Analisis Efektivitas Pengenalan Perintah Suara Menggunakan Algoritma Machine Learning untuk Mendukung Pembelajaran Digital di Wilayah Bengkulu
Keywords:
ASR, Machine Learning, Transformer, Digital Learning, Technology Inclusion.Abstract
This study aims to evaluate the effectiveness of a voice command recognition system based on Machine Learning algorithms in supporting digital learning at Panti Asuhan Kasih Sayang, Bengkulu City. Four models were tested: DNN, LSTM, CNN, and Transformer. The Transformer model demonstrated the best performance with an accuracy of 93.4%, precision of 91.8%, recall of 92.1%, and F1-score of 92.0%, followed by LSTM with 90.2% accuracy, and DNN as the baseline with 84.7%. The system was tested in real-time with an average latency of 320ms and handled up to 28 voice commands per minute. The use of this system increased participants’ post-test scores by an average of 18 points and enhanced learning engagement by 85%. Qualitative data from interviews and focus group discussions indicated that the system supported students who struggle with text-based interfaces and was perceived as interactive and easy to use. These findings show that ASR based on Machine Learning is effective in low-resource settings and supports more inclusive learning aligned with the principles of Society 5.0.
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