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All Journal Jurnal Ilmu Siber dan Teknologi Digital
Arif Farhan Bukhori
Program Studi Ilmu Komputer, Departemen Ilmu Komputer dan Elektronika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Gadjah Mada, Yogyakarta
Computer Science & IT Education Engineering Other

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Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia Thariq Iskandar Zulkarnain Maulana Putra; Suprapto Suprapto; Arif Farhan Bukhori
Jurnal Ilmu Siber dan Teknologi Digital Vol. 1 No. 1 (2022): November
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jisted.v1i1.1509

Abstract

Purpose: This research aims to improve the performance of the text classification model from previous studies, by combining the IndoBERT pre-trained model with the Long Short-Term Memory (LSTM) architecture in classifying Indonesian-language tweets into several categories. Method: The classification text based on multiclass classification was used in this research, combined with pre-trained IndoBERT namely Long Short-Term Memory (LTSM). The dataset was taken using crawling method from API Twitter. Then, it will be compared with Word2Vec-LTSM and fined-tuned IndoBERT. Result: The IndoBERT-LSTM model with the best hyperparameter combination scenario (batch size of 16, learning rate of 2e-5, and using average pooling) managed to get an F1-score of 98.90% on the unmodified dataset (0.70% increase from the Word2Vec-LSTM model and 0.40% from the fine-tuned IndoBERT model) and 92.83% on the modified dataset (4.51% increase from the Word2Vec-LSTM model and 0.69% from the fine-tuned IndoBERT model). However, the improvement from the fine-tuned IndoBERT model is not very significant and the Word2Vec-LSTM model has a much faster total training time.
Co-Authors Suprapto Suprapto Thariq Iskandar Zulkarnain Maulana Putra