Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Saludin Muis
Universitas Bina Insani, Bekasi
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Data Mining Klasifikasi Breast Cancer Menerapkan Algoritma Gradient Boosted Trees Kraugusteeliana Kraugusteeliana; Saludin Muis; Fifto Nugroho; Abdul Karim; Yessica Siagian
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6095

Abstract

Cancer is a deadly disease that is often experienced suddenly. Cancer is suffered not only among adults and the elderly, but even small children who are just born can also suffer from cancer. There are many types of cancer that have almost the same symptoms but different types and there are also levels of seriousness (danger) of these cancers, ranging from common cancers to malignant cancers that have significant changes to the body. There are many types of cancer, one of which is breast cancer, which is more common in women. This type of cancer often occurs in adult women and the elderly. In this study, to facilitate the diagnosis of breast cancer, a classification method was applied. By making an early diagnosis can reduce the mortality rate, previous diagnosis is done by utilizing image media (PET scan and CT scan) which takes a long time so it is considered less efficient. The classification algorithm used is gradient boosted trees. The test was carried out using the rapidminer application as a tester to determine the accuracy of the algorithm and also the AUC size obtained using information gain. The final result after applying the gradient boosted method produces an accuracy rate of 58.52%, this is considered less effective to use so this algorithm is not suitable to be used as a prediction of breast cancer. Precision of 64.25% and recall of only 69.44%.
Co-Authors Abdul Karim Fifto Nugroho Kraugusteeliana Kraugusteeliana Yessica Siagian