Komparasi Algoritma Machine Learning dalam Memprediksi Penyakit Gagal Ginjal

Wahyu Ardiantito S (Universitas Negeri Medan)
Rizki Agung Ramadhan (Universitas Negeri Medan)
Richard Steven Immanuel S (Universitas Negeri Medan)

Mutiara: Jurnal Penelitian dan Karya Ilmiah
Vol. 1 No. 6 (2023): Desember: Mutiara : Jurnal Penelitian dan Karya Ilmiah

Chronic Kidney Disease (CKD) is a serious health problem, with significant impact on patients' quality of life and healthcare costs. In an effort to improve early diagnosis, a comparison was made between several Machine Learning algorithms used for analysis of patient clinical data. This clinical data contains the medical history or health records of patients. The Machine Learning algorithms used in this study include K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression. By searching for the best algorithm through the calculation of Accuracy, Precision, and Recall with comparasion when using SMOTE (Synthetic Minority Oversampling) to balancing the class attribute.