Analisis Sentimen Supporter Sriwijaya FC Berbasis Manchine Learning - Farhan, Muhammad; Puspasari, Shinta; Gasim, Gasim (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
This study analyzes the sentiment of Sriwijaya FC supporters toward the club's management through comments on Instagram. Data was collected from 6,601 comments on the official @SriwijayaFC account and processed through text preprocessing stages with an 80:20 split for training and testing data. The analysis was conducted using four machine learning algorithms: SVM, Random Forest, Naïve Bayes, and KNN. The results indicate that neutral sentiment dominates (50.92%), followed by positive (25.07%) and negative (24.01%) sentiment, suggesting that most comments are informative or impartial, although there are both supporting and opposing opinions. Model performance evaluation using a confusion matrix and accuracy, precision, recall, and F1-score metrics shows that SVM achieved the highest accuracy (89%), followed by Random Forest (82%), Naïve Bayes (74%), and KNN (65%). These findings demonstrate that machine learning is effective in classifying social media sentiment. Future research may explore deep learning algorithms and expand data sources to other platforms for a more comprehensive analysis.