Evaluation of Emotional Students During the Final Project Writing Process

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-8
Year of Publication : 2019
Authors : Sulis Sandiwarno, Ariyani Wardhana
DOI :  10.14445/22312803/IJCTT-V67I8P110


MLA Style:Sulis Sandiwarno, Ariyani Wardhana"Evaluation of Emotional Students During the Final Project Writing Process" International Journal of Computer Trends and Technology 67.8 (2019):58-63.

APA Style Sulis Sandiwarno, Ariyani Wardhana.Evaluation of Emotional Students During the Final Project Writing ProcessInternational Journal of Computer Trends and Technology, 67(8),58-63.

The dynamics of information technology has changed the way of thinking in life patterns, which originally carried out activities not yet using information technology but now in various layers must use information technology. The use of information technology can be applied in the field of education, where education is the right area to be targeted in the use of information technology. In the learning process carried out between students and lecturers, lecturers must be able to understand students` feelings or emotions verbally and nonverbally. In previous studies measuring the emotional level of students carried out during the learning process by using questionnaires. But this questionnaire will not be able to capture the emotional message of students in the learning process. We propose to use sentiment analysis to measure the emotional level of students in the learning process such as the preparation of the final assignment (TA). The sentiment analysis we use utilizes several methods of machine learning such as naïve Bayes (NB) and k-Nearest Neigbors (k-NN). In this study we have several stages such as: first, collecting data about the opinions of each student`s emotional. Second, we will conduct training data with NB and k-NN in measuring the emotional level of students. Third, we will compare methods to determine which method is best used to measure student emotional. The results obtained provide information, that k-NN is a good algorithm in evaluating student emotions based on text with an accuracy value of 86%.

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learning, evaluation, machine learning classifiers