Analisa Perbandingan Algoritma Support Vector Machine dan K-Nearest Neighbors Terhadap Ulasan Aplikasi Vidio
-
Rizki Bintang Gumilar
*
Universitas Buana Perjuangan Karawang, Karawang,
Indonesia
- Yana Cahyana Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Cici Emilia Sukmawati Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Amril Mutoi Siregar Universitas Buana Perjuangan Karawang, Karawang, Indonesia (*) Corresponding Author
Abstract
Internet usage in Indonesia reached 77% of the total population in January 2023, with Over The Top (OTT) services showing user growth of 25% every year. The Vidio application, one of the popular OTT platforms with downloads exceeding 50 million, has a 3.5 star rating based on 649 thousand reviews on the Google Play Store. Despite its popularity, Vidio faces complaints regarding limited film selection, payment errors, and excessive advertising, which affects user satisfaction. This research aims to analyze the opinions of Vidio application user comments by applying the SVM (Support Vector Machine) method and the KNN (K-Nearest Neighbors) method to determine the model with the best accuracy. 15,000 review data were collected through scraping, then processed using text preprocessing and TF-IDF vectorization techniques. Model evaluation shows that SVM has an accuracy value of 82%, a precision value of 82%, a recall value of 83%, and an F1-score value of 82%, while KNN has an accuracy of 69%, precision 74%, recall 73%, and F1-score 69% . The research results show that SVM is superior to KNN in classifying the sentiment of Vidio application reviews. It is hoped that these findings can be used by application developers in an effort to improve service and satisfaction of Vidio application users.
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