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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) eProceedings of Engineering
Kurniawan Nur Ramadhan
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%.
Sistem Identifikasi Biometrika Multimodal Palmprint Dan Palmvein Menggunakan Two-dimensional Locality Preserving Projection Fuad Ikhlasul Amal; Tjokorda Agung Budi Wirayudha; Kurniawan Nur Ramadhan
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak - Dalam penelitian Tugas Akhir ini dijelaskan mengenai sistem biometrika multimodal dengan menggabungkan dua modalitas yaitu palmprint dan palmvein di level nilai kemiripan serta menggunakan Two-Dimensional Locality Preserving Projection sebagai algoritma ekstraksi ciri, melanjutkan penelitian sebelumnya oleh Wang dkk yang berhasil menggabungkan dua modalitas tersebut di level citra. Palmprint dan palmvein dipilih karena sulit untuk dipalsukan dan cara akuisisinya yang mudah. Algoritma 2DLPP diterapkan pada palmprint dan palmvein secara independen dalam proses ekstraksi ciri dengan memproyeksikan masing-masing citra ke sebuah vektor ciri menggunakan sebuah matriks transformasi. Penggabungan ciri dilakukan di level skor dengan mengombinasikan nilai kemiripan masing-masing ciri menggunakan sebuah konstanta bobot. Penelitian ini menunjukkan performansi sistem biometrika multimodal palmprint dan palmvein yang dihasilkan berupa recognition rate menggunakan dataset CASIA MS-PalmprintV1 dengan rasio data latih dan data uji 3:3 dalam mode verifikasi dan identifikasi secara berturut-turut yaitu 94,67% dan 97,33%. Kata kunci: biometrika, multimodal, region of interest, two-dimensional locality preserving projection
Pengenalan Ekspresi Wajah Menggunakan LGBP dan SVM Erwin Yulizar Fardani; Anditya Arifianto; Kurniawan Nur Ramadhan
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Ekspresi wajah merupakan komunikasi non-verbal. Ekspresi wajah memuat informasi tentang emosi dan kondisi kejiwaan seseorang. Karena memuat informasi tentang emosi pada seseorang, maka dapat digunakan pada bidang periklanan, apakah dengan iklan suatu produk orang menjadi tertarik atau tidak. Untuk hal itu penulis melakukan analisis mengenai pengenalan ekspresi wajah menggunakan metode penggabungan Local Gabor Binary Pattern (LGBP) dan Support Vector Machine (SVM). Analisis menggunakan wajah dari database Japanese Female Facial Expression (JAFFE). Hasil utama dari program yang dibuat menampilkan label dari ekspresi dari wajah yang dimasukan ke program dengan akurasi sistem sebesar 69%. Kata Kunci: Pengenalan ekspresi, Local Gabor Binary Pattern (LGBP), Support Vector Machine (SVM), Japanese Female Facial Expression (JAFFE). Abstract Facial expressions are non-verbal communication. Facial expressions contain information about one's emotions and mental state. Because it contains information about an emotion on a person, it can be used in the advertising field, whether by advertising a product people become interested or not. To that end, the authors conducted an analysis of facial expression recognition using the method of merging Local Gabor Binary Pattern (LGBP) and Support Vector Machine (SVM). The analysis uses faces from the Japanese Female Facial Expression (JAFFE) database. The main results of the program created display the label of the expression of the faces entered into the program with 69% accuracy. Keywords: Expression Recognition, Local Gabor Binary Pattern (LGBP), Support Vector Machine (SVM), Japanese Female Facial Expression (JAFFE).
Co-Authors Anditya Arifianto Erwin Yulizar Fardani Febriyanti Sthevanie Fuad Ikhlasul Amal Naufal Muhammad Athif Tjokorda Agung Budi Wirayudha