Measurement of Learning Evaluation Against Assisted by Laboratory Assistants

  IJCTT-book-cover
 
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
  10.14445/22312803/IJCTT-V67I8P111

MLA

MLA Style:Sulis Sandiwarno,Ariyani Wardhana"Measurement of Learning Evaluation Against Assisted by Laboratory Assistants" International Journal of Engineering Trends and Technology 67.8 (2019):64-68.

APA Style Sulis Sandiwarno,Ariyani Wardhana .Measurement of Learning Evaluation Against Assisted by Laboratory AssistantsInternational Journal of Engineering Trends and Technology, 67(8),64-68.

Abstract
The development of good information now triggers the use of very broad and very easy to use technology. In its development this technology is known as Information Technology (IT), where IT is a good tool in connecting the giver and recipient of information. An example of the application of IT that can measure the level of user satisfaction with the system is by using sentiment analysis. Measuring the level of satisfaction by using this sentiment analysis can also be applied in the field of learning. Previous research has been conducted to measure the level of student satisfaction with the learning process assisted by laboratory assistants based on questionnaires. However, the assessment of student satisfaction in previous studies is said to fail if only limited to questionnaires. We propose using sentiment analysis to evaluate the learning process for students assisted by laboratory assistants. In conducting research using the concept of sentiment analysis we use logistic regression (LR) and naïve Bayes (NB) methods. As for several stages such as: first, collecting data about opinions or reviews from students whose learning process is assisted by laboratory assistants. Second, we will conduct training data with both methods. Third, we will make conclusions, what methods are best used in measuring the evaluation of learning carried out by laboratory assistants. The results of this study will provide results that NB is a good algorithm in evaluating student opinion levels with an accuracy value of 80.32%.

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Keywords
learning, evaluation, machine learning classifiers