Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Erfan Subiyanta
Electrical Engineering Study Program, Faculty of Engineering, Universitas 17 Agustus 1945 Cirebon, Indonesia
Computer Science & IT Education Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Voice Recognition-Based Home Door Security System Using the BERT Model and Synonym Data Augmentation Agus Siswanto; Mutiara Syafrizal; Vidya Ikawati; Erfan Subiyanta; Mohamad Soleh; Mudofar Baehaqi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.84779

Abstract

This research develops a voice recognition-based home door security systems using the BERT model and synonym data augmentation techniques to enhance the accuracy and responsive of the system. The objective of this research is to develop a system that can accurately and rapidly recognize voice commands and function effectively in a range of environments. This research employs the “bert-base-uncased” version of the BERT model, which has been trained on a dataset comprising 382 sentence variations. The synonym data augmentation technique is employed to extend the range of recognized commands. The system was subjected to a series of tests to assess its performance in terms of voice detection frequency, command categorization accuracy, response time, noise influence, and hardware integration. The results demonstrated that the BERT model achieved an accuracy of 99.29% and F1 score of 0.9929. The system exhibited an average response time of 0.12 seconds. However, the accuracy declined when the noise level exceeded 40 dB. The findings of this research indicate that the BERT model is highly effective in classifying voice commands in home security systems and has significant potential for real-time applications. The synonym data augmentation technique enhances the system’s capacity to process sentence variations. This research makes a significant contribution to the development of the dataset and optimization of noise handling for further improvement.
Co-Authors Agus Siswanto Mohamad Soleh Mudofar Baehaqi Mutiara Syafrizal Vidya Ikawati