Penggunaan Naïve Bayes Classifier dalam Analisis Sentimen Ulasan Aplikasi McDonald's: Perspektif Pengguna di Indonesia - Kurniawan, Salsha Dara Shinta; Fauzy, Akhmad (Universitas Budi Darma, 2024)
The McDonald's mobile app has become popular among users, who often review their experiences through various review platforms. However, the sheer number of reviews suggests that the app's performance is still not satisfactory. This research aims to analyze public sentiment towards McDonald's app reviews using the Naïve Bayes Classifier algorithm. This algorithm was chosen because of its ability to classify text based on probability and its wide use in sentiment analysis. The research process began with the collection of review data totaling 4.996. Of these, 1.575 data showed neutral sentiment, while 2.137 data revealed positive sentiment, and 1.104 data showed negative sentiment towards the app. However, for the purposes of analysis using the Naïve Bayes algorithm, the focus is only on data that has positive and negative sentiment labels. Thus, the total amount of data used is 3.241 data, consisting of 2.137 positive data and 1.104 negative data. Followed by text pre-processing which includes cleaning, normalization, stopwords, stemming, tokenizing. The dataset is then divided into training data (80%) and testing data (20%). Naïve Bayes Classifier algorithm is used to classify the reviews into positive, negative, and neutral categories ignored. The results show that this model has an 90% accuracy rate in classifying sentiment. This analysis is necessary for the company's evaluation in order to know the public sentiment regarding the McDonald's app. The conclusion of this study shows that although the Naïve Bayes Classifier algorithm is quite effective in the sentiment classification of McDonald's app reviews, it is not enough to classify the sentiment of the McDonald's app.