Klasifikasi Suara Lingkungan Berbasis ResNet-50 untuk Penyandang Gangguan Pendengaran Berbasis Android - Kurniawan, Felix; Rahman, Abdul (Program Studi Teknik Informatika Universitas Multi Data Palembang, 2026)
Individuals with hearing impairments often have difficulty recognizing environmental sounds that are important for daily activities. This study presents an Android-based environmental sound classification application built on a Convolutional Neural Network (CNN) using the ResNet-50 architecture. The model was trained on the ESC-50 dataset (2,000 samples across 50 classes); each audio file was converted into three-channel images (log-Mel spectrogram, delta, delta-delta) as model input. Hyperparameter tuning identified an optimal configuration (epoch=100, batch size=16, learning rate=0.001). The trained model achieved strong performance with training accuracy ≈ 93.19% and testing accuracy ≈ 92%, and average precision ≈ 0.93, recall ≈ 0.92, and F1-score ≈ 0.92. Field tests revealed degraded performance under high noise levels and at increased distances; usability evaluation yielded Usefulness = 90.30%, Satisfaction = 87.57%, and Ease of Use = 89.09% (mean = 88.98%). These results indicate ResNet-50 is effective for environmental sound classification in controlled settings, while enhanced pre-processing (noise handling) is recommended for robust real-world deployment.