ANALISA BERMACAM OPTIMIZER PADA CONVOLUTIONAL NEURAL NETWORK UNTUK DETEKSI PEMAKAIAN MASKER PENGEMUDI KENDARAAN

Alief Wikarta, Agus Sigit Pramono, Julendra B Ariatedja

Abstract


Abstract
The COVID-19 pandemic has had a major impact on the transportation sector, especially urban
transportation in the form of regular taxis and online taxis. To overcome this, several studies have stated
that the use of masks is quite effective in preventing the spread of the virus. In this article, to detect the
use of masks by drivers and vehicle passengers, it will be done automatically by implementing the
Convolutional Neural Network (CNN) or Convnet. There are 3 types of optimizers analyzed, namely SGD,
RMSprop, and ADAM. Where the three optimizers will be compared to determine the optimal level of
accuracy. The results of CNN training show that the accuracy values for CNN with the SGD, RMSprop,
and ADAM optimizer are as follows: 0.7577, 0.9577, and 0.9654. This value indicates that the SGD
optimizer has the lowest accuracy, while the ADAM optimizer has the highest accuracy.
Keywords : Convolutional Neural Network, Optimizer, face mask detection, vehicle driver.


Pandemi COVID-19 telah memberikan dampak yang besar pada sektor transportasi, khususnya
transportasi di perkotaan berupa taksi reguler maupun taksi online. Untuk mengatasi hal tersebut, dalam
beberapa studi menyebutkan bahwa pemakaian masker cukup efektif untuk mencegah penyebaran virus.
Dalam artikel ini, untuk mendeteksi pemakaian masker oleh pengemudi maupun penumpang kendaraan
akan dilakukan secara otomatis dengan menerapkan Convolutional Neural Network (CNN) atau Convnet.
Ada 3 macam optimizer yang dianalisa, yaitu SGD, RMSprop, dan ADAM. Hasil training CNN
menunjukkan bahwa pemilihan jenis optimizer akan memberikan tingkat akurasi dan losses yang
berbeda-beda. Nilai akurasi untuk CNN dengan optimizer SGD, RMSprop, dan ADAM secara berurutan
adalah sebagai berikut: 0.7577, 0.9577, dan 0.9654. Nilai tersebut menunjukkan bahwa optimizer SGD
memiliki akurasi terendah, sementara optimizer ADAM memiliki akurasi tertinggi.
Kata Kunci : Convolutional Neural Network, Optimizer, Deteksi Pemakaian Masker, Pengemudi
Kendaraan.


Keywords


Convolutional Neural Network; Optimizer; face mask detection; vehicle driver

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References


Chollet, F. (2017). Deep Learning with Python (1st edition). Manning Publications.

Greenhalgh, T., Schmid, M. B., Czypionka, T., Bassler, D., & Gruer, L. (2020). Face masks for the public

during the covid-19 crisis. BMJ, 369. https://doi.org/10.1136/bmj.m1435

Kingma, D. P., & Ba, J. (2014). Adam: A Method for Stochastic Optimization.

https://arxiv.org/abs/1412.6980v9

Ruder, S. (2016). An overview of gradient descent optimization algorithms.

https://arxiv.org/abs/1609.04747v2

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2019). MobileNetV2: Inverted Residuals

and Linear Bottlenecks. ArXiv:1801.04381 [Cs]. http://arxiv.org/abs/1801.04381

World Health Organization. (2020). Advice on the use of masks in the context of COVID-19. Interim

guidance. Geneva, Switzerland: WHO.


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