Analisis Sentimen Aplikasi Shopee di Goole Play Store  Menggunakan Klasifikasi Algoritma Naïve Bayes

Moch Rifki Firdaus; Nining Rahaningsih; Raditya Danar Dana

doi:10.36499/jinrpl.v6i1.10302

Analisis Sentimen Aplikasi Shopee di Goole Play Store Menggunakan Klasifikasi Algoritma Naïve Bayes

Moch Rifki Firdaus, Nining Rahaningsih, Raditya Danar Dana

Abstract

Through Information Technology (IT), the internet is now used to encourage business and market activities. This research aims to conduct sentiment analysis of Shopee application reviews on the Google Play Store platform by applying the Naïve Bayes algorithm classification method. As one of the leading e-commerce applications, Shopee relies heavily on user feedback to continuously improve the quality of its services. A deep understanding of customer sentiment revealed in app reviews can be the foundation for continuous improvement and improvement. The Naïve Bayes algorithm classification method was implemented to categorize sentiment in customer reviews on the Shopee application on the Google Play Store. The data used involves a large number of reviews covering various aspects of the application. The sentiment analysis process involves data prepprocessing, feature extraction, and Naïve Bayes mode training. The results of this research provide an overview of how users respond to the Shopee application, identifying positive, neutral and negative sentiment patterns that may influence the application's reputation and popularity. These findings can be a guide for application developers to focus on certain aspects that influence user satisfaction. In addition, the application of the Naïve Bayes algorithm classification method proves its reliability in identifying customer sentiment efficiently, contributing to a deep understanding regarding the use of this algorithm in the context of analysis sentiment on the application platform.

Keywords

Sentiment Analysis, Shopee Application, Google Play Store, Algorithm Classification, Naïve Bayes

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DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10302