Online shopping has become a popular activity in Indonesia, with e-commerce playing a major role in facilitating transactions. Shopee, as a current top-ranked e-commerce company, is also in the first rank in terms of app downloads, but on the contrary, often in the second rank in terms of application or website visits because of several factors. Under the number of visits to e-commerce, customer satisfaction is an important factor that affects the customer traffic of e-commerce, and this study was conducted to understand the factors that influence customer satisfaction and the use of e-commerce websites and applications. The research found that there are nine important components of e-service quality, which are fulfillment, responsiveness, availability, ease of use, assurance, website design, credibility, reliability, and accessibility that play a role in increasing customer satisfaction. This study compared the performance of two classification algorithms, which are naïve Bayes and support vector machine. After that, this research also implemented a permutation importance algorithm in machine learning, which is used to analyze significant features that affect customer satisfaction and is employed in this study after all datasets have been classified. The results revealed that the support vector machine demonstrated superior accuracy and performance compared to naïve Bayes. Furthermore, the study found that ease of use, design, and credibility were the top three factors with the strongest impact on customer satisfaction on the Shopee e-commerce platform.