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Enthusiastic : International Journal of Applied Statistics and Data Science (e-ISSN: 2798-3153, p-ISSN: 2798-253X) is an international journal published and managed by Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia. This journal publishes original research articles or review articles on all aspect of statistics and data science field which should be written in English. ENTHUSIASTIC has the vision to become a reputable journal and publish good quality papers. We aim to provide lecturer, researchers both academic and industries, and students worldwide with unlimited access to be published in our journal.

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Volume 5 Issue 2, October 2025

Sentiment Analysis on the Rumors of the Discontinuation of IISMA Program using Support Vector Machine, Random Forest Classifier and XGBoost Classifier

Michelle Intan Handa ^a,1,, Maria Zefanya Sampe ^b,2*, Syafrudi ^c,3

^a,bBusiness Mathematics, School of Applied STEM Universitas Prasetiya Mulya, Edu Town Kavling Edu I No. 1, Banten 15339, Indonesia
^cArtificial Intelligence and Robotics, School of Applied STEM Universitas Prasetiya Mulya, Edu Town Kavling Edu I No. 1, Banten 15339, Indonesia
¹ Email First Author; ²[email protected]*; ³[email protected]

Abstract
IISMA (Indonesian International Student Mobility Award) 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 include Support Vector Machine (SVM), Random Forest Classifier (RFC), and XGBoost Classifier. The dataset consists of 630 tweets scraped from Twitter and is split into an 80:20 ratio, with 80% allocated for training and 20% for testing. The results indicate that both SVM and RFC are 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. This is because the program is perceived as non-urgent 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.