Sentiment Analysis of Honda Esaf Frame Quality Based on Reviews on Platform X using Support Vector Machine Algorithm - Setyawan, Jefri (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 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 "rust" and "damaged", while positive sentiment centered on "strong" and "safe", providing an objective picture of public perception as a basis for evaluating product quality and improving corporate communication strategies.