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PARTICLE SWARM OPTIMIZATION IN TWIN SUPPORT VECTOR MACHINE TO CLASSIFY FAKE NEWS

Junita Amalia(1), Novita Enjelia Hutapea(2), Merika Manurung(3), Tiara Octavia Situmorang(4),


(1) Institut Teknologi Del
(2) Institut Teknologi Del
(3) Institut Teknologi Del
(4) Institut Teknologi Del
Corresponding Author

Abstract


Slang word is a complex word, difficult and cannot be ignored. Slang is used by certain circles and is limited so that not everyone knows the meaning of the conversations carried out by group members. Based on previous research that has been done, namely making slang using a manual process that requires quite a lot of time to collect slang words, so that our research aims to collect slang words by applying Deep Learning, namely Natural Language Processing using the word embedding FastText method to speed up the collection process. slang words. The author implements the techniques and algorithms that have been designed in the previous stage. This stage will ensure that the processes carried out in the research can be carried out in accordance with the theories that support the research. From the combined data between YouTube comments and the Indonesian dictionary, itwas found that 421 words are slang words. These slang words are obtained by means of the process of lookingfor word similarities (similarity words) between YouTube comments and Indonesian dictionaries. In building a slang dictionary from the youtube comment dataset with a pre-trained FastText model, a preprocessing process and normalization is carried out. After the normalization process was carried out to get normal words from each slang candidate, the results of the slang dictionary were 278 rows consisting of four columns, namely the lexical column, threshold, slang candidate, and normal words using a threshold of 0.05, 0.1 and 0.2 Keywords: Slang, Pre-trained FastText, NLP, Similarity Word

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DOI: 10.58486/jsr.v6i2.176

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