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All Journal ROUTERS: Jurnal Sistem dan Teknologi Informasi
Faurika, Faurika
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Penerapan Teknik Cross-Validation untuk Menangani Overfitting pada Studi Kasus Implementasi Decision Tree untuk Prediksi Kanker Paru Faurika, Faurika; Naseh Khudori, Ahsanun; Haris, M. Syauqi
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 2 No. 2, Juli 2024
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v2i2.3631

Abstract

Lung cancer is a condition caused by cancer cells growing in the lungs. Lung cancer causes a weakened immune system, tumors, and other abnormalities that prevent the body from functioning properly. Lung cancer examination uses various technologies, namely CT Scan, X-ray, and others. However, the examination is relatively expensive and takes a long time. The use of machine learning makes it possible to support lung cancer diagnosis. With the large amount of medical data available today, machine learning can recognize patterns in the data so that it will help the process of diagnosing lung cancer more effectively. This study aims to correct overfitting in previous research which used the decision tree method to predict lung cancer with cross-validation techniques. In this research, we use a public dataset from Data World. This dataset consists of 25 data attributes and has 1000 data. The results of this research are rules obtained from decision trees which are then evaluated to produce 96.7% accuracy, 96.7% precision, 96.7% recall, and 96.7% f1-score. These results show that the decision tree method performs well in predicting lung cancer early and the cross-validation technique can overcome overfitting in decision trees with more general and stable results.
Co-Authors Ahsanun Naseh Khudori M. Syauqi Haris