ANALISIS SENTIMEN TERHADAP PENGGUNA TRANSPORTASI ONLINE GOJEK DI INSTAGRAM MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

SAVERO, MUHAMMAD JUAN and Ibrahim, Ali (2025) ANALISIS SENTIMEN TERHADAP PENGGUNA TRANSPORTASI ONLINE GOJEK DI INSTAGRAM MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE. Undergraduate thesis, Universitas Sriwijaya.

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Abstract

The development of digital technology has changed the transportation industry, including online services such as Gojek. Understanding customer sentiment is key in improving user experience and designing more effective business strategies. This research analyzes Gojek user sentiment on Instagram using Support Vector Machine (SVM). Data is obtained through web scraping, then processed through text cleaning, tokenization, common word removal, and stemming. Features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF) before being classified with SVM. The results showed that the SVM model achieved 70.82% accuracy in classifying user sentiment. Most positive comments highlight the convenience and efficiency of the service, while negative comments are more related to high tariffs, application constraints, and less responsive customer service. These findings provide insights for Gojek to improve marketing strategies, optimize customer service, and adjust fare policies based on user feedback. In addition, this analysis can help in predicting real-time customer satisfaction trends through sentiment monitoring on social media. As a development step, this research recommends further exploration with deep learning and Aspect-Based Sentiment Analysis (ABSA) to improve accuracy and understand the service aspects that have the most influence on customer satisfaction.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, Support Vector Machine, Gojek, Instagram, Marketing Strategy, Customer Satisfaction Prediction
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Muhammad Juan Savero
Date Deposited:	09 Jul 2025 03:08
Last Modified:	09 Jul 2025 03:08
URI:	http://repository.unsri.ac.id/id/eprint/175913

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