AUTOMATION OF SPEED MEASUREMENT IN MILLIKAN OIL DROP EXPERIMENT USING YOLOv5 ALGORITHM: OTOMASI PENGUKURAN KECEPATAN PADA EKSPERIMEN TETES MINYAK MILIKAN MENGGUNAKAN ALGORITMA YOLOv5 - Siska Miati Jati Ningsih; Hadi Nasbey; Haris Suhendar (Joint Prosiding IPS dan Seminar Nasional Fisika, 2025)
The Millikan oil drop experiment plays a crucial role in determining the fundamental value of the electron charge by calculating the speed of oil droplets to measure the electrostatic force. However, the manual analysis of this process is time-consuming. This study proposes the use of the YOLOv5 algorithm to automate the measurement of oil droplet speed, aiming to improve efficiency and accuracy. The YOLO algorithm is an object detection approach utilizing computer vision techniques. The methodology includes recording videos of the oil droplets, annotating data using Roboflow, training the YOLOv5 model with a dataset augmented tenfold, and evaluating the model using the mean Average Precision (mAP) metric. The results showed a mAP score of 0.355 during training, with the highest precision approaching 1.0 when recall was near 0. Speed measurements using YOLOv5 were compared to manual methods, resulting in a relative error of 3.79% and an average time difference of 0.1274 seconds. Although the mAP score was not particularly high, the model was able to consistently detect oil droplets. The model's performance heavily depends on the network type, dataset size, and dataset structure. The accuracy of speed calculations was reasonably good compared to reference data, with an error rate below 5%. Overall, the algorithm simplifies the experimental process and enhances the ease of conducting the experiment.