SENTIMENT ANALYSIS THE DAMAGE ESAF FRAME WITH SUPPORT VECTOR MACHINE AND IMPACT ON HONDA MOTORCYCLE SALES - Kinanthi Putri Ariyani; Aviolla Terza Damaliana; Trimono Trimono (Program Studi Sistem Informasi, Universitas Banten Jaya, 2025)
Damage to the Enhanced Smart Architecture Frame (eSAF) on Honda motorcycles has triggered consumer concerns and has become a public spotlight. This study analyzes public sentiment towards the problem using the Support Vector Machine (SVM) and its impact on sales at one of the dealerships in Surabaya. The data used was in the form of comments from Twitter social media which were classified into two classes, namely positive and negative. Based on the results of the analysis, the majority of 589 public sentiments (59.7%) tended to be negative towards the problem of damage to the eSAF frame, while 397 public sentiments (40.3%) showed positive sentiment. Sales results showed significant fluctuations after this issue emerged, along with increasing negative sentiment. SVM models with a Linear kernel provide the best results with 85% accuracy, 84% precision, 85% recall, and 85% f1-score. SVM was chosen because it excels in text classification compared to algorithms such as K-Nearest Neighbors (KNN), C4.5, and Naïve Bayes, and has been applied in areas such as face detection, bioinformatics, and text processing. This research provides insights for manufacturers to improve product quality, improve customer service, and restore public trust. In addition, the use of the Support Vector Machine algorithm in sentiment analysis can be a reference for similar research in other fields.