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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Yauw James Fang Dwiputra Harta
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Computer Science & IT

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Implementasi Algoritma Naïve Bayes Terhadap Klasifikasi Ulasan Aplikasi Tokopedia Yauw James Fang Dwiputra Harta; I Gede Arta Wibawa
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2023.v12.i03.p08

Abstract

Tokopedia has an average number of visitors per month which is 149.67 visitors. In addition, the Tokopedia application has been downloaded by more than 100 million users with 6.34 million reviews and received a rating of 4.7 out of 5. This study aims to classify the positive and negative reviews of the Tokopedia application on the Google Playstore. The results of the 2000 test data testing obtained 842 positive reviews and 1158 negative reviews. This means the percentage for positive reviews is only 42.1%, in contrast to negative reviews of 57.1%. The performance generated in this test against 2000 testing data is 96.19% accuracy value, with a precision value of 1. While in Class Recall the resulting value is 93.45% (positive class: negative). Then for the AUC value is 0.975.
Analisis Sentimen Berbasis Aspek Terhadap Ulasan Aplikasi Mobile Jkn Dengan Metode Random Forest Dan Information Gain Sebagai Seleksi Fitur Yauw James Fang Dwiputra Harta; I Gede Arta Wibawa; Anak Agung Istri Ngurah Eka Karyawati; Komang Ari Mogi
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the rapidly advancing digital age, applications have become integral to daily life. This study focuses on aspect-based sentiment analysis of reviews for the Mobile JKN application using the Random Forest and Information Gain methods. This technological approach is vital for understanding user opinions, guiding further improvements and developments. Google Play Store review data is employed, with rating scores serving as guides for sentiment classification. The study aims to provide in-depth insights into sentiments and aspects influencing Mobile JKN application reviews. Through this approach, the quality of healthcare services delivered via the application is anticipated to be continually enhanced.
Co-Authors Anak Agung Istri Ngurah Eka Karyawati I Gede Arta Wibawa Komang Ari Mogi