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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Mobile and Forensics Jurnal Abdi Insani Journal of Computer and Information System (J-CIS) SainsTech Innovation Journal Jurnal Ilmiah Edutic : Pendidikan dan Informatika Scientific Journal of Informatics IJMST SainsTech Innovation Journal
Ahmad Fatoni Dwi Putra
Universitas Qamarul Huda Badaruddin Bagu
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience

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Journal : mobile and forensics

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Enhancing Early Diabetes Detection Using Tree-Based Machine Learning Algorithms with SMOTEENN Balancing Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Firdaus, Asno Azzawagama; Syuhada, Fahmi; Sa'adati, Yuan
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.14495

Abstract

Diabetes continues to be a critical global health issue, demanding accurate predictive systems to enable preventive interventions. Traditional diagnostic tests lack efficiency for large-scale early screening, which has led to growing interest in artificial intelligence solutions. This research proposed an effective methodology for diabetes classification based on tree-based algorithms enhanced with SMOTEENN balancing. The study employed the Kaggle Diabetes Prediction Dataset with 100,000 instances and eight medical and demographic features. Preprocessing steps included handling missing and duplicate values, encoding categorical variables, and scaling numerical attributes with Min-Max normalization. To address severe class imbalance, SMOTEENN was adopted, producing a cleaner and more balanced dataset. Model evaluation was performed using Stratified 5-Fold cross-validation on six classifiers: Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and CatBoost. Experimental results indicated significant gains after balancing, with ensemble methods outperforming single-tree baselines. Random Forest delivered the best overall performance (98.93% accuracy, 98.96% F1-score, 99.16% recall, 99.94% AUC), followed by CatBoost and XGBoost with comparable results above 99% AUC. While Decision Tree benefited most from SMOTEENN in relative terms, it remained less competitive. Analysis of the importance of the analysis revealed HbA1c level and blood glucose level as dominant predictors, validating clinically meaningful learning. These findings suggest that integrating hybrid resampling with ensemble tree classifiers provides reliable and general predictions for diabetes risk. The approach holds promise for deployment in healthcare decision support systems.
Design and Expert Validation of AI-Supported Collaborative Digital Learning Model for Introductory Multimedia Course SPADA Indonesia Muh. Al Amin; Ahmad Fatoni Dwi Putra
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.15407

Abstract

This study develops and conceptually validates an AI-Supported Collaborative Digital Learning (AI-CDL) model for an Introduction to Multimedia course delivered through the national LMS, SPADA Indonesia. Using a Design and Development Research approach aligned with early-stage Design-Based Research, the study followed four phases: (1) contextual and needs analysis of course outcomes,  commonly referred to as CPL (Capaian Pembelajaran Lulusan) and CPMK (Capaian Pembelajaran Mata Kuliah), existing learning activities, and available LMS affordances; (2) conceptual model design grounded in collaborative learning theory and multimedia learning principles; (3) development of project-based collaborative scenarios and supporting artefacts (learning paths, assessment rubrics, and responsible AI-use guidelines); and (4) conceptual validation through expert review and alignment with recent evidence syntheses on AI-supported collaboration in higher education. The resulting AI-CDL model operationalizes AI support across three layers intelligent content support, AI-supported collaboration, and AI-augmented production workflows mapped to key multimedia topics and implemented through SPADA activities. Expert feedback informed iterative refinements, particularly in task orchestration, assessment transparency, and ethical safeguards. This study contributes a validated design blueprint and transferable design principles for integrating AI into collaborative multimedia learning within a national-scale LMS. Future work will empirically evaluate learning processes and outcomes through classroom implementation and learning analytics.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Arsan Kumala Jaya Asmaul Husna RS Asno Azzawagama Firdaus G. Tangaro, Diana May Glaiza Grancho, Bernadine Hadi San, Ahmad Syahrul Heri Wijayanto I Gede Putu Wirarama Wedashwara Wirawan Joni Saputra Joni Saputra Kumoro, Danang Tejo Mindi Richia Putri Muh. Al Amin Muhajir Yunus Muhamad Nizam Azmi Muhamad Yamin Muhammad Ari Rifqi Nuraqilla Waidha Bintang Grendis Pratama, Ramadhana Agung Sa'adati, Yuan Satria Utama Satrio Budi Santoso Sumarno Wijaya Sumiati Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Talirongan, Florence Jean B. Yuan Sa'adati