Sistem Robot Pengantar Barang Menggunakan Kamera Sebagai Detektor Objek
DOI:
https://doi.org/10.33884/psnistek.v7i1.10727Keywords:
YOLOv5, Robot, UMKMAbstract
This study discusses the design and implementation of a delivery robot system based on object detection using a camera with the YOLOv5 algorithm. The system is designed to operate autonomously in a limited-space MSME warehouse environment, equipped with a gripper and forklift mechanism to pick, lift, and deliver box-shaped goods. The dataset used includes ±300 images at various distances, positions, and lighting conditions. The test results show that the detection accuracy reaches 96.67% in low-light conditions and 83.33% in high-light conditions. The robot is able to operate for 1 hour 15 minutes with a maximum lifting capacity of 1 kg and optimal performance at loads up to 700 g. The implementation of this system has the potential to increase the logistics efficiency of MSMEs by reducing reliance on manual labor, accelerating distribution, and improving the accuracy of goods handling.
References
Ahsan Maulana, M., Soleh, M., Raya Puspitek, J., Tangerang Selatan, K., & Banten, P. (2024). Sistem Pendeteksi Physical Distance Pada Antrian Menggunakan Metode Yolo (You Only Look Once) V3 (Physical Distancing Detection System in Queue Using Yolo Method (You Only Look Once) V3).
Anandita Basyuni, R., Sugandi, D. B., Elektro, J. T., Batam, N., & Batam, P. N. (2022). QUEUE DISTANCING BASED ON IMAGE PROCESSING. Seminar Nasional Terapan Riset Inovatif (SENTRINOV) Ke-8 ISAS Publishing Series: Engineering and Science, 8(1).
Ashar, M. H., & Suarna, D. (2022). KLIK: Kajian Ilmiah Informatika dan Komputer Implementasi Algoritma YOLOv5 dalam Mendeteksi Penggunaan Masker Pada Kantor Biro Umum Gubernur Sulawesi barat. Media Online, 3(3), 298–302.
jocher, glenn. (2022, November 22). Ultralytics YOLOv5. Retrieved 17 May 2025, from https://github.com/ultralytics/yolov5
Jurnal, H., Purba, D. G., Siahaan, R. C. Y., Wani, A., Paramitha Purba, S., Siadari, F. A., & Damanik, T. H. (2024). JURNAL EKONOMI BISNIS DAN KEWIRAUSAHAAN PERTUMBUHAN DAN PERKEMBANGAN UMKM SHANAYA TEA. JEBER, 1(3).
Meidyan, M. A., & Yustanti, W. (2024). IMPLEMENTASI METODE YOU ONLY LOOK ONCE (YOLOv5) DALAM DETEKSI PELANGGARAN HELM.
Mohanty, T., Pattanaik, P., Dash, S., Tripathy, H. P., & Holderbaum, W. (2025). Smart robotic system guided with YOLOv5 based machine learning framework for efficient herbicide usage in rice (Oryza sativa L.) under precision agriculture. Computers and Electronics in Agriculture, 231. doi:10.1016/j.compag.2025.110032
Rifqi, M., Syafirly, R., Rangga, R., Firmansyah, W., Nurdiansyah, A., Adienia, A., … Wirawan, M. (2024). Implementation of YOLOv5 Algorithm for Classification and Detection of Car Types Using Deep Learning.
Sood, S., Singh Sodhi, J., & Maheshwari, P. (2022). Multiple Waypoint Navigation in Unknown Indoor Environments.
Yolanda, C., Hasanah, U., Dhien, N., & Pembangunan, S. E. (2023). PERAN USAHA MIKRO, KECIL DAN MENENGAH (UMKM) DALAM PENGEMBANGAN EKONOMI INDONESIA.
Zahra Firdausya, L., Perwira Ompusunggu, D., & kunci, K. (2023). USAHA MIKRO KECIL DAN MENENGAH (UMKM) DI ERA DIGITAL ABAD 21 MICRO, SMALL AND MEDIUM ENTERPRISES (MSME) THE DIGITAL AGE OF THE 21 ST CENTURY. TALIJAGAD, 2023(3),14–18.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
This work is licensed under a Creative Commons Attribution 4.0 International License.
