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All Journal International Journal of Advances in Intelligent Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Conference on Business, Social Sciences and Technology (CoNeScINTech)
Basiron, Halizah
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science

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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.
Student Major Subject Prediction Model for Real-Application Using Neural Network Islam, Aminul; Hoque, Jesmeen Mohd Zebaral; Hossen, Md. Jakir; Basiron, Halizah; Tawsif Khan, Chy. Mohammed
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1490

Abstract

The university admission test is an arena for students in Bangladesh. Millions of students have passed the higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue their further study. It is challenging for a student to prepare all these three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important rather than following the trend. This study developed a preliminary system to predict a suitable admission test category by evaluating students' early academic performance through data collecting, data preprocessing, data modelling, model selection, and finally, integrating the trained model into the real system. Eventually, the Neural Network was selected with the maximum 97.13% prediction accuracy through a systematic process of comparing with three other machine learning models using the RapidMiner data modeling tool. Finally, the trained Neural Network model has been implemented by the Python programming language for opinionating the possible option to focus as a major for admission test candidates in Bangladesh.
Strategic Analysis of Business Decisions PT Flextronics Techology Indonesia by Utilizing Generative Artificial Intelligence (GenAI) in the Electronics Manufacturing Aklani, Syaeful Anas; Basiron, Halizah; Zulkarnain, Nur Zareen
Conference on Business, Social Sciences and Technology (CoNeScINTech) Vol. 5 No. 1 (2025): Conference on Business, Social Sciences and Technology (CoNeScINTech)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/conescintech.v5i1.10375

Abstract

New technologies in manufacturing companies continue to develop and innovate, companies face increasing challenges related to operational efficiency, supply chain complexity, and the need for continuous innovation. PT Flextronics Technology Indonesia, as one of the large-scale industries in the Batam city, is trying to overcome this challenge by integrating Generative Artificial Intelligence (GenAI) into its strategic decision making. GenAI, a transformative part of artificial intelligence, offers unmatched capabilities in generating insights, optimizing processes, and driving innovation.  This research carries out a comprehensive strategic analysis of PT Flextronics Technology Indonesia's decision by evaluating the potential impact of using GenAI in all main operational domains such as management, engineering and quality. Using a mixed-methods approach, this research combines qualitative insights from stakeholder interviews and surveys with quantitative analysis of pilot projects to assess the feasibility and benefits of Gen AI integration. The findings highlight that GenAI significantly improves decision-making accuracy, reduces operational costs, and drives innovation in product design and production processes. However, successful implementation requires addressing challenges such as workforce readiness, data security, and initial investment costs. The study also emphasizes the importance of aligning GenAI initiatives with organizational goals and encouraging stakeholder engagement to ensure continued adoption. This research provides actionable insights and a roadmap for PT Flextronics Technology Indonesia utilizes GenAI as a strategic tool, strengthening its position as a leader in the electronics manufacturing sector while setting a benchmark for future AI-based industrial transformation.
Co-Authors Affandy Affandy Aklani, Syaeful Anas Althoff, Mohammad Noval Ardytha Luthfiarta Hoque, Jesmeen Mohd Zebaral Hossen, Md. Jakir Islam, Aminul Satya, Mohammad Wahyu Bagus Dwi Tawsif Khan, Chy. Mohammed Zulkarnain, Nur Zareen