IMPLEMENTASI BOOTSTRAP AGGREGATING PADA METODE DECISION TREE DAN REGRESI LOGISTIK UNTUK KLASIFIKASI KANKER SERVIKS

WATI, SITI HASMA and Yulia, Resti and Endang, Sri Kesnawati (2022) IMPLEMENTASI BOOTSTRAP AGGREGATING PADA METODE DECISION TREE DAN REGRESI LOGISTIK UNTUK KLASIFIKASI KANKER SERVIKS. Undergraduate thesis, Sriwijaya University.

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Abstract

Cervical cancer is one of the most common diseases suffered by women in the world. Global Burden Cancer recorded the incidence of cervical cancer worldwide in 2020 as many as 604,127 cases while the death rate from cervical cancer was recorded at 341,831 cases. The high mortality rate from cervical cancer is also related to the delay in diagnosis of the disease. Therefore, it is necessary to have a study that discusses the classification process to diagnose cervical cancer patients with high accuracy results. The purpose of this study is to classify cervical cancer based on pap smear cell image extraction using the decision tree method and logistic regression with the implementation of bootstrap aggregating (bagging) and without the implementation of bagging. The data used in this study is a dataset of Pap smear cell image extraction of cervical cancer 7 classes originating from Herlev University Hospital. The results of this study indicate that the implementation of bagging can improve the performance of a single method for cervical cancer classification. Classification using the decision tree method resulted in accuracy, precision, recall, and specificity of 85.87%, 56.09%, 54.88%, and 91.48%, respectively. While the classification using the decision tree method with bagging resulted in accuracy, precision, recall, and specificity of 87.89%, 61.85%, 61.59%, and 92.61%, respectively. Classification using logistic regression method resulted in accuracy, precision, recall, and specificity of 89.75%, 68.61%, 68.45%, and 93.75%, respectively. While the classification using the logistic regression method with bagging produces the best accuracy, precision, recall, and specificity, which are 90.53%, 70.13%, 70.70%, and 94.25%, respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Classification, Cervical Cancer, Bootstrap Aggregating, Decision Tree, Logistic Regression
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.B45 Big data. Machine learning. Quantitative research. Metaheuristics.
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Siti Hasma Wati
Date Deposited:	06 Apr 2022 02:07
Last Modified:	06 Apr 2022 02:08
URI:	http://repository.unsri.ac.id/id/eprint/68420

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