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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Fildzah Zhafarina
University of Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Pancreatic cancer classification using logistic regression and random forest Zuherman Rustam; Fildzah Zhafarina; Glori Stephani Saragih; Sri Hartini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp476-481

Abstract

In the medical field, technology machinery is needed to solve several classification problems. Therefore, this research is useful to solve the problem of the medical field by using machine learning. This study discusses the classification of pancreatic cancer by using regression logistics and random forest. By comparing the accuracy, precision, recall (sensitivity), and F1-score of both methods, then we will know which method is better in classifying the pancreatic cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that random forest has better accuracy than logistic regressions. It can be seen with maximum accuracy of logistic regressions 96.48 with 30% data training and random forest 99.38% with 20% of data training.
Twin support vector machine using kernel function for colorectal cancer detection Zuherman Rustam; Fildzah Zhafarina; Jane Eva Aurelia; Yasirly Amalia
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3179

Abstract

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artiļ¬cial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
Co-Authors Glori Stephani Saragih Jane Eva Aurelia Sri Hartini Yasirly Amalia Zuherman Rustam