Sentiment Analysis of Honda Esaf Frame Quality Based on Reviews on Platform X using Support Vector Machine Algorithm - Setyawan, Jefri (Yayasan Kita Menulis, 2026)
This study analyzes public sentiment towards Honda's eSAF frame through 1513 reviews on Platform X during the period of January 2023-October 2025, which was triggered by crucial issues related to the potential for rust, corrosion, and fracture in motorcycle frames. Using a quantitative method with a computational approach, this study applies the Support Vector Machine (SVM) Algorithm with data preprocessing (Case Folding, Cleaning, Tokenizing, Stopword Removal, Stemming), TF-IDF weighting, and Lexicon-based sentiment labeling to classify positive and negative perceptions. The evaluation results show that the SVM-TF-IDF model achieved 98% accuracy on the test data, with negative sentiment dominated by the keywords