Implementation Of The Naïve Bayes Method For Skincare Product Recommendations According To Skin Type At Dermakila Clinic

Abstract

Advances in information technology have encouraged the implementation of recommendation systems in various fields, including beauty and skincare. Selecting skincare products that are not suitable for an individual's skin type and condition may reduce treatment effectiveness and potentially lead to skin problems. Dermakila Clinic offers a wide range of skincare products with diverse characteristics, creating a need for a system that can assist users in selecting products that best suit their skin needs. This study aims to implement the Naïve Bayes method in developing a skincare product recommendation system based on user characteristics, including age range, gender, skin type, and skin concerns. The research applies a data mining approach using the Naïve Bayes classification algorithm. The dataset consists of 960 skincare product records that have undergone preprocessing and data transformation stages. The system was developed as a web-based application to provide users with fast and accurate product recommendations. The experimental results demonstrate that the Naïve Bayes method achieved an accuracy of 88%, with a precision of 89%, a recall of 88%, and an F1-score of 88%. These findings indicate that the Naïve Bayes method is effective for implementing a skincare product recommendation system at Dermakila Clinic.