Peningkatan Performa Model Gradient Boosting dalam Klasifikasi Stroke Melalui Optimasi Grid Search

Susi Handayani; Fajrizal; Taslim; Dafwen Toresa; Syahril

doi:10.37859/jf.v14i3.7893

Susi Handayani Universitas Lancang Kuning
Fajrizal Universitas Lancang Kuning
Taslim Universitas Lancang Kuning
Dafwen Toresa Universitas Lancang Kuning
Syahril Universitas Muhammadiyah Riau

https://doi.org/10.37859/jf.v14i3.7893

Keywords: hyperparamater, optimasi, grid search, stroke, klasifikasi

Abstract

Studi ini menyelidiki pengaruh optimasi hyperparameter dengan grid search pada model XGBoost dan LightGBM dalam klasifikasi stroke. Hasil penelitian menunjukkan bahwa optimasi parameter secara signifikan meningkatkan performa kedua model, terutama dalam akurasi, precision, F1 Score, dan ROC-AUC Score. Pada model XGBoost, peningkatan terutama terlihat akurasi dan precision, sementara LightGBM menunjukkan peningkatan merata di semua metrik evaluasi. Temuan ini menggarisbawahi pentingnya optimasi hyperparameter dalam membangun model klasifikasi yang efektif untuk memprediksi risiko stroke dengan lebih akurat dan dapat diandalkan. Penemuan ini dapat berkontribusi dalam pemahaman lebih lanjut tentang faktor-faktor yang mempengaruhi stroke serta mendukung penanganan yang lebih tepat dan efektif dalam praktik klinis

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