KLASIFIKASI SEL TUNGGAL PAP SMEAR BERDASARKAN ANALISIS FITUR BERBASIS NAÏVE BAYES CLASSIFIER DAN PARTICLE SWARM OPTIMIZATION

Abstract

Research from the informatics experts about cervical cancer mainly single cell of the Pap
smear, increasingly showing the almost prefect results. 20 features produced by research
conducted 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 been
able to solve is to maximize the results of the classification of the 7th grade single cells of Pap
Smear. This is due to the lack of research experts with a combination of the best methods that
produce maximum results. After reviewing previous studies, classification methods that provide
the best value to date is Naive Bayes. For the optimization method used in the present study is the
Particle Swarm Optimization. With a combination of methods Naive Bayes and Particle Swarm
Optimization, obtained better results from previous research that is 62.67% for the classification
of 7 classes and 95.70% for the classification of 2 classes.