Analisis Sentimen Opini Mahasiswa Terhadap Saran Evaluasi Kinerja Dosen Menggunakan TF-IDF dan Support Vector Machine. - Achmad Firmansyah Sulaeman; Ahmad Afif Supianto; Fitra Abdurrachman Bachtiar (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Lecturers especially in institution should always need to improve their knowledge and effective and efficient teaching methods that can be well received by students. Evaluation on the quality of teaching and academic standardization needs to be done so that the objectives of the teaching goal can be achieved. Brawijaya University especially Computer Science Faculty implements of filling the online questionnaire due to evaluating lecturers' performance at each semesters. The opinion data that held by the UJM Team of Information Systems Department is still no processed by using data analysis classification technology that is able to provide information about student perspectives. Sentiment analysis using Support Vector Machine in combining with Term Frequency Inverse Document Frequency as word weighting can be used as a method in classification of student opinion data. This research managed to used student opinion data in bahasa for 3 last semesters in 2017 and 2018. The test that have been conducted on the results of classification show the average of 82% on Accuracy. Data visualization become an output of this research based on the classification result with displaying 3 different pages of sentiment analysis lecturers performance evaluation on each semester. The average result of usability test show result in 70, which means receives a good visualization dashboard at it self.