<![CDATA[Alligator cracking, also known as fatigue cracking, is a series of interconnected cracks that occur on the surface of asphalt concrete layers as a result of fatigue failure caused by repeated vehicle loads. This type of cracking is classified into three levels, namely low, medium, and high severity. However, the detection of road cracking is still commonly performed manually, which becomes an obstacle in accurately identifying alligator cracking conditions. To address this issue, this study develops an algorithm using YOLOv11 implemented in an Android application to facilitate the identification of low, medium, and high alligator cracking. The proposed method includes dataset collection, data labeling, preprocessing, data augmentation, YOLOv11 model training, implementation of the TensorFlow Lite (tflite) model into the application, and real-time testing. The experimental results show that the developed model achieves a Precision of 83.4 percent, Recall of 83.3 percent, mAP50 of 83.1 percent, mAP50–90 of 59.3 percent, F1-Score of 83.3 percent, and an indirect testing accuracy above 90 percent. Based on these results, it can be concluded that the developed application is capable of accurately identifying alligator cracking in real time.]]>
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