Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit - Dimas Aprilianto; Rizal, Erian (METIK JURNAL (AKREDITASI SINTA 3), 2025)
Lung cancer is one of the leading causes of global mortality, often difficult to detect early due to its nonspecific initial symptoms. This study proposes a Machine Learning-based approach to classify lung cancer risks using the Random Forest algorithm optimized with GridSearchCV. The identified research gap is the lack of interactive web-based implementations that deliver real-time classification results with a user-friendly interface for general users. The objective of this study is to develop an accurate and efficient classification model and integrate it into a web application using Streamlit. The dataset was sourced from Kaggle, consisting of 5,000 patient records and 18 clinical and lifestyle-related features. The preprocessing steps included data cleaning, normalization, encoding, and feature recategorization. Model performance evaluation using Accuracy, Precision, Recall, and F1-Score metrics showed an accuracy of 90%. Feature importance analysis identified smoking habits, throat discomfort, and respiratory issues as dominant predictors of lung cancer. The model was then deployed into a Streamlit-based web application and tested via a User Acceptance Test (UAT) involving 50 respondents, resulting in a Mean Opinion Score (MOS) average above 84%. These findings indicate that the developed prediction system is not only technically accurate but also well-accepted by users, highlighting its potential as a practical and efficient tool for early lung cancer screening.