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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika: Jurnal Pengembangan IT
Amarya, Theo Krisna
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Computer Science & IT

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Optimasi Preprocessing Model Random Forest untuk Prediksi Stroke Ristyawan, Aidina; Nugroho, Arie; Amarya, Theo Krisna
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.9587

Abstract

Stroke is a serious brain dysfunction disease that occurs when blood flow to the brain suddenly stops due to blockage or rupture of blood vessels. This disease is very dangerous and life-threatening. Early detection of stroke symptoms is very important to predict and prevent long-term impacts because it can save lives. Among the early detection efforts is using the Random Forest method in machine learning to predict stroke and successfully achieving 94% accuracy. This study proposes optimization of preprocessing / data preparation with interference of missing value handlers in the stroke prediction model using KNNImputer. The result is that the Random Forest method is able to improve the accuracy performance from initially having an accuracy value of 94% to between 95% - 96%. In addition, it also reduces the standard deviation or standard deviation of the Random Forest model. However, the strategy for the sequence of work between missing value handlers and categorical feature transformations does not affect the performance of the Random Forest model.
Optimization of Random Forest Algorithm Performance for Early Detection of Stroke Disease Using Medical Record Data Amarya, Theo Krisna; Ristyawan, Aidina; Firliana, Rina
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8424

Abstract

Stroke is a medical condition that occurs when blood flow to the brain is blocked, causing damage to brain tissue. Stroke is the second largest cause of death and disability in the world, this disease can affect all ages and is influenced by various risk aspects, such as unhealthy lifestyles, high blood pressure, high blood sugar levels, and other risks. It is very important to detect stroke in patients as soon as possible to prevent it. This study proposes the optimization of the performance of the Random Forest algorithm as an early detection model for stroke by utilizing a hybrid sampling method called SMOTETomek and also conducting several experiments on the parameter settings of the Random Forest algorithm. The results of this study show an increase compared to the previous one which had an accuracy was 94% with a standard deviation of 2%, In this study, it managed to reach accuracy of 96% with a standard deviation of 0% with a ROC curve (AUC) value of 0.96 or 96%. The algorithm that has 96% accuracy in the discussion is Random Forest Algorithm as estimator of AdaBoost.
Co-Authors Achmad, Ridho Aidina Ristyawan, Aidina Andy G, Asye Candra Arie Nugroho, Arie Erna Daniati Galuh, Asye Candra Andy I, Ridho Achmad Putri, Fitria Dessela Rina Firliana Sahra, Maha Shelin