Klasifikasi Sel Tunggal PAP Smear Berdasarkan Analisis Fitur Berbasis Naive Bayes Classifier Dan Particle Swarm Optimization

Klasifikasi Sel Tunggal PAP Smear Berdasarkan Analisis Fitur Berbasis Naive Bayes Classifier Dan Particle Swarm Optimization

• By Hidayatulloh, Taufik; Herliana, Asti; Arifin, Toni;
• Jurnal Nasional Tidak Terakreditasi
• 275
• 186-193

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.

 

• Katakunci Single Cell, Pap Smear, Feature Analysis, Naive Bayes, Particle Swarm Optimization
• URL http://ejournal.bsi.ac.id/ejurnal/index.php/swabumi/article/view/1138
• Penerbit ,

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