PENERAPAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK SELEKSI ATRIBUT DALAM MENINGKATKAN AKURASI PREDIKSI DIAGNOSIS PENYAKIT HEPATITIS DENGAN METODE ALGORITMA C4.5 - Lis Saumi Ramdhani (Universitas Bina Sarana Informatika Kota Sukabumi, 2016)
Hepatitis is a chronic disease that is chronic, at which time the person has been infected, the condition isstill healthy and not showing signs and symptoms Typical but transmission continues to run. So from thatprocess are still many people who do not recognize the symptoms of hepatitis. There have been manyresearchers who conducted the study to predict hepatitis, one of which applies the method C4.5. In thisresearch, C4.5 algorithm optimization using Particle Swarm Optimization to improve predictionaccuracy. After testing the two models namely the algorithm C4.5 and C4.5 Optimization using ParticleSwarm Optimization, the results obtained are algorithms. Thus obtained test using values obtained C4.5where accuracy is 79,33% and the AUC value is 0,655, while Optimization testing using C4.5ParticleSwarm Optimization with accuracy values obtained 85,00% and AUC values were 0,718 at the level ofdiagnosis fair classification. So that the two methods have different levels of accuracy that is equal to5,67% and the difference in AUC value of 0,063.