PERBANDINGAN METODE MACHINE LEARNING UNTUK SENTIMEN ANALISIS REVIEW PENJUALAN PRODUK

Muhammad Reza, Ardiansyah Dores, Sitti Nurbaya Ambo, Popy Meilina

Abstract


Toko online atau e-commerce menurut Moossa Giant dan Samuel Ikate adalah operasi bisnis yang dilakukan secara dunia maya atau online. Pada saat pandemi di tahun 2020 sampai Mei 2023 kegiatan masyarakat dibatasi masyarakat membeli barang di toko online agar tidak terkena virus corona, akan tetapi calon konsumen membeli barang melihat ulasan pada barang yang ingin mereka tuju apakah barang yang mereka beli bagus atau pengirimannya lambat. Salah satu toko online yang memiliki ulasan komentar pelanggan yang sudah membeli barang sebagai petunjuk calon konsumen untuk membeli atau tidak, maka peneliti melakukan analisis sentimen terhadap ulasan konsumen membeli barang di produk elektronik dan produk pakaian, data ulasan konsumen dikumpulkan dari data elektronik sebanyak 925 data, dan data pakaian sebanyak 1575 data, setelah mengumpulkan data dilakukan preprocessing, pembobotan kata, pemodelan dengan supervised learning, yaitu naives bayes, decision tree, k nearest neighbor,  melakukan berbagai skenario dengan pembagian data dari 10% data uji 90% data latih, 20% data uji 80% data latih, 30% data uji 70% data latih, 40% data uji 60% data latih. Hasil terbaik pengujian dengan data pakaian menggunakan decision tree dengan split data 10% data uji dan 90% data latih menghasilkan hasil akurasi 66%, recall 66%, dan precision 65%, hasil terbaik pengujian dengan data elektronik menggunakan decision tree dengan split data 40% data uji dan 60% data latih menghasilkan hasil akurasi 66%, recall 66%, dan precision 65%


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DOI: https://doi.org/10.24853/justit.15.2.362%20–%20372

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