PREDIKSI PASIEN PENYAKIT JANTUNG MENGGUNAKAN JARINGAN SYARAF TIRUAN MULTI LAYER PERCEPTRON DAN PYTHON PADA BASIS DATA PENYAKIT JANTUNG DI CLEVELAND

Ari Kurniawan; Astried Silvanie

Ari Kurniawan Institut Bisnis dan Informatika (IBI) Kosgoro 1957
Astried Silvanie Institut Bisnis dan Informatika (IBI) Kosgoro 1957

Keywords: Jaringan Syaraf Tiruan, Prediksi Penyakit Jantung, Machine Learning, Multilayer Perceptron, Implementasi ML Dengan Python

Abstract

Penelitian ini menggunakan Jaringan Syaraf Tiruan Multi-Layer Perceptron (JST MLP) untuk memprediksi apakah pasien berpenyakit jantung berdasarkan kondisi medis mereka. Sebagai predikator fitur terdiri atas 13 atribut. Kelas target berupa nilai biner, dimana 1 = penyakit jantung dan 0 = bukan penyakit jantung. JST MLP yang diusulkan terdiri atas tiga lapisan yaitu lapisan masukan (13 neuron), satu lapisan tersembunyi (12 neuron) dan lapisan keluaran (1 neuron). Implementasi menggunakan python dan pustaka tensorflow. Pelatihan dilakukan sebanyak 300 epochs dan 10 batch. Hasil berupa bobot untuk setiap neuron dalam JST. Nilai metrik akurasi model didapatkan sebesar 81.19% dengan nilai sensitivitas 99.39% dan spesifisitas 94.93%

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