Informatics Engineering Program (TIF) is one of the existing courses at Ahmad Dahlan University (UAD) Yogyakarta. At the end of each semester the Academic Administration Department conducts an evaluation of learning services using questionnaire instruments with the answers provided along with comments or suggestions from the students. However, so far the faculty and the study program have not utilized the comments or suggestions from the students on the teaching method of lecturers as an evaluation material. So the opinions of the students do not have a very meaningful information in the evaluation of teaching methods of lecturers in each semester.

This research developed a system of sentiment analysis on lecturer performance index with Naïve Bayes method. This study includes the data collection phase, the data sharing of training and test data, labeling the train data, preprocessing text on the data, and the process of classification with Naïve Bayes. So it can provide information that is very useful to know teaching methods lecturers. The application of Naïve Bayes method to the lecturer performance index sentiment analysis used to analyze student sentiment level has optimal time performance with good classification accuracy.

The results of this study using data questionnaire as many as 13508 data that has 3 class sentiment is positive, neutral and negative. Accuracy resulted from this research using 10 K-Fold Cross Validation method that is 0,97 with 13000 data and classifier quality using Kappa Statistic has Kappa value 0,88. So in the range of quality classifier have interpretation near prefect performance.

Keywords : sentiment analysis, text mining, k-fold cross validation, kappa statistic, naïve bayes

My Journal : https://drive.google.com/file/d/1wQs494W3F-sb6b6pRYQammwNsBNH6yeZ/view?usp=sharing

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• Alvinditya Saputra - Initial work - DSS Consulting - LinkedIn Instagram Twitter