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Aceh International Journal of Science and Technology
Published by Universitas Syiah Kuala
ISSN : 20889860     EISSN : 25032348     DOI : http://10.13170/aijst
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences
Aceh International Journal of Science & Technology (AIJST) is published by the Graduate School of Syiah Kuala University (PPs Unsyiah) and the Indonesian Soil Science Association (Himpunan Ilmu Tanah Indonesia, Komda Aceh). It is devoted to identifying, mapping, understanding, and interpreting new trends and patterns in science & technology development, especially within Asian countries as well as other parts of the world. The journal endeavors to highlight science & technology development from different perspectives. The aim is to promote broader dissemination of the results of scholarly endeavors into a broader subject of knowledge and practices and to establish effective communication among academic and research institutions, policymakers, government agencies, and persons concerned with the complex issue of science & technology development. The Journal is a peer-reviewed journal. The acceptance decision is made based upon an independent review process supported by rigorous processes and provides constructive and prompt evaluations of submitted manuscripts, ensuring that only intellectual and scholarly work of the greatest contribution and highest significance is published. The AIJST publishes original conceptual and research papers, review papers, technical reports, case studies, management reports, book reviews, research notes, and commentaries. It will occasionally come out with special issues devoted to important topics concerning science & technology development issues. Scopes Starting in 2016, AIJST has focused on science and engineering aspects, and therefore now AIJST considers the topics but not limited to : Engineering (Mechanical, Chemical, Civil, Transportation) Geology and Geomorphology Environmental Science (Hydrology, Pollution, Water Treatment, Soil Science, Climatology) Physical Oceanography Mathematics Physics and Geophysics Geospatial and Information Technology
Arjuna Subject : Ilmu Lingkungan (semua ketegori) - Global dan Perubahan Planet
Articles 11 Documents
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Search results for , issue "Vol 12, No 3 (2023): December 2023" : 11 Documents clear
Tuberculosis Detection using Gray Level Co-Occurrence Matrix (GLCM) and K-Nearest Neighbor (K-NN) Algorithms Anwar, Fuad; Yunianto*, Mohtar; Putri, Rahmanisya Fani Aisha
Aceh International Journal of Science and Technology Vol 12, No 3 (2023): December 2023
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.12.3.33241

Abstract

Research has been conducted on detecting tuberculosis (TB) using machine learning. In this study, chest Xray (CXR) image data was used with a pixel value of 512 x 512 and PNG format consisting of normal lung images and TBinfected lung images in a 50:50 ratio; the number of images was 200 training data images and 80 testing data images. In the preprocessing stage, grayscaling is carried out so the image has a grayscale. Then, do the image improvement using contrast stretching. Furthermore, image extraction was carried out using 22 GLCM features with variations in the direction of the angles of 0, 45, 90, and 135. The result of feature extraction data is then identified using KNN Classification. The training results have the highest accuracy value with variations in the direction of the GLCM angle of 45 and the value of K = 3; at the testing stage, it produces an accuracy of 90%.

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