Analisis Deteksi Marker Kemunculan Objek 3D Pada Augmented Reality Menggunakan Metode MBT - Nuri Cahyono; Arif Rahmat Hidayatullah (JURIKOM (Jurnal Riset Komputer), 2023)
AbstractTechnological developments are getting advanced all the time, one of which is Augmented Reality, nowadays there are many uses of the Marker Based Tracking method used in Augmented Reality for various fields. Although many use Augmented Reality, there are still many who do not know the limitations contained in the Marker Based Tracking method, namely the minimum and maximum required levels of light intensity, distance, and angle in marker detection. This study aims to determine the effect and seek the level of minimum and maximum requirement of light intensity, distance and angle of the Marker Based Tracking method in marker detection. In this study the data collection process will use the testing method, the testing process will be carried out based on predetermined test parameters, such as light intensity, distance, and angle with several different variable indicators that have been determined, namely the variable light indicator using no light source, yellow light, white light source and sun light source then variable distance and angle indicators using 5cm, 10cm, 20cm, 30cm, 40cm, 50cm, 60cm, 70cm, 80cm, 90cm and 15°, 30°, 45°, 60° , 75°, 90°. The test results of the Marker Based Tracking method show that the marker detection distance is a minimum of 10 centimeters and a maximum of 80 centimeters, as well as a minimum marker detection angle of 45 degrees and a maximum of 90 degrees. For all light sources, Augmented Reality applications can detect markers and display 3D objects based on light intensity; however, without light, they cannot detect the marker. Testing without light intensity obtains a detection accuracy rate of 0%, for testing on yellow light sources obtains a detection accuracy rate of 48%, for testing on white light sources obtains a detection accuracy rate of 51%, for testing on solar light sources obtains a detection accuracy rate of 51%. detection accuracy of 53%.