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All Journal QALAMUNA: Jurnal Pendidikan, Sosial, dan Agama YUME : Journal of Management Jurnal Riset Informatika Jurnal Mantik Jurnal Informatika Ekonomi Bisnis Daengku: Journal of Humanities and Social Sciences Innovation Jurnal Ekonomi Jurnal Terapan Ilmu Manajemen dan Bisnis (JTIMB) Klabat Accounting Review Jurnal Informatika Ekonomi Bisnis Klabat Journal of Management
Tonny Irianto Soewignyo
Fakultas Ekonomi Dan Bisnis Universitas Klabat
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Riset Informatika

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FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Mokodaser, Wilsen Grivin; Soewignyo, Tonny Irianto; Tangka, George Morris William; Soewignyo, Fanny
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2466.493 KB) | DOI: 10.34288/jri.v7i3.369

Abstract

An insurance company is a type of non-bank financial institution that protects clients from risks and collects premiums over a certain period, these facts provide an overview of the insurance business and highlight its role in the economy, this study evaluated the performance difference between the Decision Tree Regressor and Support Vector Regression (SVR) in predicting insurance payer income. The Decision Tree model demonstrated strong predictive accuracy, achieving a Mean Absolute Error (MAE) of approximately 57 million and an R-squared (R²) value of 0.896, meaning it could explain around 89.6% of the variance in the data. Additionally, the model maintained high consistency, as evidenced by 5-fold cross-validation scores ranging from 0.908 to 0.967, indicating strong generalization and low risk of overfitting. In contrast, the SVR model significantly underperformed. It recorded a much higher MAE of over 237 million and a large Mean Squared Error (MSE), reflecting substantial deviations from the actual values. Its R² score of -0.299 suggests that SVR performed worse than a naive mean predictor, failing to identify meaningful patterns. This poor performance was consistent across all cross-validation folds, which also produced negative R² scores. The SVR model’s inadequacy is likely due to the large scale of the income data and the lack of proper preprocessing, such as normalization, or parameter tuning. Overall, these findings clearly demonstrate that the Decision Tree Regressor is a more suitable, accurate, and stable model for predicting insurance payer income.
Co-Authors Agung Sukma Adiguna Siddik Ambalao, Shapely Darvel Civlie Walone Daysi Fikka Kelejan Deske W. Mandagi dos Santos, Joni Maria Fanny Soewignyo George Morris William Tangka Jeannifer Papilaya Jeiner Maruru Joy Budiman David Sondakh Mokalu, Risca Rioline Nikoku Joshua Sepang Ronny H Walean Saputri Victoria Lolaroh Septiani Septiani Siddik, Agung Sukma Adiguna Wilsen Grivin Mokodaser