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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Handa, Michelle Intan
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Analyzing Sentiments on IISMA Discontinuation Rumors with SVM, Random Forest Classifier, and XGBoost Classifier Handa, Michelle Intan; Sampe, Maria Zefanya; Syafrudi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art5

Abstract

Indonesian International Student Mobility Award (IISMA) is a government-run student exchange program. Recently, rumors regarding its discontinuation have sparked various public opinions. This study aims to analyze these public sentiments and evaluate which machine learning model is most suitable for classifying sentiment labels in the dataset. The models tested included support vector machine (SVM), random forest classifier (RFC), and extreme gradient boosting (XGBoost) classifier. The dataset consisted of 630 tweets scraped from Twitter and was split into an 80:20 ratio, with 80% allocated for training and 20% for testing. The results indicated that both SVM and RFC were the most effective models, achieving the highest accuracy of 85.44%. Sentiment analysis reveals that the majority of public opinion is positive, suggesting that most people agree with the discontinuation of the IISMA program because the program is perceived as nonurgent and not a current national priority. These findings provide insights into public sentiment and highlight the utility of machine learning models in classifying such sentiment data effectively.
Co-Authors Maria Zefanya Sampe Syafrudi