2 research outputs found
RANCANG BANGUN PLUG - IN WHITEBOARD PICO VR MENGGUNAKAN UNITY
No full textThe development of Virtual Reality (VR) technology has opened up new opportunities in education. One innovation that can be implemented is the use of a virtual whiteboard that allows interaction and collaboration in a VR environment. This thesis entitled “Whiteboard Plugin Design for PICO VR Using Unity” aims to develop a whiteboard plugin that can be used on the PICO VR platform. This project aims to overcome the shortcomings of existing literature and practice by creating a more interactive and effective VR experience. The whiteboard plugin developed allows users to write, draw, and interact with the whiteboard in a virtual world, similar to the use of a conventional whiteboard in the classroom. The development of this plugin uses the Waterfall method, which involves the stages of needs analysis, design, implementation, and testing. The results of this project are expected to make it easier for PICO VR users to access the whiteboard feature and make a positive contribution to the world of education, especially in virtual learning scenarios. With this plugin, it is expected that learning methods can become more interactive, effective, and interesting for students, and support the development of educational technology in Indonesia
A Better Performance of GAN Fake Face Image Detection Using Error Level Analysis-CNN
No full textThe use of face images has been widely established in various fields, including security, finance, education, social security, and others. Meanwhile, modern scientific and technological advances make it easier for individuals to manipulate images, including those of faces. In one of these advancements, the Generative Adversarial Network method creates a fake image similar to the real one. An error-level analysis algorithm and a convolutional neural network are proposed to detect manipulated images generated by generative adversarial networks. There are two scenarios: a stand-alone convolutional neural network and a combination of error-level analysis and a convolutional neural network. Furthermore, the combined scenario has three sub-scenarios regarding the compression levels of the error-level analysis algorithm: 10%, 50%, and 90%. After training the data obtained from a public source, it becomes evident that using a convolutional neural network combined with compression of error level analysis can improve the model’s overall performance: accuracy, precision, recall, and other parameters. Based on the evaluation results, it was found that the highest quality convolutional neural network training was obtained when using 50% error level analysis compression because it could achieve 94% accuracy, 93.3% precision, 94.9% recall, 94.1% F1 Score, 98.7% ROC-AUC Score, and 98.8% AP Score. This research is expected to be a reference for implementing image detection processes between real and fake images from generative adversarial networks
