Aplikasi Analisa Sentimen Pada Komentar Berbahasa Indonesia Dalam Objek Video di Website YouTube Menggunakan Metode Naïve Bayes Classifier

Maximillian Christianto, Justinus Andjarwirawan, Alvin Nathaniel Tjondrowiguno

Abstract


From the increasing interest of the people in Indonesia in the use of YouTube, this has triggered the emergence of content creators who choose YouTube as a medium for work. So that the content creators are competing to produce video works that can be enjoyed by YouTube users. Various methods are used by content creators to improve the quality of the video produced.

The process carried out in this thesis is the process of processing raw data that has been collected from YouTube, before the training and classification process. The process of data preprocessing needs to be done to overcome the raw data that is varied and inconsistent so that it can affect the training process and the classification process. Data preprocessing conducted in this thesis includes Tokenization, Stopwords Removal, Stemming. The classification process is the process by which the classification algorithm is run on comments that are used as input data for comments.

Applications used in scientific papers have succeeded in producing smoothing values, where the value shows that the comments belong to the classifications of positive sentiments, negative sentiments or non-Indonesian data.

Keywords


texts; API YouTube; classification sentiment

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References


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