Perbandingan Algoritma Klasifikasi Data Mining Dalam Diagnosa Penyakit Arteri Koroner - Sudarsono, Bernadus Gunawan; Winarno, Edy (Forum Kerjasama Pendidikan Tinggi, 2024)
Coronary artery disease is one of the diseases that often attacks humans. The cause of this disease is due to narrowing or blockage of the coronary blood vessels that supply blood to the heart. The diagnosis of coronary artery disease by medical personnel has so far been constrained by the limited number of doctors, in terms of the number of doctors and time, because the number of specialist doctors is limited. The limited number of doctors causes several difficulties for medical personnel who diagnose the patient's disease and over time can become a serious problem. Information technology that can help medical personnel is by applying data mining techniques which are techniques to help diagnose coronary artery disease. Data mining can identify patterns or relationships between disease symptoms and diagnostic results, so that patients with a high risk of developing the disease can be identified. The Naïve Bayes algorithm is one of the algorithms of the Data Mining classification technique, which is based on Bayes' theorem. The C4.5 algorithm is one of the algorithms of the Data Mining classification technique, which uses decision trees in classifying data. Algorithm comparisons are carried out in order to obtain the appropriate or best algorithm for use in diagnosing a disease. The comparison process of the Naïve Bayes algorithm and the C.45 algorithm in diagnosing coronary artery disease, obtained the best algorithm results based on the largest percentage value, namely the C4.5 algorithm, with a value of 46.9%.