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All Journal International Journal of Electronics and Communications Systems Asia Information System Journal
Khan, Mujeeb Ullah
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Computer Science & IT Electrical & Electronics Engineering

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Digital Signal Processing for The Development of Deep Learning-Based Speech Recognition Technology Sari, Dita Novita; Kusnadi, Danang; Saputra, Ricco Herdiyan; Khan, Mujeeb Ullah
International Journal of Electronics and Communications Systems Vol. 4 No. 1 (2024): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v4i1.22918

Abstract

This research discusses digital signal processing in the context of developing deep learning-based speech recognition technology. Given the increasing demand for accurate and efficient speech recognition systems, digital signal processing techniques are essential. The research method used is an experimental method with a quantitative approach. This research method consists of several stages: introduction, research design, data collection, data preprocessing, Deep Learning Model Development, performance training and evaluation, experiments and testing, and data analysis. These findings are expected to contribute to developing more sophisticated and applicable speech recognition systems in various fields. For example, in virtual assistants such as Siri and Google Assistant, improved speech recognition accuracy will allow for more natural interactions and faster responses, improving the user experience. This technology can be used in security systems for safer and more reliable voice authentication, replacing or supplementing passwords and fingerprints. Additionally, in accessibility technology, more accurate voice recognition will be particularly beneficial for individuals with visual impairments or mobility, allowing them to control devices and access information with just voice commands. Other benefits include improvements in automated phone apps, automatic transcription for meetings or conferences, and the development of smart home devices that can be fully voice-operated.
ANALYSIS: ALIGNING INFORMATION SYSTEMS AND BUSINESS PROCESSES IN THE COFFEE INDUSTRY Khan, Mujeeb Ullah
Asia Information System Journal Vol. 3 No. 1 (2024): Asia Information System Journal
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/aisj.v3i1.23078

Abstract

The coffee industry, which encompasses more than 25 million smallholder farmers worldwide, requires efficient and sustainable supply chain management. Integrating Information Systems (IS) with business processes in the coffee sector can enhance operational performance. This study utilizes a combination of literature reviews and field surveys to examine the alignment of IS in the industry. The primary findings underscore the significance of web-based and desktop solutions in facilitating stakeholder access to accurate information. Entity Relationship Diagrams (ERD) and Data Flow Diagrams (DFD) ensure a comprehensive understanding of the system's structure. The integration of IS supports sustainable practices by monitoring environmental impact and promoting fair trade. The discussion highlights the benefits of digital transformation, including improved decision-making and operational efficiency. Adopting structured approaches such as ERD and DFD facilitates effective communication among stakeholders. The integration of IS with sustainable practices reflects a commitment to environmental stewardship and ethical business conduct. Recommendations include the continued development of IS customized to meet the needs of stakeholders, fostering collaboration for innovation, enhancing digital literacy, designing sustainable IS solutions, and advocating for the adoption of IS policies. The implementation of these recommendations has the potential to drive efficiency, sustainability, and stakeholder engagement in the coffee industry.
Co-Authors Danang Kusnadi Ricco Herdiyan Saputra Sari, Dita Novita