| Title | IMPLEMENTATION OF THE RANDOM FOREST METHOD FOR CLASSIFYING LUNG X-RAY IMAGE ABNORMALITIES |
|---|
| Author Order | 1 of 4 |
|---|
| Accreditation | |
|---|
| 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. |
|---|
| Publisher Name | Informatika, Universitas Jenderal Soedirman |
|---|
| Publish Date | 2025-02-13 |
|---|
| Publish Year | 2025 |
|---|
| Doi | DOI: 10.52436/1.jutif.2025.6.1.4323 |
|---|
| Citation | |
|---|
| Source | Jurnal Teknik Informatika (Jutif) |
|---|
| Source Issue | Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025 |
|---|
| Source Page | 205-210 |
|---|
| Url | https://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/4323/730 |
|---|
| Author | Dr RETNO SUPRIYANTI, S.T, M.T |
|---|
| File | 4872012.pdf |
|---|