Prediksi Stroke Menggunakan Extreme Gradient Boosting

Danang Triantoro Murdiansyah

Abstract


Stroke merupakan salah satu penyakit yang paling banyak menyebabkan disabilitas dan kematian pada orang dewasa di seluruh dunia. Salah satu hal yang penting terkait stroke adalah pengobatan dini, sehingga stroke tidak berkembang ke level yang parah pada seseorang. Oleh karena itu prediksistroke pada seseorang sebelum penyakit tersebut berkembang lebih jauh adalah sangat penting. Penelitian ini berisi prediksi stroke pada seseorang menggunakan algoritma berbasis machine learning, yaitu algoritma Extreme Gradient Boosting, disebut juga dengan XGBoost.Algoritma XGBoost dipilih karenamemiliki potensi kemampuan yang baik untuk melakukan prediksi (klasifikasi). XGBoost telah banyak digunakan oleh para peneliti untuk mencapai hasil yang bagus dalam memecahkan berbagai kasus menggunakan machine learning. Pada penelitian ini model machine learning yang dirancang dengan menggunakan XGBoost dibandingkan dengan model machine learning lain yang telah digunakan sebelumnya, yaitu model jenis Stacking, Random Forest, dan Majority Voting. Hasil pengujian menunjukkan XGBoost dapat mencapai performa yang baik dalam seluruh metrik evaluasi, termasuk akurasi yang mendapatkan nilai 95.4%, namun XGBoost pada penelitian ini performanya belum bisa mengungguli Stacking dan Random Forest, yang mana Stacking menempati performa terbaik dengan nilai akurasi 98%.

Keywords


Stroke; Prediksi; Klasifikasi; XGBoost; Extreme Gradient Boosting

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DOI: http://dx.doi.org/10.26798/jiko.v8i2.1295

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