Analisis Sentimen Review Pelanggan Lazada dengan Sastrawi Stemmer dan SVM-PSO untuk Memahami Respon Pengguna
DOI:
https://doi.org/10.32664/j-intech.v12i02.1450Kata Kunci:
analisis sentiment, sastrawi stemmer, SVM-PSOAbstrak
Di era digital, analisis sentimen memainkan peran strategis dalam memahami persepsi pelanggan terhadap produk dan layanan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pelanggan pada platform Lazada melalui penerapan teknik pemrosesan teks dan algoritma machine learning. Data diambil dari ulasan produk pada platform tersebut, yang kemudian melalui tahap praproses, meliputi tokenisasi, penghapusan stopword, dan stemming menggunakan algoritma Sastrawi. Selanjutnya, klasifikasi sentimen dilakukan dengan menggunakan Support Vector Machine yang dioptimalkan melalui metode Particle Swarm Optimization (PSO). Hasil penelitian menunjukkan bahwa kombinasi metode Sastrawi stemmer dan SVM-PSO mampu mencapai akurasi yang signifikan, yaitu sebesar 90,57%, mengalami peningkatan sebesar 6,24% dibandingkan penelitian sebelumnya. Temuan ini memberikan wawasan yang mendalam mengenai persepsi pelanggan, serta menawarkan panduan berharga bagi pengambil keputusan di Lazada dalam meningkatkan kualitas layanan dan kepuasan pelanggan. Studi ini juga menggarisbawahi pentingnya penerapan teknik Natural Language Processing dan algoritma machine learning dalam analisis sentimen pada platform e-commerce, yang terbukti mampu menghasilkan keluaran yang lebih akurat.
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