ANALISIS SENTIMEN PADA OPINI PUBLIK TENTANG ANGKUTAN ONLINE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS MOBILE q

ALTURINO, RICKY and Yusliani, Novi and Rachmatullah, M Naufal (2023) ANALISIS SENTIMEN PADA OPINI PUBLIK TENTANG ANGKUTAN ONLINE MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS MOBILE q. Undergraduate thesis, Sriwijaya University.

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Abstract

Online transportation provides convenience for people to find transportation services with fixed fares. However, online transportation has shortcomings such as inexperienced drivers, potential delays, and mismatched user expectations. Therefore, sentiment analysis is needed to assess public satisfaction with online transportation. Deep learning approaches such as Convolutional Neural Network (CNN) can be used for sentiment analysis. CNN can automatically extract relevant features from text data, recognize important patterns in the text, and efficiently handle varying lengths of text data. In this study, text data is transformed into Word Embedding using Word2Vec to implement CNN for sentiment analysis. The data used is obtained from Google Play Reviews, consisting of 6000 reviews with 3000 positive labels and 3000 negative labels. The data is divided into 3600 training data, 1200 validation data, and 1200 test data. The research results show an accuracy, precision, recall, and f1-score of 91.00%, 91.08%, 91.00%, and 91.00% on the test data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Angkutan Online, Convolutional Neural Network, Artificial Neural Network, Google Play Review, CNN.
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Ricky Alturino
Date Deposited:	03 Jul 2023 07:21
Last Modified:	03 Jul 2023 07:21
URI:	http://repository.unsri.ac.id/id/eprint/113468

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