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All Journal Techno.Com: Jurnal Teknologi Informasi Teika CogITo Smart Journal Jurnal Ilmiah Matrik Jurnal Riset Informatika Jurnal Informasi dan Teknologi Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Engineering, Science and Information Technology Global South Review Journal Of Business, Finance, and Economics (JBFE) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Jikom: Jurnal Informatika dan Komputer Servitium Smart Journal CSRID
George Morris William Tangka
Universitas Klabat
Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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.
Pemilihan Pemasok Bahan Makanan pada Kafetaria Universitas XYZ: Penerapan Metode MARCOS untuk Keputusan Multikriteria George Morris William Tangka; Raissa Camila Maringka; Erienika Meiling Lompoliu
Journal Of Business, Finance, and Economics (JBFE) Vol 6 No 2 (2025): Desember : Journal Of Business, Finance, and Economics (JBFE)
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/jbfe.v6i2.7520

Abstract

The cafeteria of XYZ University serves as an essential service hub for students, especially dormitory residents, so the selection of food suppliers must systematically consider quality, food safety, delivery reliability, service, and cost. This study aims to formulate a transparent and replicable multi-criteria decision-making model for selecting food suppliers for a university cafeteria. Five criteria are used in the evaluation, namely quality and freshness (C1), delivery reliability (C2), compliance with food safety standards (C3), service responsiveness (C4), and price (C5), applied to four supplier alternatives (Alpha, Beta, Gamma, Delta). The criteria weights are determined based on expert judgments and normalized so that their sum equals one. The MARCOS method is implemented through six main stages: construction of the decision matrix, determination of ideal and anti-ideal alternatives, normalization according to criteria type, weighting, calculation of aggregate scores, and computation of utility degrees relative to the ideal and anti-ideal conditions. The results show the final ranking Alpha > Gamma > Beta > Delta, with Alpha having the highest utility value and Delta the lowest. Sensitivity analysis with moderate variations in the weights indicates that the ranking remains stable, suggesting that the decision is robust against reasonable changes in policy preferences. In practical terms, the proposed model provides a numerical framework that is easy to audit and communicate to non-technical stakeholders, and can serve as a basis for procurement policy formulation and periodic evaluation of supplier performance in university cafeteria settings.
Klasifikasi Penyakit Retina Menggunakan Transfer Learning  Green Ferry Mandias; George William Tangka; Ivanna Junamel Manoppo
Jurnal Ilmiah Matrik Vol. 28 No. 1 (2026): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/c2mhyz46

Abstract

Retinal diseases such as Diabetic Retinopathy, glaucoma, and cataracts are major causes of global blindness that require early detection. However, manual screening faces challenges related to scalability and consistency, especially in resource-limited settings. This research aims to evaluate the comparative performance of six state-of-the-art Convolutional Neural Network (CNN) architectures to identify the most optimal model for the four-class retinal disease classification (cataract, diabetic retinopathy, glaucoma, and normal). The five models evaluated are EfficientNetB0, InceptionV3, MobileNetV2, VGG16, and VGG19. All models were trained using a transfer learning approach on the "eye_diseases_classification" dataset, compiled from various sources to ensure model generalization. The results of the comparative evaluation show that all models successfully achieved an accuracy above 90%, confirming the effectiveness of transfer learning in this task. However, the EfficientNetB0 architecture demonstrated the best performance, recording the highest accuracy of 93.67% and the best balance in precision and F1-Score metrics. The EfficientNetB0 model is established as the most reliable solution and is recommended for the development of an efficient automated early detection system to support clinical workflows.
Co-Authors Biya, Antares Nathan Andrew Cherry Lumingkewas Chrisanti, Ibrena Reghuella Delviolin, Ellie Ophelia Djamal, Eleonora Edson Yahuda Putra Edwin Melky Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo Green Arther Sandag Green Ferry Mandias, Green Ferry Ivanna Junamel Manoppo Izaac, Bryan Vallentino Jacquline Waworundeng Joe Yuan Mambu JUNIOR LAKAT Kolibu, Mario Jonatan Lahamendu, Irene Gloria Lidya Chitra Laoh Maringka, Raissa Ngangk, Stivia Yuliefri Lulij Nurwijayanti Raissa Camila Maringka Semmy Wellem Taju Sondakh, Debby Erce Stenly Ibrahim Adam Stenly Richard Pungus Tonny Irianto Soewignyo Wilsen Grivin Mokodaser