PENGKLASIFIKASIAN KANKER SERVIKS MENGGUNAKAN METODE FUZZY NAÏVE BAYES DENGAN BOOTSTRAP SAMPLING

NISA, KHOIROTUN and Resti, Yulia and Kresnawati, Endang Sri (2023) PENGKLASIFIKASIAN KANKER SERVIKS MENGGUNAKAN METODE FUZZY NAÏVE BAYES DENGAN BOOTSTRAP SAMPLING. Undergraduate thesis, Sriwijaya University.

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Abstract

Cervical cancer ranks second in the top cause of death in women in various countries, including Indonesia. The Global Burden Of Cancer Study notes that in 2020 there will be 604,127 new cases of cervical cancer worldwide. Around 351,720 new cases of cervical cancer on the Asian continent, with 190,874 cases of cervical cancer occurring in Southeast Asia. The high cases of cervical cancer are related to the late examination and diagnosis of cervical cancer. Therefore, a study is needed that discusses the classification for diagnosing cervical cancer patients with high accuracy. The purpose of this study was to classify cervical cancer based on the results of pap smear cell image extraction using the naïve bayes and fuzzy naïve bayes methods with bootstrap sampling. The data used in this study is a dataset of pap smear images of cervical cancer grade 2 and class 7 from Herlev University Hospital. The results of this study indicate that the classification of cervical cancer using the naïve bayes fuzzy method is better than naïve bayes, both for 2 classes and 7 classes. Classification using the naïve bayes method for 2 classes with bootstrap sampling produces an average value of accuracy, precision, recall and specificity of 89.27%, 86.53%, 67.83% and 96.42%, respectively. While the classification using the naïve bayes method for 7 classes with bootstrap sampling produces an average accuracy, precision, recall and specificity of 82.12%, 40.10%, 45.34% and 87.30%, respectively. Classification using the fuzzy naïve bayes method for 2 classes with bootstrap sampling produces an average value of accuracy, precision, recall and specificity of 90.26%, 89.93%, 61.94% and 97.54%, respectively. While the classification using the fuzzy naïve bayes method for 7 classes with bootstrap sampling produces an average accuracy, precision, recall and specificity of 86.94%, 60.2%, 55.17% and 90.51%, respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kanker Serviks, Fuzzy Naive Bayes
Subjects:	Q Science > QA Mathematics > QA47-59 Tables Q Science > QA Mathematics > QA71-90 Instruments and machines R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Khoirotun Nisa
Date Deposited:	06 Apr 2023 03:12
Last Modified:	06 Apr 2023 03:12
URI:	http://repository.unsri.ac.id/id/eprint/93579

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