SENTIMENT ANALYSIS APLIKASI MOBILE TIX ID DI PLAYSTORE MENGGUNAKAN ALGORITMA RANDOM FOREST

RAMADHINI, REFFINA and Sanjaya, Rudi (2025) SENTIMENT ANALYSIS APLIKASI MOBILE TIX ID DI PLAYSTORE MENGGUNAKAN ALGORITMA RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

TIX ID is one of the e-ticketing entertainment platforms for film orders that has experienced a rapid surge in Indonesia. The various features offered by the TIX ID application must of course be able to meet user expectations in order to compete in the market. The influence of reviews provided by users has a very important impact on the reputation of an application, whether it is positive reviews in the form of text and then processed into negative review information. Sentiment analysis is a study used in analyzing a review or perspective whose final result is in the form of positive or negative text information. The research that has been carried out using the Random Forest algorithm has succeeded in collecting review data of 2000 samples labeled positive and negative. Random Forest modeling in the study used the evaluation of the confusion matrix model and classification report which managed to achieve an accuracy of 87%, performance in the negative class showed high precision of 85%, negative recall rate of 92%, and f1-score of 88%. Then in the positf precision class reached 91%, recall was 83%, and f1-score was 87%. While the macro average and weighted average values for all metrics were 88%, indicating a balance of classification performance among the classes. Overall, the application of the Random Forest algorithm model provides accurate results and makes sentiment analysis a tool that helps developers understand user satisfaction and needs on the TIX ID application.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen; TIX ID; Random Forest; Ulasan; Confusion Matrix.
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Reffina Ramadhini
Date Deposited:	07 Jul 2025 07:50
Last Modified:	07 Jul 2025 07:50
URI:	http://repository.unsri.ac.id/id/eprint/177057

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