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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Sriwijaya Journal of Informatics and Applications
Reza Firsandaya Malik
Communication Network and Information Security Research Lab
Computer Science & IT Engineering

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Klasifikasi Teks Multilabel pada Artikel Berita Menggunakan Long Short-Term Memory dengan Word2Vec Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik; Iman Saladin B. Azhar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 2 (2020): April 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (639.099 KB) | DOI: 10.29207/resti.v4i2.1655

Abstract

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.
Multilabel Classification for News Article Using Long Short-Term Memory Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited when there is small labeled data and leads to the difficulty of capturing semantic relationships. In this case, it requires a multi-label text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multi-label text classification techniques. By comparing the seven proposed Long Short-Term Memory (LSTM) models with large-scale datasets by dividing 4 LSTM models with 1 layer, 2 layer and 3-layer LSTM and Bidirectional LSTM to show that LSTM can achieve good performance in multi-label text classification. The results show that the evaluation of the performance of the 2-layer LSTM model in the training process obtained an accuracy of 96 with the highest testing accuracy of all models at 94.3. The performance results for model 3 with 1-layer LSTM obtained the average value of precision, recall, and f1-score equal to the 94 training process accuracy. This states that model 3 with 1-layer LSTM both training and testing process is better.  The comparison among seven proposed LSTM models shows that model 3 with 1 layer LSTM is the best model.
Co-Authors Dian Palupi Rini Dian Palupi Rini Iman Saladin B. Azhar Winda Kurnia Sari