Analisis Sentimen Review Pelanggan Lazada dengan Sastrawi Stemmer dan SVM-PSO untuk Memahami Respon Pengguna - Nafi', Abdun; Harjanta, Aris Trijaka; Herlambang, Bambang Agus; Fahmi, Saeful (J-INTECH (Journal of Information and Technology), 2024)
In the digital era, sentiment analysis plays a strategic role in understanding customer perceptions of products and services. This research aims to analyze customer review sentiment on the Lazada platform through the application of text processing techniques and machine learning algorithms. Data is taken from product reviews on the platform, which then undergoes a preprocessing stage, including tokenization, stopword removal, and stemming using the Sastrawi algorithm. Next, sentiment classification was performed using a Support Vector Machine optimized through the Particle Swarm Optimization (PSO) method. The research results showed that the combination of the Sastrawi stemmer method and SVM-PSO was able to achieve significant accuracy, namely 90.57%, an increase of 6.24% compared to previous research. These findings provide deep insights into customer perceptions and offer valuable guidance for decision-makers at Lazada in improving service quality and customer satisfaction. This study also underscores the importance of applying Natural Language Processing techniques and machine learning algorithms in sentiment analysis on e-commerce platforms, which have proven to produce more accurate outputs.