Analisis Perbandingan Algoritma Random Forest dan Algoritma Naive Bayes untuk Memprediksi Penyakit Paru-Paru di Indonesia - Eka Wulansari Fidayanthie; Asep Sayfulloh; Mardiana Rafa Alzena; Nilam Kurnia Sari (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2025)
Lungs are vital organs in the human respiratory system, responsible for fulfilling the body's oxygen needs. If the lungs experience health problems, it can have adverse effects on the human respiratory system. Common causes of lung diseases are usually due to inhaling air contaminated by dust, smoke, viruses, and bacteria. This study aims to compare the performance of two classification algorithms, namely Random Forest and Naive Bayes, in predicting lung diseases. The data used was obtained from the Kaggle website and processed using RapidMiner software. The attributes involved include smoking habits, pre-existing conditions, staying up late, exercise activities, age, and outcomes. Based on the test results, the Random Forest algorithm demonstrated the best performance with an accuracy of 93%, while the Naive Bayes algorithm achieved an accuracy of 87%. These findings indicate that the Random Forest algorithm outperforms the Naive Bayes algorithm in terms of lung disease prediction accuracy.