Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Jurnal Edukasi Elektro REKAYASA Eduvest - Journal of Universal Studies
Candra, Wahyu Adhie
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Neuroscience Physics Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Collaboration of 2 Arm Robots in a Pick and Place Task with a Task Allocator Method Based on Computer Vision Candra, Wahyu Adhie; Anggreni, Pipit; Pardede, David Yizreel Maruli Valentino
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.49999

Abstract

Robotic arms have become integral in the manufacturing sector for automating repetitive tasks such as pick-and-place operations, which were traditionally performed manually. Manual execution is often limited by human fatigue, reduced accuracy, and the risk of injury, prompting a shift toward robotic automation. While single-arm robotic systems have been widely adopted, recent advancements now enable dual-arm collaboration, which introduces new technical challenges related to synchronization, precision, and object tracking. This study addresses these challenges by implementing an object detection system using YOLOv5 combined with HSV filtering to optimize performance under low computational constraints. The system communicates with robotic pole-arms via Python through Arduino serial communication. A perspective transformation technique is employed to ensure accurate mapping between 2D camera input and the robot's 3D operational space. The trained detection model achieved a mean Average Precision (mAP) of 97.7% at 0.5 and 60% at 0.5:0.95. Object detection testing yielded high evaluation metrics, including a precision of 1.00, a recall of 0.977, and an F1 score of 0.96. In real-world testing, the pick-and-place process demonstrated success rates of 95%, 85%, and 85% across three trials. These results indicate that the proposed system is highly effective for industrial applications and lays a foundation for further research into collaborative robot systems in dynamic environments.
Implementation of a Camera-Based Inspection System for Measuring the Diameter of 3D Printed Filaments Made from LDPE Water Bottle Caps Sunarya, Adhitya Sumardi; Candra, Wahyu Adhie; Lestari, Miranti; Taqi, Khoutal
Jurnal Edukasi Elektro Vol. 9 No. 2 (2025): Jurnal Edukasi Elektro Volume 9, No. 2, November 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i2.87996

Abstract

Advances in additive manufacturing technology are driving the need for high-quality, affordable, and sustainable filaments. In Indonesia, the demand for filaments and extruders is largely met through imports, prompting researchers to innovate and develop filament extrusion machines, including those at the Politeknik Manufaktur Bandung, which has developed a filament extrusion machine equipped with a camera-based inspection and control system to produce high-quality filaments from LDPE gallon cap waste. This system integrates a digital microscope camera and a microcontroller to monitor the diameter of the extruded filament and correct the diameter by controlling the speed of the pull motor to stabilize the diameter in real-time. Image processing uses a color-based edge detection algorithm, and camera calibration results show a precision of 0,009 mm/pixel. Diameter data is sent to the Arduino Mega, which then uses the L298N driver to control motor speed via the Sliding Mode Control (SMC) method. Test results show that at 15 RPM, the average filament diameter is 1,77 mm with an error of 1,14%, while at 20 RPM it becomes 1,56 mm with a larger error of 10,86%, compared to the standard commercial filament size of 1,75mm. SMC control also demonstrated better performance than PID in terms of system accuracy in reaching the set point. This system could serve as an economic and ecological solution for local recycled filament production, reducing dependence on imported products.
Desain Prototipe Automatic Trash Rake dengan Metode Gaussian Mixture Model Erdani, Yuliadi; Candra, Wahyu Adhie; Aisyah, Aulia
Rekayasa Vol 17, No 1: April, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i1.21148

Abstract

Trash rake is a trash netting tool used to transport trash in rivers. In its application, the operation of trash rake is still mostly done manually by the operator. In this case, there is no effectiveness in terms of the use of human resources or the effectiveness of machines. Therefore, in this study, a development was made on an automatic trash rake machine that works automatically if the waste stuck on the trash rake machine has experienced density. The image processing method used is the Gaussian Mixture Model (GMM). Used camera to capture the image of the density of the trash. The captured image is processed by GMM method. Then the data is processed on a computer and the data is displayed on the web. Arduino will drive the motor on the trash rake machine automatically with the parameters of the waste density data. The results obtained, the system can detect the level of waste density with an accuracy of 67.5% in bright, dim and dark conditions with the camera position according to the object observation area. The trash rake machine can turn on and off automatically based on the detected waste density.
Co-Authors Aisyah, Aulia Anggreni, Pipit Lestari, Miranti Pardede, David Yizreel Maruli Valentino Sunarya, Adhitya Sumardi Taqi, Khoutal Yuliadi Erdani, Yuliadi