KLASIFIKASI PARU-PARU PADA CITRA SINAR-X COVID-19 MENGGUNAKAN SEGEMENTASI OTSU, EKSTRAKSI FITUR GLCM, DAN SUPPORT VECTOR MACHINE - Irawan Dharma Sukowati; Dody Wahjudi; Isra' Nuur Darmawan; Rahardian Luthfi Prasetyo (Teodolita: Media Komunkasi Ilmiah di Bidang Teknik, 2025)
Lung damage due to Covid-19 can be analyzed through chest X-ray images, but manual interpretation is subjective. This study aims to classify lung damage in Covid-19 X-ray images using Gray Level Co-occurrence Matrix (GLCM) texture feature extraction and machine learning methods. The dataset consists of 100 labeled X-ray images stored in Excel and CSV formats. The research stages include image preprocessing, GLCM feature extraction, model training, and evaluation using precision, recall, f1-score, and accuracy. The results show that the model is capable of classifying with fairly good accuracy even though the test data used is only a portion of the dataset. The SVM model used produces an accuracy of 57% on the test data with fairly stable classification performance. This approach has the potential to be used as a support system for diagnosing lung damage due to Covid-19.