Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis
Nur Santoso, Ubaidillah Ramadhan
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

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

Found 2 Documents
Search

 1 
Deteksi Sampah Botol Plastik di Perairan Menggunakan YOLO v4-Tiny Nur Santoso, Ubaidillah Ramadhan; Gamar, Farida
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 1 (2025): Januari 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i1.1744

Abstract

This study focuses on the implementation of the YOLOv4-Tiny algorithm on Raspberry Pi 5 for detecting plastic bottle waste in aquatic environments. The primary goal is to optimize the frame per second (FPS) while maintaining detection accuracy. A dataset consisting of 914 images was augmented using RoboFlow to enhance the robustness of the model under real-world conditions. Experiments were conducted in a controlled pool environment with an input resolution of 320x320 pixels. Results demonstrated an average FPS of 7-8, with detection accuracy ranging between 67% and 80%. Further evaluation reported a total loss of 0.3, mean Average Precision (mAP) of 97.94%, precision of 93%, recall of 96%, F1 score of 0.95, and an average Intersection over Union (IoU) of 76.47%, indicating effective bounding[1] box prediction capabilities. These results highlight the potential of YOLOv4-Tiny as a lightweight and real-time detection solution, particularly for low-computational devices such as Raspberry Pi. The findings provide a solid foundation for developing efficient plastic waste detection systems, which can be deployed across various aquatic locations, supporting environmental monitoring and waste management initiatives.
Implementasi Transformasi Homografi dan YOLO v4-Tiny untuk Deteksi Botol dan Kaleng Nur Santoso, Ubaidillah Ramadhan; Gamar, Farida; Darmawan, Adytia
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 3 (2025): Juli 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i3.1981

Abstract

The increasing presence of inorganic waste such as plastic bottles and cans poses a serious environmental threat and demands efficient detection solutions. This study aims to develop a real-time detection and position estimation system for inorganic waste using the YOLO v4-Tiny algorithm combined with homography transformation. A total of 3,388 labeled images were prepared and augmented via the Roboflow platform. The detection model was trained using the Darknet framework, while homography was applied to estimate object positions in real-world coordinates. System performance was evaluated based on precision, recall, F1-score, mean Average Precision (mAP), and Intersection over Union (IoU). The results show a mAP of 86.43%, precision of 77%, recall of 90%, and an average IoU of 62.33%. The system achieved a frame rate of 3–5 FPS, demonstrating its potential for low-power embedded devices. This approach is suitable for real-time waste monitoring using computer vision in constrained environments.
Co-Authors Darmawan, Adytia Gamar, Farida