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primary topic: (IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08]

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08]
type: AcademicArticle
type: Article
abstract: This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce.
date: 2026-01-08
volume: 14
issue: 2
is part of: Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: (IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
subject: T Technology (General)

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: [2026-01-08] - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan (IJID-S3).pdf
has part: [2026-01-08] - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan (IJID-S3).pdf

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: 1. Cover.pdf
has part: 1. Cover.pdf

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: 2. Editorial Team.pdf
has part: 2. Editorial Team.pdf

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: 3. Daftar Isi.pdf
has part: 3. Daftar Isi.pdf

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: 4. Artikel.pdf
has part: 4. Artikel.pdf

(IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08] (Text)
type: Document
has file: 5. Turnitin - Similiarity Report (10%).pdf
has part: 5. Turnitin - Similiarity Report (10%).pdf

Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta
type: Organization

Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression
type: Collection
issn: 25497448

Firman Noor Hasan
type: Person
given name: Firman Noor
family name: Hasan

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primary topic: (IJID - S3) - Fathurrohman, Afandi, Wahyuningtyas, Nugroho, Hasan [2025-01-08]

T Technology (General)
type: Concept