Article Per Year (5 Year)
p-Index From 2021 - 2026
2.189
P-Index
This Author published in this journals
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

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

Found 12 Documents
Search

 1   2 
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.
Hybrid Feature Selection for Effective Heart Disease Detection: A Multi-Algorithm Machine Learning Approach Lonang, Syahrani; Putra, Ahmad Fatoni Dwi; Syuhada, Fahmi; Firdaus, Asno Azzawagama; Masitha, Alya
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38815

Abstract

Purpose: This research aims to develop an effective early detection model for heart disease with data balancing and hybrid feature selection. The study seeks to enhance predictive accuracy and minimize errors, providing a robust model for clinical decision support systems. Methods: The study used the Heart Failure Prediction dataset derived from Kaggle. A novel hybrid framework was implemented, integrating SMOTEENN (Synthetic Minority Over-sampling Technique + Edited Nearest Neighbors) for data balancing and a Hybrid Feature Selection (HFS) method combining Chi-square and Backward Elimination. Eight machine learning algorithms, including Logistic Regression, Naïve Bayes, Decision Tree, K Nearest Neighbor, Random Forest, Gradient Boosting, Support Vector Machine, and XGBoost. Performance was assessed based on accuracy, precision, recall, f1-score, specificity, AUC Score, fallout and miss rate. Result: The proposed framework significantly improved classification performance across all algorithms. The Random Forest model emerged as the optimal classifier, achieving an accuracy of 99.44%, AUC Score of 99.98%, and a specific reduction in miss rate to 0.92% (from 10.03% baseline). The HFS method successfully reduced the feature space by 54%, identifying 'ExerciseAngina', 'FastingBS', 'ST_Slope', 'ChestPainType', and 'Sex' as the most critical predictors. The model outperformed standard approaches and recent state-of-the-art benchmarks by over 10% in accuracy. Novelty: This study introduces a synergistic integration of SMOTEENN with hybrid feature selection. The combination significantly improves model performance in early heart disease detection.
Co-Authors Adipta, Marazaenal Afriansyah, M. Anggarista, Randa Arsan Kumala Jaya Asmaul Husna 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