MONITORING PENGGUNAAN MASKER PADA PENGUNJUNG STMIK “AMIKBANDUNG” MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)

Authors

  • Khoirida Aelani STMIK Bandung
  • Fajar Nazmi Fadillah STMIK BANDUNG

DOI:

https://doi.org/10.33197/jitter.vol9.iss2.2023.978

Keywords:

COVID-19, Machine Learning, Convolutional Neural Network

Abstract

The monitoring system used at STMIK "AMIKBANDUNG" is currently web-based without any support or documentation for having a machine learning-based mask detection module.. Machine learning itself is a branch of artificial intelligence which includes building systems based on gathered dataset. In this research, machine learning will be studied and applied in the development of mask detection. The final result in this study is a new mask detection system that is more effective and efficient in terms of program, supported by an interactive interface and user experience, accompanied by documentation so that the program can be developed further. By utilizing artificial intelligence, the campus can make decisions easier with the support of statistics that show the number of violations viewed from the web interface.

Keywords: COVID-19 , Machine Learning, Convolutional Neural Network

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Published

2023-04-15

How to Cite

[1]
K. Aelani and F. N. Fadillah, “MONITORING PENGGUNAAN MASKER PADA PENGUNJUNG STMIK ‘AMIKBANDUNG’ MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)”, jitter, vol. 9, no. 2, Apr. 2023.

Issue

Vol. 9 No. 2 (2023)

Section

Articles

License

Copyright (c) 2023 Khoirida Aelani, Fajar Nazmi Fadillah

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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