Analisis Sentimen Media Sosial Terhadap DPR RI: Perbandingan Akurasi Extra Trees Dan Random Forest Dengan Pendekatan Komputasi Hijau
DOI:
https://doi.org/10.26418/justin.v14i1.92556Keywords:
Sentiment AnalysisAbstract
Analisis sentimen terhadap DPR RI penting untuk memahami opini publik dan dampaknya terhadap persepsi masyarakat. Penelitian sebelumnya menggunakan metode seperti SVM, Naïve Bayes, dan Decision Tree dengan fokus utama pada akurasi, namun masih kurang mempertimbangkan efisiensi komputasi dan dampak lingkungan. Oleh karena itu, penelitian ini membandingkan performa Extra Trees Classifier dan Random Forest Classifier dalam analisis sentimen dari TikTok dan YouTube, tidak hanya dari segi akurasi tetapi juga efisiensi energi dengan pendekatan komputasi hijau. Proses mencakup pengumpulan data, preprocessing, transformasi TF-IDF, pemodelan, serta evaluasi berdasarkan akurasi, precision, recall, F1-score, dan efisiensi energi. Hasil menunjukkan Extra Trees memiliki akurasi lebih tinggi (92%) dibandingkan Random Forest (90,3%), tetapi Random Forest lebih hemat energi dengan konsumsi 0,0213 kWh dan waktu pemrosesan 8 menit dibandingkan Extra Trees yang membutuhkan 0,0248 kWh dan 22 menit. Dari perspektif komputasi hijau, Random Forest lebih ramah lingkungan karena menghasilkan emisi karbon lebih rendah. Pemilihan model bergantung pada prioritas: Extra Trees lebih akurat, sedangkan Random Forest lebih efisien dalam energi dan waktu pemrosesan. Penelitian ini diharapkan dapat memberikan wawasan bagi akademisi, pengembang teknologi, dan pembuat kebijakan dalam memilih model analisis sentimen yang optimal dan berkelanjutan.References
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