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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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Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50 Hepatika Zidny Ilmadina; Muhammad Naufal; Dega Surono Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2785

Abstract

Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results.
Incorporating AI Tool Along with Traditional Method for Speaking Assessment Liya Umaroh; Mukaromah Mukaromah; Muhammad Naufal; Ardiawan Bagus Harisa
INTERACTION: Jurnal Pendidikan Bahasa Vol 10 No 2 (2023): INTERACTION: Jurnal Pendidikan Bahasa
Publisher : Universitas Pendidikan Muhammadiyah (UNIMUDA) Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpendidikanbahasa.v10i2.4894

Abstract

This work focused on incorporating AI tool with traditional method for speaking assessment. The descriptive qualitative has been implemented to complete this research. Problem encountered during English pronunciation was failing to distinguish between short vowel and long vowel. By employing traditional and AI tool, students got numerous benefits. They may enjoy learning with the flexibility of time and they also can engage face to face interaction while using traditional method furthermore, it is essential to acknowledge AI tool with the limitation and drawbacks. AI tool do not give the same stage of personal interconnection and on-going response as a human facilitator.
ENHANCING SPEAKING SKILL THROUGH AI-POWERED TECHNOLOGY Liya Umaroh; Mukaromah Mukaromah; Muhammad Naufal
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol 3 No 1 (2023): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/semnastekmu.v3i1.209

Abstract

. Artificial Intelligent (AI) is a field of computer science devoted to solve cognitive problems, commonly associated with human intelligence, such as learning, problem solving and pattern recognition. From education point of views, it helps teachers to facilitate learning process especially for speaking skill. The research method runs with mixed method (qualitative. And quantitative) The aim of this research is investigating the students’ speaking performance with artificial intelligence. The data collection is taken from interviews, photo, and literature studies. The result of this study is the increasing student’s speaking performance with AI.
A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction Permana langgeng wicaksono ellwid putra; Muhammad Naufal; Erwin Yudi Hidayat
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023): JPIT, September 2023
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5703

Abstract

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.
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