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This Author published in this journals
All Journal East Asian Journal of Multidisciplinary Research (EAJMR) Riau Jurnal Teknik Informatika
Nancy Jeane Tuturoong
Universitas Sam Ratulangi
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering

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Binking Application: Early Detection of Computer Vision Syndrome Nancy Jeane Tuturoong
East Asian Journal of Multidisciplinary Research Vol. 2 No. 5 (2023): May, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/eajmr.v2i5.4409

Abstract

Policies regarding the use of gadgets are very necessary at this time, especially during the COVID-19 pandemic, because at this time people inevitably have to be willing to learn and use technology such as gadgets / smartphones in order to meet their respective needs, studying, buying goods, reading news and other things, without a policy in the use of this technology will certainly have a negative impact on users such as Computer Vision Syndrome, this journal aims to determine the impact of Computer Vision Syndrome and also create an eye blink detection program which aims to prevent and detect eye blinks so as to avoid the negative effects of Computer Vision Syndrome such as eye disorders. This research was conducted using four students of the Faculty of Electrical Engineering, Sam Ratulangi University as sample’s data in making this Blink Detection program, the program was made through several references and several improvements until it was finally completed and the program could detect many eye blinks of gadget users and provide warnings to the user.
Peningkatan Akurasi Deteksi Penyakit Daun Padi Menggunakan Augmentasi Data Berbasis Generative Adversarial Networks (GAN) Nasya Sunia Tubuon; Nancy Jeane Tuturoong; Salaki Reynaldo Joshua
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4585

Abstract

Rice is a staple food crop for more than half of the global population, with Asia contributing approximately 90% of global production. Leaf diseases, particularly Blast and Bacterial Blight, are major factors contributing to reduced rice productivity in Indonesia. Conventional detection methods based on visual observation are often subjective and time-consuming, highlighting the need for more reliable automated detection systems. This study aims to implement StyleGAN2-ADA to generate synthetic rice leaf disease images and evaluate its impact on the performance of Convolutional Neural Network-based classification. This research employed a quantitative experimental approach by comparing two scenarios: Baseline without GAN augmentation and Proposed with StyleGAN2-ADA synthetic image augmentation. Two CNN architectures, EfficientNetB0 and ResNet50, were evaluated using accuracy, precision, recall, F1-Score, and confusion matrix metrics. The quality of synthetic images was assessed using the Fréchet Inception Distance. The results demonstrated that StyleGAN2-ADA augmentation improved the overall F1-Score of EfficientNetB0 from 97.62% to 98.20%, with the largest improvement observed in the Blight class, increasing by 3.11%. For ResNet50, the overall F1-Score increased from 97.60% to 98.20%, although the Blast class showed no performance improvement after augmentation. GAN augmentation provided the most consistent benefits for the minority Blight class, while its impact on the Blast class varied across metrics. In EfficientNetB0, improvements in precision and F1-Score were accompanied by a decrease in recall. These findings indicate that model evaluation should consider class-specific performance and the trade-off between precision and recall rather than relying solely on aggregate metrics
Co-Authors Nasya Sunia Tubuon Salaki, Reynaldo Joshua