Pemilihan Fitur Pada Analisis Sentimen Review Travel Online Menggunakan Algoritma Naïve Bayes Dalam Penerapan Mutual Information Dan Particle Swarm Optimization (PSO)
Sari
Pertumbuhan pesat dalam aplikasi Internet di bidang pariwisata telah menyebabkan pembesaran ulasan pribadi untuk informasi terkait perjalanan di Web. Di antara kategori utama produk dan layanan, perjalanan memiliki persentase tertinggi sebelum dilakukan pembelian secara online untuk melihat bahwa 73% dari wisatawan melakukan pencarian online sebelum membuat keputusan perjalanan mereka. Algoritma Naïve Bayes dengan mutual information dan partikel swarm optimization sebagai fitur pilihan dan algoritma Naïve Bayes tanpa fitur seleksi, dapat diterapkan pada prediksi travel review online. Dari pengolahan data yang telah dilakukan, menggabungkan metode pemilihan fitur, filter menggunakan mutual information dan wrapper menggunakan partikel swarm optimization. Keduanya menghasilkan akurasi klasifikasi yang baik. Hasil prediksi dari peninjauan perjalanan online dengan algoritma Naïve Bayes tanpa fitur seleksi memiliki akurasi 81,50% dengan nilai AUC 0,500, sedangkan hasil prediksi dari travel review online menggunakan algoritma naïve Bayes dengan mutual information dan partikel swarm optimization sebagai pemilihan fitur memiliki akurasi 93,50% dengan nilai AUC 0,965. Peningkatan akurasi data 12% diperoleh dari 200 ulasan. Jadi, travel review prediksi secara online menggunakan algoritma Naïve Bayes dan algoritme seleksi fitur lebih unggul daripada menggunakan naïve bayes tanpa pemilihan fitur.
Kata kunci: analisis sentimen, klasifikasi teks, naïve bayes
Abstract
The rapid growth in Internet applications in the field of tourism has led to a large number of personal reviews for travel-related information on the Web. Among the major categories of products and services, travel has the highest percentage of pre-purchase online looking at which 73% of the tourists do a search online before making their travel decisions. Naïve Bayes algorithm with mutual information and particle swarm optimization as feature selection and naïve Bayes algorithm without feature selection, can be applied to the prediction travel review online. Of data processing that has been done, combining feature selection method, the filter uses mutual information and wrapper using particle swarm optimization. Both produce good classification accuracy. Prediction results of a review of travel online with algorithms naïve Bayes without feature selection has an accuracy of 81.50% with a value of AUC 0.500, while the prediction results of the reviews online travel using an algorithm naïve Bayes with mutual information and particle swarm optimization as feature selection has an accuracy of 93.50% with a value of AUC 0.965. Improved accuracy of 12% data was obtained from 200 reviews. So the prediction travel review online using naïve Bayes algorithm and feature selection algorithms are superior to using naïve bayes without feature selection.
Keywords: sentiment analysis, text classification, naïve bayes
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DOI: https://doi.org/10.31294/ijcit.v3i1.3763
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