Implementasi Teknik Data Mining untuk Prediksi Kanker Paru – Paru Menggunakan Algoritma Decision Tree C4.5 : Penelitian

Angela Putri Rudhiyanto; Najla Damayanti; Esthi Ayu Anggita; Ananda Putri Maharani; Amanda Chickita Aprilia Juanda

doi:10.31004/jerkin.v4i3.4976

Authors

Angela Putri Rudhiyanto Universitas Bina Sarana Informatika
Najla Damayanti Universitas Bina Sarana Informatika
Esthi Ayu Anggita Universitas Bina Sarana Informatika
Ananda Putri Maharani Universitas Bina Sarana Informatika
Amanda Chickita Aprilia Juanda Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.4976

Keywords:

Machine Learning, Linear Regression, Final Grade Prediction, Attendance, Assignment Score

Abstract

This study aims to implement data mining techniques to predict lung cancer using the Decision Tree C4.5 algorithm. The dataset used in this research is the Lung Cancer Dataset, consisting of 309 records and 16 attributes covering demographic factors, lifestyle habits, and clinical symptoms of patients. The research process was conducted using RapidMiner software through several stages, including data collection, data preprocessing, model construction, and model evaluation. The C4.5 algorithm generates a decision tree based on the Gain Ratio criterion to identify the most influential attributes in lung cancer classification.The results show that the Decision Tree C4.5 algorithm achieved excellent classification performance with an accuracy of 96.76%, a classification error of 3.24%, a Kappa value of 0.844, a weighted mean recall of 89.86%, and a weighted mean precision of 94.93%. The generated decision tree indicates that the ALLERGY attribute is the most dominant factor in classifying lung cancer, followed by other attributes such as Yellow_Finger, Peer_Pressure, and Swallowing_Difficulty. These findings indicate that the Decision Tree C4.5 algorithm is effective and highly interpretable for lung cancer prediction and has strong potential as an early decision-support tool in medical diagnosis.

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