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Yessy Yessy
Universitas Prima Indonesia
Computer Science & IT

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APPLICATION OF DATA MINING TO IDENTIFY DIABETES MELLITUS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM AND KNN Windania Purba; Yessy Yessy; Riski Nofarianus Gulo
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Damage to the performance of human organs is very detrimental Received Revised Accepted And is the source of the most problems at this time. One of the diseases that is the number one killer in the world is diabetes mellitus. Diabetes mellitus is a metabolic disease characterized by hyperglycemia caused by and obstacle in insulin secretion from insulin action or both. Diabetes mellitus is divided into several types, type 1 diabetes mellitus generally gives rise to indications before the patient is 30 years old. Although in fact the indications of the disease can arise at any time. This study aims to apply the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) method to identify diabetes mellitus and calculate the comparison value of the accuracy of the two algorithms. From the results of this study. It can be concluded that the Support Vector Machine (SVM) algorithm produces an accuracy value of 76% while the accuracy value of the K-Nearest Neighbor (KNN) algorithm is 75%
Co-Authors Riski Nofarianus Gulo Windania Purba