Klasifikasi Berita Hoax Pada Berita Berbahasa Indonesia Menggunakan Latent Dirichlet Allocation dan Vector Space Model
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Institusion

Institut Teknologi Perusahaan Listrik Negara

Author

Putri, Yolanda Prayetno
Arianto, Rakhmat
Palupiningsih, Pritasari

Subject

Teknik Informatika 

Datestamp

2022-09-20 02:32:22 

Abstract :
Hoax news is an argument that does not make sense (far-fetched) but made as if the news happened. The number of hoax news that has appeared on online news sites is one form of misuse of technological developments which makes it difficult to know the tendency of hoax news or valid for ordinary people. So the hoax news classification application is needed. Latent Dirichlet Allocation is one method of text mining that is used to summarize a document to get a discussion group of topics contained in the document or can be referred to as topic modeling. In this case, the Latent Dirichlet Allocation will be used for modeling topics in the news content dataset. The results of the topic modeling will be matched with the similarity function using the Vector Space Model to calculate the weighted of each term contained in all the results of the topics formed and the news title dataset is then classified based on the existing class. Before carrying out calculations, the text first goes through the process of tokenizing, filtering, and stemming. Based on the test results, we obtained an accuracy of 63.3%. 

File :
201531134_SKRIPSI_YOLANDA PRAYETNO PUTRI.pdf