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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Nisa R, Ajeng Arina
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Automotive Engineering Computer Science & IT Control & Systems Engineering

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Comparison Of K-Nearest Neighbor And CNN Classification Methods In Diabetic Data Sets Nisa R, Ajeng Arina; Pardede, A M H; Sihombing, Marto
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.272

Abstract

The number of diabetics worldwide is projected to increase by 204 million (48%), from 425 million in 2017 to 629 million in 2045. Indonesia ranks sixth out of ten countries with the most number of diabetics in the world or 10 million people. The majority of people with diabetes are between 20 and 64 years old, or 327 million people, compared to 123 million people between 65 and 99 years old. The incidence of diabetes increases by about 4.8% at the age of 55-64 years, and women (1.7%) suffer from diabetes more than men (1.4%). Therefore, the authors will create a program to determine the patient's diabetes. One approach is to use machine learning as a data mining classification technique. The author will do a classification comparison with the two methods, namely the KNN and CNN methods to provide the best results of the two methods for testing. So that the accuracy of the data from the diagnosis and photo images of the disease can be known to provide early treatment before the severity of the disease.
Co-Authors Pardede, Akim Manaor Hara Sihombing, Marto