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All Journal Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Al-Qardh Journal of Mathematics: Theory and Applications Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Jurnal Pengabdian Sumber Daya Manusia Media Bina Ilmiah RAGAM: Journal of Statistics and Its Application
Yuana Sukmawaty
Universitas Lambung Mangkurat
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Journal : RAGAM: Journal of Statistics and Its Application

 1 
ESTIMASI PARAMETER RANDOM EFFECT MODEL PADA REGRESI PANEL MENGGUNAKAN METODE GENERALIZED LEAST SQUARE (STUDI KASUS: KEMISKINAN DI PROVINSI KALIMANTAN SELATAN) Ariandy Hermawan; Yuana Sukmawaty; Aprida Siska Lestia
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7419

Abstract

Poverty is a condition that concerns the inability to meet the most minimum demands of life, especially from the aspects of consumption, income, education, and health. The problem of poverty is very complex and multidimensional in nature, as it relates to social, economic, cultural and other aspects. This study focuses on observation areas in South Kalimantan Province, with the PPM value in 2021 reaching (4.83%) still above the target goal of the Regional Medium-Term Development Plan (RPJMD) of South Kalimantan Province (3.96- 4.01%), so that further interventions are still needed to be able to reduce PPM in poverty cases. This study aims to estimate the parameters of the panel regression model used to analyze factors that are suspected to affect poverty cases in South Kalimantan Province in 2016-2020. The Random Effect Model (REM) is the best model used in this study, assuming that there are differences in slopes and interceptions caused by residual due to differences between individual units and between time periods. The process of estimating parameters on REM is determined through the Generalized Least Squares (GLS) Estimator method . From the results of the data processing, it was obtained that the model is influenced by economic growth, life expectancy, open unemployment rate, and labor force participation rate. From the results of the analysis of 2 (two) models, it was tested significantly and affected poverty in South Kalimantan Province in 2016-2020.  Keywords:   Poverty, Data Regression Panel, Generalized Least Square Method (GLS).
PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA DATA INDEKS HARGA KONSUMEN (IHK) 5 IBUKOTA PROVINSI DI PULAU KALIMANTAN Muhammad Aldi Relawanto; Yuana Sukmawaty; Dewi Sri Susanti
RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i2.11427

Abstract

Generalized Space Time Autoregressive (GSTAR) model is a development model from the generalized STAR (Space Time Autoregressive) model. GSTAR model have autoregressive order to see the effect of the time element and location weighting matrix to see the effect of the location element. Unlike the STAR model, it can assume that each location research has different characteristics. The purpose of this research is to apply the Generalized Space Time Autoregressive (GSTAR) model to the Consumer Price Index (CPI) data in Kalimantan Island, especially in the capital city of each province in Kalimantan Island to find out the best estimation model with the best location weight. The location weights used the distance inverse location weights and the normalized cross-correlation location weights by estimating the parameters of the GSTAR model using the Ordinary Least Square (OLS) method. The best estimated model can be seen from the smallest Akaikae’s Information Criterion (AIC) and Root Mean Square Error (RMSE) value. From the research results, it was found that the best GSTAR prediction model for CPI data for 5 cities in Kalimantan Island was the GSTAR(1,1)-I(1). These results are based on the GSTAR prediction model with the smallest AIC value and the data is differencing 1 time. The best location weight based on the smallest RMSE value for the GSTAR(1,1)-I(1) model is the normalized cross-correlation location weight.
PENERAPAN MODEL GEOGRAPHICALLY WEIGHTED PANEL REGRESSION PADA TINGKAT KEMISKINAN DI PROVINSI KALIMANTAN SELATAN Akhmad Fajar Maulana; Yuana Sukmawaty; Maisarah Maisarah
RAGAM: Journal of Statistics & Its Application Vol 2, No 2 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i2.11332

Abstract

South Kalimantan Province is one of the provinces in Indonesia which has the lowest poverty rate or percentage of poor people on the island of Kalimantan, even in Indonesia. The percentage of poor people in South Kalimantan Province in March 2022 was 4.49% or in the 2nd lowest poverty position in Indonesia, below the Bangka Belitung Islands Province and above the Bali Province. Geographically Weighted Panel Regression (GWPR) is a local regression model with repeated data at location points for each observation at different times. This study aims to estimate the GWPR model parameters and test the significance of the GWPR model parameters to determine the factors that influence poverty in South Kalimantan Province. The independent variables used affect the dependent variable in the form of the Percentage of Poor Population, namely Life Expectancy, Open Unemployment Rate, Economic Growth, Average Years of Schooling and Number of Crimes. The analysis in this study is descriptive analysis using thematic maps, panel data regression analysis to determine the global model and GWPR by combining the panel data model with the GWR model. The results of this study show that the fixed effect model is a global model and the fixed bisquare weighting function is the best weighting function for estimating the GWPR model. Based on the GWPR model formed, there are 7 model groups based on significant independent variables. Hulu Sungai Utara and Hulu Sungai Tengah districts are districts where poverty in these areas is influenced by many variables compared to other regions in South Kalimantan Province.
PENERAPAN MODEL REGRESI PANEL KOMPONEN DUA ARAH PADA POLA CURAH HUJAN PROVINSI KALIMANTAN TENGAH Vichario Indra Pradana; Yuana Sukmawaty; Nur Salam
RAGAM: Journal of Statistics & Its Application Vol 2, No 1 (2023): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v2i1.8303

Abstract

Rainfall is the climate element that is most closely related to supporting the life processes of Indonesian people such as agricultural production, plantations, fisheries, and aviation. In 2014-2016, Indonesia experienced a drought due to a global climate anomaly called the El Nino phenomenon, where annual rainfall at that time tended to decrease from other years. While in 2020-2022, rainfall in Indonesia tends to increase from other years, this event is called the La Nina phenomenon. This study aims to describe the rainfall patterns that occur in each phenomenon and analyze the regression model of rainfall panels in Central Kalimantan province with a two-way component approach. Random Effects Model (REM) is the most appropriate model to be used in the phenomenon of La Nina. Fixed Effect Model (FEM) is the most appropriate model to be used in the El Nino phenomenon. Feasible Generalized Least Square is a parameter estimation method that is focused and used to estimate regression parameters in this study. Based on the results of regression analysis of panel data, for the phenomenon of La Nina obtained R2 value of 51.66% and found that the average air temperature variable tested significant. For the El Nino phenomenon, the value of R2 is 75.35% and it is found that there are no significant independent variables tested. Therefore, it can be expected that the increase in average air temperature can decrease the average rainfall value when the La Nina phenomenon occurs in Central Kalimantan province.
Co-Authors Akhmad Fajar Maulana Annisa Halida Annisa Halida Ariandy Hermawan Azkia Azkia Dewi Anggraini Dewi Sri Susanti Farah, Nadya Fitriani Fitriani Fuad Muhajirin Farid Hayqal Hazmi Qastari Hayqal Lestia, Aprida Siska Maisarah Maisarah Muhammad Aldi Relawanto Nabila Septiani Nur Salam Sekar Wahyuningtias Selvi Annisa Siti Jubaidah Soesanto, Oni Sri Elina Herni Yulianti Syifa Ajeria Thaibatun Nissa Vichario Indra Pradana Winda Adinda Tanjung Yeni Rahkmawati Yunita Alisia