TY - GEN
T1 - X-Ray Image Based on Gray Level Cooccurrence Matrices (GLCM) K-Nearest Neighbor (KNN) to Detect Tuberculosis
AU - Suhariningsih,
AU - Bastomi, Mohammad Yazid
AU - Purwanti, Endah
AU - Hariyani, Dita Aprilia
AU - Permatasari, Perwira Annissa Dyah
AU - Astuti, Suryani Dyah
N1 - Publisher Copyright:
© 2023 American Institute of Physics Inc.. All rights reserved.
PY - 2023/5/19
Y1 - 2023/5/19
N2 - Tuberculosis is an infectious disease caused by a bacterium called bacillus mycobacterium tuberculosis. Tuberculosis is spread through coughing and sneezing which affects the lungs of people infected with pulmonary tuberculosis. One of the methods is using the thorax image. However, accuracy without a standard is the problem in this topic. It's caused by the analysis result depend on the ability of the medical experts only. In this study, a Tuberculosis detection program was designed using the k-nearest neighbor classification method and Gray Level Cooccurrence Matrices (GLCM) features as classification input. So that the detection program was expected to be a tool for medical experts who had standardized accuracy. The GLCM features were to input the k-nearest neighbor (kNN) classification which are contrast, correlation, energy, entropy, and homogeneity. The program output was divided into 2 classes namely abnormal (tuberculosis) and normal. The combination of entropy-correlation and entropy-energy-correlation features by an optimal level of accuracy, sensitivity, and specificity showed a value of k=1 that is 92%, 92%, 92%.
AB - Tuberculosis is an infectious disease caused by a bacterium called bacillus mycobacterium tuberculosis. Tuberculosis is spread through coughing and sneezing which affects the lungs of people infected with pulmonary tuberculosis. One of the methods is using the thorax image. However, accuracy without a standard is the problem in this topic. It's caused by the analysis result depend on the ability of the medical experts only. In this study, a Tuberculosis detection program was designed using the k-nearest neighbor classification method and Gray Level Cooccurrence Matrices (GLCM) features as classification input. So that the detection program was expected to be a tool for medical experts who had standardized accuracy. The GLCM features were to input the k-nearest neighbor (kNN) classification which are contrast, correlation, energy, entropy, and homogeneity. The program output was divided into 2 classes namely abnormal (tuberculosis) and normal. The combination of entropy-correlation and entropy-energy-correlation features by an optimal level of accuracy, sensitivity, and specificity showed a value of k=1 that is 92%, 92%, 92%.
KW - GLCM
KW - health service
KW - k-nearest neighbor (KNN)
KW - tuberculosis
UR - http://www.scopus.com/inward/record.url?scp=85161432954&partnerID=8YFLogxK
U2 - 10.1063/5.0118948
DO - 10.1063/5.0118948
M3 - Conference contribution
AN - SCOPUS:85161432954
T3 - AIP Conference Proceedings
BT - Proceedings of the International Conference on Advanced Technology and Multidiscipline, ICATAM 2021
A2 - Widiyanti, Prihartini
A2 - Jiwanti, Prastika Krisma
A2 - Prihandana, Gunawan Setia
A2 - Ningrum, Ratih Ardiati
A2 - Prastio, Rizki Putra
A2 - Setiadi, Herlambang
A2 - Rizki, Intan Nurul
PB - American Institute of Physics Inc.
T2 - 1st International Conference on Advanced Technology and Multidiscipline: Advanced Technology and Multidisciplinary Prospective Towards Bright Future, ICATAM 2021
Y2 - 13 October 2021 through 14 October 2021
ER -
