Implementasi Metode YOLOv8 Mendeteksi Komputer Aktif dengan Subjek Layar Monitor - Wijaya, Frisky; Hermanto, Dedy (JISKA (Jurnal Informatika Sunan Kalijaga), 2025)
Computers are one example of technological advances used in education. The use of computers that are not turned off can cause damage to computer components, and the use of electrical energy can increase. Student disobedience in turning off school laboratory computers when finished using them causes teachers to conduct manual checks by visiting each computer laboratory in the school. Deep learning is a machine learning algorithm that uses artificial neural networks. Deep learning is usually used for image recognition, voice identification, and data pattern analysis. Therefore, this study will apply the Deep Learning method, specifically YOLOv8, which aims to detect active computers based on the subject of the monitor screen and is expected to provide information about computers that are still active in the school laboratory. Based on the study's results, which detected 10 active computers, the 200-epoch model was selected with 100% accuracy at a speed of 2ms. Twenty active computers were selected, with 200 epoch models achieving 95% accuracy at a speed of 6ms per epoch. Thirty active computers were selected, with 100 epoch models achieving 96.67% accuracy at a speed of 3ms.