Analisis Kepuasan Pelanggan terhadap Beberapa Produk yang di Jual di E-Commerce Menggunakan Metode Naïve Bayes dan Logistic Regression: Penelitian

Java Diovanka Alam; Musyaffa Ramdhan; Muhammad Yuzakki Raja Rafael; Muhammad Faiz Hamka; Desmulyati Desmulyati; Imam Budiawan

doi:10.31004/jerkin.v4i3.4970

Authors

Java Diovanka Alam Universitas Bina Sarana Informatika
Musyaffa Ramdhan Universitas Bina Sarana Informatika
Muhammad Yuzakki Raja Rafael Universitas Bina Sarana Informatika
Muhammad Faiz Hamka Universitas Bina Sarana Informatika
Desmulyati Desmulyati Universitas Bina Sarana Informatika
Imam Budiawan Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.31004/jerkin.v4i3.4970

Keywords:

KIP-K, Machine Learning, Naive Bayes, Classification, Education

Abstract

Customer satisfaction is a crucial element that plays a significant role in the sustainability of businesses in the e-commerce sector. Reviews provided by consumers serve as an important source of information to assess how satisfied they are with the products they purchased. This study aims to evaluate customer satisfaction levels using product review data through two classification methods: Multinomial Naive Bayes and Logistic Regression. The data used comes from a real Indonesian-language dataset that includes review texts and buyer ratings. The research process consists of several stages, starting from text preprocessing, feature extraction using the TF-IDF method, satisfaction label grouping, model training, and evaluation using metrics such as accuracy, precision, recall, F1-score, and confusion matrix. The findings of this study indicate that both methods can predict customer satisfaction with competitive accuracy. Logistic Regression demonstrates more consistent results compared to Naive Bayes in the context of Indonesian-language text. These results can be utilized by e-commerce companies to monitor product quality and continuously improve services for consumers.

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