<![CDATA[In a school environment, policies and regulations play a vital role in teaching students discipline, particularly in adhering to uniform rules. School uniforms help instill discipline by requiring students to dress according to the rules, without modifications, and in compliance with set standards. These regulations foster equality among students, reduce social differences, and support character and moral education. However, enforcing uniform policies can pose challenges for schools. Schools need to regularly monitor compliance to ensure every student follows the uniform rules, a process that often requires significant time and effort. To address this issue, this study developed a student uniform detection system using the You Only Look Once Version 8 (YOLOv8) method. YOLOv8 is a convolutional neural network-based object detection method capable of identifying objects in real-time with high accuracy. The aim of this study is to create a system that can automatically detect student uniforms, improve record-keeping accuracy, and reduce excessive time and energy spent monitoring detection results through cameras. The research methodology includes image data collection, YOLOv8 model training, and system testing. The testing results showed that the developed model achieved a precision of 95.%, a recall of 85%, a mean Average Precision (mAP) of 92.2%.]]>
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