<![CDATA[This study conducts a comparative performance analysis of two widely utilized classification algorithms, Naive Bayes and K-Nearest Neighbors (KNN), in the context of customer satisfaction analysis based on product reviews from the Tokopedia e-commerce platform. Customer-generated reviews serve as a critical factor in shaping product reputation and perceived quality, while also influencing the purchasing behavior of prospective buyers.The methodology encompasses data collection of product reviews from Tokopedia, followed by a comprehensive preprocessing pipeline, including text cleaning, tokenization, and stemming. The processed reviews are then categorized into two sentiment classes-positive and negative-employing both Naive Bayes and KNN algorithms.The performance of these algorithms is evaluated using standard classification metrics: accuracy,recall,F1-score dan precision. Empirical results demonstrate that Naive Bayes yields superior accuracy in classifying product sentiments compared to KNN.This research offers practical insights for e-commerce businesses in selecting suitable machine learning techniques for sentiment analysis to better understand customer feedback and enhance satisfaction. Moreover, the study contributes to the academic discourse by highlighting the strengths and limitations of each algorithm, and provides recommendations for future research in developing effective sentiment classification frameworks for customer satisfaction measurement.]]>
