Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Parameter: Jurnal Matematika, Statistika dan Terapannya
Anugrah, Sri Ayu
Unknown Affiliation
Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Comparison of Random Forest and XGBoost Methods for Predicting Work Accident Claim Reserves Anugrah, Sri Ayu; Anugrawati, Sri Dewi; Sauddin, Adnan; Mariani, Andi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 3 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i3pp497-508

Abstract

The potential high claim burden in the work accident insurance sector managed by BPJS Ketenagakerjaan have an impact on the company’s financial stability. This encourages insurance companies to provide additional funds to maintain the company’s operational sustainability. Thus, preparing future fund reserves is a crucial step in risk and financial management to minimize payment delays, up to the risk of default. This study aims to determine the best method for predicting work accident claim reserves by comparing the Random Forest and XGBoost methods. The result of the analysis shows that the XGBoost method has an outstanding ability to predict work accident claim reserves on BPJS Ketenagakerjaan in the period July 2016 to August 2023, with a MAPE of 5.14% and an accuracy rate of 94.86%.
Co-Authors Adnan Sauddin Amiruddin Kade Andi Mariani Anugrawati, Sri Dewi Yusuf Kendek