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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 Bulletin of Informatics and Data Science Servitium Smart Journal
Wilsen Grivin Mokodaser
Universitas Klabat, Sulawesi Utara, Indonesia
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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An Optimized Balanced-Learning Framework for Malignant Skin Lesion Triage Using Compound-Scaled Neural Networks Argha Orion Silitonga; Raissa Camilla Maringka; Wilsen Grivin Mokodaser; George M W Tangka; Marchel Timothy Tombeng
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.165

Abstract

Skin cancer represents a prevalent global health challenge, and early detection is very important to reduce mortality risk. Manual dermoscopic diagnosis risks human bias, making deep learning classification a vital research topic. While several previous studies utilizing the ISIC 2019 dataset have demonstrated high diagnostic capabilities, they primarily focus on complex multi-class classification. However, in real-world clinical workflows, the primary necessity is a swift, dependable triage system that can confidently distinguish dangerous lesions from non-threatening ones. Furthermore, many existing models require substantial computational overhead yet still suffer from imbalanced accuracy when dealing with minority malignant classes. The novelty of this study lies in addressing these gaps by developing a streamlined, clinically practical binary screening framework optimized specifically for malignant-versus-benign triage. The original multi-class labels were transformed into binary classes where malignant lesions consist of melanoma (MEL), basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), while benign lesions consist of nevus (NV), benign keratosis (BKL), dermatofibroma (DF), and vascular lesions (VASC). The experiment applied transfer learning with ImageNet-pretrained weights, data augmentation, class weighting, and fourfold stratified cross-validation. Unlike prior works that rely on resource-heavy architectures, we leverage the compound-scaled EfficientNet-B4 backbone—delivering superior feature representational power with significantly fewer parameters evaluate on a large-scale cohort of 25,331 dermoscopic images. Experimental results show that the proposed model achieved an average accuracy of 89.77% and an average ROC AUC of 96.16%. The best fold obtained 91.49% accuracy with ROC AUC of 97.19%. Simultaneously, the framework maintained an average F1-score of 89.20%
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.
Model Random Forest Data Historis Multivariat Untuk Prediksi Pendapatan Asuransi Wilsen Grivin Mokodaser; Hartiny Koapaha; Stenly Ibrahim Adam
IDEALIS : InDonEsiA journaL Information System Vol. 8 No. 2 (2025): Jurnal IDEALIS Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v8i2.3512

Abstract

Perusahaan asuransi adalah perusahaan keuangan non-bank yang melindungi nasabah dari risiko dan mengumpulkan uang dari premi nasabah selama periode tertentu, sesuai dengan ketentuan polis. Karena perusahaan asuransi telah lama terlibat dalam perekonomian negara, masyarakat tidak begitu ragu akan layanan yang mereka tawarkan. Disebabkan oleh ketidakpastian yang terkait dengan hal-hal seperti kesehatan, pendidikan, harta-benda, dan kematian, kesadaran masyarakat tentang pentingnya asuransi terus meningkat. Asuransi menjadi alat penting bagi masyarakat untuk mengantisipasi risiko atau kerugian di masa depan. model Random Forest diterapkan untuk memprediksi pendapatan asuransi bulan berikutnya berdasarkan data historis multivariat dari bulan Januari hingga Juli/Agustus. Hasil evaluasi menunjukkan bahwa model memiliki performa yang cukup baik dalam menangkap pola pendapatan, dengan skor evaluasi Mean Absolute Error (MAE) sebesar ±25.139.426 menunjukkan bahwa rata-rata kesalahan prediksi hanya sekitar 25 juta rupiah, angka yang masih tergolong wajar jika dibandingkan dengan skala pendapatan keseluruhan. Mean Squared Error (MSE) sebesar 2.9815 × 10¹⁵ mencerminkan adanya beberapa error besar, meskipun hal ini wajar mengingat skala data dan keberadaan outlier yang sulit dihindari. R² Score sebesar 0.85 menandakan bahwa 85% variabilitas pendapatan dapat dijelaskan oleh model dari data historis, yang menunjukkan performa prediksi yang sangat baik. Kontribusi ilmiah dari penelitian ini adalah penerapan pendekatan regresi non-linear berbasis Random Forest untuk melakukan peramalan pendapatan asuransi menggunakan data multivariat historis bulanan, yang jarang dibahas secara mendalam dalam konteks industri asuransi. Pendekatan ini tidak hanya menyoroti efektivitas Random Forest dalam menangkap pola musiman dan hubungan non-linier antar variabel waktu, tetapi memberikan landasan eksplorasi metode machine learning lanjutan dalam analisis data asuransi.
Information Technology Governance Analysis Using COBIT 2019 Framework at Bank Mandiri Girian Bitung Branch Toetik Wulyatiningsih; Wilsen Grivin Mokodaser; Joe Yuan Mambu
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.642

Abstract

The advancement of information technology (IT) has become essential for organizations, including Bank Mandiri, where it underpins critical business functions. This study examines the implementation of IT governance at Bank Mandiri’s Girian branch using the COBIT 2019 framework, a comprehensive tool for managing IT processes effectively. Through a qualitative case study approach and interviews with key stakeholders, the study analyzes 40 IT processes across 11 design factors, with each factor scored between 75 and 100 to prioritize their importance. High-priority processes, such as Managed Solutions Identification and Build (BAI03), Managed Requirements Definition (BAI02), Managed IT Changes (BAI06), and Managed Projects (BAI11), are identified as critical to operational stability, customer satisfaction, and strategic alignment. These objectives play a fundamental role in resource allocation, supporting seamless IT operations and enhancing customer service. Processes with lower scores are deprioritized, allowing strategic focus on high-impact areas. This prioritization framework helps ensure efficient resource use, aligns IT governance with organizational goals, and reinforces the branch’s commitment to achieving reliable, customer-focused IT management. The study underscores the indispensable role of IT in supporting Bank Mandiri’s operations, where any IT disruption could significantly impact business continuity and customer satisfaction.
Explainable Machine Learning for Food Calorie Prediction Using Tree-Based Ensemble Models and SHAP Analysis Wilsen Grivin Mokodaser; Tonny Irianto Soewignyo; Argha Orion Silitonga; Regi Fernando Najoan
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16809

Abstract

Accurate food calorie prediction is essential for nutritional assessment, dietary planning, and intelligent health applications. However, achieving high predictive accuracy while maintaining model interpretability remains a challenge for many machine learning approaches. This study proposes an explainable machine learning framework for food calorie prediction using tree-based ensemble models and SHAP (SHapley Additive exPlanations). A dataset containing 1,346 food samples with three nutritional attributes-proteins, fat, and carbohydrate-was used. Data preprocessing included logarithmic transformation and an 80:20 train-test split. Three machine learning models, namely Linear Regression, Random Forest, and XGBoost, were developed and evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). Hyperparameter optimization for XGBoost was performed using RandomizedSearchCV with five-fold cross-validation. Experimental results showed that Random Forest achieved the best predictive performance with an MAE of 0.1149, RMSE of 0.2869, and an R² score of 0.9008, outperforming both XGBoost and Linear Regression. Cross-validation further demonstrated the robustness of the selected model, yielding a mean R² of 0.9316 with a standard deviation of 0.0239. Residual analysis indicated prediction errors centered near zero without noticeable systematic bias. SHAP analysis provided both global and local model interpretability, identifying carbohydrate as the most influential feature, followed by fat and proteins. The findings demonstrate that integrating tree-based ensemble learning with SHAP enables accurate and transparent calorie prediction, making the proposed approach suitable for nutritional decision support and explainable artificial intelligence applications.   Keywords - Food calorie prediction, Explainable artificial intelligence, SHAP, Random Forest, XGBoost, Machine learning, Nutritional analysis.
Co-Authors Argha Orion Silitonga Argha Orion Silitonga Cherry Lumingkewas Erienika Meiling Lompoliu Fanny Soewignyo George M W Tangka George Morris William Tangka Hartiny Koapaha Joe Yuan Mambu Koapaha, Hartiny Longkutoy, Desmond Marchel Timothy Tombeng Monica Dwijayanti Raissa Camilla Maringka Regi Fernando Najoan Samidi Samidi Silitonga, Argha Orion Stenly Ibrahim Adam Stenly Ibrahim Adam Stenly Richard Pungus Tangka, George Toetik Wulyatiningsih Tonny Irianto Soewignyo Tonny Irianto Soewignyo Triyono, Gandung Wagiu, Wayne Gilbert