Nafisah, Shofiya and , Diah Priyawati, S.T., M.Eng. (2026) Implementasi berbagai Jenis Hyperparameter Tuning dalam Algoritma Extreme Gradient Boosting untuk Dataset Stroke. Skripsi thesis, Universitas Muhammadiyah Surakarta.
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Abstract
Stroke was the second leading cause of death worldwide in 2019 and remains so in 2022. To prevent more serious complications, it is important to detect stroke immediately. This study aims to compare the performance of the XGBoost algorithm optimized using various hyperparameter tuning methods and determine the method that produces the best predictive performance and lower computational complexity with a stroke dataset. The XGBoost algorithm has the advantage of supporting the application of regularization to avoid overfitting and improve model generalization. The methods used to combine XGBoost include Grid Search CV, Random Search, and Bayesian Optimization. The test results show that Bayesian Optimization provides the most balanced application between performance and computational time efficiency.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | xgboost, hyperparameter tuning, klasifikasi, stroke |
| Subjects: | T Technology > Information Technology > Artificial Intelligence |
| Divisions: | Fakultas Komunikasi dan Informatika > S1 Teknik Informatika |
| Depositing User: | SHOFIYA NAFISAH |
| Date Deposited: | 26 Feb 2026 04:13 |
| Last Modified: | 26 Feb 2026 04:13 |
| URI: | http://eprints.ums.ac.id/id/eprint/143367 |
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