SURVIVAL ANALYSIS ON DATA OF STUDENTS NOT GRADUATING ON TIME USING WEIBULL REGRESSION, COX PROPORTIONAL HAZARDS REGRESSION, AND RANDOM SURVIVAL FOREST METHODS

  • Ramya Rachmawati Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Bengkulu, Indonesia https://orcid.org/0009-0007-6094-9620
  • Nur Afandi Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Bengkulu, Indonesia https://orcid.org/0000-0003-4604-0553
  • Muhammad Arib Alwansyah Mathematics Education, Faculty of Mathematics and Natural Sciences, University of Jakarta, Indonesia https://orcid.org/0009-0003-1548-778X
DOI: https://doi.org/10.30598/barekengvol19iss3pp2111-2126
Keywords: Cox Proportional Hazards Regression, Length of Study, Random Survival Forest, Survival Analysis, Weibull Regression

Abstract

This article presents a comprehensive study of the factors that influence the length of study data of undergraduate students at FMIPA UNIB class 2018 and 2019. This study is essential because observations show that many students study for more than 8 semesters. The purpose of this study is to determine the factors that significantly influence the length of study of undergraduate students. These factors can be internal and external. Survival analysis is the right method to identify these factors because ordinary regression analysis is unable to estimate survival data. Therefore, methods such as Weibull regression, Cox Proportional Hazards regression, and Random Survival Forest are used. This study does not compare the methods used because these methods are independent of each other, but have the same goal, namely, to determine the factors that influence the length of study of students. The data used in this study are data on the length of study of students from the 2018 and 2019 cohorts sourced from the academic subsection of FMIPA UNIB, with variables of GPA, gender, region of origin, university entry route, parents' occupation, type of study program, and length of study. The results showed that GPA and the type of study program significantly influenced the length of study in Weibull regression analysis. In Cox proportional hazard regression, the GPA variable is an influential factor, while using the Random Survival Forest method, all factors significantly influenced the length of study, with their respective levels of importance.

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References

R. Widyastuti, D. Wulandari, and D. Prasetyowati, “PENERAPAN REGRESI COX UNTUK MENGANALISIS VARIABEL YANG BERPENGARUH TERHADAP DURASI STUDI MAHASISWA,” vol. 13, pp. 88–98, 2024. doi: https://doi.org/10.14710/j.gauss.13.1.88-98

E. T. Lee and J. W. Wang, STATISTICAL METHODS FOR SURVIVAL DATA ANALYSIS, Third Edit. A John Wiley & Sons, INC., Publication, 2015.

U. L. Mangkurat, “ANALISIS REGRESI COX UNTUK MENENTUKAN FAKTOR-FAKTOR YANG MEMPENGARUHI LAMA STUDI MAHASISWA S1 FMIPA UNIVERSITAS LAMBUNG MANGKURAT 1,2,3,” vol. 13, pp. 1–12, 2024. doi: https://doi.org/10.14710/j.gauss.13.1.1-12

M. A. Alwansyah, “SURVIVAL ANALYSIS OF STUDENTS NOT GRADUATED ON TIME USING COX PROPORTIONAL HAZARD REGRESSION METHOD AND RANDOM SURVIVAL FOREST METHOD,” J. Stat. Data Sci., vol. 2, no. 1, pp. 13–21, 2023. doi: https://doi.org/10.33369/jsds.v2i1.24312

D. A. I. Maruddani, Tarno, A. Hoyyi, R. Rahmawati, and Y. Wilandari, SURVIVAL ANALYSIS. UNDIP Press Semarang, 2021.

J. P. Klein and M. L. Moeschberger, SURVIVAL ANALYSIS TECHNIQUES FOR CENSORED AND TRUNCATED DATA SECOND EDITION, vol. 19, no. 5. John Wiley. New Jersey., 2003. doi: https://doi.org/10.1007/b97377

Roger Koenker, V. Chernozhukov, X. He, and L. Peng, HANDBOOK OF QUANTILE REGRESSION, First Edit. CRC Press Taylor & Francis Group, 2018. doi: https://doi.org/10.1201/9781315120256

D. C. Montgomery, E. A. Peck, and G. G. Vining, INTRODUCTION TO LINEAR REGRESSION ANALYSIS, Sixth Edit. Wiley Series in Probability And Statistics, 2021.

M. Sriningsih, D. Hatidja, and J. D. Prang, “PENANGANAN MULTIKOLINEARITAS DENGAN MENGGUNAKAN ANALISIS REGRESI KOMPONEN UTAMA PADA KASUS IMPOR BERAS DI PROVINSI SULUt,” J. Ilm. Sains, vol. 18, no. 1, p. 18, 2018. doi: https://doi.org/10.35799/jis.18.1.2018.19396

C. Kartsonaki, “SURVIVAL ANALYSIS,” Diagnostic Histopathol., vol. 22, no. 7, pp. 263–270, 2016. doi: https://doi.org/10.1016/j.mpdhp.2016.06.005

D. F. Moore, APPLIED SURVIVAL ANALYSIS USING R. New York:Springer, 2016. doi: https://doi.org/10.1007/978-3-319-31245-3

N. Eliyati, S. I. Maiyanti, O. Dwipurwani, and S. W. Hamidah, “Model Regresi Cox Untuk Menganalisis Pengaruh Faktor Asupan Makanan Terhadap Risiko Kekambuhan Endometriosis,” BAREKENG J. Ilmu Mat. dan Terap., vol. 15, no. 1, pp. 103–114, 2021. doi: https://doi.org/10.30598/barekengvol15iss1pp103-114

D. Collet, MODELLING SURVIVAL DATA IN MEDICAL RESEARCH, Fourth edi. CRC texts in statistical science, 2023.

K. L. Pickett, K. Suresh, K. R. Campbell, S. Davis, and E. Juarez-Colunga, “RANDOM SURVIVAL FORESTS FOR DYNAMIC PREDICTIONS OF A TIME-TO-EVENT OUTCOME USING A LONGITUDINAL BIOMARKER,” BMC Med. Res. Methodol., vol. 21, no. 1, pp. 1–14, 2021. doi: https://doi.org/10.1186/s12874-021-01375-x

M. Rezaei, L. Tapak, M. Alimohammadian, A. Sadjadi, and M. Yaseri, “JOURNAL OF BIOSTATISTICS AND EPIDEMIOLOGY,” J Biostat Epidemiol., vol. 1, no. Iran, pp. 37–44, 2020.

S. Nurhaliza, K. Sadik, and A. Saefuddin, “A COMPARISON OF COX PROPORTIONAL HAZARD AND RANDOM SURVIVAL FOREST MODELS IN PREDICTING CHURN OF THE TELECOMMUNICATION INDUSTRY CUSTOMER,” BAREKENG J. Ilmu Mat. dan Terap., vol. 16, no. 4, pp. 1433–1440, 2022. doi: https://doi.org/10.30598/barekengvol16iss4pp1433-1440

M. Mohammed, I. B. Mboya, H. Mwambi, M. K. Elbashir, and B. Omolo, “PREDICTORS OF COLORECTAL CANCER SURVIVAL USING COX REGRESSION AND RANDOM SURVIVAL FORESTS MODELS BASED ON GENE EXPRESSION DATA,” PLoS One, vol. 16, no. 12 December, pp. 1–22, 2021. doi: https://doi.org/10.1371/journal.pone.0261625

B. M. Iskandar, “MODEL COX PROPORTIONAL HAZARD PADA KEJADIAN BERSAMA,” Universitas Negeri Yogyakarta, 2015.

Published
2025-07-01
How to Cite
[1]
R. Rachmawati, N. Afandi, and M. A. Alwansyah, “SURVIVAL ANALYSIS ON DATA OF STUDENTS NOT GRADUATING ON TIME USING WEIBULL REGRESSION, COX PROPORTIONAL HAZARDS REGRESSION, AND RANDOM SURVIVAL FOREST METHODS”, BAREKENG: J. Math. & App., vol. 19, no. 3, pp. 2111-2126, Jul. 2025.
Issue
Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
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Articles

Copyright (c) 2025 Ramya Rachmawati, Nur Afandi, Muhammad Arib Alwansyah

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