Analisis Sentimen Ulasan Aplikasi Lazada dengan Metode Naive Bayes dan Random Forest - Mohammad Rizal; Nur Azizah; Firman Jaya (Pusat Riset dan Inovasi Nasional, 2026)
The rapid growth of e-commerce applications has increased the number of user reviews that reflect public opinion on service quality and user experience. However, many previous studies rely only on rating-based sentiment analysis and do not utilize positive and negative sentiment lexicons, resulting in limited insight into public opinion. This study aims to analyze public opinion toward the Lazada application using a data mining–based sentiment analysis approach by combining user ratings and sentiment lexicons in the labeling process. The data set consists of Lazada user reviews collected from digital platforms. The research process includes data collection, data cleaning, and text preprocessing, such as text normalization, removal of emojis and symbols, and stopword elimination. Feature extraction is performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment labeling is conducted by integrating rating scores and lexicon analysis, where reviews with ratings of 4–5 or dominant positive words are classified as positive, while reviews with ratings of 1–2 or dominant negative words are classified as negative; neutral reviews are excluded. Sentiment classification is carried out using Naive Bayes and Random Forest algorithms. Model performance is evaluated using accuracy, precision, recall, and F1-score. The results show that both models perform well, with Random Forest achieving better performance than Naive Bayes. This study provides useful insights for improving e-commerce service quality based on user feedback.