Metode Support Vector Machine (SVM) dan Lexicon-Based dalam Analisis Sentiment Ulasan Pengguna Aplikasi Wink
DOI:
https://doi.org/10.55123/jumintal.v4i1.5236Keywords:
Lexicon Based, Google Play Store, Sentiment Analysis, Support Vector Machine, WinkAbstract
Sentiment Sentiment Analysis is an important method in understanding user opinions of an application. This study aims to analyze the sentiment of users of the Wink application on the Google Play Store, which is a popular application that uses Artificial Intelligence (AI) in photo and video editing that has many templates and is easy to use. Sentiment analysis was carried out using the Lexicon-Based approach and the Support Vector Machine (SVM) classification algorithm. Data was taken from reviews on the Google Play Store Wink Application which obtained 1905 review data showing that 78.6% of users gave a rating of 4 to the application, while the Lexicon-Based approach identified 69.5% of reviews with positive sentiment values with many words that appear are "good", "very" and "very" where this is in accordance with the number of good ratings given also providing sentiment reviews that are mostly positive. By using the Support Vector Machine (SVM) classification algorithm, high accuracy results were obtained, namely 95% with a recall value of 1.00 and a precision of 0.93, which shows that the combination of the Lexicon-Based approach and the Support Vector Machine (SVM) algorithm is said to be effective in analyzing sentiment in Wink application reviews.
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