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LPDP Scholarship (Education Fund Management Institution) is the most sought after by prospective students who want to continue their studies in the country, especially for those who want to continue their studies abroad. Recently, LPDP experienced problems related to students who received LPDP scholarships but did not return to Indonesia in accordance with the rules that have been stated. Starting from the incident on twitter, the topic of "LPDP" became a trending topic among twitter users. So it is our concern to find out and analyze public opinion through this twitter social media. By comparing the results of two methods, namely Support Vector Machine (SVM) and Naïve Bayes in classifying the twitter sentiment. As well as the calculation of accuracy using the Confusion Matrix, there are as many as 1000 tweets result from crawling. This research resulted in a classification that uses the Vader Lexicon Library built by NLTK, the Naïve Bayes method and Support Vector Machine (SVM) has not yet reached an accuracy rate of 70%. In contrast, the Support Vector Machine (SVM) method that uses the Vader Lexicon Library from VaderSentiment achieves an accuracy rate of 90%, with a ratio of 90:10 (training data: test data).

Keywords: LPDP, Naïve Bayes, Sentiment Analysis, Support Vector Machine (SVM), Vader Lexicon.

Article Details

How to Cite
Bagaskoro, S. A., Hasanah, A., Bahri, S., Utami, E., & Yaqin, A. (2023). Analisis Sentimen LPDP (Lembaga Pengelola Dana Pendidikan) Menggunakan SVM dan Naïve Bayes Pada Media Sosial Twitter. Pseudocode, 10(2), 65–73.


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