Pengaruh Klasifikasi Sentimen Pada Ulasan Produk Amazon Berbasis Rekayasa Fitur dan K-Nearest Negihbor
DOI:
https://doi.org/10.25126/jtiik.20241117376Kata Kunci:
Ulasan produk Amazon, analisis sentimen, Textblob Library, kombinasi fitur, K-Nearest NeighborAbstrak
Ulasan online menjadi faktor penting yang mendorong konsumen untuk membeli barang di e-commerce. Dalam e-commerce, ulasan pelanggan sebelumnya dapat membantu pembeli membuat keputusan yang lebih baik dengan memberikan informasi tentang kualitas produk, kekuatan dan kelemahan, perilaku penjual, harga, dan waktu pengiriman. Namun, keberadaan ulasan palsu menimbulkan tantangan dalam menilai sentimen yang diungkapkan oleh pelanggan asli secara benar. Dalam penelitian ini, berfokus pada analisis sentimen dan bertujuan untuk mengeksplorasi peran sentimen dalam ulasan produk Amazon. Penelitian ini menggunakan kombinasi fitur dari konten ulasan dengan menerapkan klasifikasi K-Nearest Neighbor untuk mengklasifikasikan polaritas sentimen ulasan secara akurat. Dalam mengekstrak skor polaritas dari ulasan, penelitian ini menggunakan pendekatan analisis sentimen berbasis leksikon yaitu Textblob Library dan menetapkan label sentimen dari ulasan produk. Hasil dari pemodelan yang diusulkan mencapai tingkat akurasi sebesar 83% yang menunjukkan keefektifan pemodelan yang diusulkan dalam analisis sentimen. Hasil dari penelitian ini dapat membantu konsumen dalam membuat keputusan pembelian dan membantu penjual dalam meningkatkan nilai produk dan layanan mereka berdasarkan feedback yang diberikan oleh pelanggan.
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