Sentiment Analysis on the Impact of MBKM on Student Organizations Using Supervised Learning with Smote to Handle Data Imbalance
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Abstract
Recently, there has been a decline in student interest in joining organizations. One of the causes is the MBKM program "Merdeka Belajar Kampus Merdeka". With this program from the government, more and more students are interested in entering because it is considered more profitable. Responses regarding this were conveyed by students through questionnaires, Twitter crawling, and YouTube comments. The data obtained was 1,770 (negative, positive, and neutral labeling) using Sastrawi, Nazief & Adriani, and Arifin Setiono stemming. There is an imbalance of data in labeling, so it is necessary to do SMOTE to balance the data. The algorithms used in the research focus on modeling the Naïve Bayes Classifier, Support Vector Machine, and Decision Tree with the split random method, with the best results using Support Vector Machine. Of the three algorithms, the highest results were obtained from the results of Arifin Setiono's data setmming, using a Support Vector Machine with 91% accuracy, obtained from 90% training data and 10% testing.
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