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Esther Ria Matulessy
Jurusan Matematika UNIPA
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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PENERAPAN ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN MANUSIA DI PROVINSI PAPUA BARAT Desti Setya Ningsih; Esther Ria Matulessy; Dariani Matualage
Jurnal Natural Vol. 16 No. 2 (2020): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v16i2.118

Abstract

Panel Data Regression Analysis is a combination of time series data and cross section data. The purpose of this study is to determine the best model for panel data regression analysis on HDI in West Papua Province and to determine the HDI model in West Papua Province. The data used in this study are West Papua data in the 2019 Publication Figures and 2019 Publication Human Development Index data. In the process of determining the best model, estimating model parameters with 3 approaches namely CEM, FEM and REM, then testing model selection, classical assumption test, model equation checking and finally model interpretation. The results of this study indicate that the best regression model is FEM with individual effects and time effects with a good model of 91% which means that HDI in West Papua Province is explained by GRDP, RLS, JPM and UHH. The equation model is as follows: Based on the equations that have been obtained, the variables that have a significant effect on HDI in West Papua Province are RLS and UHH.
PERBANDINGAN ANTARA MODEL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) DAN MODEL FUNGSI TRANSFER PADA PERAMALAN CURAH HUJAN DI KABUPATEN MANOKWARI Esther Ria Matulessy
Jurnal Natural Vol. 15 No. 2 (2019): Jurnal Natural
Publisher : FMIPA Universitas Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30862/jn.v15i2.138

Abstract

This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).
Co-Authors Dariani Matualage Desti Setya Ningsih