Analisis Sentimen Masyarakat di Twitter Mengenai Open AI CHATGPT Menggunakan Metode Support Vector Machine (SVM) - Septini, Ayu; Susanto; Elmayati (Bulletin of Computer Science Research, 2025)
This study aims to analyze public sentiment toward OpenAI ChatGPT technology on Twitter using the Support Vector Machine (SVM) method. The background of this research is based on the increasing global use of the internet and artificial intelligence (AI), as well as the role of social media as a platform for people to express their opinions. This study employs a qualitative research approach using the Support Vector Machine method, with data collection conducted through primary data obtained by crawling data from Twitter. The research uses data collected from 4,305 Indonesian-language tweets gathered between January and September 2023. These tweets were then classified into positive, neutral, and negative sentiments using the SVM method. The results indicate that out of the total collected data, 2,196 tweets had a neutral sentiment, 1,500 tweets had a positive sentiment, and 591 tweets had a negative sentiment. In the model performance evaluation, training data with an 80:20 ratio achieved the highest accuracy of 94.25%, while testing data with a 70:30 ratio achieved the highest accuracy of 93.16%. Additionally, the use of 10-fold cross-validation on training data resulted in an accuracy of 89.94%, while testing data achieved an average accuracy of 78.17%.