Analisis Sentimen Ulasan Pelanggan menggunakan Algoritma Naive Bayes dan Logistic Regression
DOI:
https://doi.org/10.22441/jitkom.v9i2.003Keywords:
Sentiment, Review, Customer, Naive Bayes, Logistic RegressionAbstract
Ulasan pelanggan dapat digunakan untuk menggali preferensi konsumen dan memberikan panduan untuk perbaikan produk. Dengan ketersediaan data ulasan pelanggan yang melimpah, penting untuk mendapatkan wawasan yang berharga dari data ini guna meningkatkan kepuasan pelanggan. Penelitian ini memberikan wawasan berharga bagi produsen untuk memahami kepuasan pelanggan, meningkatkan kualitas produk, dan mengambil tindakan yang sesuai berdasarkan sentimen yang diungkapkan dalam ulasan. Metode yang diterapkan dalam penelitian ini dengan penggunaan Naive Bayes dan Logistic Regression sebagai algoritma klasifikasi sentimen. Dengan penerapan metode Term Frequency-Inverse Document Frequency, dilakukan ekstraksi fitur untuk mengidentifikasi kata-kata yang memiliki tingkat penting yang tinggi dalam ulasan tersebut. Dalam penelitian ini, ditemukan bahwa setelah dilakukan balancing data dengan menggunakan metode Synthetic Minority Over-sampling Technique, akurasi dari kedua metode, yaitu Naive Bayes dan Logistic Regression, mengalami peningkatan yang sebelum dilakukan balancing, metode Naive Bayes mencapai akurasi sebesar 87,14%, yang meningkat menjadi 92,31 setelah dilakukan balancing data. Sementara itu, Logistic Regression mencapai akurasi sebesar 93,77%, yang meningkat menjadi 94,56% setelah dilakukan balancing data. Hasil penelitian ini menunjukkan bahwa kedua metode, Naive Bayes dan Logistic Regression, efektif dalam mengklasifikasikan ulasan pelanggan ke dalam kategori sentimen positif dan negatif. Penelitian ini memberikan wawasan berharga bagi produsen es krim Ben & Jerry dalam memahami persepsi pelanggan terhadap produk mereka dan mengidentifikasi area-area yang perlu diperbaiki untuk meningkatkan kualitas produk dan kepuasan pelanggan.References
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