Performance Analysis of Augmented Reality Based on Vuforia Using 3D Marker Detection

In the field of education, especially engineering, there is evidence of skills gaps. According to a survey in the UK covers 2017-2024, the demand for electricity projected to increase. In fact, at the same time, 23.7% of electricity workers are predicted to experience retirement. Therefore, skills gaps need to be minimized through the development of effective and credible learning media. One of the innovations in developing instructional media is applying augmented reality. However, augmented reality performance needs testing to find out the factors that influence the success of object detection to provide maximum results when implemented in learning media. Several existing studies analyze the performance of augmented reality based on the NyARToolkit library, template matching, and the Metaio Mobile SDK. In this research, the performance of 3D object detection performed on augmented reality based on the Vuforia. The research scenarios based on the results of the analysis of the Vuforia working principle. The study conducted with three angles of shooting and several variations of light intensity and distance of the object. The research also conducted by covering part of the object's surface. The results showed that the Vuforia was able to detect objects well in several scenarios that applied with a success rate of 87.5%. The success rate of object detection strongly influenced by the surface area of the detected object and the intensity of the light space.