Analisis Sentimen Ulasan Pengguna Aplikasi Bibit Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbors (KNN)
Keywords:
Analisis Sentimen, Aplikasi Bibit, Naïve Bayes, KNNAbstract
Perkembangan aplikasi investasi digital seperti Bibit menuntut pemahaman mendalam terhadap persepsi pengguna. Penelitian ini menganalisis sentimen ulasan pengguna aplikasi Bibit di Google Play Store menggunakan algoritma Naïve Bayes dan K-Nearest Neighbors (KNN). Sebanyak 2.586 ulasan dikumpulkan, kemudian diproses melalui pelabelan data, praproses teks, pemberian bobot menggunakan TF-IDF, dan klasifikasi dengan rasio data latih-uji 60:40, 70:30, 80:20, dan 90:10. Hasil penelitian menunjukkan bahwa sentimen positif mendominasi dengan persentase 74,2%, sedangkan sentimen negatif sebesar 25,8%. Naïve Bayes unggul dengan akurasi tertinggi 89,70% pada rasio 90:10, dengan presisi dan recall yang seimbang serta stabilitas yang lebih baik dibandingkan KNN yang mencapai akurasi tertinggi 88,84%, tetapi fluktuatif. Temuan ini merekomendasikan Naïve Bayes sebagai algoritma yang konsisten untuk analisis sentimen ulasan aplikasi investasi. Hasil penelitian ini dapat menjadi referensi berbasis data bagi calon investor dalam pengambilan keputusan.
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