Perbandingan Prediksi Harga Closing Saham BCA Menggunakan ADABoost, XGBoost, CATBoost
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Penelitian ini membandingkan kinerja tiga algoritma boosting, yaitu AdaBoost, XGBoost, dan CatBoost, dalam memprediksi harga penutupan saham Bank Central Asia (BBCA). Tujuan dari penelitian ini adalah untuk menentukan algoritma mana yang memberikan model paling akurat dan efisien dalam hal akurasi prediksi, waktu komputasi, dan pemanfaatan sumber daya. Dataset yang digunakan terdiri dari data historis saham BCA, termasuk fitur seperti opening price, high, low, volume, dan previous closing prices. Metodologi penelitian meliputi data preprocessing, feature selection, pembagian data menjadi training set dan testing set, serta pelatihan model menggunakan masing-masing algoritma dengan hyperparameter tuning. Hasil eksperimen menunjukkan bahwa AdaBoost mencapai kinerja keseluruhan terbaik, memberikan akurasi prediksi tertinggi dan hasil yang stabil dengan waktu komputasi yang relatif lebih rendah dibandingkan dengan XGBoost dan CatBoost.
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