Analisis Sentimen Terhadap Ulasan Aplikasi Tiktok Pada Google Play Store Menggunakan Metode TF-IDF dan Naïve Bayes

2026-05-05T00:22:17+08:00

Abstract: The rapid growth of short-video social media platforms such as TikTok has significantly increased the volume of user reviews that reflect public perceptions of application quality. These reviews constitute electronic word of mouth (e-WOM), which influences brand image, user trust, and adoption decisions. This study aims to analyze user sentiment toward the TikTok application using a text mining approach based on the Naïve Bayes algorithm and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The dataset consists of user reviews collected from the Google Play Store and categorized into sentiment classes. The data were processed through several preprocessing stages, including text cleaning, tokenization, stopword removal, normalization, and stemming, before feature extraction and classification.

The experimental results indicate that the proposed model achieved an accuracy of 67.35% in classifying sentiment. However, analysis of the confusion matrix and prediction distribution reveals a bias toward the majority class due to dataset imbalance. The Naïve Bayes classifier combined with TF-IDF representation demonstrates satisfactory performance in identifying dominant extreme sentiments (positive and negative), yet its effectiveness decreases in multi-class classification scenarios with uneven data distribution.

Sistem Pendukung Keputusan Pemilihan Ketua Klasis Siep Asso Menggunakan SAW

2026-05-16T09:44:05+08:00

The selection of the Head of the Siep Asso Classis is a strategic process in determining leaders who are able to manage church services and organizations effectively. This process often faces obstacles of subjectivity, lack of transparency, and the absence of measurable assessment methods. This research aims to design and build a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to produce objective and systematic decisions. The SAW method is used to calculate the preference score for each candidate based on predetermined and weighted criteria, including leadership, service experience, integrity, communication and commitment. Data was obtained through interviews, questionnaires and documentation studies. System design uses flowcharts and Data Flow Diagrams (DFD), while development is carried out using PHP and MySQL. System testing applies Black Box Testing to ensure functions run as required. The research results show that SAW-based SPK is able to provide candidate recommendations accurately, consistently and transparently. This system helps minimize subjectivity and supports a fairer and more measurable decision-making process.

Jurnal Sintaks Logika

Analisis Sentimen Terhadap Ulasan Aplikasi Tiktok Pada Google Play Store Menggunakan Metode TF-IDF dan Naïve Bayes

Sistem Pendukung Keputusan Pemilihan Ketua Klasis Siep Asso Menggunakan SAW