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All Journal Jurnal Mandiri IT Journal of Artificial Intelligence and Digital Business
Maulana, M Taufik Fajar
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mathematics

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Analisis Sentimen Ulasan Aplikasi TikTok di Google Playstore Menggunakan Algoritma Naive Bayes Maulana, M Taufik Fajar; Nugroho, Bangkit Indarmawan; Utami, Erni Unggul Sedya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2962

Abstract

Aplikasi mobile seperti TikTok telah menjadi bagian penting dari aktivitas sehari-hari sehingga perlu dipahami perspektif pengguna melalui ulasan untuk meningkatkan kualitas aplikasi. Penelitian ini menganalisis sentimen ulasan pengguna TikTok di Google Playstore, mengevaluasi performa algoritma klasifikasi, serta mengidentifikasi dinamika sentimen dan kata kunci utama sebagai dasar pengambilan keputusan strategis. Sebanyak 3.000 ulasan dikumpulkan dengan teknik web scraping dan diproses melalui tahapan pre-processing teks seperti casefolding, penghapusan tanda baca, normalisasi, stopword removal, tokenizing, dan stemming. Fitur teks diekstraksi menggunakan metode TF-IDF, sedangkan analisis sentimen dilakukan dengan algoritma Naive Bayes. Hasil klasifikasi menunjukkan 54,6% ulasan bersentimen positif, 44,1% negatif, dan 1,4% netral. Model Naive Bayes menghasilkan akurasi 83% dengan nilai precision, recall, dan F1-score yang baik pada setiap kelas sentimen. Analisis temporal dari April hingga Juni 2025 menunjukkan adanya fluktuasi sentimen, sementara identifikasi kosakata dominan pada ulasan negatif memberikan masukan langsung bagi pengembang untuk perbaikan fungsi aplikasi. Penelitian ini menyimpulkan bahwa analisis sentimen efektif sebagai dasar strategi peningkatan kualitas aplikasi secara berkelanjutan.
Application of the latent dirichlet allocation method to determine news text topics Surorejo, Sarif; Maulana, M Taufik Fajar; Andriani, Wresti; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.306

Abstract

This research discusses the application of the Latent Dirichlet Allocation (LDA) method to determine news text topics, providing new insights into media content analysis. This research aims to develop a model that can increase the accuracy and efficiency of topic identification in Indonesian news texts. The research uses a quantitative approach with experimental methods, quantitative analysis, and model validation, where news text data is processed and analyzed using LDA. The results show that the developed model can accurately identify news topics, showing significant improvements compared to existing methods. The implications are substantial for practitioners and researchers in journalism and media analysis, offering more efficient and effective strategies for managing and understanding large flows of information and opening new directions for advanced research in news text analysis.
Co-Authors Andriani, Wresti Bangkit Indarmawan Nugroho Erni Unggul Sedya Utami, Erni Unggul Sedya Gunawan Gunawan Sarif Surorejo