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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research ILKOM Jurnal Ilmiah Computer Based Information System Journal Jurnal Teknik Mesin Jurnal Ilmu Komputer dan Bisnis Jurnal Ekonomi Manajemen Sistem Informasi Indonesian Journal of Social Work Journal of Applied Data Sciences Jurnal Cahaya Mandalika International Journal of Computer and Information System (IJCIS) International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Tika Jurnal Info Sains : Informatika dan Sains Malikussaleh Journal of Mechanical Science Technology Jurnal Ilmiah Kebijakan dan Pelayanan Pekerjaan Sosial (Biyan) J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) Jurnal Puan Indonesia Journal International of Lingua and Technology JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Desain dan Analisis Teknologi Innovative: Journal Of Social Science Research Priviet Social Sciences Journal Jurnal Pengabdian Ibnu Sina Journal of Society Bridge JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Pengabdian Barelang Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) Jurnal Polimesin Computer & Science Industrial Engineering Journal Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Tukino, Tukino
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Journal : Journal of Applied Data Sciences

 1 
Application of Convolutional Neural Networks for Automated Iris Edge Detection in Sleepiness Monitoring during Blended Learning Tukino, Tukino; Yuhandri, Yuhandri; Sumijan, Sumijan
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.882

Abstract

This study introduces a novel lightweight Convolutional Neural Network (CNN) model, T-Net, designed for real-time drowsiness detection based on eye closure patterns. The model was developed to address the prevalent issue of student fatigue in resource-constrained environments, such as during prolonged online learning or blended learning sessions. Unlike traditional deep learning models, T-Net prioritizes efficiency while maintaining high accuracy, making it suitable for deployment on devices with limited computational resources. The model uses a 68-point facial landmark detection technique to extract the eye region and accurately classify eyelid states (open or closed). Evaluated on two benchmark datasets, Dataset-1 (342 eye images) and Dataset-2 (1,510 eye images), T-Net demonstrated superior performance, achieving classification accuracies of 99.33% and 99.27%, respectively, outperforming other pre-trained models such as VGG19, ResNet50, and MobileNetV2. Usability testing revealed a high acceptance rate, with a System Usability Scale (SUS) score of 84.5, indicating the system’s practicality for real-world use. Additionally, statistical analysis showed a significant correlation (r = 0.67, p 0.01) between prolonged screen time and the emergence of visual fatigue symptoms. This study highlights the effectiveness of a lightweight CNN approach for real-time fatigue monitoring, offering a balance between performance and computational efficiency. The results suggest that T-Net can be effectively integrated into student monitoring systems to ensure alertness during learning sessions. Future research will focus on expanding the dataset, integrating infrared imaging for low-light environments, and incorporating additional fatigue indicators such as yawning and head pose.
Image-Based Detection of Reduced Security Features in Indonesian Banknotes Using U-Net Architecture Andini, Silfia; Tukino, Tukino
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1087

Abstract

The circulation of fake currency banknotes in Indonesia continues to rise alongside rapid technological advancements, while conventional verification systems remain limited and often ineffective in detecting subtle authenticity cues. The main objective of this study is to develop an image-based fake currency detection system using the U-Net deep learning architecture and its modified version, T-Net, to enhance feature extraction and classification accuracy. The key contribution of this research lies in combining convolutional architectures with a practical, web-based interface that enables real-time image analysis, thus bridging the gap between model performance and user accessibility. A quantitative experimental method was employed, involving model development in Python using TensorFlow and Keras, and implementation of a Flask-based web application for real-time classification. The research utilized a dataset of 2,141 Indonesian rupiah banknote images, consisting of 1,015 genuine and 1,126 fake currency samples synthetically generated through digital modification of security features such as watermarks and color-shifting ink. Image preprocessing included resizing, normalization, and augmentation techniques such as random flipping and brightness adjustment to enhance data quality. Three convolutional architectures U-Net, ResNet-50, and the modified T-Net were trained and compared using identical hyperparameters. The T-Net model achieved the best performance, with 97.8% training accuracy, 82.6% validation accuracy, precision of 0.83, recall of 0.80, and an F1-score of 0.81. Despite the performance gap indicating overfitting, the model effectively distinguishes genuine from fake currency notes. The Flask-based interface allows users to upload images and receive classification results from all three models within 0.3–1.8 seconds per image. The findings demonstrate the feasibility and efficiency of U-Net based architectures for image-driven fake currency detection and provide a foundation for developing advanced, reliable, and real-time financial authentication systems that can strengthen digital security infrastructures in future applications.
Co-Authors AA Sudharmawan, AA Agustina, Alvi Ahmad, Sandi Ahnaf, Naufal Zubdi Akbari, Wahyu Azriel Akmal, Khafid Khaulsar Alfiah, Agry Alfiansyah, Muhammad Rindra Algifanri Maulana, Algifanri Amalia Amalia Amrizal Amrizal Angeli, Alvin Annam, Dyno Syaiful Apriani, Fitria April Lia Hananto Arif Rahman Hakim Arnomo, Sasa Ani Arsyad, Fachry Aulia, Aldi Azwanti, Nurul Baru Harahap Berkah*, Kamila Catur Nugroho ceni kirani valensyah Danny Manongga Deddy Prihadi Dede Kuswanda Dien Noviany Rahmatika Dzulqarnain, Fahmi Eichler, Luiz Elisa, Erlin Fadli, Muhammad Abil Faisal, Sutan Fajrin, Alfannisa Fauzi Ahmad Muda Ferdiansyah, Indra Fifi, Fifi FIKRI HAIKAL Guntur, Muhamad Hananto, Agustia Handayani, Citra Handoko, Koko Harman, Rika Hartono Wijaya, Sony Hendry Hilabi, Shofa Shofiah Hindriyanto Dwi Purnomo Huda, Baenil I Gede Iwan Sudipa Irawan, Bei Harira Irwan Sembiring Iwan Setiawan Jasmine Dina Sabila Karyadi Karyadi Kurnia, Nisa Kusuma P, ⁠Shafira Putri Leony, Alvina Lindo, Junius Manalu, Soli Vernika Mildawati, Milly Muammar Khaddafi Mubarok, Piky Muhammad, Daniel Muslih, Muhamad Nahampun, Mawi Nanda, Rizki Aulia Nofriani Fajrah, Nofriani Novalia, Elfina Novaria, Rachmawati Nurapriani, Fitria Oganda, Decut Della Pandiangan, Satria Patya, Dhea Intan PERNANDO, Pernando Pratama, Daffa Agung Pratiwi, Mutiana Priatna, Bayu Priyatna, Bayu Purba, rehni jayana Purnomo, Andromedo Cahyo Putria, Narti Eka Ramadhan, Muhammad Faiz Reswara, Hadaya Abhista Rivai, Samuel Saepul Aripiyanto Samosir, Epa Prima Melina Sari, Fitria Ratna Sari, Nilah Wati Indra Nur Meinda Sari Sarjon Defit Setianingsih, Krisna Dewi Sianturi, Nico Bangun Rezkyanto Silfia Andini, Silfia Silva, Tiago Simanjutak, Pastima Siregar, Amril Mutoi Situmorang, Awaljan Soleman, Soleman Sri Wahyuni Subarkah, Ade Suhara, Ade Sukoco, Dwi Heru Sulestra, Ikhwan Sumijan Sumijan Syahril Effendi Syamsiar, Syamsiar Syelfiyananda, Syelfiyananda Tiodora, Jeremy Tjong Wan Sen Triandy, Oky Versanudin Hekmatyar Wibisosno, Eko Gunawan Wong, Hendri Yana Cahyana Yohanna Siahaan, Winda Yuhandri Yuhandri, Yuhandri Zetli, Sri Zulkifli, Mohamad