Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Technology
Bradika Almandin Almandin Wisesa
Politeknik Manufaktur Negeri Bangka Belitung
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Drowsiness Detection using YOLOv12 Bradika Almandin Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2212

Abstract

Drowsiness poses significant risks in safety-critical activities such as driving, industrial operations, and online learning. While advanced deep learning models (e.g., CNN-LSTM hybrids) achieve high accuracy in driver drowsiness detection, they often require substantial computational resources, limiting deployment on embedded or resource-constrained devices. This study addresses the research gap in lightweight, real-time, non-invasive drowsiness detection by developing an embeddable library using YOLOv12, an attention-centric single-stage detector known for balancing speed and accuracy. The model was trained on a custom dataset of 2312 video frame sequences (1011 "awake" and 1301 "drowsy" states, captured from varied angles under consistent lighting), augmented with standard techniques (e.g., brightness/contrast adjustments, flips, and rotations) to enhance generalization. It was evaluated through 80 real-time trials across multiple subjects. Performance metrics include accuracy of 93%, precision of 0.94, recall of 0.91, and F1-score of 0.93. The system detects drowsiness via facial bounding boxes followed by state classification (integrating eye/mouth aspect ratios) in real time. The main contribution is a proof-of-concept YOLOv12-based approach for non-invasive drowsiness monitoring, offering faster inference suitable for embedded applications (e.g., vehicle systems, meeting tools, or industrial safety) compared to heavier hybrid models. Limitations include some remaining sensitivity to extreme lighting/angles and dataset scale; future work will expand datasets, incorporate multi-modal cues, and further test robustness in diverse real-world conditions.
Rotten Apple Detection Using YOLOv12 for Postharvest Quality Sorting Bradika Almandin Almandin Wisesa; Vivin Mahat Putri; Evvin Faristasari; Sirlus Andreanto Jasman Duli; Satria Agus Darma
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2290

Abstract

Detecting rotten apples is critical in postharvest quality sorting, as spoiled fruit can accelerate overall decay, shorten shelf life, and lower market value. This study introduces a real-time, edge-deployable object detection method using YOLOv12 to differentiate between fresh and rotten apples in RGB images. The dataset included 2,312 annotated images, with 1,011 fresh apples and 1,301 rotten apples, split into training, validation, and testing sets with an 80:10:10 stratified ratio. To enhance model generalization, data augmentation techniques such as mosaic augmentation, horizontal flipping, rotation, scaling, HSV color jitter, and mixup were applied. The YOLOv12s model was trained with an input resolution of 640 × 640 and evaluated using accuracy, precision, recall, and F1-score. The results from the confusion matrix showed that the model achieved an accuracy of 0.93, precision of 0.91, recall of 0.89, and F1-score of 0.90, indicating that YOLOv12 offers a lightweight and effective framework for rapid apple quality assessment. The primary contribution of this work lies in integrating an attention-focused YOLOv12 detector into a postharvest apple sorting workflow, accompanied by quantitative performance evaluation and robustness analysis under challenging visual conditions.
Co-Authors Evvin Faristasari Satria Agus Darma Sirlus Andreanto Jasman Duli Vivin Mahat Putri