Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm

Muhammad Nouval; Fanza Maulana Habibi; Anisya Rahmi; Muhammad Dawam Amru Bittaqwa; Rizki Agustianto

doi:10.59934/jaiea.v5i2.1830

Authors

Muhammad Nouval Universitas Bina Sarana Informatika, Jakarta, Indonesia
Fanza Maulana Habibi Universitas Bina Sarana Informatika, Jakarta, Indonesia
Anisya Rahmi Universitas Bina Sarana Informatika, Jakarta, Indonesia
Muhammad Dawam Amru Bittaqwa Universitas Bina Sarana Informatika, Jakarta, Indonesia
Rizki Agustianto Universitas Bina Sarana Informatika, Jakarta, Indonesia

DOI:

https://doi.org/10.59934/jaiea.v5i2.1830

Keywords:

Sentiment Analysis, Support Vector Machine, Text Mining, Indonesia National Team

Abstract

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.

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