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All Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Muhammad Randi Noor
Politeknik Negeri Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia Yudi Widhiyasana; Transmissia Semiawan; Ilham Gibran Achmad Mudzakir; Muhammad Randi Noor
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 4: November 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1150.961 KB) | DOI: 10.22146/jnteti.v10i4.2438

Abstract

Text classification is now a well-studied field, particularly in Natural Language Processing (NLP). The text classification can be carried out using various methods, one of which is deep learning. Deep learning methods such as RNN, CNN, and LSTM are the most frequent methods used for text classification. This research aims to analyze the implementation of two deep learning methods combination, namely CNN and LSTM (C-LSTM), to classify Indonesian news texts. News texts used as data in this study were collected from Indonesian news portals. The obtained data were then divided into three categories based on their scope: "National," "International," and "Regional." Three research variables were tested in this study: the number of documents, the batch size value, and the learning rate value of the built C-LSTM. The experimental results showed that the F1-score obtained from the classification results using the C-LSTM method was 93.27%. The F1-score value generated by the C-LSTM method was higher than that of CNN (89.85%) and LSTM (90.87%). In summary, the combination method of two deep learning methods, namely CNN and LSTM (C-LSTM), outperforms CNN and LSTM.
Co-Authors Ilham Gibran Achmad Mudzakir Transmissia Semiawan Yudi Widhiyasana