Firmansyah, Arief (2023) RANCANGBANGUN SISTEM PEMANTAU PENERAPAN SOCIAL DISTANCING BERBASIS NVIDIA JETSON NANO. S1 thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa.
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Abstract
This research is a monitoring step towards the implementation of the applicable social restrictions, namely that everyone must maintain a minimum distance of 1 meter. Therefore, Then comes the initiative to create a tool that can monitor the continuity of distance maintenance. Design and Build a Monitoring Camera for the Implementatio n of Social Distancing Based on NVDIA JETSON NANO. Detection of the human body using the TensorRT framework, the YoloV4 model is trained so that it can detect the human body. The training process is carried out at Google Collaboratory because there is a super GPU. The results of the training process are then optimized so that the file size is reduced. After the YoloV4 model is created, the next step is to create a YoloV4 program and model. Detection starts from an image taken from the NYK A-96 Wabcam camera and is immediately calculated using Euclidean distance calculations. In the experiment, 2 people in the experiment facing the camera got 94.874% accuracy, in the experiment facing the right the camera got the greatest accuracy, namely 97.909%, in the experiment facing the left the camera got 97.368% accuracy. in the rear-facing experiment the camera got 91.892% accuracy. In the experiment, 3 people in the experiment facing the camera got an accuracy of 94.71%, in the experiment facing the right the camera got an accuracy of 92.854%, in the experiment facing the left the camera got an accuracy of 92.854%. in the rear-facing experiment the camera got an accuracy of 89.853%.
| Item Type: | Thesis (S1) | |||||||||
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| Additional Information: | Penelitian ini merupakan langkah pemantaun terhadap penerapan dari pembatasan sosial yang berlaku, yaitu setiap orang harus menjaga jarak minimal 1 meter. Maka muncul inisiatif membuat alat pemantau keberlangsungan penjagaan jarak. Rancang Bangun Sistem Pemantau Penerapan Social Distancing Berbasis NVDIA JETSON NANO. Deteksi tubuh manusia menggunakan framework TensorRT, model YoloV4 dilatih agar dapat melakukan deteksi tubuh manusia. Proses latih dilakukan di Google Colaboratory karena terdapat super GPU. Hasil dari proses latih, kemudian di optimalisasi agar ukuran filenya mengecil. Setelah model YoloV4 dibuat, maka selanjutnya membuat program dan model YoloV4. Pendeteksian bermula dari gambar yang di ambil dari kamera wabcam NYK A-96 dan langsung dihitung dengan menggunakan metode Euclidean distance. Pada percobaan yang dilakukan 2 orang di percobaan menghadap kamera mendapatkan akurasi 94.874%, percobaan menghadap kanan kamera mendapatkan akurasi paling besar yaitu 97.909%, percobaan menghadap kiri kamera mendapatkan akurasi 97.368%. percobaan menghadap belakang kamera mendapatkan akurasi 91.892%. Pada percobaan yang dilakukan 3 orang di percobaan menghadap kamera mendapatkan akurasi 94.71%, percobaan menghadap kanan kamera mendapatkan akurasi 92.854%, percobaan menghadap kiri kamera mendapatkan akurasi 92.854%. percobaan menghadap belakang kamera mendapatkan akurasi 89.853%. | |||||||||
| Subjects: | T Technology > T Technology (General) T Technology > TG Bridge engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TR Photography |
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| Divisions: | 03-Fakultas Teknik 03-Fakultas Teknik > 20201-Jurusan Teknik Elektro |
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| Depositing User: | Mr Arief Firmansyah | |||||||||
| Date Deposited: | 21 Sep 2023 08:51 | |||||||||
| Last Modified: | 21 Sep 2023 08:51 | |||||||||
| URI: | http://eprints.untirta.ac.id/id/eprint/29829 |
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