| Autor: |
Yuhandri, Musli Yanto, Eka Naufaldi Novri |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 7, Iss 4, Pp 922-929 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2580-0760 |
| DOI: |
10.29207/resti.v7i4.5059 |
| Popis: |
The spread of Coronavirus Disease (Covid-19) is still a serious problem that we are currently facing. Spread occurred very quickly through the face-to-face interaction process. The face-to-face interaction process that occurs both in public spaces and in closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is to increase the use of masks in both public and closed spaces. On the basis of this, this study aims to develop an object detection process in image processing techniques. Object detection development using the convolution neural network (CNN) method to provide optimal output. CNN can process the input image, which is converted into a pixel matrix and then sent to the convolution layer. The research data set consists of 2000 images of masks and not masks. The images were obtained from open sources, github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used in the monitoring of public services for the community to increase the use of masks. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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