| dc.description.abstract | Acne is one of the most common skin problems experienced by people. Acne can occur due to excess oil production, blockage of pores on the face, bacterial infections, food and cosmetic factors. It is very important to know the types of acne to make it easier to treat. People often make mistakes in providing ingredients that are suitable for this type of acne. Therefore, we need an application system that can classify the types of acne. In this research, three categories of acne, namely nodules, papules, and cystic acne, will be classified using the Faster Region Convolutional Neural Network (Faster R-CNN) method. The research utilizes a dataset comprising 900 entries, which is divided into training, testing, and validation sets. Upon conducting the experiments for this research, it can be concluded that the outcomes of this investigation are deemed satisfactory for distinguishing the three acne types, highlighting their similarities. Additionally, the study demonstrates that the selected method is proficient in achieving a classification accuracy of 92%. | en_US |
