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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTERACTION: Jurnal Pendidikan Bahasa Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika: Jurnal Pengembangan IT
Muhammad Naufal
Dian Nuswantoro University, Semarang
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection Muhammad Niko Andrean; Guruh Fajar Shidik; Muhammad Naufal; Farrikh Al Zami; Sri Winarno; Harun Al Azies; Permana Langgeng Wicaksono Ellwid Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7167

Abstract

Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
Pengenalan Ekspresi Wajah Menggunakan Transfer Learning MobileNetV2 dan EfficientNet-B0 dalam Memprediksi Perkelahian Ni Made Kirei Kharisma Handayani; Erwin Yudi Hidayat; Muhammad Naufal; Permana Langgeng Wicaksono Ellwid Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7048

Abstract

Expressions play an important role in recognizing someone's emotions. Recognizing emotions can help understand someone's condition and be a sign of their possible actions. Fighting is one of the violences that occur due to someone's negative emotions that need to be prevented and treated immediately. In this study, expression recognition is used to predict the possibility of a fight based on the expression shown by a person. The dataset used is FER-2013 which has been modified into two labels, namely "Yes" and "No". The data undergoes a preprocessing step which includes resizing and normalization. Model experiments using transfer learning from the MobileNetV2 and EfficientNet-B0 architectures have been modified by performing hyperparameter and fine tuning which includes freezing the layer by 25% in the first layers of each model and adding several layers such as flatten and dense. In the training process, some parameters used are 30 epochs, batch size 32, and Adam optimization with a learning rate of 0.0001. Model performance evaluation is measured using Confusion Matrix, then the results are compared and obtained the model that produces the best accuracy value is EfficientNet-B0 which is 82%. Meanwhile, based on the training time and model weight, MobileNetV2 is 1 hour 1 minute 43 seconds faster and 21.57 MB smaller than EfficientNet-B0.
Co-Authors Ardiawan Bagus Harisa Dega Surono Wibowo Erwin Yudi Hidayat Erwin Yudi Hidayat Farrikh Al Zami Guruh Fajar Shidik Harun Al Azies Hepatika Zidny Ilmadina Liya Umaroh Muhammad Niko Andrean Mukaromah Mukaromah Ni Made Kirei Kharisma Handayani Permana Langgeng Wicaksono Ellwid Putra Permana Langgeng Wicaksono Ellwid Putra Permana langgeng wicaksono ellwid putra Sri Winarno