Detection Of Worker Presence Using The Yolo Model Based On Digital Image


  • Jazmi Matondang Magister of Computer Science, Potensi Utama University
  • Rika Rosnelly Magister of Computer Science, Potensi Utama University
  • Roslina Roslina Magister of Computer Science, Potensi Utama University



Face Detection, YOLO, Deep Learning


The large number of workers requires a long time for administrative officers to check worker attendance manually so that work efficiency cannot be achieved. It takes quite a long time to check worker attendance manually. Attendance reports are sent to office social media, including the names of each worker and so on. This is considered less efficient and sufficient as a basis for providing a solution by detecting worker presence using the YOLO (You Only Look Once) method, so that the process of checking worker attendance can be more efficient. The test results with low pixels and training of 100 epochs, namely 224x192 pixels, obtained an accuracy of 86%, while the best test results were using dimensions of 1088x640 pixels in the worker's photo as test data with original photo dimensions of 1080x1920 pixels in the YOLO model which succeeded in detecting faces with 100% accuracy. So, it can be concluded that the higher the pixel value, the better the accuracy tends to be. However, in this case it also has pixel limitations that are recognized by the model.