Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal SAINTEK
Linda Wahyu Setyoningsih
Unknown Affiliation
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : SAINTEK

 1 
Komparasi Algoritma Support Vector Machine (Svm) Dan Logistic Regression Menggunakan Metode Smote Untuk Klasifikasi Penyakit Diabetes Melitus Donny Maulana; Linda Wahyu Setyoningsih
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

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.
Co-Authors Donny Maulana