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All Journal SMATIKA Smatika Jurnal : STIKI Informatika Jurnal
Faldy Irwiensyah
Teknologi Industri dan Informatika, Universitas Muhammadiyah Prof. Dr. Hamka, Indonesia
Computer Science & IT Education

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Analisis Sentimen Terhadap Aplikasi Tiktok Dari Ulasan Pada Google Playstore Menggunakan Metode Naïve Bayes Nizar Fawwazun Hilmi; Faldy Irwiensyah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1210

Abstract

In this research, we use reviews from the Google Play Store platform to conduct sentiment analysis on the TikTok application. Reviews can be categorized into positive, negative, or neutral sentiment groups using a Naïve Bayes approach. The distribution of user sentiment towards TikTok is displayed in the sentiment analysis results. Exposing model performance allows you to observe the level of understanding, accuracy, and precision of emotion classification. Our findings provide marketers and app developers with in-depth information about how users view TikTok. The accuracy level, precision level, and recall level of the Naive Bayes algorithm used for sentiment analysis for evaluating users of the TikTok application on the Google Play Store are 83.66%, 82.97%, and 91.97%, respectively. From the accuracy figures, it is clear that the Naive Bayes method provides reliable results so it is suitable for use in data categorization.
Analisis Sentimen Terhadap Aplikasi Tiktok Dari Ulasan Pada Google Playstore Menggunakan Metode Naïve Bayes Nizar Fawwazun Hilmi; Faldy Irwiensyah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1210

Abstract

In this research, we use reviews from the Google Play Store platform to conduct sentiment analysis on the TikTok application. Reviews can be categorized into positive, negative, or neutral sentiment groups using a Naïve Bayes approach. The distribution of user sentiment towards TikTok is displayed in the sentiment analysis results. Exposing model performance allows you to observe the level of understanding, accuracy, and precision of emotion classification. Our findings provide marketers and app developers with in-depth information about how users view TikTok. The accuracy level, precision level, and recall level of the Naive Bayes algorithm used for sentiment analysis for evaluating users of the TikTok application on the Google Play Store are 83.66%, 82.97%, and 91.97%, respectively. From the accuracy figures, it is clear that the Naive Bayes method provides reliable results so it is suitable for use in data categorization.
Co-Authors Nizar Fawwazun Hilmi