Implementasi Sistem Identifikasi Senjata Real Time Menggunakan YOLOv7 dan Notifikasi Chat Telegram


  • Muhammad Rizqi Sholahuddin Politeknik Negeri Bandung, Bandung Barat, Indonesia
  • Firas Atqiya * Mail Universitas Muhammadiyah Bandung, Bandung, Indonesia
  • Sri Ratna Wulan Politeknik Negeri Bandung, Bandung Barat, Indonesia
  • Maisevli Harika Politeknik Negeri Bandung, Bandung Barat, Indonesia
  • Sofy Fitriani Politeknik Negeri Bandung, Bandung Barat, Indonesia
  • Yusuf Sofyan Politeknik Negeri Bandung, Bandung Barat, Indonesia
  • (*) Corresponding Author
Keywords: YOLOv7; Telegram; CCTV; Flask; Chatbot

Abstract

This research produces an application that can send automatic notifications in the form of a chat on the Telegram platform when a weapon object is detected on CCTV. This application was created utilizing computer vision technology and artificial intelligence, in particular YOLOv7. As demonstrated by the mAP@0.5 value of 0.837 after 50 epochs of training, this application can detect weapon objects such as people, pistols, and knives with a reasonable degree of accuracy. This application is also linked to the telepot library, which enables it to send chats on the Telegram platform. These applications can aid in enhancing security and safety in a variety of environments and have numerous practical applications in fields such as public safety, law enforcement, and others. However, there are still deficiencies in this study that can be addressed in future research, such as the small number of training epochs and the size of the dataset.

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References

S. Megawan, W. S. Lestari, and A. Halim, “Deteksi Non-Spoofing Wajah pada Video secara Real Time Menggunakan Faster R-CNN,” J. Inf. Syst. Res. JOSH, vol. 3, no. 3, Apr. 2022, [Online]. Available: https://ejurnal.seminar-id.com/index.php/josh/article/view/1519

F. Atqiya, N. Ihsani, M. R. Sholahuddin, F. M. Dwivany, and S. Suhandono, “Segmentasi Citra Digital Objek Hasil Pengamatan In Situ Localization Gen gfp pada Tanaman Transforman,” Edsence J. Pendidik. Multimed., vol. 1, no. 2, pp. 53–60, Dec. 2019, doi: 10.17509/edsence.v1i2.21575.

I. A. Dahlan, D. Ariateja, M. A. Arghanie, M. A. Versantariqh, M. David, and U. D. Fatmawati, “Sistem Deteksi Senjata Otomatis Menggunakan Deep Learning Berbasis CCTV Cerdas,” J. Sist. Cerdas, vol. 4, no. 2, pp. 126–141, Aug. 2021, doi: 10.37396/jsc.v4i2.172.

R. Ivandhani, I. I. Tritoasmoro, and N. Ibrahim, “Perancangan dan Implementasi Sistem Deteksi Manusia Menggunakan Citra Webcam Dengan Fitur Notifikasi pada Ponsel,” in eProceedings of Engineering, vol. 7, pp. 3877–3883. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/12940/12625#

M. Harika, D. R. Ramdania, S. Rahmadika, N. A. Suwastika, and G. G. A. Delilah, “Designing R-CNN Algorithm to Detect Halal Composition of Korean Food and Beverages,” in 2022 8th International Conference on Wireless and Telematics (ICWT), Yogyakarta, Indonesia, Jul. 2022, pp. 1–5. doi: 10.1109/ICWT55831.2022.9935428.

M. Abdul Hadi, R. Ferdian, and L. Arief, “Klasifikasi Tingkat Ancaman Kriminalitas Bersenjata Menggunakan Metode You Only Look Once (YOLO),” CHIPSET, vol. 2, no. 01, pp. 33–40, Apr. 2021, doi: 10.25077/chipset.2.01.33-40.2021.

R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66–72, Jan. 2018, doi: 10.1016/j.neucom.2017.05.012.

M. R. Sholahuddin and F. Atqiya, “Sistem Tanya Jawab Konsultasi Shalat Berbasis RASA Natural Language Understanding (NLU),” J. Pendidik. Multimed. Edsence, vol. 3, no. 2, pp. 93–102, Dec. 2021, doi: 10.17509/edsence.v3i2.38732.

A. Mahmudi, “Deteksi Senjata Tajam dengan Metode Haar Cascade Classifier Menggunakan Teknologi SMS Gateway,” MATICS, vol. 1, no. 1, Mar. 2014, doi: 10.18860/mat.v1i1.2646.

I. Romadhanti, I. Kurniastuti, and T. D. Wulan, “Pemrosesan Citra Kuku Jari Tangan Menggunakan Metode GLCM (Grey Level Co-Occurrence Matrix),” Natl. Conf. UMMAH NCU 2020, vol. 1, no. 1, Jan. 2021, Accessed: Dec. 28, 2022. [Online]. Available: https://conferences.unusa.ac.id/index.php/NCU2020/article/view/658

J. Pustejovsky and A. Stubbs, Natural Language Annotation for Machine Learning, 3rd ed. O’Reilly Media, 2013.

E. Mozef, “Algoritma Labeling Citra Biner dengan Performansi Optimal Processor-Time,” J. Inform., vol. 5, no. 2, pp. 67–77, Mar. 2005, doi: 10.9744/informatika.5.2.pp.

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, Jul. 06, 2022. Accessed: Dec. 22, 2022. [Online]. Available: http://arxiv.org/abs/2207.02696

G. Shobha and S. Rangaswamy, “Machine Learning,” in Handbook of Statistics, vol. 38, Elsevier, 2018, pp. 197–228. doi: 10.1016/bs.host.2018.07.004.

N. Workspace, “3clase Dataset,” Roboflow Universe. Roboflow, Jul. 2022. [Online]. Available: https://universe.roboflow.com/new-workspace-bjaa4/3clase


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Article History
Submitted: 2022-12-28
Published: 2023-01-29
Abstract View: 639 times
PDF Download: 729 times
How to Cite
Sholahuddin, M., Atqiya, F., Wulan, S., Harika, M., Fitriani, S., & Sofyan, Y. (2023). Implementasi Sistem Identifikasi Senjata Real Time Menggunakan YOLOv7 dan Notifikasi Chat Telegram. Journal of Information System Research (JOSH), 4(2), 598-606. https://doi.org/10.47065/josh.v4i2.2774
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