KLASIFIKASI PENYAKIT JANTUNG MENGGUNAKAN PENDEKATAN HYBRID IINFORMATION GAIN dan BACKPROPAGATION NEURAL NETWORK (BPNN)

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Diterbitkan Mei 28, 2025
https://doi.org/10.59737/jpi.v17i1.347
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Vol 17 No 1 (2025): Volume 17, Nomor 1, Mei 2025

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Azis Wahyudi
Politeknik Kesehatan Permata Indonesia
Haryo Nugroho
Harinto Nur Seha
Rina Yulida

Abstrak

Penyakit jantung merupakan penyebab utama kematian di seluruh dunia. Untuk mendeteksi risiko penyakit jantung secara dini, dibutuhkan metode klasifikasi yang akurat dan efisien. Penelitian ini mengusulkan pendekatan hybrid dengan menggabungkan seleksi fitur Information Gain (IG) dan algoritma klasifikasi Backpropagation Neural Network (BPNN). Dataset yang digunakan adalah Heart Disease dari UCI Repository dengan total 303 data pasien. Sebanyak 8 fitur terbaik dipilih menggunakan Information Gain. Model BPNN dilatih menggunakan parameter hidden_layer_sizes=(16, 8), activation='relu', dan learning_rate_init=0.01. Hidden layer dengan 16 dan 8 neuron memungkinkan jaringan mempelajari pola kompleks, ReLU mempercepat konvergensi pelatihan, dan learning rate mengatur kecepatan pembaruan bobot. Hasil evaluasi menunjukkan model mencapai akurasi 79.12%, presisi 84.44%, recall 76.00%, F1-Score 80.00%, dan AUC 85.95%. Validasi silang 5-Fold menghasilkan rata-rata akurasi 82.15%. Hasil ini menunjukkan bahwa pendekatan IG + BPNN memberikan performa klasifikasi yang baik dan stabil dalam mendeteksi penyakit jantung.

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