PERBANDINGAN ALGORITMA BOOSTING UNTUK KLASIFIKASI LINGKUNGAN TEKTONIK GEOKIMIA VULKANIK
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Abstract
Penelitian ini bertujuan untuk menentukan model machine learning paling efektif untuk klasifikasi lingkungan tektonik (tectonic setting) berdasarkan komposisi geokimia. Menggunakan dataset dari database GEOROC, tiga algoritma gradient boosting XGBoost, LightGBM, dan CatBoost diuji melalui beberapa skenario, termasuk pembagian data 70:30 dan 80:20. Model dengan kinerja terbaik kemudian dioptimalkan menggunakan Grid Search Cross-Validation (GridSearchCV). Hasil menunjukkan bahwa model LightGBM, setelah melalui proses hyperparameter tuning pada skenario 80:20, mencapai performa tertinggi dengan akurasi 80,87%. Analisis kepentingan fitur (feature importance) lebih lanjut mengidentifikasi bahwa Al₂O₃ (Aluminium Oksida), Na₂O (Natrium Oksida), dan FeOT (Besi Oksida Total) merupakan tiga prediktor paling signifikan. Studi ini membuktikan bahwa LightGBM adalah pendekatan yang superior dan andal untuk tugas klasifikasi geokimia otomatis.
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