Public Opinion Sentiment Analysis of the Brain Drain Phenomenon on Social Media X Using the Naive Bayes Classifier Algorithm
DOI:
https://doi.org/10.30865/jurikom.v12i5.9131Keywords:
Brain Drain, Sentiment Analysis, X Social Media, Naive Bayes Classifier, TF-IDFAbstract
The brain drain phenomenon is an important issue in Indonesia due to the increasing number of young professionals choosing to work abroad, which reduces the quality of human resources within the country. This study aims to analyze public opinion toward the brain drain phenomenon through the X (Twitter) social media platform and classify public sentiment using the Naive Bayes Classifier algorithm. Data were collected through a web crawling process within the last two years, resulting in 1,170 relevant Indonesian-language tweets. The preprocessing stage included cleaning, case folding, tokenizing, normalization, stopword removal, and stemming to produce clean and structured data. Word weighting was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method to measure the significance of each term. The findings show that public opinion is divided into two main sentiments: positive and negative. Positive sentiment reflects the perception that working abroad offers career advancement and experience, while negative sentiment expresses concern about the loss of skilled human resources. The classification model achieved a high level of accuracy in categorizing sentiment data. This research contributes to understanding public perceptions and provides a foundation for developing strategic policies to address the brain drain issue in Indonesia
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