KURNIAWAN, ARDI (2021) DETEKSI MASKER MENGGUNAKAN TENSORFLOW DENGAN METODE CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Universitas Stikubank.
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Abstract
Merebaknya pandemi virus covid-19, WHO (World Health Organization) menyarankan agar masyarakat rajin mencuci tangan, jaga jarak atau physical distancing, serta wajib menggunakan masker. Presiden melalui gugus tugas covid-19 menyampaikan pemberitahuan WHO ini kepada masyarakat Indonesia agar mematuhi anjuran tersebut, namun terkadang masih banyak masyarakat kita yang tidak mematuhi protokol kesehatan untuk memakai masker di tempat umum atau yang terdapat banyak keramaian yang rentan akan penyebaran virus covid-19 ini. Penelitian kali ini akan melakukan pendeteksian masker dengan tensorflow menggunakan metode convolutional neural network. Metode convolutional neural networks digunakan untuk untuk melakukan pendeteksian dan pengenalan dari sebuah objek dengan mengolah data dua dimensi berbentuk gambar. Penelitian ini menggunakan dataset yang berjumlah 1300 gambar yang dibagi kedalam dua folder yaitu mask dan no mask, dimana masing-masing folder berisikan 650 gambar. Kemudian akan masuk ke proses training data serta testing data sehingga akan menghasilkan akurasi pada data testing yaitu diatas 90%. The outbreak of the COVID-19 virus pandemic, the WHO (World Health Organization) recommends that people be diligent in washing their hands, keeping a distance or physical distancing, and must wear masks. The President through the COVID-19 task force conveyed this WHO notification to the Indonesian people to comply with these recommendations, but sometimes there are still many of our people who do not comply with health protocols to wear masks in public places or where there are many crowds who are vulnerable to the spread of the Covid-19 virus . This study will detect masks with tensorflow using the convolutional neural network method. Convolutional neural networks method is used to detect and recognize an object by processing two-dimensional data in the form of images. This study uses a dataset of 1300 images which are divided into two folders, namely mask and no mask, where each folder contains 650 images. Then it will go into the data training process and data testing so that it will produce an accuracy of the testing data which is above 90%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | SKR.I.05.01.1997 NIM 17.01.53.0071 |
| Uncontrolled Keywords: | Deteksi Masker, Tensorflow, Convolutional Neural Network (CNN) Mask Detection, Tensorflow, Convolutional Neural Network (CNN) |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty / Institution: | Fakultas Teknologi Informasi > Program Studi Teknik Informatika |
| Depositing User: | Teteh Hayati |
| Date Deposited: | 08 Nov 2021 07:26 |
| Last Modified: | 08 Nov 2021 07:26 |
| URI: | https://eprints.unisbank.ac.id/id/eprint/8003 |
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