Skripsi

PERBANDINGAN METODE NAÏVE BAYES, SUPPORT VECTOR MACHINE(SVM) DAN LONG SHORT-TERM MEMORY(LSTM) UNTUK ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI SAYURBOX DI GOOGLE PLAY STORE

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Penilaian

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Sayurbox is an e-commerce application that provides fresh products directly from farmers to consumers. To understand user perception namely how users express their evaluations of the services and experiences when using the application this study conducts sentiment analysis on user reviews of Sayurbox from the Google Play Store using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM). Feature extraction is performed using TF-IDF for Naïve Bayes and SVM, and pre-trained Word2Vec for LSTM. The dataset consists of thousands of user reviews written in Indonesian. Evaluation results show that the SVM model with a linear kernel and C=100 achieves the best accuracy at 91.42%, followed by LSTM with 85.92%, and Naïve Bayes with 81.63%. These findings indicate that SVM is the most effective method for classifying the sentiment of Sayurbox user reviews. Keywords: Sentiment Analysis, Naïve Bayes, Support Vector Machine, LSTM, TF-IDF, Word2Vec, Sayurbox.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003802	T177243	T1772432025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1772432025

Publisher

Indralaya : Prodi Manajemen Informatika, Fakultas Ilmu komputer Universitas Sriwijaya., 2025

Collation

xvi, 136 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Manajemen Informatika

Specific Detail Info

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Statement of Responsibility

MI

Other version/related

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