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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Jurnal Teknik IPTEK The Journal for Technology and Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) ELINVO (Electronics, Informatics, and Vocational Education) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Applied Information System and Management Indonesian Journal of Electrical Engineering and Computer Science Lontara: Journal of Health Science and Technology Jurnal Algoritma International Journal of Community Service (IJCS) International Journal of Scientific Multidisciplinary Research (IJSMR) Lontara Journal Of Health Science And Technology Journal of Information Engineering and Technology
Fatchul Arifin
Universitas Negeri Yogyakarta
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A System Identification of Diabetes Based on Ensemble Method: Bagging, Random Forest, and Extreme Gradient Boosting Jonas de Deus Guterres; Fatchul Arifin
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 10 No. 2 (2025): November 2025
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v10i2.89649

Abstract

Diabetes is a prevalent chronic illness that is recognized worldwide, with an estimated prevalence in adults ranging from 42% to 170% globally. To reduce the likelihood of developing diabetes, it is vital for individuals at an increased risk to understand the importance of embracing healthy lifestyles and managing their consumption of foods that can potentially raise insulin levels in the body. Therefore, it is crucial to detect early pre-symptoms to minimize the incidence of individuals being afflicted by this condition without their awareness. Machine learning has emerged as a contemporary tool that aids in the prediction of various diseases, including diabetes, by analyzing patient data. Despite numerous research attempts using various machine learning techniques, achieving high accuracy in predicting diabetes has remained challenging. Therefore, this study implemented an ensemble approach that combined bagging, random forest, and Extreme Gradient Boost (XGBoost) algorithms to enhance the predictive performance for diabetes. This approach involved evaluating selected features based on their highest correlation and incorporating all available features in the analysis. Based on the results, the bagging technique demonstrated the highest accuracy of 0.83 in predicting model 6. Following closely behind was the random forest algorithm, which achieved an accuracy of 0.82, and XGBoost with an accuracy of 0.81.
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Herman Dwi Surjono; Fatchul Arifin; Rohmat Gunawan; Randi Rizal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7320

Abstract

This study proposes a web-based deepfake detection system that integrates Vision-Based Excitation technology and Transformer-based intelligence, called VERITAS (Vision-based Excitation and Robust Intelligence for Transformer-Assisted Deepfake Detection). The system is designed to automatically detect manipulated images and videos by leveraging the Vision Transformer (ViT) model architecture, equipped with the Grad-CAM mechanism for interpretability of detection results. The study conducted a series of tests to measure the system's performance in various scenarios and ensure its reliability in dealing with various types of input. Load testing results showed that up to 30 simultaneous users, the system can operate with good responsiveness (average response time of 130 ms) without experiencing errors. However, when the number of users reaches 40 or more, the system performance drops drastically with a very high error rate, reflecting limitations in handling server load. Real-world testing showed the system can detect deepfakes with an accuracy of 73.61%, with results varying depending on the quality of the tested images. Furthermore, unit functional testing and coverage analysis demonstrated an excellent test pass rate (85%), with all major functions running smoothly and error handling needed to be fixed in some code sections. Overall, the VERITAS system demonstrates strong potential for web-based deepfake detection, with high reliability under low load and adequate performance in functional testing. However, further optimization is needed to handle higher user loads.
Co-Authors Agus Setyawan Al Matiin, Muhammad Azril Haidar Alam Rahmatulloh Alfath, Muhammad Rofiq Banu Ali Sophian Ali Sophian Anggun Winursito Anis Nurashikin Nordin Ardy Seto Priambodo Aris Nasuha Arselan Ashraf Arselan Ashraf Bekti Wulandari Caryn, Femilia Hardina Dessy Irmawati Dwi Kurniawan, Prabowo Dwi Marlina Dyah Muslihah Erman Agusta, Gino Faisal Ahmed Assaig Fauziah, Khasna Nur Fendi Aji Purnomo Gunawan, Rohmat Gunawan, Teddy Surya Gusti, Wahyu Ramadhani Habiburrahman, M. Hazlan, Mohamed Alfian Al-Zikry Hendri Maradona, Hendri Herjuna Artanto Herman Dwi Surjono Indradewa, Rhian Iskandar, Ranu Jonas de Deus Guterres Junior, Andy Sadewa Kartiwi, Mira Lahiya, Indah Wardati Malik Arman Morshidi Mashoedah Mashoedah Mauridhy Hery Purnomo Mir Suhail Alam Mira Kartiwi Mira Kartiwi Moh Ali Aljauhari Mohamed Hadi Habaebi Mohamed Hadi Habaebi Muhammad Faozan Mulad Khalik Muhammad Rofiq Banu Alfath Muslikhin Ningrum Astriawati Nur Hasanah Pahtoni, Tri Yuli Prabowo, Hernawan Prasetyo Prasetyo Raharjo, Muhammad Mukti Rahmahwati, Febri Suci Rajif, R. Amirur Ramadhan, Rayhan Dwi Rashidah Funke Olanrewaju Richard Sambera Sagala Rizal, Randi Rosminazuin Ab. Rahim Rozainanee Mohd Zain sapina, Sapina Sri Andayani Suprapto Suprapto Swadyaya, Putu Yana Syaiful Rijal, Bait Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Teddy Surya Gunawan Tri Arief Sardjono Yaacob, Mashkuri Zainal Arifin Zainihariyati Mohd Zain