SISTEM PREDIKSI PENYAKIT JANTUNG KORONER MENGGUNAKAN METODE NAÏVE BAYES

• Main Author: Devina Larassati, (Author)
• Format: Book
• Published: 2022-01-14.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata
• Notes: http://repository.upnvj.ac.id/15563/13/ABSTRAK.pdf; http://repository.upnvj.ac.id/15563/14/AWAL.pdf; http://repository.upnvj.ac.id/15563/15/BAB%20I.pdf; http://repository.upnvj.ac.id/15563/16/BAB%20II.pdf; http://repository.upnvj.ac.id/15563/17/BAB%20III.pdf; http://repository.upnvj.ac.id/15563/18/BAB%20IV.pdf; http://repository.upnvj.ac.id/15563/19/BAB%20V.pdf; http://repository.upnvj.ac.id/15563/20/DAFTAR%20PUSTAKA.pdf; http://repository.upnvj.ac.id/15563/22/DAFTAR%20RIWAYAT%20HIDUP.pdf; http://repository.upnvj.ac.id/15563/21/LAMPIRAN.pdf; http://repository.upnvj.ac.id/15563/11/HASIL%20PLAGIARISME.pdf; http://repository.upnvj.ac.id/15563/8/ARTIKEL%20KI.pdf
• Keywords: Q Science (General); QA Mathematics; QA76 Computer software; T Technology (General)

Coronary heart disease, caused by blockage of coronary arteries, is a disease that gets attention from all walks of life, given the impact it causes. Coronary heart disease causes high morbidity and mortality with increasing prevalence (incidence) every year. This study was conducted to make predictions that can later assist a doctor in determining the correct diagnosis and early treatment of coronary heart disease. One of the data mining classification algorithms used in this research is the Naïve Bayes Classifier algorithm. This algorithm is applied to calculate the probability of a patient based on the patient's medical record data. Patient medical records are obtained from Kaggle for experiments on the system to be created. The initial dataset contains 303 records after preprocessing contains 296 records. In this study, three experiments were conducted by dividing training data and test data. In the first experiment the training data and test data were 60% and 40%, in the second experiment the training data and test data were 70% and 20%, while in the third experiment the training data and test data were 80% and 20%, respectively. The results obtained in the first experiment had the highest accuracy of 83.1%. It is hoped that this system can help doctors to diagnose coronary heart disease.