<![CDATA[Facial cleansers are one of the skincare products widely used by individuals ranging from children to adults. The main benefit of facial cleansers is to remove dirt from the user’s face. The numerous brands and types of ingredients in facial cleansers often lead to users making incorrect choices when selecting a cleanser that meets their skin needs. Therefore, this research develops a facial cleanser recommendation system using the Naïve Bayes algorithm, aimed at facilitating the selection of products that match the condition of the user's skin. The Naive Bayes algorithm is chosen for its simplicity and efficiency in processing text data, as well as its ability to provide fast and accurate results in classification. The stages of system development include data collection and preprocessing, feature and label separation, and model training. The dataset used is sourced from official websites. The outcome of this research is a recommendation system for facial cleansers, where users input their skin concerns into the system, and it automatically generates a list of facial cleanser products that correspond to the user’s complaints. For example, if the user has oily skin, the recommended products will be those suitable for oily skin.]]>
Copyrights © 2024
