Analisis Perbandingan Algoritma XGBOOST dan BiLSTM Dalam Klasifikasi Sentimen Publik Terhadap Kebijakan Penempatan Dana Pemerintah di Bank
DOI:
https://doi.org/10.59696/prinsip.v4i2.237Keywords:
Analisis sentimen, XGBoost, BiLSTM, Klasifikasi Teks, Kebijakan Publik, Media SosialAbstract
Penempatan dana pemerintah di sektor perbankan merupakan salah satu kebijakan strategis yang bertujuan untuk menjaga stabilitas ekonomi dan meningkatkan likuiditas perbankan. Namun, kebijakan ini memunculkan beragam respons dari masyarakat yang tercermin dalam opini publik di media sosial. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja algoritma Extreme Gradient Boosting (XGBoost) dan Bidirectional Long Short-Term Memory (BiLSTM) dalam melakukan klasifikasi sentimen publik terhadap kebijakan tersebut. Data yang digunakan berasal dari platform media sosial yang dikumpulkan melalui teknik scraping, kemudian melalui tahap preprocessing yang meliputi tokenisasi, normalisasi, dan penghapusan noise. Selanjutnya, fitur diekstraksi menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk XGBoost dan word embedding untuk BiLSTM. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model BiLSTM memiliki kemampuan yang lebih baik dalam menangkap konteks kalimat sehingga menghasilkan akurasi yang lebih tinggi dibandingkan XGBoost. Namun, XGBoost menunjukkan keunggulan dalam hal efisiensi komputasi dan waktu pelatihan. Penelitian ini memberikan kontribusi dalam pemilihan model klasifikasi sentimen yang optimal untuk analisis opini publik berbasis data teks, khususnya dalam konteks kebijakan ekonomi pemerintah.
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