Prediksi Retweet Berdasarkan User-Based dan Content-Based Menggunakan Metode Ensemble Stacking
Abstract
Abstrak-Twitter merupakan salah satu social media yang sangat popular dan mudah digunakan untuk mendapatkan informasi secara cepat. Fitur Retweet merupakan salah satu alasan mengapa penyebaran informasi tersebut dapat tersebar dengan cepat. Retweet terjadi jika seorang follower men-tweet ulang tweet dari followee-nya. Pada penelitian ini dilakukan pemodelan untuk prediksi retweet berdasarkan feature user-based dan content-based dengan menggunakan metode Ensemble Stacking melalui proses K-fold Cross Validation. Ensemble Stacking ini dibentuk dengan 3 base-learner yaitu Random Forest, Gradient Boosting, dan Support Vector Machine(SVM). Sedangkan meta-learner yang digunakan adalah Support Vector Machine(SVM) Pemodelan ini menunjukan hasil terbaik ketika sudah dilakukan Imbalanced Class Handling menggunakan Teknik SMOTE dan K-fold Cross Validation dengan k=10. Hasil F1-score menunjukkan 86.46%. Dengan hasil demikian, bisa disimpulkan bahwa pemodelan yang dibentuk mampu meningkatkan hasil prediksi dari base-learnernya.
Kata kunci-twitter, retweet, ensemble stacking, k-fold cross validation, oversampling
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