Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Muhammad Hafizh Bayhaqi
Unknown Affiliation
Computer Science & IT

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

Found 1 Documents
Search

 1 
Prediksi Klaim Asuransi Perjalanan Menggunakan Machine Learning untuk Optimasi Manajemen Risiko Andy Hermawan; Nila Rusiardi Jayanti; Adam Praharsya Rahmadian; Muhammad Hafizh Bayhaqi; Amira Afdhal; Kerin Aurelia
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 3 No. 2 (2025): April : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v3i2.2476

Abstract

Travel insurance provides financial protection for individuals during their trips, both domestically and internationally. With the increasing demand for travel insurance, insurance companies face challenges in efficiently managing claims. This study aims to develop a predictive model to classify whether an insurance policy will be claimed based on historical customer and transaction data. This research utilizes a dataset containing various features related to travel and policyholders, such as agent type, distribution channel, insurance product, travel duration, and premium amount. The methods used include data exploration, feature processing, and the application of machine learning algorithms such as Logistic Regression, Random Forest, and XGBoost. Experimental results indicate that the XGBoost model performs the best, achieving the highest accuracy compared to other models. With this predictive model, insurance companies can optimize claim evaluation processes, reduce fraud risks, and improve operational efficiency in handling travel insurance claims.
Co-Authors Adam Praharsya Rahmadian Amira Afdhal Andy Hermawan Kerin Aurelia Nila Rusiardi Jayanti