<![CDATA[The incidence of lung cancer in Indonesia has shown a significant increase, positioning the country as the eighth highest in Southeast Asia, with a growth rate of 10.85% over the past five years. A considerable number of lung cancer cases remain undiagnosed at earlier stages due to difficulties in detection, which contributes to the high mortality rate associated with this disease. Consequently, there is a need for a relatively efficient and straightforward technique to uncover knowledge, patterns, and interrelationships among data. The objective of this study is to develop a classification model for lung cancer using the C4.5 decision tree method and to evaluate its predictive performance. The methodology comprises several stages, including data preprocessing, exploratory data analysis (EDA), handling of missing values, identification of duplicate records, assessment of feature correlations, separation of features and target variables, partitioning of data into training and testing sets, model implementation, and performance evaluation through a confusion matrix. The experimental results demonstrate that the proposed model achieves a recall of 90%, a precision of 86%, an F1-score of 88%, and an overall accuracy of 89%. These findings indicate that the C4.5 decision tree method is effective in classifying lung cancer cases and holds potential as a reliable approach in medical data analysis for early detection and diagnosis.]]>
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