Analisis Perbandingan Algortima Support Vector Machine, Random Forest dan Naive Bayes Untuk Prediksi Penyakit Kanker Paru-Paru - Rizky, Rendy Alfa; Fauzi, Ahmad; Kusumaningrum, Dwi Sulistya; Novita, Hilda Yulia (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
The lungs are one of the vital organs responsible for the processes of respiration and blood circulation, with smoking habits being the primary factor contributing to the development of lung cancer. In Indonesia, the prevalence of this disease continues to increase, placing it eighth in the Southeast Asian region. Globally, lung cancer accounts for approximately 11.6% of all cancer cases and 18% of total cancer-related deaths.This study aims to analyze and compare the performance of Support Vector Machine (SVM), Random Forest, and Naïve Bayes algorithms in predicting lung cancer, as well as to determine the best-performing algorithm based on accuracy, precision, and recall metrics. The study utilizes the Lung Cancer Prediction dataset obtained from Kaggle, consisting of 309 instances and 16 attributes. The approach involves the implementation of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The research process includes data collection, preprocessing, data transformation, feature selection, model development, and evaluation using a confusion matrix. The experimental results show that both SVM and Naïve Bayes achieve the same accuracy of 91.07%, while Random Forest obtains an accuracy of 89.28%. In terms of evaluation metrics, SVM demonstrates more consistent performance with a precision of 95% and recall of 93%, whereas Naïve Bayes shows a higher recall of 95% with a precision of 93%. On the other hand, Random Forest exhibits limitations in identifying non-cancer cases. Based on the overall results, SVM is considered the most optimal method as it provides a better balance of performance. This study indicates that machine learning has significant potential as a supporting tool for early detection of lung cancer in a more accurate and efficient manner.