ANALISIS KEPUASAN PENGGUNA MAXIM BERDASARKAN ONLINE REVIEW GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)

PRATIWI, RADHAISYAH and Fathoni, Fathoni (2023) ANALISIS KEPUASAN PENGGUNA MAXIM BERDASARKAN ONLINE REVIEW GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM). Undergraduate thesis, Sriwijaya University.

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Abstract

Maxim is one of the most popular online transportation companies in Indonesia that entered the counstry in 2018. To be able to compete with other competitors and increase popularity, user satisfaction is one of the important things that Maxim must pay attention to. Based on the results of identification of online reviews of the Maxim application on the Google Play store, there are differences in user perceptions that indicate the inequality of services received by each user, resulting in various positive and negative reviews. This study aims to determine user satisfaction by utilizing online data review of Maxim application on google play store. This research applies a sentiment analysis method using the Support Vector Machine (SVM) algorithm and data validation using K Fold Cross Validation with a trial fold value = 3, 5, 7, and 9 to get a model with the best accuracy results. From the tests that have been carried out using the RapidMiner application, the best accuracy results are obtained in the fold = 5 model with an accuracy value of 76.72%, a precision value of 77%, and a recall value of 68.53%. The test results conducted on 8779 online review datasets classify 7120 positive reviews and 1659 negative reviews. The test results indicate that users who feel satisfaction with the Maxim application are more than users who feel dissatisfied. From the wordcloud analysis, there are service aspects that affect the satisfaction and dissatisfaction of Maxim users including "driver", "price", "pay", "location", and "application". Keywords : Sentiment Analysis, Google Play Store, User Satisfaction, Online Review, Support Vector Machine (SVM)

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Analisis Sentimen, Google Play Store, Kepuasan Pengguna, Online Review, Support Vector Machine (SVM)
Subjects: T Technology > T Technology (General) > T1-995 Technology (General)
Divisions: 09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User: Radhaisyah Pratiwi
Date Deposited: 03 Apr 2023 02:12
Last Modified: 03 Apr 2023 02:12
URI: http://repository.unsri.ac.id/id/eprint/92155

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