KLASIFIKASI SENTIMENT ANALYSIS PADA KOMENTAR PESERTA DIKLAT MENGGUNAKAN METODE K-NEAREST NEIGHBOR
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Siregar, Zuhdiyyah Ulfah
Subject
Teknik Informatika
Datestamp
2019-06-13 06:51:27
Abstract :
The process of analyzing and classifying comment data done by reading and sorting one by one negative comments and classifying them one by one using Ms.Excel not effective if the data to be processed in large quantities. Therefore, this study aims to apply sentiment analysis on comment data using K-Nearest Neighbor (KNN) method. The comment data used is the comments of the participants of the training on Udiklat Jakarta filled by each participant who followed the training. Furthermore, the comment data is processed by pre-processing, weighting the word using Term Frequence-Invers Document Frequence, calculating the similarity level between the training data and test data with consine similarity. The process of applying sentiment analysis is done to determine whether the comment is positive or negative. Furthermore, these comments will be classified into four categories, namely: instructors, materials, facilities and infrastructure. The results of this study resulted in a system that can classify comment data automatically with an accuracy of 94.23%
The process of analyzing and classifying comment data done by reading and sorting one by one negative comments and classifying them one by one using Ms.Excel not effective if the data to be processed in large quantities. Therefore, this study aims to apply sentiment analysis on comment data using K-Nearest Neighbor (KNN) method. The comment data used is the comments of the participants of the training on Udiklat Jakarta filled by each participant who followed the training. Furthermore, the comment data is processed by pre-processing, weighting the word using Term Frequence-Invers Document Frequence, calculating the similarity level between the training data and test data with consine similarity. The process of applying sentiment analysis is done to determine whether the comment is positive or negative. Furthermore, these comments will be classified into four categories, namely: instructors, materials, facilities and infrastructure. The results of this study resulted in a system that can classify comment data automatically with an accuracy of 94.23%
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