Application of Linear Discriminant Analysis Method With Gray Level Cooccurrence Matrix Method for Classification of Lung Disease Diagnosis Based on X-Ray Results
DOI:
https://doi.org/10.30871/jaic.v9i4.9908Keywords:
Lung Disease, X-ray, Linear Discriminant Analysis, GLCM, Texture ClassificationAbstract
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.
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[1] F. Meila Azzahra Sofyan, A. Voutama, and Y. Umaidah, “Penerapan Algoritma C4.5 Untuk Prediksi Penyakit Paru-Paru Menggunakan Rapidminer,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 2, pp. 1409–1415, 2023, doi: 10.36040/jati.v7i2.6810.
[2] S. Salmi, “Penggunaan Pemodelan Paru-Paru sebagai Upaya Meningkatkan Pemahaman Peserta Didik tentang Mekanisme Pernapasan di Kelas VIII.8 MTsN 2 Kota Bima,” Lamda J. Ilm. Pendidik. MIPA dan Apl., vol. 2, no. 2 SE-Articles, pp. 86–94, Nov. 2022, doi: 10.58218/lambda.v2i2.326.
[3] A. Naseh Khudori and M. Syauqi Haris, “Implementasi Decision tree Untuk Prediksi Kanker Paru-Paru,” J. Ris. Sist. Inf. Dan Tek. Inform. (JURASIK, vol. 9, no. 1, pp. 94–106, 2024, [Online]. Available: https://tunasbangsa.ac.id/ejurnal/index.php/jurasik
[4] S. Maharani and W. R. Aryanta, “Dampak Buruk Polusi Udara Bagi Kesehatan Dan Cara Meminimalkan Risikonya,” J. Ecocentrism, vol. 3, no. 2, pp. 47–58, 2023, doi: 10.36733/jeco.v3i2.7035.
[5] L. B. Diantara, H. Hasyim, I. P. Septeria, D. T. Sari, G. T. Wahyuni, and R. Anliyanita, “Tuberkulosis Masalah Kesehatan Dunia: Tinjauan Literatur,” J. ’Aisyiyah Med., vol. 7, no. 2, pp. 78–88, 2022, doi: 10.36729/jam.v7i2.855.
[6] H. Fahmi and Sutisna, “Implementasi Data Mining Klasifikasi Gejala Penyakit TB Menggunakan Algoritma Naive Bayes pada Studi Kasus Puskesmas Pegangsaan Dua B,” vol. 5, no. 3, pp. 2888–2898, 2024, doi: doi.org/10.35870/jimik.v5i3.970.
[7] M. Sabir and Sarifuddin, “Analisis Faktor Risiko Tingginya kasus Tuberkulosis Paru di Indonesia : Literatur Revieu,” J. Kolaboratif Sains, vol. 6, no. 6, pp. 453–468, 2023, doi: 10.56338/jks.v6i6.3662.
[8] J. Scott, A. M. Biancardi, O. Jones, and D. Andrew, “Artificial Intelligence in Periodontology: A Scoping Review,” Dent. J., vol. 11, no. 2, 2023, doi: 10.3390/dj11020043.
[9] B. Nugroho and E. Y. Puspaningrum, “Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, pp. 533–538, 2021, doi: 10.25126/jtiik.2021834515.
[10] C. Wijaya, H. Irsyad, and W. Widhiarso, “Klasifikasi Pneumonia Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi Glcm,” J. Algoritm., vol. 1, no. 1, pp. 33–44, 2020, doi: 10.35957/algoritme.v1i1.431.
[11] W. N. Afifah and V. Lusiana, “Klasifikasi jenis batik semarangan menggunakan metode convolution neural network (cnn),” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 1, pp. 542–553, 2025, doi: doi.org/10.29100/jipi.v10i1.5873.
[12] A. Nurdiansyah, H. Erlanda, Y. B. Roza, and R. Sovia, “Klasifikasi Citra Dalam Identifikasi Kol Dan Wortel Menggunakan Algoritma LDA dan KNN,” J. Sci. Soc. Res., vol. 4307, no. May, pp. 1895–1902, 2025, doi: doi.org/10.54314/jssr.v8i2.2894.
[13] M. Muchtar and R. Arjaliyah Muchtar, “Integrasi Fitur Warna, Tekstur Dan Renyi Fraktal Untuk Klasifikasi Penyakit Daun Kentang Menggunakan Linear Discriminant Analysis,” J. Mnemon., vol. 7, no. 1, pp. 77–84, 2024, doi: 10.36040/mnemonic.v7i1.9258.
[14] A. Razi and D. Yulisda, “Klasifikasi Tingkat Keberhasilan Survival Rate (Sr) Pada Produksi Udang Vaname Menggunakan Algoritma Naïve Bayes,” Ekasakti J. Penelit. dan Pengabdi., vol. 4, no. 2, pp. 206–212, 2024, doi: doi.org/10.31933/ejpp.v4i2.
[15] E. Utaminingsih, R. Silviani, and Z. Fitri, “Pengelompokan Fitur Color Structure Descriptor (CSD) Menggunakan Support Vector Machine (SVM) Untuk Citra Busana Tradisional Aceh,” vol. 4, no. 1, pp. 139–147, 2021.
[16] R. Amelia et al., “Analisis Perbedaan Permukaan Buah Segar Dan Busuk Menggunakan Model Rgb Dan Gray-Level Co-Occurrence Matrix ( GLCM ),” vol. 9, no. 4, pp. 7219–7226, 2025, doi: doi.org/10.36040/jati.v9i4.14461.
[17] F. W. Nugraha, A. Harjoko, M. A. Muslim, and N. N. Alabid, “Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning,” J. Soft Comput. Explor., vol. 4, no. 1, pp. 13–22, 2022, doi: 10.52465/joscex.v4i1.99.
[18] A. Ramola, A. K. Shakya, and D. Van Pham, “Study of statistical methods for texture analysis and their modern evolutions,” Eng. Reports, vol. 2, no. 4, pp. 1–24, 2020, doi: 10.1002/eng2.12149.
[19] D. H. U. Ningsih, E. Zuliarso, M. R. Radyanto, and D. B. Santoso, “Feature Extraction Dengan Gray Level Co-Occurrence Matrix Warna Alami Dari Tanaman Ketapang Berbasis Geolokasi Dewi,” Featur. Extr. Dengan Gray Lev. Co-Occurrence Matrix Warn. Alami Dari Tanam. Ketapang Berbas. Geolokasi, vol. 29, pp. 1–14, 2024, doi: doi.org/10.35315/dinamik.v29i2.9851.
[20] R. Destriana, D. Nurnaningsih, D. Alamsyah, and A. A. J. Sinlae, “Implementasi Metode Linear Discriminant Analysis (LDA) Pada Klasifikasi Tingkat Kematangan Buah Nanas,” Build. Informatics, Technol. Sci., vol. 3, no. 1 SE-Articles, Jun. 2021, doi: 10.47065/bits.v3i1.1007.
[21] I. Rashad, R. R. Isnanto, and C. E. Widodo, “Klasifikasi Penyakit Jantung Menggunakan Algoritma Analisis Diskriminan Linier,” J. Sist. Info. Bisnis, vol. 13, no. 1, pp. 29–36, 2023, doi: 10.21456/vol13iss1pp29-36.
[22] A. Rasyid and L. Heryawan, “Klasifikasi Penyakit Tuberculosis (TB) Organ Paru Manusia Berdasarkan Citra Rontgen Thorax Menggunakan Metode Convolutional Neural Network (CNN),” J. Manaj. Inf. Kesehat. Indones., vol. 11, no. 1, pp. 35–44, 2023, doi: 10.33560/jmiki.v11i1.484.
[23] D. A. Rizqiana and H. S. Indri, “Asuhan Keperawatan Bersihan Jalan NafasTidak Efektif Pada Pasien Bronkhitis Fisiotrapi Dada Di Ruang Edelweis Atas RSUD Kardinah kota Tegal,” J. Inov. Penelit., vol. 3, no. 3, pp. 1–4, 2022, doi: doi.org/10.47492/jip.v3i3.1881.
[24] G. Annisa and R. Khairani, “Rokok Dan Alergi Berhubungan Dengan Bronkitis Akut Pada Pasien Dewasa,” J. Akta Trimedika, vol. 1, pp. 316–326, 2024, doi: doi.org/10.25105/aktatrimedika.v1i3.19974.
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