Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science The Indonesian Journal of Computer Science
Bustami, M Irwan
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

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

Found 3 Documents
Search

 1 
Deteksi Objek Boneka Korban pada Kontes Robot SAR Indonesia Menggunakan ESP32-cam Taupiq, Arahmad; Pratama, Yovi; Bustami, M Irwan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5979

Abstract

The 2024 Indonesian SAR Robot Contest demands the ability of robots to differentiate between dummy dolls and victim dolls in emergency situations. This SAR robot has the main goal of rescuing victims and bringing them to a safe zone, so the author explores the implementation of object detection on SAR robots using ESP32-cam to detect victim dolls. The authors used the Edge Impulse platform, a TinyML platform, to train an object detection model using the Faster Objects, More Objects (FOMO) architecture. This model is optimized to run efficiently on resource-limited devices such as the ESP32-cam microcontroller. Training data was obtained by taking pictures of dummy dolls and victim dolls in various angles, lighting conditions and backgrounds using a camera from the ESP32-cam. The confusion matrix results from the model training process showed that the F1 score reached 100% and when testing the model, the object detection model was able to detect the victim doll with adequate accuracy, even though there were challenges such as variations in position and environmental conditions so the researchers used additional algorithms to increase detection accuracy. . The use of FOMO allows faster object detection and is able to detect more objects in one frame. This implementation shows great potential in the development of more efficient and autonomous SAR robots for rescue missions. These findings contribute to improving robotic technology, one of which is in SAR operations and provide a basis for further research in the application of object detection.
Kontrol Navigasi Robot Hexapod berbasis Inverse Kinematic dan Body Kinematic untuk Stabilitas Optimal di Medan Ekstrem Pratama, Yovi; Saputra, Chindra; Toscany, Afrizal Nehemia; Bustami, M Irwan; Taupiq, Arahmad
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6007

Abstract

This study discusses the application of Inverse Kinematics (IK), Body Kinematics (BK), and Bézier Curves in a hexapod robot to efficiently control leg movements in a three-dimensional space. IK is used to calculate joint angles based on the desired target position, while BK enables adjustments to the robot's body posture to maintain stability during movement. Simulations demonstrate that these two approaches can produce accurate and controlled movements. Additionally, Bézier Curves are applied to the foot trajectory, significantly enhancing the smoothness of movements and the robot's stability during transitions from one step to the next. Testing the hexapod robot over a distance of 2.10 meters showed a 70% success rate with an average error of 4.2 cm. Further testing of the robot's stability on an inclined X-axis revealed that the robot could adapt to inclines up to 35 degrees; however, at inclines exceeding 35 degrees, the robot was unable to maintain balance. Based on the results, it can be concluded that the combination of IK, BK, and Bézier Curves effectively supports the hexapod robot's movement with a step accuracy of 70% and high stability when adapting to inclines up to 35 degrees. Improving stability in more extreme terrains and enhancing performance in more diverse environments are the primary focuses for maximizing the hexapod robot's capabilities.
PENGGUNAAN YOLO UNTUK DETEKSI ROBOT DAN GAWANG PADA ROBOT SEPAK BOLA BERODA Surya, Muhammad; Toscany, Afrizal; Saputra, Chindra; Pratama, Yovi; Bustami, M Irwan
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4575

Abstract

The ability to detect objects in real-time is a crucial factor in enhancing a robot's performance in understanding and adapting to dynamic environments. This research aims to develop and implement an object detection system on a wheeled soccer robot using the YOLOv11 algorithm, applied to images generated by omnidirectional and front-facing cameras. The system leverages deep learning technology for data labeling, model training, and performance evaluation. Testing was conducted by comparing the object detection results from both types of cameras, as well as analyzing performance metrics such as precision, recall, F1-score, and accuracy. The results show that the YOLOv11 model is effective in detecting objects in real-time, with a detection accuracy of 95.91% for the front camera and 96.7% for the omnidirectional camera. The highest precision and recall were recorded in the robot class, with precision of 99.12% and recall of 97.40% for the front camera, and precision of 96.5% and recall of 97.8% for the omnidirectional camera. The use of a combination of cameras proved to expand the robot's field of vision, enhancing object detection accuracy in dynamic environments. This research contributes to the implementation of object detection systems in robotics, particularly in the context of robot soccer competitions.
Co-Authors Afrizal Nehemia Toscany Chindra Saputra MUHAMMAD SURYA taupiq, Arahmad Toscany, Afrizal Yovi Pratama