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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Linguistik Komputasional Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF)
Dzikrillah, Akhmad Rizal
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Journal : Building of Informatics, Technology and Science

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Penerapan Metode Naïve Bayes untuk Analisis Sentimen pada Ulasan Pengguna Aplikasi ChatGPT di Google Play Store Hermawan, Tri Ramadhani Putra; Dzikrillah, Akhmad Rizal
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5400

Abstract

ChatGPT is a chatbot application developed by OpenAI. It has attracted a large number of users in a short period of time. User comments are categorized into positive and negative, indicating their sentiments on using this app. Although ChatGPT provides convenience from its various features, it also has its downside if misused. Some people think that people will depend on the information provided by this chatbot and reduce the desire to find out the information themselves, because the information from ChatGPT still uses the old generation model. From this concern, a deeper research on sentiment analysis of people who have used the ChatGPT application is made. It is hoped that this research will be able to collect data on public responses to ChatGPT, both pros and cons. Research data will be taken from reviews of ChatGPT application users in the Play Store. Google Collaboratory with Google Play Scraper will be used during the data collection process. The data that has been obtained will go through a preprocessing stage to be cleaned. After the data is successfully cleaned, the data will go through the process of labeling positive and negative data, and will be classified through the Naïve Bayes method. The study results show that the application of the Naïve Bayes method is able to classify user sentiment using Confusion Matrix with a percentage accuracy value of 94.05%, a percentage precision value of 95% for positive and 81.25% for negative. Then the percentage of recall value for positive is 98.84%, and for negative is 48%.
Co-Authors Aditya, Muhammad Rifky Al-Fadillah, Syafira Nadia AMURU, ISMAT Hermawan, Tri Ramadhani Putra Irwansyah Irwansyah Makhfuz, Maulana Muhammad Mulia, Adi Nova, Sabrina Qodri Somoal, Muhammad Gabriel