Komparasi Algoritma Support Vector Machine (Svm) Dan Logistic Regression Menggunakan Metode Smote Untuk Klasifikasi Penyakit Diabetes Melitus

Donny Maulana
Linda Wahyu Setyoningsih

SAINTEK
Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024

Diabetes Mellitus is a metabolic disease characterized by increased blood sugar levels due to impaired insulin production, insulin action or both. Utilization of data mining is used in the health sector and also in the technology industry. Processing data so that it can be used as a source of fresh knowledge and information is one of the many advantages of data mining. In this study, two algorithms are used, namely Support Vector Machine and Logistic Regression. Both of these algorithms use the SMOTE (Synthetic Minority Over-sampling Technique) method to overcome data imbalances. Based on tests carried out using the Confusion Matrix, the results of measuring the performance values of Accuracy, Precision, Recall and f1-score using the Support Vector Machine (SVM) algorithm and Logistic Regression using the SMOTE method, it can be concluded that the best algorithm in the classification of diabetes mellitus is the Support Vector Machine (SVM) algorithm with an Accuracy value of 0.81, Precision of 0.80, Recall of 0.82 and f1-score of 0.81.