East Java HDI in 2020 (Y), Life Expectancy (X1), Infant Mortality Rate (X2), Mean Years of Schooling (X3), Expected Years of Schooling (X4), Open-Unemployment Rate (X5), Average Household Expenditure per Capita (X6), and Labor Force Participation Rate (X7). Based on MSE value, the Gulud Regression method is better than Principal Component Regression (PCR) method in dealing with multicollinearity problem. Based on adjusted  score that is 0,954, feasibility test of the best model of Gulud Regression method is a strong model.
PEMODELAN PRODUK DOMESTIK BRUTO DI INDONESIA DENGAN PENDEKATAN SEMIPARAMETRIK POLINOMIAL LOKAL DILENGKAPI GUI-R Muftia Lutfi Cahyani; Suparti Suparti; Budi Warsito
Jurnal Gaussian Vol 12, No 2 (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.12.2.189-198

Abstract

Gross domestic product is a measuring tool for a country's economy that needs to be known so that the country is able to consider decisions taken regarding future economic policies. The local polynomial semiparametric method that combines parametric regression and local polynomial nonparametric can be one way of predicting a country's GDP. This method is used because in GDP modeling there is one independent variable that has a linear relationship while the other variables have a pattern that tends to cluster. The modeling aims to obtain a semiparametric local polynomial model on GDP in Indonesia with the influence of coal export volume as a parametric independent variable and world oil prices as a nonparametric independent variable from the first quarter of 2005 to the second quarter of 2021 which is equipped with a GUI to simplify calculations. Based on experiments on several types of kernels, bandwidth and model degrees, the best model is local polynomial semiparametric model with Gaussian kernel weighting at degree 2 which has the smallest GCV. This model also has an R-Square value of 89.2% where the value of GDP is strongly influenced by world oil prices and coal export volumes together. The forecasting ability of this best model is said to be good because it has a MAPE of 17.127%.
Co-Authors Arief Rachman Hakim Budi Warsito Muftia Lutfi Cahyani Raihandika Ari Indhova