Jurnal Riset Informatika
Vol. 5 No. 2 (2023): March 2023

Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification

Mukhamad Rizal Ilham (Universitas Amikom Yogyakarta)
Arif Dwi Laksito (Universitas Amikom Yogyakarta)

Article Info

Publish Date
25 Mar 2023


A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically. From part-of-speech (POS) parsing and tagging to machine translation and dialogue systems, NLP enables computers to carry out various natural language-related activities at all levels. In this research, we compared word embedding techniques FastText and GloVe, which are used for text representation. This study aims to evaluate and compare the effectiveness of word embedding in text classification using LSTM (Long Short-Term Memory). The research stages start with dataset collection, pre-processing, word embedding, split data, and the last is deep learning techniques. According to the experiments' results, it seems that FastText is superior compared to the glove technique. The accuracy obtained reaches 90%. The number of epochs did not significantly improve the accuracy of the LSTM model with GloVe and FastText. It can be concluded that the FastText word embedding technique is superior to the GloVe technique. Keywords: Word Embedding; ; ;

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Journal Info





Computer Science & IT


Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik ...