Evaluasi Sentimen Review Produk Roundup Menggunakan Algoritma Support Vector Machine: Evaluasi Sentimen Review Produk Roundup Menggunakan Algoritma Support Vector Machine

Mohamad Khoiron
Dian Ahkam Sani
Mohammad Zoqi Sarwani
Muhammad Mahrus Ali
Khoirul Anwar
Muhammad Udin

J-Innovation
Vol. 13 No. 2 (2024): Jurnal J-Innovation

In today's digital era, more and more internet users are sharing their experiences and opinions about certain products. Sentiment analysis can be used to extract valuable information from the data generated by the shopee application users. This study aims to conduct a sentiment analysis of Roundup product reviews. The method used is the Support Vector Machine (SVM). SVM is an effective machine learning method for classifying text based on positive or negative sentiments. The purpose of this study is the SVM model which can be used to perform sentiment analysis automatically on Roundup product reviews. The results of this analysis can provide important insights for Roundup producers in understanding consumer perceptions of their products. In addition, this research can also be a guide for consumers in choosing and understanding weed killer products that suit their needs and preferences. In this study, the accuracy value was 80%, the precision value was 80%, the recall value was 100% and the value F1 score of 88.89%.