IMPLEMENTATION OF THE RANDOM FOREST METHOD FOR CLASSIFYING LUNG X-RAY IMAGE ABNORMALITIES

Retno Supriyanti; M. Gus Solhan Fadlola; M. Syaiful Aliim; Yogi Ramadhani

doi:10.52436/1.jutif.2025.6.1.4323

Retno Supriyanti Electrical Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia
M. Gus Solhan Fadlola Electrical Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia
M. Syaiful Aliim Electrical Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia
Yogi Ramadhani Electrical Engineering Department, Jenderal Soedirman University, Purwokerto, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.4323

Keywords: COVID-19, X-ray Image, Random Forest, Classification, Cross-validation

Abstract

The Covid-19 pandemic has caused a severe global health crisis. Rapid and accurate diagnostics are essential in combating this disease. In this regard, lung X-ray images have become critical for identifying Covid-19 infections. The method used in this study is random forest, a classification method based on ensemble modeling of decision trees. The lung X-ray images used in this study were taken from a datasheet containing images from COVID-19 patients and images from non-Covid-19 patients. The data pre-processing process involves extracting features from the images using image processing techniques and statistical analysis. The random forest model is trained using the processed datasheet to classify the lung X-ray images. The model's performance is evaluated using accuracy, sensitivity, and specificity metrics. In addition, cross-validation is used to measure the reliability and generalization of the model. The study results showed that the random forest method achieved good classification performance in distinguishing COVID-19 lung X-ray images from normal ones. The resulting model provided high accuracy and good sensitivity in identifying Covid-19 cases. These results show the potential of the random forest method in supporting early diagnosis and treatment of COVID-19 disease.

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