Klasifikasi Aritmia Pada Sinyal Elektrokardiogram Menggunakan Gated Recurrent Unit
Abstract
Deteksi aritmia jantung secara otomatis umumnya dilakukan melalui analisis sinyal elektrokardiogram (EKG) untuk meningkatkan akurasi dan efisiensi diagnosis. Penelitian ini mengevaluasi pengaruh variasi panjang window (3R, 5R, 10R) terhadap performa klasifikasi aritmia, membandingkan kinerja varian arsitektur Gated Recurrent Unit (GRU0–GRU4) dan Bidirectional GRU (BiGRU0–BiGRU4), serta menganalisis dampak konfigurasi hyperparameter. Dataset yang digunakan adalah MIT-BIH Arrhythmia, dengan segmentasi sinyal menggunakan metode sliding window berbasis jumlah puncak R. Model dilatih menggunakan fungsi loss categorical crossentropy dan optimizer Adam, dengan tuning pada jumlah unit (32, 64, 128), dropout (0.2, 0.5), dan learning rate (0.001, 0.0001). Evaluasi dilakukan menggunakan akurasi, ROC AUC, precision, recall, dan F1-score. Hasil menunjukkan bahwa window 3R dan model GRU0 dengan konfigurasi 128 unit, dropout 0.2, dan learning rate 0.001 menghasilkan performa terbaik, dengan akurasi 95.99%, F1-score 0.9599, dan akurasi validasi akhir 96.72%. Temuan ini membuktikan bahwa arsitektur GRU sederhana dengan konfigurasi optimal mampu memberikan klasifikasi aritmia berbasis EKG dengan performa tinggi.
Kata kunci— Aritmia, BiGRU, EKG, GRU, Hyperparameter Tuning, Sliding Window
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