Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal CoreIT
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT

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

Found 1 Documents
Search

 1 
Benchmarking Various Machine Learning Models to Detect Lung Cancer Iis Afrianty; Liza Afriyanti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study benchmarked and evaluated the performance of various machine learning techniques to detect lung cancer using public datasets. The techniques used include Random Forest, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, C4.5, Bayesian Network, Reptree, Naive Bayes, and P.A.R.T. Evaluation was carried out using metrics such as Accuracy, F-measure, Precision, TPR, ROC, FPR, PRC, and MCC. The results showed that the Support Vector Machine algorithm performed best on balanced dataset distribution, while Random Forest showed stable performance on unbalanced datasets. This study confirms the importance of selecting appropriate algorithms and data distribution to improve lung cancer detection.
Co-Authors Liza Afriyanti