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Sufiyandi, Ilham
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Computer Science & IT

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Diabetes Mellitus Disease Analysis using Support Vector Machines and K-Nearest Neighbor Methods Nusantara Habibi, Ahmad Rizky; Sufiyandi, Ilham; Murni, Murni; Jayed, A K M; Nakib, Arman Mohammad; Syukur, Abdul; Furizal, Furizal
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.22-27.2025

Abstract

Diabetes Mellitus (DM) is a chronic disease characterized by high blood sugar levels and can cause various serious complications if not treated properly. This study aims to analyze the effectiveness of Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) methods in classifying diabetes mellitus patient data. The methodology used includes collecting diabetes datasets, preprocessing data, and applying SVM and KNN algorithms to perform classification. The performance of both methods is analyzed using evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the SVM method provides more optimal performance in classifying diabetes data compared to KNN, with higher accuracy and lower error rate. This finding indicates that SVM is more suitable for early detection of diabetes mellitus in the dataset used in this study.
Co-Authors Abdul Syukur Furizal, Furizal Jayed, A K M Murni Murni Nakib, Arman Mohammad Nusantara Habibi, Ahmad Rizky