PREDIKSI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINE (SVM)

• NOVI NIKMATUL KHASANAH
• 2024

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Novi Nikmatul Khasanah (19236057), Diabetes Disease Prediction Using Random
Forest Algorithm and Support Vector Machine (SVM)
Diabetes mellitus (DM) is a chronic degenerative disease caused by the body's
inability to produce or use insulin effectively, with hyperglycemia as the main
indicator. The prevalence of Diabetes mellitus in Indonesia is predicted to increase
significantly, ranking the country 4th in the world after the United States, India, and
China. This increase highlights the importance of early detection and accurate
classification. This study aims to compare the accuracy performance of two
classification algorithms, namely Random Forest and Support Vector Machine (SVM),
in predicting Diabetes mellitus. The dataset used consists of 100,000 records with class
0 (no diabetes) of 60,000 records and class 1 (diabetes) of 40,000 records, which are
divided into 90% training data and 10% testing data. This study found that Random
Forest has an accuracy of 97.47%, which is higher than SVM with an accuracy of
96.36%. This result shows that Random Forest is more effective in classification
strategy for Diabetes mellitus detection, by showing the superiority of Random Forest
in the accuracy performance metri.

• Diabetes prediction
• Random Forest
• SVM
• accuracy

https://repository.bsi.ac.id/repo/56641/PREDIKSI-PENYAKIT-DIABETES-MENGGUNAKAN-ALGORITMA-RANDOM-FOREST-DAN-SUPPORT-VECTOR-MACHINE-(SVM)
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