Perbandingan Algoritma Random Forest Dan Maximum Entropy Untuk Analisis Sentimen Pada Ulasan Aplikasi My Pertamina Di Google Playstore Menggunakan Python

Auliya, Rahmat (2023) Perbandingan Algoritma Random Forest Dan Maximum Entropy Untuk Analisis Sentimen Pada Ulasan Aplikasi My Pertamina Di Google Playstore Menggunakan Python. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

In an ever-evolving digital world, user reviews on platforms like Google PlayStore play an important role in shaping user perceptions and decisions regarding an app. To understand user sentiment and views on the My Pertamina application, sentiment analysis on user reviews is important. Therefore this study aims to find out an overview of review data about the My Pertamina application based on the Google Play website and how precise machine learning using the Random Forest and Maximum Entropy methods is in classifying Indonesian-language text regarding user reviews of the My Pertamina application based on the Google Play website. The method used in this research is Random Forest and Maximum Entropy. This research was conducted using the My Pertamina application review data on the Google Playstore as many as 1,000 review data. Owned data is divided into nine scenarios of comparison of train and test data. Experiments on Random Forest have superior accuracy with values of 99.62%, 99.62%, 99.25%, 99.16%, 98.65%, 98.75, 97.06%, 94.26%, and 88.26%. Whereas Maxium Entropy has an accuracy value of 98.88%, 98.50%, 98.50%, 98.69%, 97.76%, 96.57%, 95.30%, 94.16%, 89.46 %. Keywords: Sentiment analysis, Random Forest, Maximum Entropy, My Pertamina, Google Playstore

Item Type: Thesis (Undergraduate)
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorAnggraeny, Fetty TriNIDN0711028201fettyangraeny.if@upnjatim.ac.id
Thesis advisorRizki, Agung MustikaNIDN0025079302agung.mustika.if@upnjatim.ac.id
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science > Departemen of Informatics
Depositing User: Rahmat Auliya
Date Deposited: 26 Jul 2023 07:49
Last Modified: 26 Jul 2023 07:49
URI: http://repository.upnjatim.ac.id/id/eprint/16475

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