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All Journal Jurnal Informatika Upgris Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri JURNAL ILMIAH INFORMATIKA ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi JUTIS : Jurnal Teknik Informatika STUDI PELAKSANAAN SELFMEDICATION PADA PENDERITA DIARE BERDASARKAN TINGKAT PENGETAHUAN MASYARAKAT PESISIR KECAMATAN SOROPIA KABUPATEN KONAWE JUSTIAN - Jurnal Sistem Informasi Akuntansi Jurnal Abdimas Komunikasi dan Bahasa sudo Jurnal Teknik Informatika INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH JURNAL KESEHATAN, SAINS, DAN TEKNOLOGI (JAKASAKTI) Journal of Informatics, Electrical and Electronics Engineering
Hasan, Fuad Nur
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Analisis Sentimen Pengguna TikTok Terhadap Postingan Tiktok Smartfrenworld Menggunakan Algoritma Logistic Regression Mulya, Arga Budi; Putra, Rifky Wahyu Eka; Giovanni, Muhammad Arif; Putravani, Adheiktyo Februarjuna; Putra, Arya Ade; Hasan, Fuad Nur
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.9945

Abstract

Abstrak - Penelitian ini bertujuan melakukan analisis sentimen pengguna TikTok terhadap postingan akun resmi Smartfrenworld menggunakan algoritma Logistic Regression. Metode ini dipilih karena keunggulannya dalam efisiensi komputasi dan efektivitasnya yang tinggi dalam menangani klasifikasi biner pada data teks, sehingga cocok untuk analisis sentimen. Data yang digunakan adalah 4.200 komentar pengguna TikTok yang dikumpulkan melalui proses scraping. Metodologi penelitian mencakup tahap pengumpulan data, prapemrosesan data yang mencakup Case folding, Normalisasi Kata Slang, dan penghapusan Stopword, serta pelabelan sentimen menggunakan metode Rule-Based. Hasil pelabelan menunjukkan bahwa sentimen negatif lebih mendominasi, yaitu sebesar 52,31% atau 2.197 sampel, dibandingkan dengan sentimen positif sebesar 47,69% atau 2.003 sampel. Evaluasi kinerja model Logistic Regression sebagai algoritma Machine Learning menunjukkan hasil yang sangat baik dan stabil, dengan nilai akurasi keseluruhan mencapai 0,9286 (92,86%). Selain itu, model ini juga mencapai nilai Presisi, Recall, dan F1-Score yang seimbang, yaitu 0,93 untuk kedua kelas sentimen. Analisis WordCloud menunjukkan bahwa sentimen negatif berkaitan dengan berbagai kendala teknis layanan seperti "sinyal", "jaringan", dan "gangguan", sementara sentimen positif berkaitan dengan interaksi dan pelayanan pelanggan yang baik, terlihat dari kata kunci seperti "teman" dan "trimz".Kata kunci: Analisis Sentimen; TikTok; Logistic Regression; Smartfrenworld; Machine Learning; Abstract - This study aims to analyze TikTok user sentiment towards Smartfrenworld's official account posts using the Logistic Regression algorithm. This method was chosen because of its advantages in computational efficiency and its high effectiveness in handling binary classification on text data, making it suitable for sentiment analysis. The data used are 4,200 TikTok user comments collected through a scraping process. The research methodology includes data collection stages, data preprocessing including Case folding, Slang Normalization, and Stopword removal, as well as sentiment labeling using the Rule-Based method. The labeling results show that negative sentiment is more dominant, namely 52.31% or 2,197 samples, compared to positive sentiment at 47.69% or 2,003 samples. The performance evaluation of the Logistic Regression model as a Machine Learning algorithm shows excellent and stable results, with an overall accuracy value reaching 0.9286 (92.86%). In addition, this model also achieves balanced Precision, Recall, and F1-Score values, namely 0.93 for both sentiment classes. WordCloud analysis shows that negative sentiment is related to various technical service issues such as “signal,” “network,” and “interference,” while positive sentiment is related to good customer interaction and service, as seen from keywords such as “friends” and “thanks”.Keywords: Sentiment Analysis; TikTok; Logistic Regression; Smartfrenworld; Machine Learning;
Co-Authors Ade Christian, Ade Alfarizi, Faisal Antonius Yadi Kuntoro Atang Saepudin Bachtiar, Yusuf Bittaqwa, Muhammad Dawam Amru Dandi Rosadi Dini Nurlaela Dwi Yuni Utami Eka Kusuma Pratama Erni Erni Fachrian, Mohammad Akmal Fauzi Ahmad Muda Giovanni, Muhammad Arif Gumilang, Rapanca Cahya Habibi, Fanza Maulana Harefa, Kristine Hermawan, Muhamad Taufik Hikmah, Noer Indryani, Azizah Wardah Kartika Handayani Krisnandi, Daiva Rakha Laia , Metodius Modianus Lestari Yusuf Lila Dini Utami Maulana, Dimas Mulya, Arga Budi Nabilatul Adzra, Salsa Nouval, Muhammad Nurlaelatul Maulidah Nurlelah, Elah Panny Agustia Rahayuningsih Pratama, Mr. Aldiansyah Putra, Arya Ade Putra, Rifky Wahyu Eka Putravani, Adheiktyo Februarjuna Qodri Nurfalah Rahmat, Ferdy Rahmi, Anisya Ramadhan, Muhammad Ridwan Ramanda, Dea Riki Supriyadi Riska Aryanti Rizki Agustianto Santi Santi Sri Murni Taufik Hidayatulloh tri wahyuni Tuasamu, Abdulrahman Yulianti Yulianti