Stacking Ensemble Model berbasis SVM, Random Forest, dan XGBoost untuk Klasifikasi Emosi dari Sinyal EEG
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Abstract
Klasifikasi emosi berbasis sinyal EEG (Electroencephalography) merupakan bidang penting dalam pemrosesan sinyal biomedis karena perannya dalam pengambilan keputusan, interaksi sosial, dan evaluasi kondisi psikologis. Namun, tingginya dimensi data, kompleksitas pola, serta kerentanan terhadap noise menjadi tantangan utama dalam analisis EEG. Penelitian ini menerapkan Stacking Ensemble Model yang mengombinasikan Support Vector Machine (SVM), Random Forest (RF), dan XGBoost (XGB) dengan Logistic Regression sebagai meta-learner untuk meningkatkan akurasi dan stabilitas prediksi emosi. Dataset yang digunakan berasal dari Kaggle dengan 2132 data dan 2549 fitur, mencakup tiga kelas emosi: negatif, netral, dan positif. Tahapan penelitian meliputi pengecekan missing value, normalisasi menggunakan StandardScaler, encoding label, serta pembagian data menjadi data latih dan uji. Hasil eksperimen menunjukkan akurasi model tunggal yang tinggi, yaitu SVM 94,8%, RF 98,6%, dan XGB 99,5%, sedangkan model stacking mencapai akurasi 99,5% dengan nilai precision, recall, dan f1-score mendekati 1,00. Hasil ini menunjukkan bahwa stacking ensemble mampu meningkatkan keandalan klasifikasi emosi berbasis EEG dan berpotensi diterapkan pada HCI, pemantauan kesehatan mental, serta sistem pengenalan emosi real-time.
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