The Implementation of You Only Look Once Version 5 (YOLOv5) for Detecting Types of Acne on the Face
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Abstract
This research aims to detect acne on the face using the YOLOv5 method. Acne disrupts a person's
appearance and comfort. The causes of acne include increased sebum production, Propionibacterium
breakout bacteria, smoking, excessive calorie intake, and stress. The presence of acne has an impact
on psychosocial development and self-confidence. YOLOv5 method was chosen for its superior speed,
accuracy, and multi-class detection capabilities. Performance evaluation of the model using Accuracy,
Precision, Recall, and F1-Score metrics showed relatively good accuracy, with training values
reaching 99% for Accuracy, Precision, and Recall, and 99.5% for F1-Score. Testing values reached
41% for Accuracy, 66% for Precision, 51% for Recall, and 57% for F1-Score. The model demonstrated
strong capabilities in predicting acne on the face. This research is expected to contribute to the
development of better acne detection methods for individual facial care, leading to more effective
solutions for acne problems.
Keywords: Acne, Detection, YOLOv5, Accuracy, Precision
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