Acne is a common skin condition that can significantly impact the quality of
life of those affected. Many individuals struggle to identify different types of acne,
such as blackheads, nodules, papules, pustules, and whiteheads. This study aims to
develop and evaluate an acne image classification system using the Naive Bayes
algorithm. The study employs RGB color feature extraction and GLCM texture
feature extraction to improve the accuracy of acne type identification from images.
The classification model was tested on 35 testing images and 316 training images,
yielding an accuracy of 63%. The evaluation matrix indicates that the model
achieved a recall of 62.86% for actual acne cases and a precision of 63.57%, with
an F1-score of 0.6054. This research contributes to the accurate identification of
acne types based on images.