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All Journal Jurnal Jaminan Kesehatan Nasional
Hanif Noer Rofiq
a:1:{s:5:"en_US";s:20:"Kementerian Keuangan";}
Decision Sciences, Operations Research & Management Dentistry Health Professions Medicine & Pharmacology Public Health

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Deteksi Inefisiensi pada Klaim BPJS Kesehatan dengan menggunakan Machine Learning Hanif Noer Rofiq
Jurnal Jaminan Kesehatan Nasional Vol. 3 No. 1 (2023): Jurnal Jaminan Kesehatan Nasional
Publisher : BPJS Kesehatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53756/jjkn.v3i1.134

Abstract

Inefficiency in claims submitted by health facilities to BPJS Kesehatan is a problem that needs attention. With the growing number of claims, traditional method such as manual verification is unable to handle enormous amounts of data swiftly. Simultaneously, there is a demand to accelerate the settlement of claims with a limited number of verifiers. One way that can be adopted is to use Machine Learning to detect potential inefficient transactions rapidly. This study compares several Machine Learning algorithms: Random Forest, Gradient Boosting Classifier, Decision Tree, Support Vector Machine, Naive Bayes, CatBoost, and XGBoost. In addition, oversampling and under sampling methods are also used because the dataset is imbalanced. The best results were obtained using the Random Forest + Tomek Links model, which produced an F1 score of 19,53, with the five most influential variables: location of the health facility, participant's age, first-level health facilities diagnosis, participant's primary diagnosis, and health facility type.
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