Analisis Sentimen Evaluasi Kinerja Dosen menggunakan Term Frequency-Inverse Document Frequency dan Naive Bayes Classifier - Sri Wulan Utami Vitandy; Ahmad Afif Supianto; Fitra Abdurrachman Bachtiar (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Effective and efficient in teaching can help students achieve maximum results. An evaluation is needed to improve the quality of learning and academic standardization, also it can improve the quality of the student. Therefore, the Information Systems Department always evaluates performance using questionnaires and filled by students at the end of each semester. The results of the suggestion column can be sentiment analysis to find out whether the suggestion is positive, negative or neutral. The classifier is a method that can classify data into several classes. Naive Bayes Classifier can be used to classify opinions into positive, negative and neutral classes. The comment data was collected 3502 comments which were divided into 3 semesters. Then, this comment data processed in preprocessing, weighting TF-IDF and classification using Naive Bayes Classifier. The test result on 4 parameters resulted in an accuracy of 80,1%, Precision 80,3%, Recall 80,3% and F1-Score 80%. The results of Usability testing obtained an average value of SUS Score of 75. So it can be concluded that the Dashboard is included in the Acceptance category and in the rating of "Good"