Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm - Nouval, Muhammad; Habibi, Fanza Maulana; Rahmi, Anisya; Amru Bittaqwa, Muhammad Dawam; Agustianto, Rizki (Yayasan Kita Menulis, 2026)
The Indonesian National Team's failure in the 2026 World Cup qualifiers has generated diverse responses on social media, particularly on Ferry Irwandi's YouTube channel. This study aims to analyze public sentiment towards the national team's performance based on YouTube user comments. The method used is a Support Vector Machine (SVM) with stages of data scraping, pre-processing (cleaning, case folding, normalization, tokenization, stopword removal, stemming), lexicon-based automatic labeling, and model evaluation using a confusion matrix. The data consists of 8,353 comments divided with a ratio of 80:20 for training and testing. The results show that the SVM algorithm is able to classify comments into two classes, positive and negative, with an accuracy of 81%, a precision of 82%, a recall of 83%, and an F1-score of 82%. These results demonstrate the effectiveness of SVM in accurately and stably identifying public opinion towards the Indonesian National Team's failure.