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All Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika
Althoff, Mohammad Noval
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Computer Science & IT 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.
Comparative Analysis of T5 Model Performance for Indonesian Abstractive Text Summarization Bagus Dwi Satya, Mohammad Wahyu; Luthfiarta, Ardytha; Althoff, Mohammad Noval
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.4884

Abstract

The rapid growth of digital content has created significant challenges in information processing, particularly in languages like Indonesian, where automatic summarization remains complex. This study evaluates the performance of different T5 (Text-to-Text Transfer Transformer) model variants in generating abstractive summaries for Indonesian texts. The research aims to identify the most effective model variant for Indonesian language summarization by comparing T5-Base, FLAN-T5 Base, and mT5-Base models. Using the INDOSUM dataset containing 19,000 Indonesian news article-summary pairs, we implemented a 5-Fold Cross-Validation approach and applied ROUGE metrics for evaluation. Results show that T5-Base achieves the highest ROUGE-1, ROUGE-2, and ROUGE-L scores of 73.52%, 64.50%, and 69.55%, respectively, followed by FLAN-T5, while mT5-Base performs the worst. However, qualitative analysis reveals various summarization errors: T5-Base exhibits redundancy and inconsistent formatting, FLAN-T5 suffers from truncation issues, and mT5 often generates factually incorrect summaries due to misinterpretation of context. Additionally, we assessed computational performance through training time, inference speed, and resource consumption. The results indicate that mT5-Base has the shortest training time and fastest inference speed but at the cost of lower summarization accuracy. Conversely, T5-Base, while achieving the highest accuracy, requires significantly longer training time and greater computational resources. These findings highlight the trade-offs between accuracy, error tendencies, and computational efficiency, providing valuable insights for developing more effective Indonesian language summarization systems and emphasizing the importance of model selection for specific language tasks.
Co-Authors Affandy Affandy Ardytha Luthfiarta Bagus Dwi Satya, Mohammad Wahyu Basiron, Halizah Satya, Mohammad Wahyu Bagus Dwi