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All Journal Jurnal Transformatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science and Information Technologies Aiti: Jurnal Teknologi Informasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Tematik International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
Lenny Margaretta Huizen
Universitas Semarang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Social Sciences Transportation

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Speech-Driven Visitor Notification System Using Telegram Bot and Voice Activity Detection for Real-Time Retail Applications Aria Setiaji; Aria Hendrawan; Bernadus Very Christioko; Lenny Margaretta Huizen
International Journal of Artificial Intelligence and Science Vol. 2 No. 2 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v2i2.44

Abstract

In the retail industry, fast and responsive service is essential for maintaining customer satisfaction and loyalty. A key challenge faced by store owners is delayed responses to customer arrivals, leading to dissatisfaction and potential lost sales. This project develops an automatic notification system using Speech-to-Text technology and a Telegram bot to detect voice keywords and send real-time notifications to store owners. The system was developed using the prototype methodology, allowing for iterative testing and refinement to ensure it met user needs and functional requirements. It integrates the Deepgram API for accurate speech transcription, the Telegram Bot API for notifications, and a web interface for managing keywords and monitoring system status. To enhance efficiency, a Voice Activity Detection (VAD) module was added, ensuring that only human speech is processed, thereby reducing unnecessary processing. Experimental results showed that the system achieved 100% accuracy in quiet environments and 80% in noisy conditions. The system's response time was also impressive, with an average time of 3.72 seconds in quiet conditions and 3.8 seconds in noisy environments. Word Error Rate (WER) and Character Error Rate (CER) evaluations indicated perfect accuracy in quiet conditions (WER 0%, CER 0%) and slight errors in noisy conditions (WER 13.33%, CER 12.5%). Overall, the system effectively improved service speed and responsiveness, offering store owners a valuable tool for enhancing customer experience in retail environments.
Co-Authors Agus Hartanto Agus Hartanto Agusta Praba Ristadi Pinem Alauddin Maulana Hirzan April Firman Daru Ardima, Muhammad Basyier Aria Hendrawan, Aria Aria Setiaji Azri Saputra, Jeremia Maheswara Badroe Zaman Charis Maulana Cholil, Saifur Rohman Faradilah, Yanti Febrian Wahyu Christanto Firman Daru, April Hassanudin, Wahyu Maulana Idris, Mochamad Kurnia, Rizal Kusumaningrum, Yulinda Maulana, Charis Muhammad Basyier Ardima Nurtriana Hidayati Prayoga, Mohamad Adrian Rastri Prathivi Setiaji, Aria Siti Asmiatun, Siti Surono Surono Titis Handayani Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Willyarnandi, Muchammad Chadavi