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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Dari, Aprillia Wulan Nanda
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Computer Science & IT Decision Sciences, Operations Research & Management

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Penerapan Algoritma K-Nearest Neighbor (KNN) Untuk Klasifikasi Resiko Penyakit Jantung Dari, Aprillia Wulan Nanda; Fajri, Ika Nur
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6038

Abstract

Heart disease is one of the deadliest diseases in the world, where there is a disruption in the function of the heart and blood vessels that causes chest pain, irregular heartbeat, and difficulty breathing. According to data from the World Health Organization (WHO), there are 17.9 million deaths each year due to heart disease. The difficulty in classifying heart disease accurately and quickly is a significant problem. From this problem, researchers conducted data mining research using the KNN algorithm to classify the risk of heart disease by taking data from the official Kaggle website. In this study, there are 4 stages, namely data collection, model formation, mode evaluation, and prediction interface. By using the KNN algorithm, the analysis results obtained an accuracy of 83%, precision 0.88, recall 0.77 and f1-score 0.82. With the results of the model evaluation data, it shows that the classification of heart disease risk using the KNN algorithm has quite good performance. The results of the modeling are then presented in the form of a website by deploying the model.
Co-Authors Fajri, Ika Nur