(Similarity) Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes

OKTAVIANTO, HARDIAN and HANDRI, RAHMAN PUJI (2019) (Similarity) Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes. Universitas Jember.

[img] Text
artikel Hardian INFORMAL plagiasi-1-7.pdf

Download (233kB)

Abstract

Breast cancer is one of the highest causes of death among women, this disease ranks second cause of death after lung cancer. According to the world health organization, 1 million women get a diagnosis of breast cancer every year and half of them die, in general this is due to early treatment and slow treatment resulting in new cancers being detected after entering the final stage. In the field of health and medicine, machine learning-based classification has been carried out to help doctors and health professionals in classifying the types of cancer, to determine which treatment measures should be performed. In this study breast cancer classification will be carried out using the Naive Bayes algorithm to group the types of cancer. The dataset used is from the Wisconsin breast cancer database. The results of this study are the ability of the Naive Bayes algorithm for the classification of breast cancer produces a good value, where the average percentage of correctly classified data reaches 96.9% and the average percentage of data is classified as incorrect only 3.1%. While the level of effectiveness of classification with naive bayes is high, where the average value of precision and recall is around 0.96. The highest precision and recall values are when the test data uses a percentage split of 40% with the respective values reaching 0.974 and 0.973

Item Type: Peer Review
Subjects: 000 Computer Science, Information, & General Works > 004 Data Processing, Computer Science
Divisions: Faculty of Engineering > Department of Informatics Engineering (S1)
Depositing User: Hardian Oktavianto
Date Deposited: 18 Jan 2023 02:20
Last Modified: 30 Mar 2023 00:57
URI: http://repository.unmuhjember.ac.id/id/eprint/16057

Actions (login required)

View Item View Item