Komparasi Algoritma Random Forest dan XGBoost dalam Prediksi Premi Asuransi Kesehatan 619 497
DOI:
https://doi.org/10.26623/transformatika.v23i1.11914Kata Kunci:
Asuransi Kesehatan, Komparasi, Random Forest, XgboostAbstrak
Asuransi kesehatan saat ini menjadi salah satu hal yang banyak orang persiapkan dikarenakan adanya ketidakpastian risiko kesehatan dan biaya layanan kesehatan yang semakin naik. Perhitungan premi tiap individu dapat berbeda dikarenakan terdapat perbedaan profil kesehatan seperti usia, BMI maupun gaya hidup seperti merokok yang membuat perusahaan asuransi harus memperhitungkan premi dengan akurat agar tidak menimbulkan kerugian finansial dan sesuai dengan tingkat risiko terjadinya klaim. Adapaun tujuan dari penelitian ini adalah melakukan komparasi antara algoritma Random Forest dan XGBoost dalam memprediksi premi asuransi kesehatan berdasarkan beberapa faktor yang sulit dihitung secara manual. Evaluasi dilihat berdasarkan metrik regresi yaitu MAE, MSE, RMSE, dan R2. Pada penelitian ini, algoritma Random Forest berhasil memprediksi premi asuransi kesehatan lebih baik dari XGBoost dengan nilai MAE 2.573, MSE 24199792,43 RMSE 4919, 33 dan R2 sebesar 84.04%.
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