A SISTEM ELEKTROKARDIOGRAM BERBASIS KECERDASAN BUATAN UNTUK DETEKSI DINI ARITMIA MENGGUNAKAN ALGORITMA DEEP LEARNING
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Penelitian ini bertujuan mengembangkan sistem klasifikasi kondisi jantung berbasis sinyal elektrokardiogram (EKG) dengan pendekatan algoritma deep learning. Berdasarkan hasil yang diperoleh, sistem yang dibangun menunjukkan performa klasifikasi yang sangat tinggi, dengan akurasi mencapai 99%. Sistem ini mampu secara otomatis mengklasifikasikan lima kondisi jantung: Normal, Abnormal, High Risk Potential, Potential for Arrhythmia, dan Very High Risk Potential. Temuan ini secara langsung menjawab tujuan utama penelitian, yaitu menguji efektivitas model deep learning dalam deteksi dini aritmia.
Meskipun hanya menggunakan data dari 82 subjek, model mampu mencapai precision dan recall yang sangat tinggi, bahkan mencapai skor sempurna (1.00) pada hampir semua kelas, kecuali satu kasus salah klasifikasi pada kelas Normal. Hasil ini menunjukkan bahwa model memiliki kapabilitas generalisasi yang baik, dan mampu mengenali pola kompleks dalam sinyal EKG meskipun data terbatas. Kesalahan klasifikasi tunggal yang terjadi kemungkinan disebabkan oleh kemiripan pola fisiologis antar kelas atau noise pada data, dan ini menjadi catatan penting untuk pengembangan sistem di masa depan.
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