KLASIFIKASI SENTIMEN ULASAN PRODUK SUNSCREEN PADA FEMALE DAILY MENGGUNAKAN METODE NAÃVE BAYES
DOI:
https://doi.org/10.31000/jika.v9i3.14461Abstrak
Perkembangan teknologi internet mendorong konsumen untuk lebih aktif membagikan pengalamannya melalui ulasan, salah satunya pada platform Female Daily. Ulasan produk tabir surya dari pengguna memberikan wawasan sentimen yang berharga. Namun, menganalisis data dalam skala besar secara manual tidaklah efektif. Studi ini bertujuan untuk menganalisis sentimen ulasan produk tabir surya menggunakan algoritma Naïve Bayes Classifier. Data dikumpulkan melalui web scraping, diikuti oleh pra-pemrosesan teks dan pelabelan sentimen menurut skor peringkat menjadi tiga kategori: sangat cocok, cocok, dan tidak cocok. Distribusi dalam distribusi kelas diatasi menggunakan teknik oversampling, dan data kemudian diubah menjadi format numerik dengan TF-IDF. Model dibor dengan algoritma Multinomial Naïve Bayes dan dievaluasi menggunakan matriks konfusi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 83,33%, dengan presisi 0,84, recall 0,83, dan skor F1-score 0,83. Visualisasi WordCloud digunakan untuk mengidentifikasi kata-kata dominan di setiap kategori sentimen. Temuan ini menunjukkan efektivitas algoritma Naïve Bayes dalam mengklasifikasikan opini konsumen dengan baik dan menyoroti potensinya untuk mengembangkan sistem rekomendasi produk berbasis ulasan, serta untuk memahami persepsi konsumen dalam industri kecantikan.
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