Analisis Sentimen Komentar Pengguna Instagram Mengenai Pelaksanaan Pemilu 2024 dengan Naïve Bayes dan Lexicon-Based - Dewi, Cahyani Rahma; Iskandar, Agus (Forum Kerjasama Pendidikan Tinggi (FKPT), 2024)
The debate surrounding the implementation of the 2024 General Election has taken centre stage in Indonesia, especially on social media platforms that are favoured by the public. The change of leaders in Indonesia and the emotional differences that emerge in society are of significant concern. The search for leadership figures brings up various complex theoretical, conceptual, and cultural perspectives. This paper aims to analyse people's sentiment related to the 2024 general election by classifying sentiment as positive, negative, or neutral, aiding understanding of people's perceptions of candidates, relevant political issues, and voter behaviour patterns. The methodology involved collecting data using scrapping techniques from the social media platform Instagram using a combination of both Naïve Bayes Classifier and Lexicon-Based labelling algorithms. These two methods were used to conduct sentiment analysis towards the general election in this study. Sentiment analysis of the 2024 General Election using the Naive Bayes and InSet Lexicon models showed good results with an accuracy of 72% (precision negative 74%, neutral 54%, positive 70%; recall positive 62%, neutral 22%, negative 87%). This study successfully surpassed the accuracy of the previous model (72% accuracy, 70% precision, 72% recall) and revealed that negative sentiments were more prevalent in public opinion towards the 2024 General Election. This indicates that there is public dissatisfaction and doubt regarding the implementation of the election, which is thought to be triggered by technical problems and political uncertainty.