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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Statistics and Its Applications
Budi Susetyo
School of Data Science, Mathematics, and Informatics, IPB University, Indonesia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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Application of the Spatial Durbin Panel Model and Geographically Weighted Panel Regression on Poverty Data in West Java Province Anis Sulistiyowati; Mohammad Masjkur; Budi Susetyo
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p240-260

Abstract

Poverty is one of the priority issues in the Sustainable Development Goals. In 2024, West Java Province became the province with the second-highest number of people living in poverty in Indonesia. This study aims to identify the variables that significantly affect the percentage of people living in poverty in districts/cities of West Java Province from 2019 to 2023, using the spatial Durbin panel model and geographically weighted panel regression. The data used is secondary data on poverty indicators in West Java Province from 2019 to 2023, sourced from Statistics Indonesia of West Java. The spatial Durbin panel model developed in this study is a fixed-effects spatial Durbin panel model. The model shows that average years of schooling and expenditure per capita have significant effects. In addition, the spatial lags of the percentage of households living in appropriate housing, the percentage of the population covered by local health insurance, and average years of schooling also have significant effects. The geographically weighted panel regression model, estimated using a fixed effect panel regression with a Gaussian fixed kernel as the optimal weighting function, produces distinct models for each region. The average year of schooling is the dominant factor influencing the percentage of people living in poverty in districts/cities in West Java Province.
SURVIVAL ANALYSIS OF CHRONIC KIDNEY FAILURE PATIENTS USING THE COX STRATIFIED MODEL AND RANDOM SURVIVAL FOREST Assyifa Lala Pratiwi Hamid; Budi Susetyo; Anang Kurnia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1527-1540

Abstract

This study aims to analyze the factors influencing the survival of chronic kidney failure patients undergoing hemodialysis and to compare the performance of the Cox Stratified Model with the Random Survival Forest (RSF) using retrospective data from 741 patients at Asy-Syifa General Hospital, Indonesia. Data were analyzed using the Cox Stratified Model to address violations of the proportional hazards assumption and RSF to capture non-linear patterns and complex interactions among variables. The results showed that age, hypertension, diabetes, anemia, and hemodialysis frequency significantly affected survival, with a C-Index of 0.66 for the Cox Stratified Model and 0.6558 for RSF. The limitations of this study include its single-center retrospective design, which may limit generalizability, potential residual confounding from unmeasured variables, as well as the interpretability limitations and higher computational demands of RSF. The originality of this research lies in the direct comparison between advanced statistical models and machine learning methods in a cohort of chronic kidney failure patients in Indonesia, providing new insights for improving risk stratification and clinical prediction.
Co-Authors Anang Kurnia Anis Sulistiyowati Assyifa Lala Pratiwi Hamid Mohammad Masjkur