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All Journal Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JOIV : International Journal on Informatics Visualization ComTech: Computer, Mathematics and Engineering Applications Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science
Chowanda, Andry
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Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics

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Journal : CommIT (Communication

 1 
Object Detection Model for Web-Based Physical Distancing Detector Using Deep Learning Chowanda, Andry; Sariputra, Ananda Kevin Refaldo; Prananto, Ricardo Gunawan
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.8669

Abstract

The pandemic has changed the way people interact with each other in the public setting. As a result, social distancing has been implemented in public society to reduce the virus’s spread. Automatically detecting social distancing is paramount in reducing menial manual tasks. There are several methods to detect social distance in public, and one is through a surveillance camera. However, detecting social distance through a camera is not an easy task. Problems, such as lighting, occlusion, and camera resolution, can occur during detection. The research aims to develop a physical distancing detector system that is adjusted to work with Indonesian rules and conditions, especially in Jakarta, using deep learning (i.e., YOLOv4 architecture with the Darknet framework) and the CrowdHuman dataset. The detection is done by reading the source video, detecting the distance between individuals, and determining the crowd of individuals close to each other. In order to accomplish the detection, the training is done with CSPDarknet53 and VGG16 backbone in YOLOv4 and YOLOv4 Tiny architecture using various hyperparameters in the training process. Several explorations are made in the research to find the best combination of architectures and fine-tune them. The research successfully detects crowds at the 16th training, with mAP50 of 71.59% (74.04% AP50) and 16.2 Frame per Second (FPS) displayed on the web. The input size is essential for determining the model’s accuracy and speed. The model can be implemented in a web-based application.
Modeling Emotion Recognition System from Facial Images Using Convolutional Neural Networks Kusno, Jasen Wanardi; Chowanda, Andry
CommIT (Communication and Information Technology) Journal Vol. 18 No. 2 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i2.8873

Abstract

Emotion classification is the process of identifying human emotions. Implementing technology to help people with emotional classification is considered a relatively popular research field. Until now, most of the work has been done to automate the recognition of facial cues (e.g., expressions) from several modalities (e.g., image, video, audio, and text). Deep learning architecture such as Convolutional Neural Networks (CNN) demonstrates promising results for emotion recognition. The research aims to build a CNN model while improving accuracy and performance. Two models are proposed in the research with some hyperparameter tuning followed by two datasets and other existing architecture that will be used and compared with the proposed architecture. The two datasets used are Facial Expression Recognition 2013 (FER2013) and Extended Cohn-Kanade (CK+), both of which are commonly used datasets in FER. In addition, the proposed model is compared with the previous model using the same setting and dataset. The result shows that the proposed models with the CK+ dataset gain higher accuracy, while some models with the FER2013 dataset have lower accuracy compared to previous research. The model trained with the FER2013 dataset has lower accuracy because of overfitting. Meanwhile, the model trained with CK+ has no overfitting problem. The research mainly explores the CNN model due to limited resources and time.
Co-Authors Ahmadi, Hero Akbar Arifin, Yulyani Benhard, Stefanus Diansari, Marsella Iglesias, Glen Kusno, Jasen Wanardi Kusuma, Juanietto Forry Ndruru, Samson Prabowo, Benard H. Prananto, Ricardo Gunawan Putra, Arya Krisna Santika, Satriadi Putra Sariputra, Ananda Kevin Refaldo SATRIYAS ILYAS Tambunan, Fiqri Ramadhan Widardo, Fernandi