Public Sentiment Analysis of the Indonesian National Team's Performance Under Patrick Kluivert Using the Random Forest Algorithm - M Rifqi Tri Putra; Rendra Gustriansyah; Lastri Widya Astuti (CV. AFDIFAL MAJU BERKAH, 2026)
This study aims to analyze public sentiment towards the performance of the Indonesian National Football Team during the Patrick Kluivert era using the Random Forest Classifier algorithm. The research data was obtained from 1,000 Twitter tweets collected through web scraping with relevant keywords, between January and March 2025. The obtained data was processed through the stages of cleaning, case folding, tokenization, stemming, and stopword removal, then converted into numeric form using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Sentiment was categorized into two classes, namely positive and negative. The test results showed that the Random Forest model was able to classify sentiment with an accuracy rate of 83% with a precision value of 100%, a recall of 33.33%, and an F1-score of 50%. This finding confirms that public opinion towards the Indonesian National Team during the Kluivert era is divided into two main tendencies: positive support and negative criticism. This study proves that the Random Forest algorithm is effective for social media-based sentiment analysis and can be a reference for the PSSI and related parties in understanding public perception to improve the quality of team strategy and performance.