Jurnal Komtika (Komputasi dan Informatika)

The phenomenon of labor migration abroad has become a widely discussed social issue on social media, particularly through the hashtag #KaburAjaDulu on the X platform. This study aims to analyze public sentiment toward the phenomenon using machine learning classification methods with the implementation of Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The research was conducted using 1,750 data collected from the X platform through several stages, including data collection, text preprocessing, sentiment labeling, TF-IDF weighting, SMOTE implementation, and classification using Support Vector Machine (SVM), Naive Bayes, Random Forest, and Logistic Regression algorithms. Model evaluation was carried out using accuracy, precision, recall, and f1-score metrics. The results show that the implementation of SMOTE significantly improved classification performance. Logistic Regression achieved the best performance with an accuracy of 91.36%, followed by Random Forest at 90.30%, Support Vector Machine at 88.89%, and Naive Bayes at 82.89%. These findings indicate that Logistic Regression has the best capability in recognizing sentiment patterns within unstructured social media data. This study proves that data balancing using SMOTE plays an important role in improving sentiment classification performance and in understanding public opinion regarding social phenomena developing in digital media.