Perbandingan Algoritma Naïve Bayes dan K-Nearest Neighbor (K-NN) Untuk Klasifikasi Penyakit Gagal Jantung - Firman Zahri; Fitri Insani; Jasril Jasril; Lola Oktavia (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
A condition known as heart failure, where the heart is unable to pump enough blood to meet the body's needs for oxygen and nutrients, should not be taken lightly. This can result in a number of symptoms, such as fatigue, fluid retention, and dyspnea. The World Heart Federation estimates that up to 1.8 million people in Southeast Asia suffered from heart failure in 2014. For prompt and efficient treatment, heart failure is a medical problem that needs to be identified. This disease has the potential to worsen if not treated immediately. Several machine learning methods can be used to help diagnose and categorize this disease. One of them is the popular algorithm, namely Naive Bayes and K-Nearest Neighbors. Naive Bayes is a simple but very efficient probability-based machine learning algorithm, especially in classification applications. K-Nearest Neighbors is comparing the data to be predicted with a number of its closest data in a feature space based on a certain distance, such as Euclidean distance, Manhattan, or others. This study was conducted using Confusion Matrix to evaluate and compare the Naive Bayes and K-Nearest Neighbor algorithms in the categorization of heart failure disease by collecting data totaling 918 heart failure patient data from kaggle. Based on the research findings, the K-Nearest Neighbor method achieved an accuracy score of 76%, while the Naive Bayes approach achieved 90% accuracy using a ratio of 80:20.