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All Journal Journal of Software Engineering and Information System (SEIS)
Indayana, Nurul Sakhila
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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PEMODELAN MACHINE LEARNING DENGAN ALGORITMA RANDOM FOREST DALAM MEMPREDIKSI RISIKO STROKE Arman, Doni; Indayana, Nurul Sakhila; Okmayura, Finanta; Anjani, Suci Putri; Dayani, Fitri Nur; Farhan, Muhammad; Faturrahman, Ariya
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 2 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i2.9590

Abstract

Stroke is one of the diseases that significantly affects health and economy, becoming the second most common cause of death in the world after coronary heart disease. Based on data from the World Health Organization (WHO), stroke is ranked second as the leading cause of death in the world after ischemic heart disease. In 2019, stroke was responsible for around 11% of total global deaths. One important way to reduce the death rate from stroke is to make prevention efforts through early prediction. Machine learning methods, especially Random Forest, are used in this study to predict the risk of stroke. The data used comes from a public dataset that includes age, gender, blood pressure, blood sugar, smoking status, and other medical history. The research process includes data pre-processing stages (data cleaning, outlier handling, and category coding), model training using the Random Forest algorithm, and model evaluation using a confusion matrix to evaluate accuracy, precision, recall, and F1 score. The evaluation results show an accuracy value of 97.55%, which indicates very good predictive performance so that this model has very good predictive performance.
Co-Authors Anjani, Suci Putri Arman, Doni Dayani, Fitri Nur Faturrahman, Ariya Finanta Okmayura Muhammad Farhan