Klasifikasi Sentimen Presepsi Masyarakat di Instagram Terhadap Paslon Pilpres 2024 Menggunakan Naïve Bayes Classifier (NBC) - Akbar, Lionita Asa; Haerani, Elin; Syafria, Fadhilah; Nazir, Alwis; Budianita, Elvia (Jurnal Komtika (Komputasi dan Informatika), 2024)
The 2024 presidential election has attracted considerable attention as it has become a controversial issue among the public. Various positive and negative opinions generated can potentially turn into rumors. One of the means used by the public to express their opinions is the social media platform Instagram. Data on public opinions on Instagram can be processed into valuable information through sentiment classification. This research conducted sentiment classification on public perceptions towards the 2024 presidential candidates using a naïve Bayes classifier. The study utilized a dataset consisting of 1000 comments. These comments were collected from several posts on the social media platform Instagram discussing the presidential and vice-presidential candidates. The comments were manually labeled by an expert who is a lecturer in the Indonesian language. Classification was carried out after preprocessing and weighting TF-IDF stages. Based on the research findings, the naïve Bayes classifier method showed an accuracy of 82% and an F1-Score of 83.93% obtained from a 90%:10% split of training and testing data. These results indicate that the naïve Bayes classifier method is effective in classifying the sentiments of the public on Instagram towards the 2024 presidential candidates.