Metode Hybrid Naive Bayes Dan K-Means Dalam Kategorisasi Sentimen Ulasan Pengguna Aplikasi Chatgpt Menggunakan Fitur N-Gram Tf-Idf Dan Word2vec (Studi Kasus : Ulasan Pengguna Aplikasi Chargpt Yang Berasal Dari Indonesia)

Abstract

The rapid development of Artificial Intelligence (Al) has significantly influenced nearly all aspects of life. One Al product widely used by people worldwide is the Chat Generative Pre-Training Transformer (ChatGPT), which can respond to questions conversationally. Although data indicates that the use of ChatGPT in Indonesia is less widespread than in other countries, a Populix survey reveals that half of the respondents have utilized ChatGPT, using Al more than once a month. This indicates its crucial role among the Indonesian population. ChatGPT is not limited to browsers, it is also available as a downloadable application on the Google Play Store. The ChatGPT application has garnered various user reviews, particularly those from Indonesia. Therefore, this research employs the Naive Bayes Classifier and K-Means Clustering to classify sentiments and group user reviews of the ChatGPT application originating from Indonesia. The study utilizes TF-IDF and Word2Vec as feature extraction methods, combining various N-Gram in data preprocessing to consider the context of sequentially arranged words that may carry meaning. The best classification results are obtained from the trigram classification model, as indicated by precision, recall, and accuracy values of 0.99 each, along with an Fl-score of 1. Clustering also yields positive results, with some overlapping, yet words within clusters exhibit high similarity. Categorization results suggest that user reviews of the ChatGPT application from Indonesia tend to be positive, expressing satisfaction impressions, providing feedback for feature development, and expressing hope for the continued availability of the accessible version of ChatGPT due to its remarkable benefits.