Feature Weights Menggunakan Particle Swarm Optimization Untuk Sentiment Analysis Penilaian Kepuasan Pelanggan Makanan Kuliner

Oman Somantri, Dyah Apriliani

Abstract


Mendapatkan rekomendasi makanan kuliner dengan rasa enak dan pelayanan restoran yang terbaik merupakan hal yang paling diharapkan oleh setiap para penikmat kuliner, dan hal ini masih sangat terbatas. Sentiment analysis diterapkan sebagai solusi dengan menggunakan algoritma Support Vector Machine (SVM) karena dapat mengatasi permasalahan yang ada dan merupakan model terbaik saat ini untuk sentiment analysis. Feature weight dengan menggunakan Particle Swarm Optimization (PSO) diusulkan untuk dapat meningkatkan tingkat akurasi klasifikasi. Hasil eksperimen dengan model SVM+PSO dihasilkan sebuah peningkatan akurasi yang lebih baik, hal ini terlihat dari evaluasi yang dilakukan. Berdasarkan hasil yang didapatkan makan model SVM+PSO dapat meningkatkan tingkat akurasi klasifikasi sentiment analysis pada penilaian kepuasan pelanggan terhadap makanan kuliner.

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References


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