SENTIMENT ANALYSIS FOR EXTRACTING STUDENT OPINION DATA ON HIGHER EDUCATION SERVICES USING THE NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE METHODS (CASE STUDY AKPRIND INSTITUTE OF SCIENCE AND TECHNOLOGY YOGYAKARTA)

Uning Lestari (Informatics Department Institut Sains & Teknologi AKPRIND Yogyakarta, Yogyakarta)
Tri Romadhani (Informatics Department Institut Sains & Teknologi AKPRIND Yogyakarta, Yogyakarta)
Suraya Suraya (Informatics Department Institut Sains & Teknologi AKPRIND Yogyakarta, Yogyakarta)
Erfanti Fatkhiyah (Informatics Department Institut Sains & Teknologi AKPRIND Yogyakarta, Yogyakarta)

Jurnal TAM (Technology Acceptance Model)
Vol 13, No 1 (2022): Jurnal TAM (Technology Acceptance Model)

Opinions are ideas, opinions, or the results of someone's subjective thoughts in explaining or addressing something. IST AKPRIND Yogyakarta provides comment and suggestion box facilities in the learning evaluation questionnaire. Opinions that have been collected can be used to determine the sentiment of the campus community. This sentiment information can be used in future campus development. The development of a system that can analyze sentiment automatically is designed by comparing the Naive Bayes Classifier (NBC) method and the support vector machine (SVM) optimized by selecting the Information Gain (IG) feature. Prior opinion data needs to be prepared before being analyzed. Preprocessing (text preprocessing) used includes: cleanning, text folding, normalization, stemming, stopword removal, convert negation, and tokenization. The results of this study show that the SVM method produces higher accuracy than NBC. The accuracy test shows the highest accuracy of SVM reaches 99.09% while NBC is 96.56%. The application of IG did not significantly affect the accuracy of the analysis. GI greatly influenced the analysis duration of the SVM method, which could shorten the time by 195.71%.