Text Mining Menggunakan Naive Bayes Berbasis Particle Swarm Optimization Untuk Sentiment Restaurant Jurnal Text Mining Rizki Aulianita

Tanggal

2021-06-19

Penulis

Abstraksi

Berbagai website dengan hasil review dan ulasan memudahkan kita dalam memenentukan

sebuah keputusan. Namun keputusan tersebut belumlah maksimal dan akurat. Seperti review

makanan di yelp.com. Pengguna cukup banyak melihat review pada web tersebut sebelum

memutuskan untuk memesan makanan. Permasalahan disini adalah jika hasil review terbukti

kurang objektif, maka hasil keputusan menjadi tidak akurat. Tujuan dari penelitian ini untuk

membantu para pengguna review untuk menghasilkan sebuah keputusan yang optimal. Naïve

Bayes terbukti sebagai slah satu metode klasifikasi text yang menghasilkan akurasi tinggi.

Sedangkan Particle Swarm Optimization dikenal sebagai algoritma optimasi yang baik untuk

penyelesaian masalah berdasarkan parameter proses yang ada.  Pada penelitan ini akan

digunakan metode Naïve Bayes yang dilakukan ujicoba menggunakan Particle Swarm

Optimization untuk pembobotan sehingga hasil akurasinya lebih tinggi. Berdasarkan hasil

pengolahan data maka dihasilkan nilai akurasi Naïve Bayes sebesar 81.00% dan 83.80% adalah

hasil pengolahan akurasi untuk Naïve Bayes Berbasis Particle Swarm Optimization (PSO).

Kesimpulan pada percobaan metode Naïve Bayes tersebut yaitu bahwa PSO dapat

meningkatkan nilai optimasi dari sebuah algoritma Naïve Bayes sehingga mampu diterapkan

sebagai solusi untuk pemecahan masalah di atas.


Kata Kunci: Text mining; Naïve Bayes; Particle Swarm Optimization

URI
https://jurnal.univbinainsan.ac.id/index.php/jutim/article/view/1300/724

Bidang ilmu
Sistem Informasi

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