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Ainunnisa, Isna Riaandita
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Computer Science & IT

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Analisis Sentimen Aplikasi Tiktok dengan Metode Support Vector Machine (SVM), Logistic Regression dan Naïve Bayes Ainunnisa, Isna Riaandita; Sulastri, Sulastri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 3 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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Abstract

The tiktok application is a platform application specifically for photos, music and videos that many people like, from children, teenagers, and even adults. Tiktok is in great demand because there is a lot of interesting and useful content. Currently, reviews on the TikTok application have reached 16 million reviews with a rating of 4.4 on the Google Play Store, which has reached 500 million downloads. Many users also have many positive and negative reviews on the TikTok application. Because of this, the researcher conducted a sentiment analysis which was used to analyze user opinions by grouping positive, neutral or negative reviews. The data taken in this study was 2100, using the Python programming language. Then the preprocessing stage is carried out, namely case folding, tokenizing, filtering and stemming. The methods used in this study are the Support Vector Machine (SVM) method, Logistic Regression, and Naïve Bayes. The results of applying the 3 sentiment analysis methods are the Sopport Vector Machine method producing an accuracy value of 82%, Precision 82%, Recall 81% and F1 score 81%. The Naïve Bayes method produces an accuracy value of 79%, Precision 81%, Recall 77% and F1 score 78%, Logisstic Regression Method an accuracy value of 84%, precision 83%, recall 82%, F1 score 83%.
Co-Authors Sulastri Sulastri