PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINE

Authors

  • Valonia Inge Santoso Universitas Kristen Duta Wacana
  • Gloria Virginia Universitas Kristen Duta Wacana
  • Yuan Lukito Universitas Kristen Duta Wacana

DOI:

https://doi.org/10.26623/transformatika.v14i2.439

Keywords:

sentiment analysis, support vector machine, inverse matrix

Abstract

The quality of lectures can be determined by some feedbacks from students. From the feedbacks, we can give appreciations for those lectures who get good feedback from students, and evaluations for those who get bad feedback. The problem is classifying large size of feedbacks manually isn t effective and took a lot of time. Therefore, we need a system that can classify feedbacks automatically. These feedbacks will be classified into positive, negative, and neutral, usually called as sentiment analysis. Sentiment analysis implementation can be done by several methods, one of them that has a good accuracy is Support Vector Machine (SVM). SVM performance in this research is measured with the level of accuracy. The number of accuracy indicate the success level of system. The conclusion of this research is factors that affects the accuracy. The factors are the range of each classes and number of unique words in the training document.

References

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Published

2017-01-30

Issue

Vol. 14 No. 2 (2017): January 2017

Section

Artikel

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