KLASIFIKASI SEL TUNGGAL PAP SMEAR BERDASARKAN ANALISIS FITUR BERBASIS NAÏVE BAYES CLASSIFIER DAN PARTICLE SWARM OPTIMIZATION - Taufik Hidayatulloh; Asti Herliana; Toni Arifin (Universitas Bina Sarana Informatika Kota Sukabumi, 2016)
Research from the informatics experts about cervical cancer mainly single cell of the Papsmear, increasingly showing the almost prefect results. 20 features produced by researchconducted by Jantzen, Norup, Dounias and Bjerregaard, has now been developed and reviewed.This assessment takes precedence on efficiency features that make a significant contribution(assessed based on the percentage of best feature tool). Until now, the problems that have not beenable to solve is to maximize the results of the classification of the 7th grade single cells of PapSmear. This is due to the lack of research experts with a combination of the best methods thatproduce maximum results. After reviewing previous studies, classification methods that providethe best value to date is Naive Bayes. For the optimization method used in the present study is theParticle Swarm Optimization. With a combination of methods Naive Bayes and Particle SwarmOptimization, obtained better results from previous research that is 62.67% for the classificationof 7 classes and 95.70% for the classification of 2 classes.