Sistem Pendeteksi Premature Ventricular Contraction Berbasis K-Nearest Neighbors Menggunakan Elektrokardiograf Portabel
Abstract
Premature Ventricular Contraction (PVC) merupakan jenis aritmia yang ditandai dengan kompleks QRS yang melebar lebih dari 120 ms tanpa disertai gelombang P. Penelitian ini bertujuan mengembangkan sistem portabel untuk merekam sinyal EKG dan menerapkan algoritma K-Nearest Neighbors (KNN) guna mendeteksi PVC. Sistem dirancang menggunakan sensor ADS1293 dan mikrokontroler ESP32 yang mengirimkan data EKG ke aplikasi mobile untuk dianalisis. Hasil pengujian menunjukkan sinyal EKG yang terekam memiliki kualitas baik dan heart rate yang sesuai dengan alat referensi. Model KNN yang dilatih menggunakan data MIT-BIH menghasilkan akurasi 92,85% dan F1-score 0,93. Sistem juga berhasil diterapkan pada aplikasi mobile untuk memvisualisasikan hasil deteksi. Meskipun belum divalidasi secara klinis, sistem ini telah berfungsi sesuai tujuan dan dapat dikembangkan lebih lanjut untuk klasifikasi secara real-time. Kata kunci— Elektrokardiogram (EKG), Aritmia, Premature Ventricular Contraction (PVC), K-Nearest Neigbors (KNN)
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