Optimasi Deteksi Aritmia Pada Sinyal Ekg Menggunakan Pendekatan Divergence Kullback-Leiber
Abstract
Aritmia jantung merupakan gangguan irama jantung yang berpotensi memicu kondisi kardiovaskular serius apabila tidak terdeteksi secara dini. Kompleksitas morfologi sinyal elektrokardiogram (EKG), dimensi data yang tinggi, dan ketidakseimbangan distribusi kelas pada dataset menjadi tantangan dalam pengembangan sistem deteksi berbasis kecerdasan buatan. Penelitian ini bertujuan mengembangkan sistem klasifikasi aritmia berbasis sinyal EKG dari MIT-BIH Arrhythmia Database dengan menggabungkan Discrete Wavelet Transform (DWT) dan Kullback–Leibler Divergence (KL Divergence) untuk ekstraksi fitur. Data diseimbangkan menggunakan random undersampling sebelum ekstraksi, dengan empat pendekatan distribusi pada KL Divergence, yaitu Uniform, Exponential, Gaussian, dan Combined. klasifikasi dilakukan menggunakan Support Vector Machine (SVM) dengan kernel RBF, serta dievaluasi menggunakan metrik akurasi, F1-score, ROC AUC, log loss, average precision (AP), efisiensi komputasi, dan Coefficient of Variation (CV). Hasil menunjukkan bahwa KL Combined memberikan performa terbaik dengan akurasi 0,8895, F1-score 0,9039, AUC 0,9406, dan log loss uji 0,3012. KL Combined dinilai optimal untuk implementasi klinis karena menggabungkan akurasi tinggi, kestabilan, dan efisiensi, menjadikannya pilihan unggulan dalam sistem deteksi aritmia yang konsisten dan andal.
Kata kunci: Aritmia jantung, Divergence Kullback-Leibler, Discrete Wavelet Transform, EKG, MIT-BIH, Support Vector Machine
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