Deteksi Kelainan Jantung Berdasarkan Sinyal EKG Menggunakan Deep Neural Network
DOI:
https://doi.org/10.30865/json.v6i4.8662Keywords:
Deep Neural Network, Sinyal EKG, Deteksi Kelainan Jantung, Klasifikasi, Deep LearningAbstract
Penyakit jantung merupakan salah satu penyebab utama kematian di dunia, termasuk di Indonesia. Deteksi dini kelainan jantung melalui sinyal elektrokardiogram (EKG) sangat penting, namun interpretasi manual oleh tenaga medis sering kali memerlukan keahlian khusus dan rentan terhadap kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis kelainan jantung menggunakan metode Deep Neural Network (DNN) berdasarkan sinyal EKG. Dataset yang digunakan berasal dari PTB Diagnostic ECG Database (PTBDB) yang diperoleh dari Kaggle, dengan dua kategori data: normal dan abnormal. Data diproses melalui tahap balancing, normalisasi, dan pembagian menjadi data latih dan uji. Model DNN dilatih menggunakan data terstruktur berdurasi pendek dengan 187 fitur, dan dievaluasi menggunakan metrik akurasi, precision, recall, f1-score, ROC, serta Precision-Recall Curve. Hasil pelatihan menunjukkan bahwa model mampu mencapai akurasi validasi sebesar 95% dan nilai AUC sebesar 0,98, yang mengindikasikan kemampuan klasifikasi yang sangat baik. Dengan performa tersebut, model ini memiliki potensi besar untuk diterapkan dalam sistem pendukung diagnosis medis secara real-time, terutama untuk membantu deteksi dini gangguan jantung secara efisien dan akurat
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Copyright (c) 2025 Michael Robert,Yennimar,Andy Wyjaya,, Steven Ebert,Mhd. Ali Ramadhan
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