Evaluasi Performa Algoritma Klasifikasi dalam Prediksi Gagal Jantung: Studi Kasus Dataset Heart Failure Prediction

Authors

  • Wahyu Nugraha Universitas Bina Sarana Informatika
  • Muhamad Syarif Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.62411/tc.v23i4.11685

Abstract

Kegagalan jantung (Heart Failure) merupakan salah satu penyebab utama kematian dan morbiditas secara global, dengan lebih dari 26 juta orang terdampak di seluruh dunia. Deteksi dini kegagalan jantung sangat penting untuk mencegah komplikasi yang lebih serius, namun diagnosis sering kali sulit dilakukan karena gejala yang tidak spesifik. Pembelajaran mesin (machine learning) telah banyak digunakan untuk membantu prediksi risiko kegagalan jantung melalui analisis data medis. Berbagai algoritma klasifikasi telah diimplementasikan dalam berbagai penelitian sebalumnya, namun belum ada konsensus mengenai algoritma terbaik. Penelitian ini bertujuan untuk melakukan komparasi performa beberapa algoritma klasifikasi dalam memprediksi gagal jantung, menggunakan dataset Heart Failure Prediction dari Kaggle. Algoritma yang diuji termasuk Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, GaussianNB, dan LightGBM. Hasil menunjukkan bahwa algoritma Random Forest memberikan performa terbaik dengan akurasi 95 persen, sedangkan algoritma KNN menunjukkan akurasi terendah sebesar 76 persen. Penelitian ini diharapkan dapat berkontribusi dalam pengembangan sistem pendukung keputusan klinis yang lebih baik untuk prediksi kegagalan jantung.   Kata Kunci: Heart Failure, mesin pembelajaran algoritma klasifikasi

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Published

2024-11-27

Issue

Vol. 23 No. 4 (2024): November 2024

Section

Articles

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Copyright (c) 2024 Wahyu Nugraha, Muhamad Syarif

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