Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Riska Chairunisa
Universitas Telkom
Computer Science & IT Engineering

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

Found 1 Documents
Search

 1 
Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray Riska Chairunisa; Adiwijaya; Widi Astuti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 5 (2020): Oktober 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (434.683 KB) | DOI: 10.29207/resti.v4i5.2083

Abstract

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.
Co-Authors Adiwijaya Widi Astuti