Application Of The Term Frequency-Inverse Document Frequency (TF-IDF)-Based Support Vector Machine (SVM) Method For Sentiment Classification Of Customer Reviews On My Lova Bengkulu

Muhammad Sihab (Universitas Dehasen Bengkulu)
Prahasti Prahasti (Universitas Dehasen Bengkulu)
Ahmad Asyhari (Universitas Dehasen Bengkulu)

Jurnal Media Computer Science
Vol 5 No 3 (2026): Juli

This study aims to apply a Term Frequency-Inverse Document Frequency (TF-IDF)-based Support Vector Machine (SVM) method for sentiment classification of My Lova Bengkulu customer reviews. The research data consisted of 109 reviews obtained from Google Reviews, which were then subjected to a preprocessing process involving cleaning and stemming. Next, weighting was performed using TF-IDF, and classification was carried out using the SVM algorithm. The results showed that 93 reviews (85.32%) were positive, while 16 reviews (14.68%) were negative. The model achieved an Accuracy of 81.82%, Precision of 81.80%, Recall of 100%, and an F1-Score of 90.00%. These results demonstrate that the TF-IDF-based SVM method is capable of effectively classifying customer sentiment.