IKHSAN, MUHAMMAD (2021) RANCANG BANGUN ALAT PHYSICAL DISTANCING MENGGUNAKAN OPEN CV DENGAN METODE DEEP LEARNING YOLOv3. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The findings of the problem in this research are diseases caused by a new type of coronavirus, Sars-CoV-2, can cause symptoms of acute respiratory disorders such as fever above 38°C, cough and shortness of breath for humans. It can also be accompanied by weakness, muscle aches, and diarrhea. In severe sufferers, it can cause pneumonia, acute respiratory syndrome, kidney failure even until death. COVID-19 can be transmitted from human to human through close contact and droplets (splashes of liquid during sneezing and coughing), Physical Distancing is one example and the government's efforts to reduce the spread of the COVID-19 virus. From the collection of facts and problems, the purpose of this research is to design a physical distancing prototype using OpenCV with Yolov3 Deep Learning Method. The hypothesis in this research is that this prototype tool serves as a detection of distance between individuals. .jpg and .jpeg format. The image objek capture in observation uses a clear image so that the program can recognize the image objekcapture. The results in this research STB 96 Max Plus TV Box can work as well as a Computer for programming and data processing. in this research in thisresearch objects will be given a red mark on the Pyshical Distancing tool when between objects close to each other and given a green mark if objects are far apart Conclusions in response to detection with an average response time of 9.525133 second. This is influenced by the size of the file and the number of objek captures generated Keywords: Covid-19, Physical Distancing, Python, OpenCV, Deep Learning, Yolov3, Image Temuan masalah didalam riset ini adalah penyakit yang disebabkan oleh jenis coronavirus baru yaitu Sars-CoV-2, dapat menimbulkan gejala gangguan pernafasan akut seperti demam diatas 38°C, batuk dan sesak nafas bagi manusia. Selain itu dapat disertai dengan lemas, nyeri otot, dan diare. Pada penderita yang berat, dapat menimbulkan pneumonia, sindroma pernafasan akut, gagal ginjal bahkan sampai kematian. COVID-19 dapat menular dari manusia ke manusia melalui kontak erat dan droplet (percikan cairan pada saat bersin dan batuk), Physical Distancing merupakan salah satu contoh dan upaya pemerintah untuk mereduksi penyebaran virus COVID-19. Dari pengumpulan fakta dan masalah tersebut, maka pada Tujuan riset ini merancang suatu alat purwarupa physical distancing menggunakan OpenCV dengan Metode Deep Learning Yolov3. Hipotesis didalam riset ini adalah alat Purwarupa ini berfungsi sebagai pendeteksi jaga jarak antara perorangan. tangkapan gambar berformat .jpg dan .jpeg. Tangkapan objek gambar dalam pengamatan menggunakan gambar yang jelas sehingga program dapat mengenali tangkapan objek gambar. Hasil dalam riset ini STB 96 Max Plus TV Box dapat bekerja dengan baik seperti layak nya Komputer untuk pemrograman dan pemrosesan data. dalam riset ini dalam riset ini Objek akan diberi Tanda merah pada alat Pyshical Distancing apabila antara objek saling berdekatan dan diberi akan tanda hijau jika objek saling berjauhan Kesimpulan dalam menanggapi pendeteksian dengan nilai rata-rata waktu respon 9.525133 second. Hal ini dipengaruhi dari besarnya ukuran file dan jumlah tangkapan objek yang dihasilkan Kata kunci : Covid-19, Physical Distancing, Python, OpenCV, Deep Learning, Yolov3, Image Capture,
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41419120115 |
| Uncontrolled Keywords: | Covid-19, Physical Distancing, Python, OpenCV, Deep Learning, Yolov3, Image Capture, |
| Subjects: | 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 621 Applied Physics/Fisika terapan |
| Divisions: | Fakultas Teknik > Teknik Elektro |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 02 Nov 2023 01:12 |
| Last Modified: | 02 Nov 2023 01:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/83679 |
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