Classification of Acne Severity Using K-Nearest Neighbor (KNN) and Random Forest Method

Gloria Flourin Maitimu (Telkom University)
Putu Harry Gunawan (Telkom University)
Muhammad Ilyas (Kuwait College of Science and Technology)

Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Vol. 16 No. 02 (2025): Vol.16, No. 02 August 2025

The development of machine learning technology, especially in dermatology, offers excellent opportunities for classifying and diagnosing skin conditions such as acne. This study aims to apply and compare two machine learning methods, K-Nearest Neighbors (KNN) and Random Forest methods, to classify acne severity into three levels: mild, moderate, and severe. The acne density and average confidence features were extracted from facial images using the YOLOv8 model based on acne bounding boxes. While the KNN model achieves 95% accuracy, the Random Forest model reaches 97%, indicating superior performance with excellent precision, recall, and F1-score values. With its level of accuracy, the integration of the Random Forest model and the features extracted using the YOLOv8 model appear to be a promising tool in dermatology for classifying acne severity in a more accurate and effective way.