KLASIFIKASI SENTIMEN ANALISIS APLIKASI JAKET BOAT PADA ULASAN PLAYSTORE MENGGUNAKAN ALGORITMA NAÏVE BAYES
Abstract
Kemajuan teknologi informasi telah mendorong penggunaan aplikasi transportasi digital, termasuk Jaket Boat. Seiring bertambahnya user, review di Google Play Store menjadi sumber data penting bagi pengembang dalam meningkatkan layanan. Namun, analisis manual terhadap ulasan dalam jumlah besar kurang efisien. Tujuan penelitian ini adalah mengklasifikasikan review pengguna dengan algoritma Naive Bayes. Data dikumpulkan melalui web scraping dan diproses dengan Natural Language Processing (NLP). Proses pre-processing melibatkan pembersihan teks, case folding, tokenisasi, stopword removal, dan stemming. Data yang telah diproses kemudian dikonversi ke bentuk numerik dengan metode TF-IDF (Term Frequency-Inverse Document Frequency) sebelum diklasifikasikan menggunakan model Naive Bayes. Evaluasi model dilakukan dengan menggunakan metrik akurasi, recall, precision, dan F1-score. Penelitian ini menunjukan hasil bahwa algoritma Naive Bayes mampu mengklasifikasikan sentimen ulasan dan akurasi yang didapat yang cukup tinggi. Dengan demikian, penelitian ini dapat membantu pengembang aplikasi dalam memahami permasalahan yang dialami pengguna dan meningkatkan kualitas layanan berdasarkan hasil sentimen analisis.
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