Analisis Sentimen Pengguna terhadap Aplikasi Shopee Menggunakan Metode Naive Bayes sebagai Pendukung Pengambilan Keputusan
DOI:
https://doi.org/10.61722/jmia.v3i4.11411Keywords:
Analysis Sentiment, Decision Making, Google Play Store, Naive Bayes, ShopeeAbstract
Shopee is one of the e-commerce applications with millions of active users and a rating of 4.6 on the Google Play Store. The large number of users generates various reviews that reflect user experiences, satisfaction levels, criticisms, and suggestions regarding the services provided. These reviews are valuable sources of information to understand user perceptions and can be utilized as a basis for decision-making processes. However, the large amount of available review data makes manual analysis ineffective and time-consuming. Therefore, a sentiment analysis method is needed to process textual data automatically and efficiently. This study aims to analyze user sentiment toward the Shopee application based on reviews obtained using the Naive Bayes method. The research stages include review data collection, data preprocessing consisting of cleaning, case folding, tokenizing, stopword removal, and stemming, followed by sentiment classification into two categories: positive sentiment and negative sentiment. The Naive Bayes method was selected because it has low complexity, fast computational processing, and good capability in classifying textual data. The results of sentiment analysis are expected to provide information regarding the tendency of user opinions toward the Shopee application and identify various service aspects that receive positive or negative responses. This information can be utilized by developers as evaluation material to improve service quality, enhance application features, and increase user satisfaction. In addition, the research results can serve as a reference for general users in understanding public perceptions of the Shopee application. Thus, sentiment analysis using the Naive Bayes method can be an effective approach in transforming user review data into valuable information to support decision-making.
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