Analisis Sentimen Ulasan Aplikasi Shopee Food Driver Di Playstore Menggunakan Algoritma Multinomial Naïve Bayes - Endah Wiji Lestari; Dahlia Dahlia; Nuraeni Herlinawati; Farhan Ramadhan (Universitas Bina Sarana Informatika, 2026)
Online food delivery services such as ShopeeFood are a practical solution for modern society in fulfilling culinary needs without having to leave the house. However, from the driver's perspective, using the ShopeeFood Driver application is not free from technical challenges and limited features that impact job satisfaction. This study aims to analyze user review sentiments for the ShopeeFood Driver application using the Multinomial Naïve Bayes algorithm. Data were collected through web scraping techniques from the Google Play Store and the data used were 2000 user reviews collected from May to June 2025. The analysis process involved the stages of text preprocessing, data labeling, TF-IDF weighting, classification, and model evaluation. The classification results showed an accuracy of (82.35%), precision (81.76%), recall (82.35%), and an F1-score of 83.36%, indicating excellent model performance in identifying sentiment. These findings provide important insights for application developers in responding to user reviews more effectively and strategically. This research is expected to contribute to the development of more relevant features and improving the quality of digital services in the online transportation sector.