Perbandingan Naive Bayes Classifier dan SVM untuk Analisis Sentimen Desain Seragam Atlet Indonesia pada Media Sosial X di Olimpiade Paris 2024 - Azizah Salma Nida; Irwansyah; Ade Davy Wiranata; Zuhri Halim (PT Ajira Karya Indonesia, 2026)
The Olympics is an international sporting event held every four years and serves as a platform for countries to showcase their athletic capabilities and national identity. One aspect that attracts public attention is the design of athletes' uniforms, which not only have aesthetic value but also support athletic performance. Differences in public perception of these designs generate various opinions expressed on social media X. This study aims to analyze public sentiment toward the design of Indonesian athletes' uniforms at the Paris 2024 Olympics on social media X and to compare the performance of Naive Bayes Classifier and Support Vector Machine algorithms. The dataset consists of textual data collected from social media X and processed through preprocessing stages and split into training and testing data with an 80:20 ratio. The results show that there are 1,014 positive and 728 negative sentiments. Model evaluation indicates that the Naive Bayes Classifier achieved an accuracy of 80.5%, while the Support Vector Machine achieved 94.2%, outperforming the former. These findings demonstrate that the Support Vector Machine is more effective than the Naive Bayes Classifier for sentiment analysis of social media text data related to the design of Indonesian athletes' uniforms at the Paris 2024 Olympics.