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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Sistem Cerdas
Satya, Mohammad Wahyu Bagus Dwi
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Leveraging Label Preprocessing for Effective End-to-End Indonesian Automatic Speech Recognition Althoff, Mohammad Noval; Affandy, Affandy; Luthfiarta, Ardytha; Satya, Mohammad Wahyu Bagus Dwi; Basiron, Halizah
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14257

Abstract

This research explores the potential of improving low-resource Automatic Speech Recognition (ASR) performance by leveraging label preprocessing techniques in conjunction with the wav2vec2-large Self-Supervised Learning (SSL) model. ASR technology plays a critical role in enhancing educational accessibility for children with disabilities in Indonesia, yet its development faces challenges due to limited labeled datasets. SSL models like wav2vec 2.0 have shown promise by learning rich speech representations from raw audio with minimal labeled data. Still, their dependence on large datasets and significant computational resources limits their application in low-resource settings. This study introduces a label preprocessing technique to address these limitations, comparing three scenarios: training without preprocessing, with the proposed preprocessing method, and with an alternative method. Using only 16 hours of labeled data, the proposed preprocessing approach achieves a Word Error Rate (WER) of 15.83%, significantly outperforming the baseline scenario (33.45% WER) and the alternative preprocessing method (19.62% WER). Further training using the proposed preprocessing technique with increased epochs reduces the WER to 14.00%. These results highlight the effectiveness of label preprocessing in reducing data dependency while enhancing model performance. The findings demonstrate the feasibility of developing robust ASR models for low-resource languages, offering a scalable solution for advancing ASR technology and improving educational accessibility, particularly for underrepresented languages.
Leveraging BERT and T5 for Comprehensive Text Summarization on Indonesian Articles Satya, Mohammad Wahyu Bagus Dwi; Luthfiarta, Ardytha
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025): August
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.458

Abstract

One of the main challenges in the field of Natural Language Processing (NLP) is developing systems for automatic text summarization. These systems typically fall into two categories: extractive and abstractive. Extractive techniques generate summaries by selecting important sentences or phrases directly from the original text, whereas abstractive techniques focus on rephrasing or paraphrasing the content, producing summaries that resemble human-written ones. In this research, models based on Transformer architectures, including BERT and T5, were used, which have been shown to effectively summarize texts in various languages, including Indonesian. The dataset used was INDOSUM, consisting of Indonesian news articles. The best results were achieved with the T5 model, using the abstractive approach, recorded ROUGE-1, ROUGE-2, and ROUGE-L scores of 69.36%, 61.27%, and 66.17%, respectively. On the other hand, the extractive BERT model achieved ROUGE-1, ROUGE-2, and ROUGE-L scores of 70.82%, 63.99%, and 58.40%.
Co-Authors Affandy Affandy Althoff, Mohammad Noval Ardytha Luthfiarta Basiron, Halizah