Klasifikasi Suara Paru Normal Dan Abnormal Berbasis Algoritma CNN (Convolutional Neural Network)
Abstract
Deteksi dini dan diagnosis penyakit paru-paru sangat penting untuk perawatan kesehatan yang efektif. Suara paru merupakan salah satu indikator utama dalam identifikasi kondisi paru-paru yang normal maupun abnormal. Penelitian ini bertujuan untuk mengembangkan model klasifikasi suara paru normal dan abnormal menggunakan algoritma Convolutional Neural Network (CNN). Model CNN yang diusulkan terdiri dari beberapa lapisan konvolusi dan pooling yang dioptimalkan untuk ekstraksi fitur dari spektrogram suara paru. Dataset dibagi menjadi data latih dan data validasi, dan visualisasi data dilakukan menggunakan Analisis Data Eksplorasi (EDA) dengan diagram lingkaran. Model dilatih melalui 20 iterasi, dengan setiap iterasi mempengaruhi kinerja model. Eksperimen pertama menggunakan dataset audio terpisah, menghasilkan nilai loss 0,2926 dan akurasi 0,8664. Evaluasi kinerja model menunjukkan skor evaluatif 89%, sehingga prediksi terbaik dan akurat telah dicapai.
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