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All Journal Jurnal Mantik
Nita Sintami
Universitas Prima Indonesia, Indonesia
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Implementation of KNN algorithm in classifying diabetic ulcers in patients with diabetes mellitus Saut Dohot Siregar; Yusra Uli Rasita Ginting; Nita Sintami; Hari Salim Butar-Butar; Riki Marthin Simanjuntak
Jurnal Mantik Vol. 7 No. 2 (2023): Agustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i2.3928

Abstract

The rapid development of the times, humans face various types of diseases that exist, even some of these types of diseases can be fatal and even death, one of the most famous diseases is diabetes mellitus. Diabetic ulcers are caused by diabetes mellitus. The risk of people with diabetes mellitus is 29 times that there will be diabetic ulcer complications, and the potential to experience diabetic ulcers is 15-25% during their lifetime and within 5 years has a recurrence rate of 50-70%. This research uses the K-Nearest Neighbor algorithm with the Gray Level Co-Occurrence model which is the extraction of image or texture features from images. Using the calculate_glcms(image) function to calculate some features of the GLCM matrix given an image as input. GLCM: greycomatrix(image,[1], [0, np.pi/4, np.pi/2,3 np.pi/4], levels=256, symmetric=True, normed=True) calculates the GLCM matrix of the image with pixel distance 1 and angle 0, np.pi/4, np.pi/2, and3 np.pi/4. Conclusion of this research is the test result matrix, the accuracy is 0.94 (94%), precision 0.928, recall 0.905, F-1 Score 0.907 and support of 66.
Co-Authors Hari Salim Butar-Butar Riki Marthin Simanjuntak Siregar, Saut Dohot Yusra Uli Rasita Ginting