OPTIMASI ALGORITMA NAÏVE BAYES, RANDOM FOREST, DAN LOGISTIC REGRESSION MENGGUNAKAN METODE MAJORITY VOTE UNTUK ANALISIS SENTIMEN PADA ULASAN APLIKASI SATUSEHAT

Wiratama, Yohanes Dimas Wisnu (2024) OPTIMASI ALGORITMA NAÏVE BAYES, RANDOM FOREST, DAN LOGISTIC REGRESSION MENGGUNAKAN METODE MAJORITY VOTE UNTUK ANALISIS SENTIMEN PADA ULASAN APLIKASI SATUSEHAT. Undergraduate thesis, Universitas Pembangunan Nasional "Veteran" Jawa Timur.

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Abstract

The SATUSEHAT application, which is an update of the Peduli Lindungi app, has become a general health application for the Indonesian public. In its development, various user feedback is needed to improve its services in the future. This feedback can be found in the reviews on the Google Play Store. To facilitate the categorization of these reviews in the future, sentiment analysis has been used in several previous studies and has yielded quite good results. Various classification algorithms can be used for sentiment analysis. In this study, the researcher chose to use the Multinomial Naïve Bayes, Random Forest, and Logistic Regression algorithms, which have proven effective in several sentiment analysis cases in prior research. The researcher will also combine the results of each algorithm using the Majority Vote method to produce sentiment analysis with higher accuracy. The results show that the Majority Vote method successfully provided the highest test accuracy compared to the three individual algorithms. The highest accuracy achieved was 97.6%, with a precision of 98%, recall of 98%, and an F1-score of 98%.

Item Type: Thesis (Undergraduate)
Subjects: Q Science > QA Mathematics > QA76.87 Neural computers
Divisions: Faculty of Computer Science > Departemen of Informatics
Depositing User: Yohanes Dimas Wisnu Wiratama
Date Deposited: 03 Dec 2024 01:56
Last Modified: 03 Dec 2024 01:56
URI: https://repository.upnjatim.ac.id/id/eprint/32287

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