<![CDATA[This study aims to determine the optimal batch size in training the YOLOv8 model for object detection in autonomous vehicles. With the increasing need for accurate and efficient object detection technology, this study explores the effect of batch size variation on the performance of the YOLOv8 model. The dataset used in this study is a traffic simulation dataset from CARLA, obtained from the Roboflow universe, consisting of 1719 images divided into training, validation, and testing data. The research methodology includes data collection, pre-processing, and data analysis using the YOLOv8 technique with different hyperparameter settings. The results showed that increasing the number of epochs and batch size contributed to the increase in the mean Average Precision (mAP) value of the model. The best training scheme was identified with the highest mAP value of 98.2%, using 100 epochs, batch size 32, and image resolution 640x640. These findings provide important insights for further development in object detection technology, as well as provide guidance for researchers who want to optimize training parameters for object detection models using YOLOv8 in the context of autonomous vehicles. This research is expected to serve as a reference for future studies in this field.Kata kunci: YOLOv8, object detection, autonomous vehicle, optimal batch size, CARLA dataset, mean Average Precision (mAP), hyperparameters, model training]]>
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