Pelatihan Implementasi Hand Gesture Recognition Berbasis YOLO untuk Pengenalan Kecerdasan Buatan Siswa SMAN 1 Pabuaran

Widyawati Widyawati; Leo Sandi

doi:10.59025/jpb3c803

Authors

Widyawati Universitas Pamulang
Leo Sandi Universitas Pamulang

DOI:

https://doi.org/10.59025/jpb3c803

Keywords:

Artificial Intelligence, Computer Vision, Experiential Learning, Hand Gesture Recognition, Object Detection, YOLO

Abstract

Perkembangan teknologi digital pada era Revolusi Industri 4.0 mendorong pentingnya peningkatan literasi kecerdasan buatan di lingkungan pendidikan, khususnya bagi siswa sekolah menengah. Namun, pemahaman siswa SMA Negeri 1 Pabuaran mengenai penerapan kecerdasan buatan masih terbatas karena proses pembelajaran yang diterapkan cenderung bersifat teoritis dan belum memberikan pengalaman praktik secara langsung. Kondisi tersebut menyebabkan siswa belum memahami implementasi teknologi kecerdasan buatan dalam kehidupan nyata, terutama pada bidang computer vision dan deteksi objek. Untuk mengatasi permasalahan tersebut, kegiatan Pengabdian kepada Masyarakat dilaksanakan melalui pelatihan implementasi Hand Gesture Recognition berbasis You Only Look Once. Kegiatan ini bertujuan meningkatkan pemahaman, keterampilan, dan minat siswa terhadap teknologi kecerdasan buatan melalui pendekatan pembelajaran yang interaktif dan aplikatif. Metode pelaksanaan menggunakan pelatihan partisipatif dan experiential learning dengan tahapan sosialisasi, praktik pengumpulan data, pelabelan citra, pelatihan model, hingga implementasi sistem deteksi gerakan tangan secara real-time. Hasil kegiatan menunjukkan adanya peningkatan pemahaman siswa berdasarkan hasil pre-test dan post-test serta meningkatnya antusiasme peserta terhadap pengembangan teknologi kecerdasan buatan. Dengan demikian, kegiatan ini mampu meningkatkan literasi digital siswa dan memberikan pengalaman belajar yang lebih kontekstual serta aplikatif.

References

W. Widyawati, E. R. Putra, and S. Septiani, “AI Implementation to Enhance Creativity in Designing Book Covers at SMK YP Fatahillah 1 Kramatwatu,” Jurnal Masyarakat Madani Indonesia, vol. 5, no. 2, pp. 1263–1275, Apr. 2026, doi: 10.59025/t4ajp059. DOI: https://doi.org/10.59025/t4ajp059

W. Widyawati and W. Gunawan, “Early detection of sick chicken using artificial intelligence,” Teknika: Jurnal Sains dan Teknologi, vol. 18, no. 2, pp. 136–141, Nov. 2022, doi: http://dx.doi.org/10.36055/tjst.v18i2.17337. DOI: https://doi.org/10.36055/tjst.v18i2.17337

W. Widyawati and R. Febriani, “Real-time detection of fruit ripeness using the YOLOv4 algorithm,” Teknika: Jurnal Sains dan Teknologi, vol. 17, no. 2, p. 205, Nov. 2021, doi: 10.36055/tjst.v17i2.12254. DOI: https://doi.org/10.36055/tjst.v17i2.12254

A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” ArXiv, vol. arXiv:2004.10934, Apr. 2020, doi: https://arxiv.org/abs/2004.10934.

Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO Series in 2021,” ArXiv, vol. arXiv:2107.08430, pp. 1–7, May 2021, doi: https://doi.org/10.48550/arXiv.2107.08430.

S. Xu et al., “PP-YOLOE: An evolved version of YOLO,” ArXiv, vol. arXiv:2203.16250v3, pp. 1–7, Dec. 2022, doi: https://doi.org/10.48550/arXiv.2203.16250.

C. Lyu et al., “RTMDet: An Empirical Study of Designing Real-Time Object Detectors,” ArXiv, vol. arXiv:2212.07784, pp. 1–15, Dec. 2022, doi: https://doi.org/10.48550/arXiv.2212.07784.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” ArXiv, vol. arXiv:2207.02696, p. 15, Jul. 2022, doi: https://doi.org/10.48550/arXiv.2207.02696. DOI: https://doi.org/10.1109/CVPR52729.2023.00721

G. Jocher and U. Ultralytics, “YOLOv8: Ultralytics Documentation,” Ultralytics. Accessed: Mar. 12, 2026. [Online]. Available: https://docs.ultralytics.com/models/yolov8/

S. Liu et al., “Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection,” ArXiv, vol. arXiv:2303.05499v5, pp. 1–33, Jul. 2024, doi: https://doi.org/10.48550/arXiv.2303.05499. DOI: https://doi.org/10.1007/978-3-031-72970-6_3

C.-Y. Wang, I.-H. Yeh, and H.-Y. M. Liao, “YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information,” ArXiv, vol. arXiv:2402.13616v2, pp. 1–18, Feb. 2024, doi: https://doi.org/10.48550/arXiv.2402.13616. DOI: https://doi.org/10.1007/978-3-031-72751-1_1

Y. Zhao, W. Lv, and S. Xu, “DETRs Beat YOLOs on Real-Time Object Detection,” in CVPR, CVPR, 2024. Accessed: Mar. 12, 2026. [Online]. Available: https://openaccess.thecvf.com DOI: https://doi.org/10.1109/CVPR52733.2024.01605

A. Wang et al., “YOLOv10: Real-Time End-to-End Object Detection,” ArXiv, vol. arXiv:2405.14458v2, pp. 1–21, Oct. 2024, doi: https://doi.org/10.48550/arXiv.2405.14458.

Z. Wang et al., “A Comprehensive Survey on Data Augmentation,” ArXiv, vol. 14, no. 8, pp. 1–20, Oct. 2025, doi: 10.48550/arXiv.2405.09591.

C. K. Chan, “Applying Experiential Learning in Digital Learning Environments,” Journal of Education and Learning, vol. 12, no. 3, pp. 45–54, 2023, doi: 10.5539/jel.v12n3p45.

W. Rahmi, “Analytical Study of Experiential Learning : Experiential Learning Theory in Learning Activities,” EDUKASIA: Jurnal Pendidikan dan Pembelajaran, vol. 5, pp. 115–126, 2024, doi: 10.62775/edukasia.v5i2.1113. DOI: https://doi.org/10.62775/edukasia.v5i2.1113