Analisis Sentimen Lexicon-Based Penggunaan ChatGPT pada Siswa dan Guru SMAN 2 Klari

Abstract

The development of Artificial Intelligence (AI) technology, particularly ChatGPT, has increasingly been utilized in learning activities. However, the perceptions of students and teachers regarding the use of ChatGPT in schools still need to be objectively mapped. This study aims to analyze the sentiments of students and teachers at SMAN 2 Klari toward the use of ChatGPT in learning activities using a Lexicon-based approach with the InSet sentiment lexicon. The research employed a quantitative approach using 777 responses collected from 742 students and 35 teachers through open-ended questionnaires. The research stages included data collection, preprocessing (case folding, text cleaning, tokenizing, stopword removal, slang word normalization, and stemming), Lexicon-based sentiment analysis, and evaluation using Fleiss’ Kappa and Confusion Matrix. The results showed that the majority of students expressed positive sentiment toward the use of ChatGPT in learning, accounting for 91.1%, while negative and neutral sentiments accounted for 6.9% and 2.0%, respectively. Among teachers, all respondents expressed positive sentiment, reaching 100%. The annotator evaluation using Fleiss’ Kappa obtained a score of 0.7745, categorized as Substantial Agreement, indicating strong agreement among annotators. Furthermore, the Confusion Matrix evaluation produced an accuracy of 79%, demonstrating that the Lexicon-based method performed reasonably well in classifying sentiments related to ChatGPT usage. Overall, the findings indicate that ChatGPT is perceived positively by both students and teachers as a technology that supports the learning process, although concerns remain regarding potential dependency and inappropriate use.