Klasifikasi Teks Quick Count Pemilihan Presiden 2024 pada Twitter menggunakan Metode TF-IDF dan Naive Bayes - Pranata, Aditya; Rudiman, Rudiman; Verdikha, Naufal Azmi (Jurnal Informatika Terpadu, 2024)
The 2024 Indonesian Presidential Election generated various responses on X Twitter platform related to the Quick Count. The large number of diverse opinions makes identifying and categorizing sentiments difficult. This study aims to evaluate the accuracy of the Naive Bayes method with TF-IDF weighting in text classification regarding the Quick Count of the 2024 Presidential Election on X Twitter. Data was obtained through crawling, resulting in 2113 tweets, which experts in data labelling then labelled. The preprocessing stage includes case folding, cleansing, stopword removal, and stemming. Words are weighted using TF-IDF, and then the data is divided into 80% for training and 20% for testing. Text classification using the Naive Bayes algorithm achieved an accuracy of 74.46%, indicating a pretty good accuracy in classifying text related to the 2024 Presidential Election Quick Count on X Twitter.