Komparasi tingkat akurasi support vector machine (svm) dan naive byes dalam klasifikasi keganasan kanker payudara

Indira, Sarasmitha Batu Bara (2018) Komparasi tingkat akurasi support vector machine (svm) dan naive byes dalam klasifikasi keganasan kanker payudara. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Cancer is one of the leading causes of death worldwide. Cancer has many types, depending on where it develops. One of the most common cancer is breast cancer. The difficulty of classifying the malignancies of breast cancer can lead to delays in treatment, which leads to a higher risk of death. Currently in the field of data mining developed an algorithm that can classify malignancy of breast cancer, some of which are Support Vector Machine (SVM) and Naïve Bayes. SVM is a supervised learning algorithm that classifies the class using hyperplane. The advantages of SVM is over the high level of accuracy produced. Naïve Bayes is a learning algorithm that uses the probability of classifying data. Naïve Bayes has an advantage on the capability of classifying large amounts of data. The study used a dataset from Wisconsin Breast Cancer with 699 data obtained from http://archive.ics.uci.edu/ml/index.php. The results showed SVM accuracy was 98.56% and Naïve Bayes 97.74%. Keywords: Classification, Breast Cancer, SVM, Naïve Bayes

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