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All Journal Desimal: Jurnal Matematika MATHunesa: Jurnal Ilmiah Matematika
Widarti, Widiarti
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Education Mathematics Social Sciences

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KAJIAN MODEL REGRESI DATA PANEL PADA DATA INDEKS PEMBANGUNAN MANUSIA PROVINSI DKI JAKARTA TAHUN 2019-2023 Widarti, Widiarti
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v13n1.p117-124

Abstract

Regression analysis on panel data is a regression technique that utilizes the panel data structure, which combines information from time series and cross section data. Panel data regression analysis in economics is usually used for Human Development Index (HDI) data. HDI is a measure used to assess the success of a region in developing the quality of life of its population. In panel data regression, there are three estimation models, namely CEM, FEM and REM. The CEM method assumes that the intercept and slope in the cross section and time series units are the same, the FEM method assumes that the intercept is different between cross section units, while the slope between cross section units remains the same, while the REM method assumes that differences in unit characteristics and time periods are accommodated in the residual model. As a result of this study, the best panel data regression model is using the Random Effect Model (REM) with individual effects. The variables of life expectancy, school expectancy, number of poor people and per capita expenditure are able to explain HDI in DKI Jakarta Province by 95.45%. Keywords: Panel Data Regression, HDI, Random Effect Model.
Application of singular spectrum analysis (SSA) method on forecasting train passengers data in sumatera Fitriani, Debi Nur; Widarti, Widiarti; Nuryaman, Aang; Setiawan, Eri
Desimal: Jurnal Matematika Vol. 6 No. 3 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i3.19040

Abstract

A time series is a series of observations of a variable that is collected, recorded, or observed over a period of time in sequence. Singular Spectrum Analysis is a powerful method to analyze time series data by decomposing the original time series data into several small components that can be identified, such as trend, periodic, and noise components. One of the datasets that can be used is data on the number of train passengers in Sumatera in 2013–2022. In this study, the Singular Spectrum Analysis method is used to forecast the number of train passengers in Sumatera in 2013–2022. The best Singular Spectrum Analysis model in this study was obtained at a window length of 22 and a number of groups of 8, with a MAPE value of 19.55%.
Co-Authors Aang Nuryaman Eri Setiawan Fitriani, Debi Nur