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All Journal Teknika ITEj (Information Technology Engineering Journals) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) International Journal of Engineering, Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Malikussaleh Mengabdi INOVTEK Polbeng - Seri Informatika
Zahratul Fitri, Zahratul
Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Bumi Persada
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results Nuriana, Nuriana; Fitri, Zahratul; Razi, Ar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9908

Abstract

This study aims to classify lung diseases from X-ray images using a combination of Gray Level Cooccurrence Matrix (GLCM) and Linear Discriminant Analysis (LDA) methods. GLCM was used to extract texture features such as contrast, correlation, energy, and homogeneity from 300 lung X-ray images representing four categories: Normal, Pneumonia, Tuberculosis, and Bronchitis. The LDA method was then applied for classification based on these features. The results showed that Tuberculosis had the highest classification accuracy at 80%, while the overall model accuracy was 61.67%. Evaluation using precision, recall, F1-score, and confusion matrix confirmed that the GLCM and LDA combination performed best in identifying tuberculosis cases. However, overlapping features between Normal, Bronchitis, and Pneumonia classes reduced the classification performance. These findings suggest that the proposed method provides promising results and could be improved further with advanced feature extraction or classification techniques.
Co-Authors - Fakhrurrazi Ade Irfan Aidilof, Hafizh Al Kautsar Akbar, Muhammad Zulfat Ananda Faridhatul Ulva Andik Bintoro Angga Pratama ANNISA KARIMA Asrianda Asrianda Bakhtiar Bakhtiar Dahlan Abdullah Delianda, Anggun Dita Amelia, Dita Emi Maulani Eva Darnila Fadliani, Fadliani Fuadi, Wahyu Hisyam, Mochammad Irwansyah, Defi Kausar, M Reza Khairul Amna Leni Maulida Liza Afra Merdu, Bunga Mawarni MUHAMMAD TEGUH, MUHAMMAD Muhibuddin Muhibuddin, Muhibuddin Mutammimul Ula Nisa, Miftahul Nunsina, Nunsina Nurdin Nurdin Nurdin Nurdin Nuriana, Nuriana Putri, Rizki Amelia Rahma, Putri Razi, Ar Rizal S.Si., M.IT, Rizal Safriana Safriana Safwandi Safwandi, Safwandi Samsuar, Samsuar Saptari, Mochamad Ari Siregar, Siti Chairani Ummi Kalsum Zainal Abidin Zul Akli