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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) eProceedings of Engineering
Febriyanti Sthevanie
Telkom University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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FACE MASK DETECTION UNDER LOW LIGHT CONDITION USING CONVOLUTIONAL NEURAL NETWORK (CNN) Naufal Muhammad Athif; Febriyanti Sthevanie; Kurniawan Nur Ramadhan
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 1 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i1.3324

Abstract

The COVID-19 pandemic has been around for 3 years, and the virus is still spreading until now and using mask is an alternative for people to not get infected, but some people tend to let go of the mask for inconvenience reasons, especially under low light conditions which is difficult for humans to identify. Thus, this paper proposed and implemented a face mask detection model which can accurately detect a person that using a mask or not in such a condition as low light by using Convolutional Neural Network (CNN) architecture with OpenCV, TensorFlow and Keras. To achieve this, the first step is to transform the data by using Python Imaging Library (PIL) to create a low light image, then we process the data by using Contrast Limited Adaptive Histogram Equalization and with Gamma Correction. The second step is to augment the data by using TensorFlow ImageDataGenerator and define the CNN model. The final step is to create the face mask prediction by using Haar Cascade Algorithm to detect the face mask. The results of this research shows that CNN model can be trained with a recreational low light images to detect face mask under low light conditions. The result of the model produced an accuracy of 98%.
Deteksi Katarak Dan Konjungtivitis Menggunakan Hough Transform Naufal Ihsan Kusumayadhi; Febriyanti Sthevanie; Kurniawan Nur Ramadhani
eProceedings of Engineering Vol 6, No 1 (2019): April 2019
Publisher : eProceedings of Engineering

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Abstract

Abstrak Mata adalah salah satu organ tubuh yang paling penting karena mata merupakan sensor dari indra pe-nglihatan. Penyakit pada mata dapat mengakibatkan resiko yang berbeda-beda tergantung dari penya-kit apa yang sedang diderita. Keterbatasan pengetahuan tentang resiko dari sebuah penyakit mata bisa mengakibatkan penyakit menjadi lebih parah apabila tidak segera ditangani oleh dokter. Oleh karena itu, dibutuhkan sebuah sistem yang dapat mendeteksi setidaknya nama penyakitnya apa. Pada penelitian ini akan dibangun sebuah sistem yang dapat mendeteksi penyakit mata apa yang diderita berdasarkan gambar menggunakan Hough Transform. Sistem akan memberitahu apakah mata pada gambar masukan terkena sebuah penyakit atau normal. Penyakit yang dapat dideteksi oleh sistem adalah Katarak dan Konjungtivi-tis. Setelah dilakukan implementasi dan evaluasi sistem, didapatkan akurasi pada pemodelan sistem sebesar 79,16% pada penyakit katarak dan 62,5% pada penyakit konjungtivitis. Sedangkan untuk pengujian dida-patkan akurasi pada deteksi katarak sebesar 57,9% dan deteksi konjungtivitis sebesar 68,4%. Kata kunci : Deteksi, Katarak, Konjungtivitis Abstract Eye is one of the most important organs because eye is the sensor of the sense of sight. Eye disease can lead to different risks depending on what illness is being suffered. Limitations of knowledge about the risk of an eye disease can lead to more severe disease if not handled immediately by the doctor. Therefore, it takes a system that can detect at least the name of the disease. This study will build a system that can detect what illnesses are suffered based on the picture using Hough Transform. The system will tell whether the eye on the input image is exposed to a disease or healthy. Diseases that can be detected by the system are cataracts and conjunctivitis. After implementation and evaluation of the system, the modeling system indicates an average accuracy of 79,16% over cataract and 62,5% over conjunctivitis. The testing system indicates an average accuracy of 57,9% over cataract and 68,4% over conjunctivitis. Keywords: Detection, Cataract, Conjunctivitis.
Co-Authors Kurniawan Nur Ramadhan Kurniawan Nur Ramadhani Naufal Ihsan Kusumayadhi Naufal Muhammad Athif