Sistem Otomatis Pendeteksi Wajah Bermasker Menggunakan Deep Learning

Baay, M.; Irfansyah, Astria Nur; Attamimi, Muhammad

doi:10.12962/j23373539.v10i1.59790

Cited by 10 publications

(12 citation statements)

References 4 publications

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“…Furthermore, Fig. 2 this output will be entered on the second dense layer which has 1 neuron so that it will produce an output with a size (1). The output of this last layer is what is used as the final result of the model for the classification of mask use, why using output 1 aims to see if there is an error in classifying one class object whether it is a class wearing a mask or not wearing a mask.…”

Section: Fig 1 Masked Face Classification Flowchartmentioning

confidence: 99%

“…The Covid-19 virus has been claimed by the world health agency, namely WHO (World Health Organization) as a pandemic around the world including in Indonesia, the impact is so greatly felt by the Indonesian people as the rate of increasing positive cases of Covid-19 and also deaths that occur. Several aspects are also affected, both in terms of economic, social, and health [1]. The government took swift steps to impose the New Normal stage, as a form of government in handling cases of the spread of the Covid-19 virus in Indonesia by enacting this policy so that the wheels of the economy can run as it should.…”

Section: Introductionmentioning

confidence: 99%

“…Here are some similar studies on the classification of masked faces with the convolutional neural network method, including research conducted by [1] designing an automated system to be able to detect masked faces and not by utilizing the help of deep learning and combining face detector methods accompanied by a tracking program with a graphic user interface. The results obtained in the tests that have been carried out get an accuracy value of 99% on the testing data.…”

Section: Introductionmentioning

confidence: 99%

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masked face identification using the convolutional neural network method

Furqon

Murinto²

2022

Jurnal Informatika

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In times of a pandemic like this, masks are part of the main needs in daily activities when outside the home. Because masks can help us avoid the Covid-19 virus, it often happens among people when doing activities outside the home that they forget to wear masks, therefore the level of public awareness of the importance of wearing masks is decreasing. This study aims to create a system that can classify people who wear masks and do not wear masks as an evaluation material for the level of public awareness of the importance of wearing masks. The total data used in this study were 600 data samples which were divided into two, namely 300 data samples wearing masks and 300 data samples not wearing masks. The CNN architecture in this study is the same as the CNN architecture in general, the difference is the depth level of the convolution layer and pooling which consists of 5 convolution layers, 5 max pooling, and finally, 2 layers dense In the training process, it gets the highest accuracy rate of 98%, while in the validation process it gets the highest level of accuracy at 95%. Therefore, the results of these two processes show that the application of deep earning by utilizing the convolutional neural network can classify objects that wear masks and do not wear masks properly. The results of testing the research dataset are quite maximal by using 40 new dataset testing data to test the convolutional neural network that has been created by the researchers to get an overall accuracy result of 97.5%.

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Section: Fig 1 Masked Face Classification Flowchartmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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masked face identification using the convolutional neural network method

Furqon

Murinto²

2022

Jurnal Informatika

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“…Teknologi deteksi wajah (Face Detection) adalah teknologi komputer yang digunakan untuk mendeteksi bagian wajah manusia (Baay et al, 2021;Luthfillah Ahmad et al, 2021;Septiana et al, 2020). Teknologi ini dibangun dengan menggunakan algoritma tertentu yang akan menentukan dimana bagian wajah yang muncul pada citra masukan.…”

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Face Mask Detection Menggunakan Python Dan Opencv Untuk Mendeteksi Pelanggaran Protokol Kesehatan Covid-19

Sopian¹,

Kurniati²,

Dewi

2023

JEIS

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Face Detection Technology is a computer technology for detecting human faces. The popular method used is the Convolutional Neural Networks (CNN) method. Using a combination of object detection classification, image and object tracking, for masked or non-masked face detection systems in the form of images or videos. Face detection in Python can be done using the OpenCV library and the method applied is the Convolutional Neural Networks (CNN) method. The problem in this research is how to build a computer program with the Python programming language, using Google Collab, and apply and test it in the face detection process using a mask or not to detect violations of the Covid-19 health protocol. The goal to be achieved through this research is to produce an image-processing system that uses Image processing aims to help prevent Covid-19, especially regarding calls for the use of masks in public spaces.Teknologi Deteksi Wajah adalah teknologi komputer untuk mendeteksi wajah manusia, metode yang populer digunakan adalah metode Convolutional Neural Networks (CNN). Menggunakan kombinasi klasifikasi deteksi objek, gambar, dan pelacakan objek, untuk sistem pendeteksian wajah bermasker atau tidak bermasker dalam bentuk gambar atau video.Pendeteksian wajah dengan Python dapat dilakukan dengan menggunakan library OpenCV dan Metode yang diterapkanadalah metode Convolutional Neural Networks (CNN).Permasalahan dalam penelitian ini bagaimana membangun program komputer dengan bahasa pemrograman Python, menggunakan Google Collab, serta menerapkan dan mengujinya dalam proses pendeteksian wajah menggunakan masker atau tidak untuk mendeteksi pelanggaran protokol kesehatan Covid-19.Tujuan yang ingin dicapai melalui penelitian ini adalah menghasilkan sistem pengolahan citra yang menggunakan image processing bertujuan untuk membantu pencegahan Covid-19 khususnya terkait himbauan penggunaan masker pada ruang publik.

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“…Suara pernapasan didengarkan menggunakan stetoskop kemudian direkam untuk mendapatkan suatu analisis sehingga menghasilkan informasi yang bermanfaat [1]. Saat ini, sistem kesehatan pernafasan telah memanfaatkan metode machine learning untuk melakukan klasifikasi penyakit pernapasan melalui visualisasi suara stetoskop yang merupakan informasi grafik bunyi dalam rentang waktu dan frekuensi tertentu [2].…”

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Klasifikasi Penyakit Pernapasan Berbasis Visualisasi Suara Menggunakan Metode Support Vector Machine

Rijal¹,

Adnan²,

Achmad³

2022

JIKOM

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Suara merupakan parameter yang sangat penting dalam sistem kesehatan pernapasan. Proses klasifikasi pernapasan dipengaruhi oleh dukungan tubuh dalam mendapatkan oksigen yang menghasilkan keadaan normal dan abnormal. Keadaan abnormal yang dimaksud adalah penyakit asma, bronkitis dan tuberkolosis. Tujuan paper ini adalah menerapkan metode Support Vector Machine untuk mengklasifikasi penyakit asma, bronkitis dan tuberkolosis. Pemilihan metode Support Vector Machine berdasarkan keunggulan dalam proses generalisasi dengan meminimalkan terjadinya salah prediksi dan estimasi parameter agar ditemukan hyperplane yang terbaik untuk memisahkan kelas. Hasil pengujian mengemukakan bahwa metode Support Vector Machine berhasil diterapkan dalam mengklasifikasi penyakit asma, bronkitis dan tuberkolosis dengan akurasi sebesar 46.37%.

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Sistem Otomatis Pendeteksi Wajah Bermasker Menggunakan Deep Learning

Cited by 10 publications

References 4 publications

masked face identification using the convolutional neural network method

masked face identification using the convolutional neural network method

Face Mask Detection Menggunakan Python Dan Opencv Untuk Mendeteksi Pelanggaran Protokol Kesehatan Covid-19

Klasifikasi Penyakit Pernapasan Berbasis Visualisasi Suara Menggunakan Metode Support Vector Machine

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