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All Journal Informatik : Jurnal Ilmu Komputer 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
An Intelligent Real-Time Detection and Classification System for Sustainable Aquatic Ecosystem Monitoring in Tropical Waters Nancy Jeane Tuturoong; Jimmy Reagen Robot; Djuwita Aling
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12806

Abstract

Sustainable monitoring of aquatic ecosystems in tropical waters requires effective, adaptive, and intelligent technological approaches. This work proposes a design and evaluation of an intelligent real-time system for detecting and classifying freshwater fish species using You Only Look Once version 8 (YOLOv8), a recent deep learning architecture. The dataset used consists of 13,018 images of fishes constituting seven primary species: Catfish, Piranha, Tilapia, Betta, Milkfish, Gourami, and Koi. The research process includes preprocessing images (resizing and augmentation) and training the model using transfer learning techniques to expedite convergence and enhance accuracy. The evaluation findings show that the created system attained a maximum classification accuracy of 100% on the testing dataset. The model was successfully able to recognize species with distinct morphological traits, but a minor decrease in accuracy was reported in classifying fish with inductive body shapes. Overall results substantiate that YOLOv8 has solid potential as an efficient and replicable artificial intelligence-based approach to assisting sustainable aquatic ecosystem monitoring in tropical waters.
Co-Authors Djuwita Aling Jimmy Reagen Robot Nasya Sunia Tubuon Salaki, Reynaldo Joshua