ANALISIS PERBANDINGAN RATING-BASED DAN INSET LEXICON-BASED DALAM PROSES LABELING ANALISIS SENTIMEN (STUDI KASUS: ULASAN APLIKASI GOBIZ DI GOOGLE PLAY STORE)

FIRDA, HILIAH and Suarli, Pacu Putra (2025) ANALISIS PERBANDINGAN RATING-BASED DAN INSET LEXICON-BASED DALAM PROSES LABELING ANALISIS SENTIMEN (STUDI KASUS: ULASAN APLIKASI GOBIZ DI GOOGLE PLAY STORE). Undergraduate thesis, Sriwijaya University.

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Abstract

Digital transformation has had a significant impact on various sectors, including Micro, Small and Medium Enterprises (MSMEs). The GoBiz application, as Gojek's business partner platform for GoFood services, plays an important role in supporting digital transformation in the MSME sector, so it is necessary to understand users' views on this application. To analyze user perceptions of this application, sentiment analysis research was conducted using 5,000 GoBiz user reviews from the Google Play Store. This research compares two labeling methods, namely Rating-based and Inset Lexicon-based, then evaluated with the Support Vector Machine (SVM) algorithm. The analysis process includes data selection, text preprocessing, data transformation using TF-IDF, SVM application with 10-fold cross-validation, and visualization of results through WordCloud. The test results show that Rating-based labeling gets 87% accuracy, 86.7% precision, 87.1% recall, and 86.8% f1-score. Meanwhile, Inset Lexicon-based labeling achieved 89.7% accuracy, 89% precision, 89.8% recall, and 89.3% f1-score. These findings show that the combination of Inset Lexicon-based labeling method and SVM algorithm is more effective in classifying the sentiment of user reviews and provides a more accurate understanding of users' perceptions of the GoBiz app.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Inset lexicon-based, Rating-based, Aplikasi GoBiz, Support Vector Machine
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Hiliah Firda
Date Deposited:	22 Jan 2025 06:24
Last Modified:	22 Jan 2025 06:24
URI:	http://repository.unsri.ac.id/id/eprint/166015

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