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Abstract
Penelitian ini membandingkan kinerja Vision Transformer (ViT-Base) dan MobileNet untuk deteksi objek pada perangkat edge. Evaluasi dilakukan pada Jetson Orin Nano dengan menggunakan empat parameter: akurasi, latensi, konsumsi energi, dan efisiensi komputasi. Hasil pengujian menunjukkan MobileNet mencapai akurasi 100%, latensi 41,38 ms, konsumsi energi 0,3937 joule/frame, dan efisiensi 0,8332 %/msW. Sementara itu, ViT-Base memperoleh akurasi 93,72%, latensi 63,58 ms, konsumsi energi 0,5306 joule/frame, dan efisiensi 0,4466 %/msW. MobileNet lebih unggul dalam hal akurasi, efisiensi, kecepatan, dan penggunaan energi. Temuan ini membuktikan, MobileNet direkomendasikan untuk aplikasi edge real-time yang menuntut respon cepat dan hemat daya, serta sesuai untuk skenario yang membutuhkan akurasi tinggi.
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References
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References
M. Nurul Achmadiah, N. Setyawan, and A. D. Risdhayanti, “Deteksi Kepadatan Objek di Stasiun Kereta Api Berbasis ViT-Base pada Jetson Orin Nano,” Jurnal Elektronika dan Otomasi Industri, vol. 12, no. 1, pp. 153–161, May 2025, doi: 10.33795/elkolind.v12i1.7495.
S. Terabe, T. Kato, H. Yaginuma, N. Kang, and K. Tanaka, “Risk Assessment Model for Railway Passengers on a Crowded Platform,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2673, no. 1, pp. 524–531, Jan. 2019, doi: 10.1177/0361198118821925.
L. Jiao et al., “A Survey of Deep Learning-Based Object Detection,” IEEE Access, vol. 7, pp. 128837–128868, 2019, doi: 10.1109/ACCESS.2019.2939201.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 6, pp. 1137–1149, Jun. 2017, doi: 10.1109/TPAMI.2016.2577031.
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick, “Mask R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, pp. 2961–2969.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” Apr. 2020.
W. Liu et al., “SSD: Single Shot MultiBox Detector,” Dec. 2015, doi: 10.1007/978-3-319-46448-0_2.
M. Nurul Achmadiah, A. Ahamad, C.-C. Sun, and W.-K. Kuo, “Energy-Efficient Fast Object Detection on Edge Devices for IoT Systems,” IEEE Internet Things J, vol. 12, no. 11, pp. 16681–16694, Jun. 2025, doi: 10.1109/JIOT.2025.3536526.
M. N. Achmadiah, N. Setyawan, A. A. Bryantono, C.-C. Sun, and W.-K. Kuo, “Fast Person Detection Using YOLOX With AI Accelerator For Train Station Safety,” in 2024 International Electronics Symposium (IES), IEEE, Aug. 2024, pp. 504–509. doi: 10.1109/IES63037.2024.10665874.
J. Pan et al., “EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers,” 2022, pp. 294–311. doi: 10.1007/978-3-031-20083-0_18.
N. Setyawan, M. N. Achmadiah, C.-C. Sun, and W.-K. Kuo, “Multi-Stage Vision Transformer for Batik Classification,” in 2024 International Electronics Symposium (IES), IEEE, Aug. 2024, pp. 449–453. doi: 10.1109/IES63037.2024.10665807.