Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Mutiara: Jurnal Penelitian dan Karya Ilmiah Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Rizki Agung Ramadhan
Universitas Negeri Medan
Religion Humanities Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Komparasi Algoritma Machine Learning dalam Memprediksi Penyakit Gagal Ginjal Wahyu Ardiantito S; Rizki Agung Ramadhan; Richard Steven Immanuel S
Mutiara : Jurnal Penelitian dan Karya Ilmiah Vol. 1 No. 6 (2023): Desember: Mutiara : Jurnal Penelitian dan Karya Ilmiah
Publisher : STAI YPIQ BAUBAU, SULAWESI TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59059/mutiara.v1i6.781

Abstract

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.
Komparasi Algoritma KNN dan SVM dalam Memprediksi Penyakit Stroke Rahel Lina Simanjuntak; Rizki Agung Ramadhan; Theresia Romauli Siagian; Vina Anggriani
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 3 No. 3 (2023): November : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v3i3.2474

Abstract

Stroke is a serious medical condition that affects many people around the world. The ability to predict a person's stroke risk can help in effective prevention, treatment and care. In this study, a comparison between the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms was conducted to predict stroke risk. The KNN algorithm is a method that searches for the nearest neighbors among the data points to be predicted and assigns the most common label among its neighbors. Experimental results show that both KNN and SVM can provide fairly accurate stroke predictions. However, from an operational point of view, SVM consistently performed better than KNN in terms of accuracy and precision. This research provides insight into the differences between KNN and SVM algorithms in the context of stroke prediction. The results can provide guidance for researchers and practitioners in choosing the right algorithm to predict stroke risk based on the characteristics of the available datasets.
Co-Authors Rahel Lina Simanjuntak Richard Steven Immanuel S Theresia Romauli Siagian Vina Anggriani Wahyu Ardiantito S