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All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Teknik Komputer AMIK BSI Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics Journal of Students‘ Research in Computer Science (JSRCS) Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Reputasi: Jurnal Rekayasa Perangkat Lunak Indonesian Journal Computer Science (ijcs) Jurnal Semesta Ilmu Manajemen dan Ekonomi Bianglala Informatika
Dahlia Dahlia
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences Other

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Analisis Sentimen Ulasan Aplikasi Shopee Food Driver Di Playstore Menggunakan Algoritma Multinomial Naïve Bayes Endah Wiji Lestari; Dahlia Dahlia; Nuraeni Herlinawati; Farhan Ramadhan
Bianglala Informatika Vol. 14 No. 1 (2026): Maret 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v14i1.12410

Abstract

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.
Co-Authors Anggi Hardiansyah Atang Saepudin Devi Pebriana Alawiyah Eka Fitriani Endah Wiji Lestari Endah Wiji Lestari Lestari Endang Puji Astuti Farhan Ramadhan Ganda Wijaya Leliyanah Leliyanah Lia Mazia, Lia Lutfi Hidayat Muhamad Fajar Assidiq Assidiq Nuraeni Herlinawati Nurlina Sari Riska Aryanti Saridawati Shasa Puja Amritha Siti Juleha Suci Indah Permatasari Umi Khultsum Umniati Afifah Verry Riyanto