Design and Build a Door Security System Using a Raspberry-Pi based Liveness Detection - Saputra, Reinaldo Riswanto Saputra; Rasyid, Abdul; Anshori, Mohammad Abdullah; Adzikirani, Adzikirani (JURNAL JARTEL: Jurnal Jaringan Telekomunikasi, 2024)
Various problems with the door threaten the security of the room owner such as theft. Many are done by thieves to carry out their actions. In the data from the National Police Center, there are 158,267 cases of theft with aggravation, the act of theft with aggravation is included in the theft at home or in a boarding room. This causes the need for a door security system. The purpose of this research is to make door security using Convolutional Neural Network (CNN) as accuracy detection and face prediction with Liveness Detection as real face detection or not. The overall test results of facial accuracy obtained based on epoch 25, epoch 50, epoch 100, epoch 150, epoch 200, and epoch 250. With average accuracy test results of 95.1% at epoch 25, 98.2% at epoch 50, 97.9% at epoch 100, 98.6% at epoch 150, 96.3% at epoch 200, and 95.7% at epoch 250. Prediction results were 95.3% at epoch 25, 98.1% at epoch 50, 97.7% at epoch 100, 98.6% at epoch 150, 96.3% at epoch 200, and 95.7% at epoch 250. Overall, the use of Liveness Detection and Convolutional Neural Network (CNN) technology in the door security system can improve security with more than 95% accuracy. This system also provides convenience for users to monitor the state of the door remotely through Android applications, so that it can be relied upon to improve room efficiency and security.