Article Per Year (5 Year)
p-Index From 2021 - 2026
1.835
P-Index
This Author published in this journals
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

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
GreenPoin: Mobile Application with Reward Point System at Klabat University Adam, Stenly Ibrahim; Mokodaser, Wilsen Grivin; Wagiu, Wayne Gilbert
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.22163

Abstract

At Klabat University, the conventional method of deducting Sabbath points poses challenges for students and supervisors due to its inefficiency. To address this, the GREENPOIN smartphone application was developed as an innovative solution for managing Sabbath points. The application enables administrators to approve point redemptions, add supervisors, manage tasks, and monitor students' total Sabbath points. Students earn points by completing environmental cleaning tasks assigned by supervisors. The system was designed using prototype models and use case diagrams to evaluate user requirements. Key features include Mission Management and Validation for supervisors; Supervisor Management, Point Management, and Point Redemption Approval for administrators; and Login, Registration, Point Viewing, Mission Viewing, Task Submission, Point Redemption, History, and Point Reset for students. GREENPOIN has proven to enhance the efficiency of Sabbath point management, accelerate task validation, and streamline mission oversight. The application increases process transparency and encourages student participation in campus cleanliness initiatives. Future enhancements will include push notifications, an iOS version, mission-specific comments, and API integrations, such as Google Maps, to further improve functionality.
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.
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