Sentiment Analysis of Gojek App Reviews on Google Play Store with Natural Language Processing Using Naive Bayes' Algorithm

Zumardi Rahman; Putri  Sakinah; Yomei  Hendra; Budy  Satria; Fajar  Maulana; Aisyah Qurrata  Ayun

doi:10.54209/jatilima.v6i03.1189

Zumardi Rahman Infomatika, Universitas Metamedia
Putri Sakinah Infomatika, Universitas Adzkia
Yomei Hendra Infomatika, Universitas Adzkia
Budy Satria Infomatika, Universitas Andalas
Fajar Maulana Infomatika, Universitas Adzkia
Aisyah Qurrata Ayun Infomatika, Universitas Adzkia

DOI: https://doi.org/10.54209/jatilima.v6i03.1189

Keywords: Sentiment Analysis, Naïve Bayes, Data Preprocessing, Machine Learning

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Abstract

In the digital era, sentiment analysis is an important tool to understand user perceptions of applications, including the Gojek application. This study aims to analyze the sentiment of Gojek application user reviews on the Google Play Store using the Naive Bayes algorithm. The research process involved collecting 5,000 reviews, preprocessing the text, weighting with TF-IDF, and applying the Naive Bayes algorithm to classify sentiment into negative, neutral, and positive. The evaluation results show that the model has the best accuracy of 76% after applying the data balancing technique. The model's performance for negative sentiment is very good with a precision of 91% and an F1 score of 87%. Positive sentiment shows quite good performance with a precision of 76% and an F1 score of 65%. However, neutral sentiment has low precision (23%) although recalls increased to 51%. Sampling techniques such as SMOTE have succeeded in improving the model's ability to recognize underrepresented classes. With an overall evaluation of weighted average precision of 82% and an F1 score of 78%, this model is considered quite reliable in analyzing the sentiment of Gojek app reviews. This research provides insights for application developers in improving service quality based on user perception..

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