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Amir Mujahid
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Implementasi Data Mining untuk Prediksi Penyakit Diabetes pada Anak Menggunakan Algoritma K-Nearest Neighbor Arif Siswandi; Amir Mujahid
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Diabetes Mellitus (DM) is defined as a chronic disease or metabolic disorder with multiple etiologies characterized by high blood sugar levels accompanied by disturbances in carbohydrate, lipid, and protein metabolism as a result of insufficient insulin function. According to the International Diabetes Federation (IDF), the number of Type 1 diabetes patients was collected based on age in 2022. Globally, the estimated number of Type 1 diabetes patients reached 8.75 million people in 2022. Among them, 1.52 million people, or 17% of the total, were below 20 years of age. This age category includes children, adolescents, or young adults. The objective of this study is to implement data mining to improve the effectiveness of decision-making in the treatment of diabetes. In this research, the prediction process was conducted on a diabetes dataset using the K-Nearest Neighbor algorithm. The testing results on the dataset yielded an accuracy of 97%, precision of 100%, and recall of 95%. These results demonstrate that the K-NN algorithm provides excellent outcomes in predicting diabetes.
Co-Authors Arif Siswandi