Klasifikasi Tingkat Kepuasan Pengguna Produk Body Care Menggunakan Algoritma Decision Tree

Abstract

The increasing competition in the body care industry encourages companies to understand customer satisfaction as a basis for improving product quality and service performance. However, analyzing user satisfaction often produces complex data that are difficult to process manually. This study aims to apply the Decision Tree algorithm to classify the satisfaction levels of body care product users based on user characteristics and product evaluations. The research data were collected through questionnaires distributed to 250 respondents, including attributes such as gender, age, frequency of use, product quality, price, service quality, and satisfaction level as the target variable. The research stages consisted of data preprocessing, attribute selection, data transformation, splitting data into training and testing datasets, and building a classification model using the Decision Tree algorithm. Model evaluation was carried out using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the model was able to classify user satisfaction into four categories: very satisfied, satisfied, fairly satisfied, and dissatisfied, with an accuracy of 58%, precision of 57%, recall of 57%, and F1-score of 57%. This study contributes to the implementation of data mining for customer satisfaction analysis in the body care industry and helps companies identify dominant factors influencing user satisfaction, particularly product quality and service quality. In addition, the findings are expected to serve as a reference for developing customer satisfaction analysis systems based on data mining in the beauty and body care industry.