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All Journal Techno.Com: Jurnal Teknologi Informasi CogITo Smart Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Riset Informatika Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology Servitium Smart Journal
Wilsen Grivin Mokodaser
Universitas Klabat
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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FORECASTING HEALTH INSURANCE PAYER INCOME: A COMPARATIVE ANALYSIS OF DECISION TREE AND SVR ALGORITHMS Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; George Morris William Tangka; Fanny Soewignyo
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.
PRODUCT SALES PREDICTION USING XGBOOST WITH FEATURE IMPORTANCE ANALYSIS FOR ADVERTISING MEDIA EVALUATION Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; Fanny Soewignyo
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.537

Abstract

Product sales prediction plays a crucial role in supporting data-driven marketing strategies and optimizing advertising expenditures. Although previous studies have demonstrated the effectiveness of machine learning techniques for sales forecasting, most of them primarily focus on prediction accuracy and provide limited insights into the contribution of individual advertising channels to sales performance. This limitation reduces the interpretability and practical value of predictive models for business decision-making. Therefore, this study proposes a product sales prediction framework using Linear Regression as a baseline model and XGBoost Regression combined with Feature Importance Analysis for advertising media evaluation. The novelty of this study lies in integrating predictive modeling and interpretable analysis within a single framework, enabling both accurate sales prediction and the identification of influential advertising factors. Hyperparameter optimization and five-fold cross validation were employed to improve model reliability and robustness. Experimental results show that Linear Regression outperformed XGBoost, achieving an R² score close to 1.0, while XGBoost achieved an R² score of 0.953 with a mean cross-validation R² score of 0.950, indicating stable predictive performance. Feature Importance Analysis revealed that Affiliate Marketing was the most influential factor, followed by Billboards and Social Media. These findings contribute to marketing analytics by providing interpretable insights that support advertising budget optimization and more effective data-driven business decision-making.
Co-Authors Cherry Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo George Morris William Tangka Joe Yuan Mambu Koapaha, Hartiny Longkutoy, Desmond Monica Dwijayanti Samidi Samidi Silitonga, Argha Orion Stenly Ibrahim Adam Stenly Richard Pungus Tangka, George Toetik Wulyatiningsih Tonny Irianto Soewignyo Triyono, Gandung Wagiu, Wayne Gilbert